personal statement examples for data analyst

Sample Personal Statement Data Science and Analytics

by Talha Omer, MBA, M.Eng., Harvard & Cornell Grad

In personal statement samples by field.

The demand for data science experts is increasing in every industry, not just in technology. Moreover, it is a high-paying job with a guaranteed placement even before graduation. Hence, the competition is also increasing, and with every passing year, the ease of getting into a top data analytics program keeps getting harder. So, start early with your application and make sure you put good time into drafting your perfect application essays.

Here is a sample personal statement of data science professional with two years of experience working in a big data consulting firm. This candidate was able to secure admission into top data science programs like Vanderbilt and CMU. He has graciously shared his successful essay so that prospective applicants can benefit from it.

Related Personal Statements 1) Sample Personal Statement Business Analytics 2) Sample Personal Statement in Advanced Analytics (admitted to NCSU) 3) Sample Personal Statement in Analytics (admitted to Georgia Tech) 4) Sample Personal Statement in Management and Analytics (admitted to LBS)

Sample Personal Statement for Big Data/Data Science/Data Analytics

I want to play a critical role as a big data architect who translates business problems into solvable analytics. In the short run, I want to work for a leading FMCG firm like Unilever, P&G, or Nestle and define procedures and models to determine what IT systems gather and remove information silos across different departments. In the long run, however, I want to extend my expertise in the public sector and advise corporates and governments alike across the globe to solve several social and business problems through big data.

My undergraduate has equipped me with extensive quantitative knowledge and technical experience around different themes in Computer Engineering. I’ve focused most of my studies on GUI in C++, apps and game development, and intensive numerical analysis. This was further honed when I joined Afiniti Experience Ltd as Software Engineer.

I have written scripts using MySQL and MSSQL to process large datasets and troubleshoot and configure the company’s operations. At Afiniti, I have developed a strong skillset in collecting, storing and managing big data. I plan to translate business problems into analytics-driven solutions, which I would embed into business operations. However, I must first curate my leadership skills and polish my skillset in designing computational pipelines for high-dimensional and large-scale complex data.

At Vanderbilt, I want to develop my theoretical basis of operations and decision technologies which ideally dovetails with my career interests of applying quantitative techniques in business operations. Beyond the classroom, I would greatly appreciate the opportunity to learn from and collaborate with Vanderbilt’s influential faculty. The Data Science Institute will allow me to learn data-driven research and train me as a future leader.

In particular, the techniques of Gautam Biswas on learner modeling and adaptivity are foundational for my current work. Moreover, Jeffrey D. Blume’s expertise in statistical inference and methodology for analyzing and interpreting receiver operating characteristic curves will equip me with tools through which I can excel in my future career.

My future aspirations require strong leadership qualities recognized in a data-driven world. For this purpose, I would greatly benefit from Data Science Institute’s capstone development and lead a project from scratch. This will mold my personality into a global leader’s persona.

Lastly, I will exploit the locational advantage of living at Vanderbilt and gain access to multiple fortune 500 companies where I can seek pro bono consulting opportunities and enhance my problem-solving acumen. I am also confident in acquiring the necessary communication skills to present solutions to Product Managers, Sales Associates, Engineers, and Marketing Teams.

To sum up, owing to my aspirations and professional expertise in big data synthesis, I am confident of using the vibrant opportunities at Vanderbilt’s master’s in data science and converting it into an ideal segue for my future career aspirations.

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Data Science Personal Statement Sample and Examples

Home Blog Data Science Data Science Personal Statement Sample and Examples

Data science is one of the most popular career options for students, especially those pursuing a Bachelor's degree. It is also one of the most sought-after courses in universities today. If you just want some ideas on how to write a personal statement for data science, then this article is for you. Also, if you’re someone who is willing to secure a career in the field of data science, then it is recommended that you pursue Data Science Courses that will enable you to learn all aspe that will enable you to learn all aspects and principles of data science. 

What is a Data Science Personal Statement?

In a nutshell, the personal statement for data science is a document that you write to explain why you are interested in pursuing the subject and what you can bring to the table. It should be written in a way that shows your interest in the subject and why you want to study it. You may want to include information about the following in your data science personal statement.  

• What led up to your decision to pursue this field? 
• Why do data scientists matter? What problems need solving by them? What value do they provide society as well as individuals? 
• How will studying this specific field help prepare you for future careers or additional educational opportunities (e-learning programs, etc.)?

You can also include the following:  

• What are your goals for this degree?  
• How will it benefit you?  
• What do you hope to achieve from studying data science?  
• Why is this field important in today’s society?  
• What are the challenges that you see in this field?  
• How will you address those challenges?  
• What do you think the future of data science is?  
• How do you plan on staying relevant as technologies and trends change?

The Importance of Creating a Data Science Personal Statement

Data science personal statement is a formal document that will be used by the company to evaluate your skills. If you are applying for a Data Science job and want to impress the hiring manager, then you must write a strong data science personal statement.

A good personal statement for a master's in data science must be unique, creative, informative and interesting to read. It should describe not only your skills and experience but also showcase your ability to think critically and creatively.

A well-written data science personal statement will help you stand out from other applicants and make yourself an ideal candidate for the job that you want. Here are some useful tips for writing a strong data science personal statement: 

• Be honest and straightforward in your personal statement. 
• Don’t exaggerate or lie about your skills, experience and achievements. If you don’t have any relevant work experience, then focus on other areas where you can showcase your skills, such as volunteering or community projects. 
• Know the company that you are applying to and tailor your personal statement accordingly. A generic resume won’t help if you are applying for a specific job position. Instead, write a customized letter that shows how well-suited you are for this role. 
• Keep it short and sweet. The best personal statements are between a few hundred to a few thousand words long. Don’t try to cram everything in one big paragraph; break it up into smaller sections that will make it easier for readers to digest. 

So now you might have understood how important data analytics personal statements are. To learn how to create a personal statement, it is recommended that you enroll in the Best Data Science Bootcamps . 

Data Science Personal Statement Sample

I am writing this Data Science Personal Statement for the MS in Data Science program at UC Berkeley. My goal is to explain why I want to pursue a career in data science and how my experience as an undergraduate student has prepared me for graduate school. As you can see from my resume, I have had many opportunities to work with large amounts of data through internships and research projects over the course of my academic career. These experiences have given me valuable insight into how large-scale computational problems can be tackled by applying statistical methods under tight deadlines while still maintaining quality control over your results. 

In addition, I have taken classes such as AI/ML Systems Design & Implementation and Machine Learning Algorithms. These classes have helped me develop new ways of approaching problems while also providing an understanding of why certain algorithms work better than others when applied in specific situations. 

I am a Data Science Major at UC Berkeley and have been for two years. In order to graduate with a major in Data Science, you must complete four required classes, one of which is an independent study project. 

I have chosen to take this independent study project in order to gain hands-on experience with a data science problem of my choosing and to learn how to effectively apply machine learning algorithms in the real world. My goal is to create an application that can accurately predict where students need tutoring based on their past grades. This project will require me to use various classes of statistical models, such as regression, decision trees, and neural networks. 

How to Write a Personal Statement for Masters Programme in Data Science?

If you are looking for the best way to write a sample personal statement for a master in data science, you should follow these steps: 

• Step 1: The first step is to find out what courses are available in your area and how long it takes to complete them. You can find this information on websites online. 
• Step 2: Once you have this information, you need to think about how much time you will have available each day. It is important that you do not leave your studies until you finish all of your courses because once you finish your degree program, there will be no more work available for you. Your ability to continue working will depend upon how well your personal statement for data science courses was received by universities and whether or not they offer scholarships for those who want to study abroad or online. 
• Step 3: In order to write a good personal statement for M.Sc data science, you will need to think about why you want to continue your education after completing your bachelor's degree program. This could be because of what happened during college or because of something else entirely (such as family obligations). If it is something that happened during college, then you will need to explain what that event was and how it has affected your desire to continue your education. If it is something that happened outside of college, then you should talk about how that event impacted your academic performance and why you want to continue studying.

