How to Build a Data Analyst Portfolio That Will Get You Hired?

The best guide for building a data analyst portfolio that will get you hired at one of the top tech companies.

How to Build a Data Analyst Portfolio That Will Get You Hired?
 |  BY Daivi

Are you seeking a job as a data analyst? Your data analyst portfolio is an opportunity to demonstrate your ability to tell a story, which is a crucial data analyst skill. This time, though, the tale is about you as a professional, not simply facts. Nothing beats facts when it comes to conveying the power of a tale, and your data analyst portfolio is your chance to illustrate how your story may connect with that of the organization you're applying to.

how to build a data analyst portfolio


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The Ultimate Guide to Build a Data Analyst Portfolio

In this blog, we'll provide you with some pointers to show you how to build a data analyst portfolio. We'll discuss what to put in your portfolio, the abilities you'll want to highlight, and how to create and present your work.

So, let us begin the journey towards building the perfect portfolio as a Data Analyst!!

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Data Analyst Portfolio Platforms

The first step in putting together a data analytics portfolio is deciding where it will be hosted. You need not invest a lot of money or start from scratch to create your own data analyst portfolio website. Aside from determining what should be included in a data analyst portfolio, consider your options for putting one together. While having a printed copy in your portfolio or briefcase is always a good idea, it may be big, costly, and time-consuming to update. Regardless of how amazing your physical portfolio appears, you'll want to adopt a digital one.

Consider these free data analyst portfolio platforms if you're just getting started:

  • Github: GitHub, an open-source community of 56 million developers, is one such popular choice for hosting your portfolio for free. After creating an account, you may begin contributing data analytics portfolio projects to a public repository, where you can publish items such as code and Jupyter Notebooks.

  • Linkedin: LinkedIn makes adding, modifying, and removing projects from your profile, which may also serve as an online portfolio, quite simple. You may upload and share a variety of content types because the platform supports a variety of formats (.jpeg, PDF, PowerPoint, Word, and others). You can include data analyst projects for portfolio in your Featured, Experience, or Education categories on LinkedIn.

  • Kaggle: Kaggle, a Jupyter Notebooks cloud environment that may be modified, can also be used as a free data analyst portfolio template of your work. You may showcase the results of any Kaggle data science competitions you've entered, as well as any data sets or code you've developed.

You might want to consider switching your portfolio to a separate website as your career progresses and your portfolio expands. Use a site like SquareSpace or Wix to host your portfolio, which offers pre-made templates and easy drag-and-drop customization. If you're comfortable with HTML, you can host your site using WordPress, which gives you even more choices.

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Skills to Showcase On Your Portfolio for Data Analyst

The need for professionals who can harness critical information inside data science to deliver value to a business is growing in a world dominated by big data and automation. A comprehensive approach is the greatest way to become a well-rounded and professional data analyst. Not only will you require great technical skills, but also effective soft skills, a thorough comprehension of statistical analysis, and good data visualization talents. The following is a list of the data analyst skills you'll need to learn if you’re willing to work on entry-level data analyst portfolio examples-

1) SQL 

The common language for communicating with databases is SQL or Structured Query Language. Knowing SQL allows you to alter data structures as well as modify, organize, and query data contained in relational databases (schema). SQL is likely the most necessary skill to master to gain a job because practically all data analysts will need to access data from a company's database. In reality, a technical screening using SQL is a regular part of data analyst interviews. Fortunately, SQL is among the simplest languages to grasp.

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2) Math, Probability, and Statistics

Statistics is a branch of mathematics and science concerned with the collection, analysis, interpretation, and presentation of statistics. That may seem familiar since it closely resembles the job description of a data analyst. You'll be best able to:

1) detect patterns in data

2) avoid distortions, inconsistencies, and logical errors in your assessment,

3) produce accurate and consistent outcomes if you have a solid base in probability and statistics.