Data Science Personal Statement Example 

Following is a data science personal statement example. You can refer to this data science statement of purpose example and keep in mind the necessary points.

Source: personal-statement-examples.com

Tips to Write an Effective Data Science Personal Statement

The following tips will help you write an effective personal statement for a master in data science: 

1. Use a Template

It's best to use a template that has been created by experienced admissions officers and other professionals in the field. This means you can skip the writing process entirely since they've already done most of it for you. 

2. Keep Your Sentences Short and Simple

Your goal should be no more than one or two paragraphs per section (including your application summary), which means keeping your sentences as short as possible without compromising clarity or coherence. If there are too many adjectives or numbers used in an otherwise simple sentence, try replacing them with action verbs like "ran" instead of "ran fast." 

3. Avoid Clichés

In your data science personal statement sample, instead of saying things like "I am dynamic," try saying something more descriptive such as “I am highly dynamic” instead because this shows off how creative your mind works while also showing off how well-rounded personas are important traits needed by anyone working at companies when writing an M.Sc data science personal statement.  

Do’s and Don’ts While Writing Personal Statement

Data science is a booming field with a lot of opportunities. You can work anywhere and make a good salary with this skill. If you think that it’s not for you, then it’s time to think again. The world has changed and so have our needs as individuals. Data science professionals will be needed in the future because of their role in shaping our lives as we know them today. In order to pursue a career in this broad field of data science, it is recommended that you pursue KnowledgeHut to learn its principal aspects and gain in-depth knowledge about data science. Data Science Courses to learn its principal aspects and gain in-depth knowledge about data science.

Frequently Asked Questions (FAQs)

Find out what diploma courses are available in your area and how long it takes to complete them. Once you have this information, you need to think about how much time you will have available each day. After evaluating all these things, start writing your personal statement using templates. 

• The reason(s) why you selected this subject(s) 
• Your chosen area of study and how it relates to the current studies 
• Your experiences in relation to your chosen subject(s) 
• What are your interests and responsibilities in relation to the subject you are studying? 
• After university, what's next? 
• A summary of why you will make an excellent student 
• Don’t use quotes 
• Don’t let spelling and grammatical errors spoil your statement. 
• Don’t copy and paste 

During the writing of the letter of intent for the MS in Data Science course, it is important to take into account the basic questions asked by the institution, including what kind of ambitions the prospective candidate has and the inspiration behind those ambitions. If the students do not want to sound conversational in their essays, then they should keep in mind that the tone should be formal instead of informal.

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Data Science Personal Statement Samples with Examples

A data science personal statement is crucial for pursuing a career in the field as it allows individuals to showcase their qualifications, experiences, and aspirations.

It serves as a platform to express their passions and highlight their academic backgrounds, technical skills, and practical experiences. The statement enables individuals to articulate career goals, research interests, and potential contributions. 

In that sense, a personal statement examples are valuable as it offers guidance and examples of successful statements, providing insights into key elements, structure, and content. Samples also inspire and motivate by showcasing possibilities and achievements in the data science field. They help individuals understand how to express their passion, highlight relevant skills, and align career goals. 

If you are a data science enthusiast, consider enrolling in an Advanced Certificate Programme in Data Science to boost your resume.

What is a Data Science Personal Statement?

Data science personal statements are written statements or essays by individuals aiming to pursue courses like a Master of Science in Data Science from LJMU or aspiring to pursue a career in data science. These statements allow applicants to showcase their passion, skills, and experiences related to data science. Personal statements must be submitted as a part of the application process. 

They typically highlight the individual’s motivation for choosing data science, their relevant academic background, technical skills, and any practical experience they have gained. Personal statements also allow applicants to express their career goals, research interests, and how they envision contributing to the field of data science. 

As a student or professional in this field, referring to a data science personal statement sample is crucial in conveying your suitability and enthusiasm to the admissions committee or potential employers.

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Key Elements of a Strong Data Science Personal Statement

The essential components of a compelling personal statement for data science comprise:

• Genuine Passion and Drive: Demonstrating a deep-rooted enthusiasm for data science and a solid motivation to pursue a career in this field.
• Academic Background: Showcasing academic accomplishments relevant to data science, such as degrees, coursework, or research projects in quantitative disciplines.
• Technical Proficiency: Exhibiting expertise in programming languages (like Python or R), statistical analysis, machine learning techniques, data manipulation, and visualisation tools.
• Practical Exposure: Highlighting practical experiences, such as internships, projects, or industry engagements that have honed data science skills.
• Areas of Interest in Research: Creatively ideating your areas of interest in data science, like computer vision, predictive modelling, and natural language processing and discussing research contributions or projects in these domains.
• Analytical Problem-Solving: Demonstrating skills in problem-solving, critical thinking, and applying appropriate data science methods to real-world challenges.
• Efficient Collaboration and Communication: Emphasising efficient communication skills, teamwork capabilities, and the ability to convey complex concepts to non-technical audiences.
• Future Aspirations: Outlining long-term career goals in data science and how one envisions utilising this field to impact the industry or domain of their choice.

Example 1: Personal Statement for an Entry-Level Data Science Position

I am eager to apply for this role as I have a degree in Computer Science and a course concentration on statistics and machine learning. Along with a strong base in data analytics, I have commendable analytical skills and an aptitude for problem-solving. I have also taken an active part in multiple relevant projects in which I was required to work with algorithms to derive insights from numerous datasets. 

Additionally, my internship experience exposed me to real-world challenges, refining my expertise in data cleansing and preprocessing for effective modelling. I am excited about the opportunity to contribute to a dynamic team, leveraging my technical skills and enthusiasm to drive data-informed decision-making and deliver impactful solutions. I believe I am a potential candidate for an entry-level data science role. 

Example 2: Personal Statement for a Data Analyst Role

As an ambitious individual aiming for a data analyst role, I am enthusiastic about applying my analytical abilities and dedication to data-driven insights within a vibrant organisation. Equipped with a degree in statistics and hands-on involvement in data manipulation and visualisation, I have established a strong foundation in data analysis. 

Throughout my academic endeavours, I have refined my aptitude for extracting valuable insights and effectively communicating them to key stakeholders. Moreover, my internship at a well-known company allowed me to apply statistical methodologies to extensive datasets, further augmenting my analytical proficiencies. 

I eagerly look forward to applying my technical acumen and problem-solving prowess to unearth meaningful insights, facilitate data-led decision-making, and contribute to the organisation’s triumphs.

Example 3: Personal Statement for a Data Scientist Position

I am enthusiastic about data science and am beyond elated by its potential to take my area of interest to greater heights. I have cultivated a profound understanding of statistical modelling, machine learning algorithms, and the art of data visualisation required to fulfil my role as a data scientist. I have upskilled myself with strong analytical and problem-solving skills, a Master’s in Data Science and hands-on experience in research projects. 

Throughout my academic trajectory, I actively participated in endeavours that harnessed advanced analytics to extract profound insights from expansive datasets. Furthermore, my professional background has honed my collaborative acumen, bolstered my contributions to data-driven initiatives, and engendered actionable recommendations. 

I eagerly anticipate harnessing my expertise in data analysis, programming prowess, and problem-solving aptitude to propel innovation, facilitate data-driven decision-making, and profoundly contribute to the organisation’s achievements as a data scientist.

Example 4: Personal Statement for a Data Engineer Role

As a dedicated advocate of harnessing the transformative potential of data, I am thrilled to pursue a data engineer role where I can utilise my technical aptitude and drive impactful solutions. 

I have a Bachelor’s degree in Computer Science and professional experience in database management, ETL processes, and data pipeline development. I have established a strong foundation in data engineering principles. Proficient in SQL, Python, and cloud platforms, I can efficiently transform raw data into valuable insights. Throughout my academic journey, I actively participated in projects that involved designing and implementing robust data architectures, optimising query performance, and ensuring data integrity. 

I am eager to contribute my expertise and collaborate with cross-functional teams to construct scalable and reliable data infrastructure that empowers data-driven decision-making.

Example 5: Personal Statement for a Machine Learning Engineer Position

As a passionate advocate of leveraging machine learning to drive innovation, I am excited to pursue a position as a machine learning engineer where I can apply my technical expertise and problem-solving skills to develop cutting-edge solutions. 

I have gained a solid understanding of the field with a strong academic background in computer science and a focus on machine learning algorithms and model development. I have honed my data preprocessing, feature engineering, and model evaluation skills through my project work and internships.

I am eager to collaborate with interdisciplinary teams, apply my knowledge in practical settings, and contribute to creating intelligent systems that positively impact industries and society.

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Tips for writing an effective data science personal statement.

Here are some tried and tested tips for writing an impactful data science personal statement:

• Clearly express your deep enthusiasm for the field of data science: Begin your statement by articulating your genuine passion and excitement for data science. Elucidate what captivates you about data analysis, machine learning, or any particular aspect of data science that ignites your motivation.
• Emphasise your pertinent academic background: Highlight your educational qualifications, such as degrees, coursework, or certifications, that directly relate to data science. Discuss specific subjects you have studied, projects you have engaged in, and any research experience you have gained.
• Showcase your technical proficiencies: Outline the technical skills you have acquired, including programming languages (Python, R, SQL), statistical analysis, machine learning algorithms, data visualisation, or frameworks for big data processing. Offer concrete and quantifiable examples of how you have applied these skills in practical projects or academic assignments.
• Share your practical experiences: Discuss any internships, industry projects, or research opportunities where you have gained hands-on experience with real-world data and problem-solving. Highlight notable achievements, challenges you have confronted, and strategies to overcome them.
• Demonstrate your problem-solving capabilities: Elaborate on your approach to resolving intricate problems through data-driven methodologies. Expound upon how you analyse data, formulate hypotheses, design experiments, and assess results to extract meaningful insights and make well-informed decisions.
• Establish a connection between your goals and the field: Articulate your career aspirations clearly and explain how embarking on a data science career aligns with those ambitions. Discuss specific areas or industries that interest you, and elucidate how your skills and expertise can contribute to tackling challenges in those domains.
• Highlight your communication and teamwork skills: Acknowledge that data science entails effective communication and collaboration. Showcase instances where you have adeptly conveyed complex concepts to non-technical stakeholders or collaborated within multidisciplinary teams to achieve project objectives.
• Tailor your statement to the specific role or programme: Customise your statement to match the requirements and expectations of the data science position or programme for which you are applying. Conduct thorough research on the organisation or university to grasp their focal points, projects, or faculty expertise, and integrate pertinent details to demonstrate your suitability and alignment.
• Strive for concise and well-structured content: Maintain focus and conciseness throughout your statement, ensuring each sentence contributes value and reinforces your overall message. Employ a logical structure, commencing with an engaging introduction, developing well-articulated body paragraphs, and culminating in a firm conclusion.
• Carefully review and edit: Before submission, meticulously review your personal statement for grammatical, spelling, and punctuation errors. Verify consistency in tone, smoothness of flow, and compelling content. Seek feedback from reliable sources, such as mentors or professors, to obtain valuable insights and recommendations for refinement.

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Data science personal statement samples provide invaluable guidance and inspiration for aspiring data scientists. They catalyse self-reflection and help applicants align their own experiences and aspirations with the dynamic field of data science. By studying successful personal statements, you can gain insights into the key elements, structure, and content required to create a compelling personal statement. 

Armed with a professional certification like an Executive PG Programme in Data Science from IIIT Bangalore , you can craft personal statements that effectively convey your unique qualities, setting you apart and increasing your chances of securing admission or job opportunities in the competitive field of data science.

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Frequently Asked Questions (FAQs)

The statement of purpose for data science should encompass the reasons for selecting data science as a specialisation, aspirations for a career in the field, and a strategic plan outlining how the acquired data science education will be utilised to attain those goals.

It is advisable to share pertinent details of employment, internships, work experience, or voluntary engagements, particularly those relevant to the chosen course of study. Establishing a connection between the experiences and the skills or qualities that contribute to a candidate's potential for success is crucial.

The introduction of a personal statement should commence with an explanation of the rationale behind choosing the field of study, summarised in one or two sentences. Be original and avoid clichéd opening sentences, quotes, or overused expressions to maintain a fresh and engaging introduction.

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How To Write An Appealing Personal Statement For Masters Programme In Data Science

Illustration by How To Write An Appealing Personal Statement For Masters Programme In Data Science

• Published on July 31, 2020
• by Sejuti Das

Besides submitting test scores, recommendation letters, and an undergraduate transcript, one essential requirement of applying to a data science masters programme is the application essay — aka personal statement. The personal statement is where applicants need to convince the professors their ability and worth of getting selected in the master programme . 

In fact, many a time, a personal statement acts as a deciding factor for getting chosen for a prestigious masters programme in the field of data science . Thus, one needs to be extremely cautious while writing a personal statement for their master’s application. Not only does it help the university authorities determine the sincere interest of the applicant to enrol for the course, but also provide a chance to the students to stand out of the crowd highlighting their skills and relevancy.

Having said that, data scientists are experts in mathematics, but writing might not always be their expertise , and a personal statement is usually longer than you think and requires to be well crafted in order to grab the attention of professors and administrators. So, if decided to pursue higher studies in the data science field and have narrowed down universities to apply, this article can help you write a winning personal statement required to apply for the data science masters’ programme.

Also Read: The 10 Most Promising Data Science Masters Programs In US

Planning Is The Key: Highlight The Reason To Study Data Science Masters

Although it is stated as ‘personal,’ a personal statement doesn’t require applicants to share the intimate details of their life; instead, it needs to highlight the intention of the applicant for the particular master’s programme. To avoid any confusion or mistakes, the first step to write a personal statement for a data science masters programme is to brainstorm around it and plan it before actually starting to write. It is critical to make notes and use bullet points when planning, which can later be referred to while writing the personal statement. One should research thoroughly about the course requirements and the university, and prepare a list of their achievements and goals that can come handy while writing the essay. 

Most universities expect their applicants to adhere to a specific word limit for the personal statement, and thus a good brainstorming will help applicants to keep their essay relevant and to the point. Planning will help in setting the context, creating a structure and forming a narrative of the piece that is critical for drafting a compelling statement.

Also Read: What Not To Include In Your Data Science Resume

Have A Killer Intro & A Concise Conclusion: Relevant To The Passion For The Field

An attention-grabbing intro and a hard-hitting conclusion are again critical for writing a compelling personal statement. The first paragraph can create the first impression of the applicant in front of the professors, and a sharp end will help them remember that candidate among the crowd. The readers of the personal statement are the experts from the data science industry and academics; thus, they expect the writeup to be extremely intriguing in terms of content. 