3) Programming Languages

Programming languages, such as R or Python, allow you to conduct complex analysis, i.e., you can clean, analyze, and display massive data sets more effectively using these languages. Since both languages are open-source, listing at least one of them will be a great add-on to your portfolio. Both languages are capable of doing similar data science tasks. While R was created with analytics in mind, Python is the more popular of the two and is generally easier to pick up.

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4) Data Visualization

The data analysis process includes more than just extracting useful insights from data. Another important aspect is to use those insights to construct a narrative that will help you make better business decisions. This is where data visualization enters the picture. As a data analyst, you may present your results in an easy-to-understand manner by using charts, graphs, and many other visual representations of data. Learning visualization tools, such as Tableau, is a common way to improve your data visualization abilities. This industry-standard application allows you to turn your data into dashboards, data models, visualizations, and business intelligence reports.

5) Critical Thinking

Critical thinking entails studying a situation, weighing the many choices, and coming up with workable solutions. You may demonstrate to a potential employer that you can apply critical thinking to develop their business by outlining the issues, actions, findings, and solutions that your data analysis projects have accomplished.

A good data analyst portfolio template will demonstrate to potential companies that you can use data to solve issues and discover new possibilities. A visually engaging presentation that emphasizes the importance of your analysis to an organization might propel you to the top of their priority list.

How to build a Data Analyst Portfolio- Data Analyst Portfolio Template

There are three things you must keep in mind while preparing a data analytics portfolio- simplicity, easy navigation, and visual appeal. For data analytics careers, communication is the key. Make sure your portfolio is accessible to both technical and non-technical audiences. To a non-technical audience, a data analytics portfolio must clearly describe the value of a data product or a model that you've created. Here's an overview of what you should include in your portfolio for data analyst role:

1) An ‘About Me’ Section

The "About me" section allows you to explain to potential employers a little bit about yourself, what you do, and why it's important to you. While writing the content for this section, keep in mind the following questions:

1) How did you get started with the field of data analysis?

2) What aspect of data intrigues you the most?

3) What are your areas of interest with respect to data analytics?

Also, this is a great place to include your contact information and links to your social network profiles, as it allows employers to contact you about potential analytics work opportunities that perfectly suit your expertise.

2) Your Data Analytics Projects for Portfolio

Understanding a business problem, extracting data with SQL, data cleansing and validation using Python or R, and lastly, visualizing the insights for successful business choices are all part of a data analyst's job description. Model-based analytics projects that demonstrate all of the data analytics abilities necessary to turn data into meaningful insights should be included in a data analytics portfolio. A data analytics portfolio isn't the place to include every project you've ever worked on. Only include data analytics portfolio projects that are relevant to the job position you're looking for, and each one must be carefully picked.

Focus on showcasing the following while compiling your portfolio and considering what kind of projects to include:

1) The potential to collect (or "scrape") relevant data from several sources.

2) The skill to clean datasets completely.

3) The ability to do various kinds of analysis (e.g., descriptive or diagnostic), as well as the application of various analytic techniques (e.g., time series analysis, A/B testing, regression analysis, and so on).

4) The ability to illustrate your findings using graphs, charts, geomaps, and other appropriate visualizations.

5) A solid fundamental knowledge of SQL. For this purpose, you should try looking for some SQL projects for data analyst portfolio.

6) The skill to convey complicated results in a concise manner (for example, through your project write-up or in a blog post).

7) The ability to operate as part of a team—include a group project if possible.

8) Any expertise you've acquired, such as Python knowledge, that can make you stand out as a data analyst.

Data Analyst Projects for Portfolio

Furthermore, there are three major types of data analytics portfolio projects-

  1. Data Scraping projects

  2. Data Visualization projects

  3. Exploratory Data Analysis projects

Here are some project ideas belonging to these categories that will help you start building your data analytics portfolio from the ground up-

  1. Customer Market Basket Analysis

The Market Basket Analysis is a data mining technique that helps businesses reach a wider audience and, as a result, increase revenue. Customers' purchasing patterns are studied in order to identify various product combinations that are purchased together. The notion is that if a consumer buys a specific item or set of things (let's call it Product 'A'), it enhances the possibility that the customer will also buy another item or group of items (let's call it Product 'B'), meaning that interest in A could also entail interest in B. By assisting with customized advertising, referrals, and cross-selling, Market Basket Analysis can help enhance sales. In fact, the patterns discovered through market basket analysis can be used to write menus. Aisles and commodities in grocery shops can be organized to accommodate things that are regularly purchased together. The Apriori and Fp Growth data mining techniques can be used to do client market basket analysis.