Personal statements are usually lengthy but require to be extremely clear in sending out the message. Rather than starting the essay with some cliches, data science applicants should begin their personal statement highlighting their passion for the stream and their domain proficiency. And to have a definite ending, these data scientists must ensure to convey their genuine interest in pursuing the master’s programme, and how their skills are relevant to the stream.

Also Read: Tips And Templates For A Data Scientist Resume

Be A Good Story Teller: Highlight Experiences & Skill Sets

Thirdly, data science applicants must showcase their skill sets and experience in their personal statements without repeating the information that is already mentioned in their application form. And that’s why it is critical to be a good storyteller with their statement, where applicants can highlight their skills by talking about a particular data science research project that helped in solving real-life problems. One can also point out their experiences, knowledge, and quantify their expertise in the field that can help them in pursuing further studies.

The job of the personal statement is to let the administrators and professors know the abilities of the applicants to be qualified for the master’s programme. Data scientists can also mention their thesis, publications, journals or any relevant activities that can help them in getting selected. A well crafted personal statement avoids clichés, jargons, and too many details, and should be presented formally with a clear narrative.

Also Read: How To Create A Compelling Cover Letter To Land A Data Science Job

Focus On Your Domain & The Programme

Unlike undergraduate courses, masters programmes are more specific as well as require applicants to understand the domain they are pursuing. Consequently, while writing a personal statement, one needs to sync their interest according to the requirement of the programme and emphasise on the specific skills that match the area of expertise. One can also network with relevant faculty members and seniors to get a better understanding of the requirements of the program.

Many universities are also working on several ongoing data science projects, citing one of them corresponding to the interest, can also be a great addition to the personal statement. Furthermore, applicants can also write about what inspired them to pursue this particular domain and how their work will contribute to the field. One can also share their personal experiences and how that has helped in pursuing this course.

Also Read: What Data Science Graduates Need To Do To Get Hired During Covid-19

Don’t Be Generic: Customise The Write Up For The Course

Lastly, it is critical that the essay is unique and thus requires to be customised according to the university and its requirements. Applicants don’t have to start from scratch every time they are applying to a university, but they must ensure that they still provide a unique draft personal statement to each application. Professors and administrators read thousands of personal statements in a day, and therefore to be unique, applicants cannot pick up generic content to build their essay.

Applicants can also make the personal statement unique by adding up personal experiences relevant to the field, which will not only make the read interesting but also will allow the readers to empathise with the applicant. One can also add up a few of their failures to make it sound genuine as well as relatable. Usually, masters programmes don’t conduct face to face interviews; thus, personal statement plays a vital role in the applicants’ admission process.

Also Read: 10 Commonly Asked Puzzles In A Data Science Interview

Other Things To Keep In Mind

• Personal statements are not university applications, so don’t be repetitive.
• Highlight why this university is the right choice for the career you are planning to pursue.
• Although it’s the life experiences one shares in their personal statements, it indeed requires to be professional and to the point. 
• Avoid grammar, spelling, and punctuation errors.

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Data science personal statement example.

Ever since the commencement of high school studies, I was keen to know application of mathematics in factuality. Its abstract nature intrigued me to question my teachers about relevance and usefulness to mankind. I wanted to apply my learning and understanding invariably in real world circumstances.

It was until the introduction of Operations Research, while studying at college, which satiated my yearning to a certain extent. The problem solving and analytical techniques in Operations Research fostered the logic and decision making, which I believe, is one key component for developing models in real world or an approach towards mathematical modeling.

The process of creating mathematical representation of a real-world scenario to make predictions or provide insights captivated my interest. Although real world problems are often open ended and require iterative methods, but would also promote problem solving skills, creativity and innovation.

Besides Math, my interest in computing initiated right while coding the first program in C++ during bachelors. The backend scripting while using mobile phone, social media, scanning barcode at grocery or making online transactions, escalated my enthusiasm. I believed in the vigorous nature of programming and its effectiveness to engage world dynamically.

Also the symbiotic relation of computing and mathematics led me towards researching pathways blending the two disciplines, to evolve more powerful outcomes which would inherit the scientific and analytic approaches. While discussing it with a friend last year, I got introduced to the big data world and the interdisciplinary field of Data Science, combining aspects of statistics, mathematics, programming and domain expertise to address real problems. I was looking forward to traverse a similar realm and hence Data Science receives preference.

My previous studies helped to gain essential strategic and adaptive reasoning in the study of Algebra, Geometry, Statistics and Calculus. Integration of mathematics with computing widens the scopes and facilitates challenging yet interesting opportunities. This has instilled passion in me to explore computing specializations like Algorithms, Data structures and Programming fundamentals.

For the past six months I have been working diligently, enrolled in several courses to learn programming fundamentals with Python, SQL basics, Foundations of Data Science, k-Means clustering in Python and also Machine Learning concepts on self-learning platforms like Coursera, Udemy, Simplilearn and Youtube. Successful execution of programs like Conversion scales, time zones, board game and DNA processing using Python, exhilarated me and imparted encouragement. Although this helped me immensely to acquire foundational understanding of the subject. However, I needed more holistic education to gain mastery by pursuing formal studies and enrolling in university.

I started looking for reputed universities to pursue masters in Data science or related computer science streams. I figured that the role of data scientist is one of the most in-demand jobs currently in UK and US. Most likely its demand will originate in other regions of the world. My preference towards UK was obvious for couple of reasons. One year master’s program itself is an advantage from an international student perspective. Ever since inception, British universities are known for quality education and accreditation across the world. Many prominent world personalities from all walks of life, have graduated from English universities.

Joining on campus program at foreign land is much more than just universities. It is about the place, environment, people and also the facilities prevailing. As an expatriate living in Saudi Arabia, for about ten years, I have learnt this significantly. Cities like London, Birmingham, Manchester and Leeds provide residence to several expatriate communities exhibiting diverse vibrant cultures.

The world has reached a stage when every conceivable organization is becoming data-driven. Like the vast and ever-expanding universe, the big data fields are in a perpetual expansion mode, both fascinate me. Data is becoming more valuable in fast-paced life and this is creating a plethora of opportunities for data-centric roles in reputed organizations. It is no exaggeration to state that Data Science is making astonishing progress in the multiple domains of technology, economy, commerce and medicine.

After completing masters, I look forward to join established organization to acquire mastery and gain hands on experience in Data Science. However, time ahead, I would like to start a company where I can design advance models with the acquired knowledge and expertise from all the domains. For instance model like – ‘Accident free zones’ by collecting information from traffic flow, speed, regulations to make informed decisions regarding public safety or a ‘model school’ to be implemented by combining the best from several curriculums, adaptable for future generations.

For the past ten years I have been in the field of education, gaining expertise in teaching mathematics, designing curriculums and mentoring learning community. Although teaching-learning is my passion, and would definitely continue it as a hobby, however I am also a kind of person who believe in constant change and progress in life. This instinct supports me to accept demanding expeditions. My future endeavors would be to take slight detour from my current profession to establish myself in another exciting province, to leverage my knowledge, skills and career.

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BUILDING STELLAR PROFILES FOR DREAM ADMITS

Personal Statement that got selected to MS in Data Science, University of Pennsylvania (UPenn)

Penn’s Master of Science in Engineering (MSE) in Data Science prepares students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications.

It blends leading-edge courses in core topics such as machine learning, big data analytics, and statistics, with a variety of electives and an opportunity to apply these techniques in a domain specialisation – a depth area – of choice.

So, what does it take to get into one of the top 15 programs of the USA in Data Science? I mean, look at it! Wouldn’t you want to spend 2 years here?

So, let’s look at the ‘Personal Statement’ requirements what the UPenn lists out in its Admission Requirements:

• No more than two pages in a readable font/size:
• Why are you interested in this program?
• What have you done that makes you a great candidate?
• How will you benefit from the program?
• How do you plan to contribute to the student community in SEAS while you’re here?
• Why will you succeed in the program?
• What will you do/accomplish once you have completed the program?

As it is amply clear, the AdCom doesn’t want the details of EVERYTHING that you have done in your academic projects. You should answer only these 6 questions, in a way that best justifies your interest in this program. Right?

Here is where the Mridul got it wrong.

About Mridul

Mridul grew up in Mumbai, went to KJ Somaiya College Of Engineering in Mumbai, and had a GPA of 8.46.

He had a GRE score of 322 (167 Q 155 V) and a TOEFL score of 103.

His academic projects reasonably aligned with the research work at UPenn, one of his dream schools.

When he sent us the first draft, it was his entire story of all the things he has done till now. In total, it was 4 PAGES LONG!

So, our first revision was to cut short the massive piece of self-appreciation, to a workable draft of around 1000 words.

Then, we cherry picked some of the most notable projects of Mridul, and tried to address the questions that the AdCom was really looking for.

After 3 rounds of revision, we arrived at the final draft which looked like this:

As it can be seen, each paragraph of Mridul’s Personal Statement, tries to address a question which helps the AdCom to decide if you are the ‘RIGHT FIT’ for the class.

Since there was no hard limitation on the number of words, we stuck to around 1100 words to clearly and concisely tell Mridul’s story to the AdCom.

Mridul was accepted to three graduate programs – University of Pennsylvania (MS in Data Science), University of California Irvine (MS in Computer Science), and CMU (MS in Data Science)

We couldn’t be happier! Like this happy puppy.

Read Shrishti’s application journey to MS in Computer Science, University of Southern California (USC)

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Guide to Writing Data Science Personal Statements

Table of Contents

A  data science personal statement  is an integral part of the application process for aspiring data scientists. It provides recruiters and admissions board representatives insight into a candidate’s motivations, skills, and abilities related to their chosen field of study. 

The statement should demonstrate a clear understanding of the concepts and practices that make up the data science discipline. It showcases an applicant’s technical aptitude and professional experience. A successful personal statement will convey passion for the profession through emotionally resonant language and examples. 

Personal statements are everyday encounters in job applications as well as applications to special programs and postgraduate studies.

While personal statements and resumes both demonstrate an applicant’s qualifications, the former does so in paragraph form. This is crucial because it allows applicants a reasonable degree of creativity to create vivid depictions and powerful messages.

This allows them to not only create a good impression on readers but also to evoke emotions.

The Importance of a Personal Statement

The primary function of a personal statement is to give insight into the type of person you are. It provides recruiters and admissions board officers a glimpse into your qualifications . 

The actual value of a personal statement lies in its exposition. While resumes and summaries give readers the information they need pertaining to your qualifications, personal statements have a more intimate feel.

They read like stories. They take readers on a journey that helps them fully appreciate an applicant’s skills, experience, and character. Personal statements are particularly beneficial because they encourage recruiters and admissions board members to see candidates as more than just their qualifications. They are a way to show evaluators the person behind the application.

But, as good as all these sound, you can reap these benefits only through a compelling personal statement. If you’re unsure of how to write your data science personal statement, heed the following tips.

Tips for Writing a Data Science Personal Statement

Highlight Your Most Relevant Experience

Demonstrate your skills and accomplishments in data science by including concrete examples. Include experiences such as projects you have worked on or organizations/industries in which you have experience. Doing so will help to demonstrate that you possess the necessary qualifications for a successful career in data science. 

Showcase Personal Passion

Showcasing your passion for data science. You can do this by highlighting the personal challenges, successes, and motivations which led to your interest in the field. Explain what inspired you and how this has driven you to pursue further education and, ultimately, a career in data science. 

Be Specific

Make sure that when describing both experiences and achievements, they are as specific as possible.

Doing so will allow an admissions panel to better understand the nature of your work and its relevance to data science. Providing evidence to support statements (e.g., screenshots, code snippets, sample analyses) is also beneficial. 

Use Clear Language

Being clear and concise is essential when writing about complex topics like data science.

Aim to use language which conveys your points without overcomplicating them with jargon or technical terms. This will make it easier for an admissions panel to understand your application, increasing the chances of being accepted onto their program. 

Leverage Emotional Writing

The tone of your statement should reflect a human quality, using emotions and speaking authentically about why data science excites you. If appropriate, include colloquial language throughout; while ensuring it does not detract from the overall clarity of your essay.

Data Science Personal Statement Example

I have been deeply invested in the burgeoning field of data science for almost a decade. My expertise has allowed me to explore its nuances and applications with avid enthusiasm. I utilized my specialized knowledge to contribute significantly to many successful projects. As a result, I have accrued an immense portfolio of experience. My experience ranges from predictive analytics to natural language processing. This sets me apart as a leader in a rapidly-evolving industry.

From analyzing complex datasets to constructing scalable machine learning systems, my tenaciousness drives me to continually seek out dynamic challenges. Although I am thoroughly versed in all theoretical aspects of data science, I thrive on uncovering new possibilities through experimentation and creative problem-solving. I pride myself on being able to translate technical jargon into actionable solutions. 

A personal statement is a short paragraph that outlines a candidate’s skills, experiences, and motivation . It is an essential document because it allows applicants to connect with readers and establish a good impression. Remember our simple tips. While they won’t make you an expert overnight, they will help you cement good writing habits that will serve you well in the future.

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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Data Science MSc personal statement

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Like the vast and ever-expanding universe, the big data fields are in a perpetual expansion mode. Both fascinate me. The sheer volume and complexity of data being generated in today’s world present unprecedented opportunities for exploration and analysis. Harnessing the power of data can lead to groundbreaking discoveries, transformative insights, and innovative solutions to some of the most pressing challenges we face.

My education has provided me with a strong foundation to navigate this data-driven landscape. Through my coursework in computer science, I have gained a deep understanding of algorithms, data structures, and programming languages, all of which are crucial components in extracting meaningful information from large datasets. This knowledge has not only enabled me to work comfortably with computers and numbers but has also fostered my passion for leveraging data to uncover valuable insights.

Building on my educational foundation, my work experience in the field of technology and business management has allowed me to put theory into practice. I have had the opportunity to work on large-scale, data-intensive projects that have exposed me to the challenges faced by various industries and governments. The experience has strengthened my ability to analyze complex datasets, identify patterns, and derive actionable intelligence that can drive informed decision-making.

In this era of big data, organizations have come to realize the importance of transitioning from traditional methods to data-driven approaches. It is now essential to understand and process all aspects of data and analyze it effectively to arrive at optimal choices for decision-making. This realization has sparked a surge in demand for professionals who possess the skills and expertise to transform raw data into meaningful insights. I have been fortunate enough to develop hands-on experience with programming languages like SQL and JSON, as well as data visualization tools like Excel and Tableau. These practical experiences have allowed me to deepen my understanding of data analysis techniques and strengthen my ability to communicate complex information visually.

Furthermore, my passion for big data extends beyond technical proficiency. I am captivated by the immense potential of predictive analytics and artificial intelligence (AI). The ability to leverage advanced algorithms and machine learning models to uncover hidden patterns, forecast trends, and make data-driven predictions holds tremendous promise for addressing complex challenges across industries. By exploring these fields, I aim to contribute to the development and application of AI techniques that can empower organizations and individuals to make informed decisions and drive positive change.