Customer Market Basket Analysis with Source Code

  1. Churn Prediction using Telecom dataset

Businesses rely on new consumers to succeed, but it is necessary to keep existing clients in order to reduce customer acquisition costs. When a client decides to cease consuming a company's products or services, this is known as customer churn. Existing customers must maintain their relationship with the company, just as they must in other industries. Call dropouts, network unavailability, service quality, and reduced pricing from competitors can all contribute to customer churn in the telecom business. The chance of customer churn for individual customers can be calculated by studying customer data. A thorough analysis of the results of this study might alert telecom businesses to areas that need to be paid attention to in order to improve customer happiness. This can aid telecom firms in providing better service to their customers, reducing customer churn, and maximizing profit. Customer churn prediction can be made using machine learning approaches.

Churn Prediction using Telecom Dataset with Source Code

  1. Event Data Analysis

The method of applying business logic to process and analyze data that is streamed at the event level in order to produce data that is more suitable for querying is known as event data analysis. The activities taken by numerous entities are referred to as event data. A single data point in event data might be connected with several entities, all of which contribute to the occurrence of a certain event. It's vital to remember while working with event data that events can be dynamic, constantly influenced by outside events, and non-linear, meaning they don't happen at regular intervals. This is why event data analysis is such a challenging feat. The purpose of event data analysis is for the system to be able to identify key events based on patterns found in past data occurrences. Opportunities and threats can be predicted ahead of time so that responses can be made before they occur. If the prediction isn't feasible, event data processing can ensure that timely reactions are available after an event takes place when such a situation occurs.

Event Data Analysis with Source Code

If you’re building an entry-level data analyst portfolio, you can check out some beginner-friendly data analyst project ideas to help you get started.

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3) Blogs, Testimonials (Clients, Previous Employers, etc.)

Consider writing blog articles on your approach and insights as you work on projects. In this way, you can flaunt your communication skills while also reiterating what you've learned. Testimonials or feedback from former employers or any freelance customer with whom you've worked is a great way to convince prospective employers that they'll be content with the analytics projects you make for them. Make it a point to put in your portfolio if you were highlighted in an analytics newsletter or if an analytics expert you like tweeted about your innovative research. Testimonials regarding your work reflect the fact that you possess the data analytics skills you mention on your CV.

Apart from the above-mentioned items, make sure to mention any significant analytics events you've attended, such as analytics conferences, meetings, webinars, or workshops. Mention your takeaways from these events and how they aided you in expanding your knowledge. 

Data Analyst Portfolio Examples - What You Can Learn From Them?

Given the complexities of data analytics, it may appear that a visual portfolio isn't the greatest solution. The precision of data analytics projects can be tedious at times, which is why having a solid portfolio is so important. Use images (where feasible) to bring your job to life rather than simply telling them what you do. Using the data analyst portfolio examples below as inspirations, you'll quickly learn how to blend words, projects, and pictures to build a portfolio that stands out and will get you hired.

1) Ger Inberg

Ger is a data scientist with a background in software engineering who works as a freelancer in the Netherlands. Data visualization and machine learning are two of his strong suits.

Highlights of His Portfolio:

Ger Inberg's portfolio is a regular WordPress template-based website. His portfolio projects are the next item we come to after a quick introduction.

 

 

Ger's portfolio is notable for his selection of intriguing and timely datasets for his portfolio projects. Speed skating Winter Olympic medalists, worldwide life expectancy, US airline delays, and a slew of other topics are among them.