As I look ahead, my goal is to be a Data Scientist and contribute to an organization’s data-driven decision-making processes. I am particularly excited about the prospect of applying my skills and knowledge to real-world scenarios, where I can utilize data to uncover insights and create innovative solutions. Ultimately, I envision establishing my own enterprise that focuses on mentoring and guiding the next generation of data scientists, fostering a community of individuals dedicated to using data for social good and addressing pressing challenges.

The MSc Data Science program with an industry placement at Essex University aligns perfectly with my aspirations. Its comprehensive curriculum and emphasis on practical application will provide me with the theoretical foundation and hands-on experience necessary to excel in the field. I am eager to immerse myself in an environment that fosters collaboration, innovation, and critical thinking, where I can learn from esteemed faculty and engage with like-minded peers.

In conclusion, my passion for big data, combined with my educational background and industry experience, fuels my desire to pursue the MSc Data Science program at Essex University. By expanding my knowledge, developing advanced analytical skills, and immersing myself in real-world applications, I am confident that I will be well-prepared to make a meaningful impact in the world of data science .

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9 of the Best Data Analytics Portfolios on the Web

Seeking some inspiration for your data analytics portfolio?

Whether you’re a newly qualified data analyst or a seasoned data scientist, you’ll need a portfolio that pops. While data analytics portfolios are traditionally about highlighting your work, they also need to show off your personality, your communication skills, and your personal brand.

In this post, we highlight our top nine data analytics portfolios from around the web. This includes screenshots, tips, and examples for how you can show your best side. While your portfolio should naturally include some strong projects, how you present yourself and your work is just as crucial as the content you’re sharing.

We’ll start by explaining why a data analytics portfolio is so important. If you want, you can skip straight to the good stuff using the menu below.

• Harrison Jansma
• Naledi Hollbruegge
• Anubhav Gupta
• Jessie-Raye Bauer
• Maggie Wolff
• Data analyst portfolio FAQ

First, though…

What’s the point of a data analytics portfolio?

As the first thing an employer sees, a strong data analytics portfolio needs to highlight your best work.

Given the complexity of data analytics, it might seem that a visual portfolio isn’t the best approach. The detail of data analytics projects can indeed be a bit mundane at times, but this is why a strong portfolio is so vital. Creating an engaging narrative is far more effective than simply linking to pre-existing code (although that’s required, too, of course).

Rather than simply telling people what you do, use visuals (where possible) to bring your work to life. After all, storytelling is a key skill for data analytics, a field where facts and figures are used to weave a narrative. Taking inspiration from the following, you’ll soon see how you can combine words, projects, and visuals to create a portfolio that shines.

1. Harrison Jansma

Who is harrison jansma.

Harrison Jansma is a US-based data process manager at Capital One. His website shows a clear passion for automating tedious tasks using tech. He also has a presence on  Medium .

What makes Harrison Jansma’s data analytics portfolio so great?

Harrison’s data analytics portfolio is a good example of how to use a portfolio to show off your personality. While he includes some sample projects of his work, just as much focus goes into creating a sense of his personal brand, using fun graphics, choice words, and a taste of his interests.

Right on the homepage, we see a large photograph of Harrison’s friendly face, and a short quote: ‘Contemplative coder and analyst. Inspired by tough problems.’ This description is intriguing and drives us on. Below, Harrison highlights his interest areas and his three most recent projects as exemplars.

This is a clever approach. Sometimes it’s hard to know which projects to share. Incorporating exemplar projects on your homepage is a good way to highlight them while having the option for people to see more if they want to.

It’s also worth noting that Harrison doesn’t get into detailed case studies on his website. Instead, he links directly to his projects. This is quite common practice.

What can we learn from Harrison Jansma?

These days, it’s important to cultivate your personal brand. Harrison Jansma shows us how to introduce personality to your portfolio. He even has a page including pictures of his dog! While it’s your choice how much you want to share about yourself, Harrison’s approach humanizes him while remaining unobtrusive and professional.

Key takeaway

Showcase your best work, with an option for viewers to read more. And if you add a dash of personality, your portfolio examples needn’t be super slick.

View Harrison Jansma’s full portfolio website

2. Naledi Hollbruegge

Who is naledi hollbruegge.

Naledi is a freelance consulting analyst and social researcher based in the UK. She believes that data has the power to make the world a better place, and wants to play her part in that process. Sounds pretty admirable!

What makes Naledi Hollbruegge’s data analytics portfolio so great?

There’s a clear drive in Naledi’s portfolio to find clients. With this in mind, the first thing Naledi flags is her ability to carry out all the key jobs of a data analyst (collecting, processing, and visualizing data). She then dives right in with a quick introduction followed by some project samples.

This portfolio is a prime example of good storytelling. First, Naledi tells us what she can do. Next, she demonstrates it with some projects that highlight those skills, adding an extra layer to the tale. While remaining professional, she also gives us a taster of her interests.

For instance, she has a clear focus on social justice. This is shown with her link to a Tableau project exploring perceptions of discrimination . Another project explores  girls’ rights and well-being . Naledi has implicitly shown us her ethics, strengthening the value of her business proposition. This is something to consider when creating your portfolio projects.

What can we learn from Naledi Hollbruegge?

Naledi demonstrates how to use your portfolio to tell a story. She achieves this brilliantly with a combination of personal statements and supporting projects. In addition to her portfolio,  she also maintains a blog where she writes about her interests. Combined, these aspects all tell us that she believes in the power of data analytics to change the world. That would certainly make us want to hire her!

For some, data analytics is just a day job. That’s fine. But combining client work with personal projects  will show that data analytics is more than just a professional interest—it’s something you’re dedicated to.

View Naledia Holbruegge’s full portfolio website

3. Tim Hopper

Who is tim hopper.

Tim Hopper is a data scientist, machine learning engineer , and cybersecurity software developer based in the US.

What makes Tim Hopper’s data analytics portfolio so great?

Tim’s is a great example of a multimedia portfolio. Rather than bombarding us with a list of his past projects, he’s given us a taste of his interests and expertise using a variety of different methods. This includes a combination of humor, podcasts, articles, and videos that tell us what he does and show us how he works. The details of his projects, meanwhile, are available on his GitHub (which he links to in numerous places on his website).

On his homepage, Tim quickly grabs our attention with unobtrusive but bold visuals, and a headline that tells us his key skills: ‘Machine Learning. Cybersecurity. Python. Software Engineering. Math Jokes.’ This provides a nice taste of his experience, as well as his personality. Notice that the top menu includes links to Tim’s podcasts, talks, and articles, as well as other sites of interest. He also links out to his social media: Twitter, LinkedIn, and GitHub. These offer more in-depth information about his professional background.

Interestingly, Tim doesn’t use his portfolio to discuss specific projects. This is an admittedly bold move. Instead, he sells himself as a thought leader and data influencer , writing about his experiences and sharing talks and podcasts about his time as a data scientist.

This is a high-risk but high pay-off strategy, and Tim executes it well. While this approach is better suited to more experienced data scientists, that doesn’t mean we can’t learn from it. Using articles, videos, and podcasts gives him a legitimate excuse to enrich his portfolio with additional media. His website is also laden with personality. He has a sense of humor, which is an appealing quality in itself.

What can we learn from Tim Hopper?

Tim shows us that you don’t need a traditional portfolio to make an impact. By creating a distinctive personal brand, you can boost your profile with more than just sample projects. By including his articles, videos, and podcasts, Tim has given us direct proof that he is a competent communicator and data scientist. If we want to find out more, we can contact him via his numerous social media platforms.

Think about the different ways you can spice up your portfolio. While we always recommend including sample projects (which Tim hasn’t done) you can definitely still enhance your offering by evidencing your other interests. Create a unique offering that nobody else can compete with.

View Tim Hopper’s full portfolio website

4. Ger Inberg

Who is ger inberg.

Ger is a Dutch freelance data scientist with a background in software engineering. He has a flair for data visualization and machine learning.

What makes Ger Inberg’s data analytics portfolio so great?

Ger Inberg’s portfolio is a standard website, created using a WordPress template. When we see this, we might wonder why he hasn’t used the opportunity to demonstrate his web design skills. But no matter…After a brief introduction, Ger’s portfolio projects are the very next thing we arrive at.

Using a simple but effective menu, it’s clear at a glance where Ger’s skills lie: data visualization, machine learning, and web development. Viewers can also filter projects by clicking on the relevant topic. But then, Ger directs us to a page where we can view data visualization apps that he’s created using R-Shiny (an R package for interactive web apps). Now we see his web design expertise in action! By showing data right in the app, he’s demonstrating both his visualization and web development skills—a great combination that highlights his expertise without shouting about it.

What can we learn from Ger Inberg?

Something noteworthy about Ger’s portfolio is that he has chosen interesting and topical datasets for his portfolio projects. These include things like global life expectancy, the spread of the coronavirus, and even an overview of which major cities are dominated by digital nomads (i.e. those who use tech to work remotely—a bit like Ger himself!)

While data analytics portfolios need to include code and other technical information, it’s good to balance granular detail with interesting datasets, and something a bit more interactive and visual.

Keep things visual if you can. Creating dedicated interactive apps or dashboards will show your coding capabilities (if you have them) as well as your ability to create memorable visualizations.

View Ger Inberg’s full portfolio website

5. James Le

Who is james le.

James Le is a data scientist, machine learning researcher, journalist, and podcaster. His enthusiasm is infectious—his portfolio makes it clear that he eats, sleeps, and breathes data science!

What makes James Le’s data analytics portfolio so great?

James Le’s website is nothing if not comprehensive, detailing all his data science exploits. While this could easily be overwhelming, he neatly breaks his website down to help visitors focus on what they’re looking for. Namely, his journalism, his academic research, and—in our case—his data analytics expertise.

Clicking through to James’ data analytics portfolio, the header is immediately attention-grabbing. The headline ‘The sexiest job of the 21st century’ tells us that he doesn’t take himself too seriously. His portfolio remains professional, though. He also has a separate coding portfolio, as we see below.

Once again, James uses a headline that isn’t afraid to show a little personality. This helps bring to life what could otherwise be quite dry content. What do we mean? Well, most of James’ projects are code-based, linking directly to files on GitHub. However, he’s still managed to keep his portfolio popping by using bright splash images. These provide a nice visual front-end. To illustrate this, the first image below is what we see on James’ website. The second is the notebook document that we click through to on GitHub.

What can we learn from James Le?

James does a fantastic job of presenting all his projects using Jupyter Notebook and R-Notebook. These formats (a bit like interactive MS Word documents) combine interactive code with text and visual elements to present data analytics work clearly and consistently. Readers know what they’re getting.

If data visualization isn’t your strongpoint, create portfolio projects using Jupyter Notebook or R Notebook. These tools are designed for presenting data analytics findings. You can host them on GitHub and hide the link behind a more appealing visual image on your portfolio.

View James Le’s full portfolio website

6. Yan Holtz

Who is yan holtz.

Yan is a data analysis and visualization specialist. He works as a software engineer at Datadog, a global cloud-monitoring service headquartered in the USA.

What makes Yan Holtz’s data analytics portfolio so great?

For style and substance combined, Yan Holtz’s data analytics portfolio is something to aim for. From the moment you land on his homepage, the interactive design (by Yan himself) grabs attention and shows off his skills.

See those geometric shapes on the homepage? They’re not just pretty to look at. They’re also dynamic and interactive, responding to the movement of the mouse. This is technical stuff; Yan’s clearly no novice. Of course, your own portfolio doesn’t need these fancy extras, but it highlights what you can achieve if you’re feeling ambitious.

What’s more, this is not a case of style over substance. It would have been easy for Yan to simply create a slick front-end that links out to other websites, such as GitHub. Instead, for each project, he’s created an appealing pop-up, offering a clear overview of what he’s worked on. As a reader, this makes for a satisfying user experience. Once we’re done, we can finally explore his projects in more detail via an app, or on GitHub.

Lastly, Yan also offers a broad range of project types from genotype sequencing, to where surfers travel. He’s implicitly telling us that (although he specializes in data visualization) he can work with all kinds of datasets. He tops this all off with customer testimonials, something many portfolios neglect to include.

What can we learn from Yan Holtz?

Yan’s eye for detail is what makes his portfolio a winner for us. He’s executed the entire thing with seemingly-effortless panache. This shows what a difference it makes if you invest extra time into your portfolio. While the level of interactivity on Yan’s website is by no means necessary, it’s a nice demonstration that a little extra focus can make a big difference. But it’s not just the code—even just the testimonials, or keeping his case studies self-contained (rather than linking directly to projects on GitHub) makes an impact.

Show that you have an eye for detail. Pay attention to your design, the language you use (and the spelling!) as well as the projects you’re promoting.

View Yan Holtz’s full portfolio website

7. Anubhav Gupta

Who is anubhav gupta.

Anubhav Gupta is a data analyst and graduate from the School of Information at UC Berkeley. He’s worked at several global cybersecurity companies.

What makes Anubhav Gupta’s data analytics portfolio so great?

What makes Anubhav’s portfolio stand out is how compact it is. All the supporting information is contained on one short web page—two quick scrolls from top to bottom. The benefit of this approach is that nobody will get bored or lose track of where they’re at.

The first thing we see is a clear, unfussy headline. This tells us everything we need to know—who Anubhav is and what he does.

Next, Anubhav introduces himself in a little more detail—still nothing heavy, he’s saved the details for his resume—but it gives us a taste of his interests, experience, and personality.

Finally, Anubhav dives right in with his projects. His projects take a slightly different approach from many of the others we’ve looked at. He primarily focuses on his roles, e.g. as a product manager or machine learning engineer, rather than the project content. He saves the detail for his case study pages. Each of these makes good use of headings, images, and neat layout to keep the messaging clear, compelling, and consistent.

What can we learn from Anubhav Gupta?

Despite having the skills to create a ‘flashy’ portfolio, Anubhav has gone for clarity and precision first and foremost. He’s provided bite-sized information which nevertheless covers everything that it needs to. Ultimately, his portfolio shows us that less is sometimes more and that a little humility goes a long way. Remember, it often demonstrates greater confidence to include a smaller handful of projects rather than stuffing in everything along with the kitchen sink. Sometimes less is more!

Keep your portfolio simple. A few short, confident sentences about who you are and a couple of sample projects are all you need.

View Anubhav Gupta’s full portfolio website

8. Jessie-Raye Bauer

Who is jessie-raye bauer.

Jessie-Raye Bauer is a data scientist working at Apple. She has a Ph.D. from The University of Texas at Austin and is trained in cognitive psychology and statistics.

What makes Jessie-Raye Bauer’s data analytics portfolio so great?

Jessie-Raye Bauer’s portfolio is an interesting example of where a career in data analytics can take you. Just like many of the other portfolios we’ve explored, Jessie-Raye focuses on her skills, adding a dash of personality. However, her portfolio is distinctly academic in feel. You might expect a data scientist of Jessie-Raye’s caliber to show off more. But she doesn’t need to. As a data scientist at Apple, she is at a point in her career where her experience largely speaks for itself.

Unlike many data science portfolios, Jessie-Raye hasn’t included links to projects in the traditional sense. Instead of linking right to GitHub projects or using traditional case studies, Jessie-Raye has chosen to showcase her work via her blog. This is more appropriate for her skill level. The complexity of the work she’s doing lends itself well to the detailed medium of a blog. It’s also in keeping with her more academic background.

A blog is an interesting way of exploring the data analytics journey alongside the author. By blogging, readers can share experiences, rather than merely reading about completed projects. This is always more engaging and insightful. Jessie-Raye also uses topics that interest her personally. For instance, one blog post is all about how to create your own Fitbit API , which begins with an explanation that she is a new Fitbit owner.

What can we learn from Jessie-Raye Bauer?

There’s nothing overly ambitious about Jessie-Raye’s portfolio. It’s even been built using a template. This tells us that those at the top of their game (who, it should be noted, are also those who often have hiring power) aren’t necessarily focused on how slick or flashy your portfolio is. As we can see from Jessie-Raye Bauer, it’s ultimately the content that matters.

View Jessie-Raye Bauer’s full portfolio website

Consider alternative options for showcasing your work. Could you use a blog? An app? Heck, could you even create a visual data analytics essay, a  bit like this one ? Consider what novel approaches you can take that will help you to stand out.

9. Maggie Wolff

Who is maggie wolff.

Last, but by no means least, Maggie is a seasoned data scientist and product analytics aficionado currently working for American Express Global Business Travel.

What makes Maggie Wolff’s data analytics portfolio so great?

Maggie has created an excellent example of a clear, attractive, accessible GitHub portfolio site. Don’t confuse the simplicity for the work of a rookie—this is a thoughtful site, showing a bit about her, her CV/resume, portfolio projects, and her related passions of the talks she gives as well as some blog articles exploring her journey into data science.

What can we learn from Maggie Wolff?

It’s easy to get wrapped up in the design and layout of your own site, wanting to show things off in the coolest way possible. But ease off, and make sure that your website is simple to negotiate around. It’s vital when constructing your site to think about the user , in this case, potential hiring managers and future contacts.

View Maggie Wolff’s data portfolio site

While it might not be one of the flashiest examples out there, that is precisely the point—Maggie’s data analyst portfolio is effective. For someone who is involved in so many things, it lays them out easily and accessibly.

10. Data analyst portfolio FAQ

What should i put in my data analyst portfolio.

Your data analyst portfolio should showcase your skills and experience in the field. This can include projects you’ve completed, data visualizations you’ve created, and analyses you’ve conducted. It’s important to ensure that your portfolio demonstrates your ability to work with real-world data and solve complex problems using data analysis techniques.

Do data analysts need portfolios?

While a data analyst portfolio isn’t strictly necessary, it can be a valuable tool in showcasing your skills and experience to potential employers. A well-crafted portfolio can help you stand out from other candidates and demonstrate your ability to work with real-world data. Additionally, creating a portfolio can help you develop your skills and gain practical experience in data analysis.

Can I make 100k as a data analyst?

Yes, it is possible to make 100k as a data analyst, but it depends on a number of factors, such as your level of experience, the size and location of the company you work for, and the specific job responsibilities. Generally, more senior roles, such as data science manager or data architect, are likely to pay higher salaries than entry-level data analyst positions.

How do I start a data portfolio?

To start a data portfolio, begin by identifying projects or analyses that showcase your skills and experience in data analysis. This can include analyzing publicly available data sets or completing projects for non-profit organizations or local businesses. Use data visualization tools, such as Tableau or Power BI, to create visually compelling representations of your findings. Finally, ensure that your portfolio is well-organized and easy to navigate, with clear descriptions of each project and your role in completing it.

And that’s the end of our list! If you’re considering a new career path in data analytics, why not check out our list of the best online data analytics courses to get your career-changing journey on its way, or get a taster with our free, five-day data analytics short course? You can also find more portfolio inspiration below:

• How to build your data analytics portfolio from scratch
• 9 Project ideas for your data analytics portfolio
• 10 Great places to find free datasets for your next project

Data Governance Analyst CV Example

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CV Tips for Data Governance Analysts

• Specify Your Data Governance Expertise : Highlight your knowledge and experience in data governance principles, data management, data privacy, and compliance. Mention any specific industries you've worked in, such as finance, healthcare, or IT.
• Quantify Your Achievements : Use specific metrics to demonstrate your impact, such as "Implemented a data governance framework that improved data quality by 30%" or "Reduced data breaches by 20% through stringent data governance policies".
• Align Your CV with the Job Description : Tailor your CV to match the specific requirements of the job. If the role emphasizes data privacy, highlight your experience in implementing data privacy laws and regulations.
• Highlight Your Technical Skills : Mention your proficiency in data governance tools such as Collibra, Informatica, or IBM's data governance solutions. Also, highlight your skills in SQL, data analysis, and data modeling.
• Demonstrate Your Soft Skills : Showcase your communication skills, leadership abilities, and your knack for problem-solving. Mention instances where you've collaborated with different teams or led a data governance project.

The Smarter, Faster Way to Write Your CV

• Implemented a comprehensive data governance framework, resulting in a 30% improvement in data quality and a 20% reduction in data-related issues across the organization.
• Championed the adoption of a new data management system, leading to a 40% increase in data processing speed and enhancing the accuracy of data-driven decision making.
• Managed cross-functional teams to ensure adherence to data governance policies, resulting in a 15% decrease in compliance risks and potential penalties.
• Developed and enforced data governance policies, leading to a 25% improvement in data integrity and consistency across all departments.
• Conducted regular data audits, identifying and rectifying data discrepancies that saved the company an average of $60,000 annually in potential fines.
• Collaborated with IT to design a custom data dashboard, providing real-time data metrics that supported strategic decision-making and increased operational efficiency by 20%.
• Played a key role in the development of a data governance strategy, leading to a 10% improvement in data accuracy and a 15% increase in data utilization for strategic planning.
• Conducted detailed data analysis, uncovering insights that led to a 5% increase in revenue and a 10% reduction in operational costs.
• Facilitated training sessions on data governance policies and procedures, improving staff compliance by 30% and reducing data-related errors by 20%.
• Data Governance Framework Implementation
• Data Management System Adoption
• Cross-Functional Team Management
• Data Governance Policy Development and Enforcement
• Data Auditing
• Collaboration with IT for Data Dashboard Design
• Data Governance Strategy Development
• Detailed Data Analysis
• Data Governance Training Facilitation
• Data-Driven Decision Making

Data Governance Analyst CV Template

• Worked closely with [teams/departments] to implement [data governance initiative, e.g., data quality improvement, data privacy regulations], demonstrating strong [soft skill, e.g., collaboration, project management].
• Managed [data governance function, e.g., data classification, data lineage mapping], improving [process or task, e.g., data cataloging, metadata management] to enhance [business outcome, e.g., data accessibility, decision-making].
• Implemented [system or process improvement, e.g., data governance software, data stewardship program], resulting in [quantifiable benefit, e.g., 20% increase in data quality, reduced data breaches].
• Contributed significantly to [project or initiative, e.g., data governance framework development, data privacy compliance], leading to [measurable impact, e.g., improved data integrity, regulatory compliance].
• Conducted [type of analysis, e.g., data quality assessment, data risk analysis], utilizing [analytical tools/methods] to guide [decision-making/action, e.g., data policy formulation, data strategy development].
• Key player in [task or responsibility, e.g., data governance training, data lifecycle management], ensuring [quality or standard, e.g., data consistency, adherence to data governance principles] across all data assets.
• Major: Name of Major
• Minor: Name of Minor

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