 

While data analytics portfolios must contain technical details, Ger’s portfolio manages to strike a balance between granular detail and fascinating datasets. What you can learn from his portfolio is that he tries to keep things as much visual as possible. Dedicated interactive applications or dashboards will demonstrate your coding skills, as well as your ability to develop distinctive visuals.

2) Naledi Hollbruegge

Naledi is a social researcher and consultant located in the United Kingdom. She feels that data has the potential to transform the world, and she wants to be a part of that process.

Highlights of Her Portfolio:

 

 

Having a quest to seek clients, Naledi emphasizes her ability to perform all of the essential functions of a data analyst (collecting, processing, and visualizing data). She then jumps right in with a short overview and some project examples.

 

2.

Naledi teaches how to convey a message with your portfolio. Using a combination of personal remarks and supporting projects, she does this beautifully. She has a blog where she talks about her hobbies, in addition to her portfolio. These characteristics, taken together, indicate that she believes in the ability of data analytics to transform the world. Naledi’s portfolio shows you that combining client work with personal initiatives will demonstrate that data analytics is more than just a hobby for you—it's something you're passionate about.

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3. Claudia Ten Hoope

Based in the Netherlands, Claudia Ten Hoope is a data scientist and freelance data analyst. Her stated purpose is to assist companies in making the best data-driven decisions possible.

Highlights of Her portfolio:

 

 

An excellent illustration of keeping things simple and basic, Claudia’s portfolio concentrates on the task at hand. Her goal as a freelancer is to find clients, and her portfolio clearly depicts it.

The site is straightforward and uncomplicated. It employs a subtle pastel palette to avoid any flashy design. It's short and to-the-point, implying that Claudia is trustworthy and direct, which are the two important qualities for any data analyst.

 

Claudia instantly mentions the services she provides, following a brief section outlining her abilities. Data analysis, dashboards, interactive visualizations, and training are just a few of the services available. This is significant since these are the fundamental abilities that each data analyst need. Most importantly, each skill is then linked to a real-life case study. These are also available in the portfolio area lower down. Claudia's user journey shows that she has given it a lot of attention. She then directs visitors to her projects.

Claudia's project examples address common business issues, such as how you can construct KPI dashboards or what can be done to improve sales funnels. While the material is geared toward business, these are the abilities that employers are looking for. Claudia does an excellent job of merging images and headlines to bring all the contents to life. Claudia's style is notable for its confidence and sincerity. Keeping in mind how busy employers are, the page has been designed to auto-scroll to the essential information via a clickable menu, which is time-efficient. 

The best thing you can learn from this data analyst portfolio example is to concentrate on your aim of finding a job. Experienced professionals can discuss their research in-depth. However, an entry-level data analyst portfolio needs to be more focused. Use real-world examples to emphasize your data analyst skills such as data manipulation, analytics, and visualization.

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4) Yan Holtz

Yan Holtz is an expert in data visualization and data analysis. He is a software developer at Datadog, a US-based cloud-monitoring MNC.

Highlights of His Portfolio:

 

Yan Holtz's data analytics portfolio is highly inspirational in terms of both aesthetic and content. The interactive design (created by Yan) attracts your attention and demonstrates his abilities at the same time. The geometric forms seen on the page are not only for the sake of visual appeal. They're also interactive and dynamic in nature, i.e., they respond to mouse movement.

 

He then goes on to mention the data analysis services that he provides, such as data management, data mining, dashboard creation, training courses, etc., followed by his blogs and websites.

 

Furthermore, he has built an attractive pop-up for each data analysis project, providing a clear picture of everything he's worked on. This provides a good user experience for the reader. When we're done, we'll be able to dig further into his projects on Github. What makes his portfolio so appealing is Yan's attention to detail. This demonstrates how much of a difference it makes if you invest additional time in building your portfolio. The website's degree of interaction is a good example of how a little additional emphasis can make a tremendous impact. It's not only the code that makes a difference; it's also the client testimonies and the fact that he keeps his case studies self-contained (rather than connecting directly to projects on GitHub). Yan’s data analyst portfolio website teaches you to showcase your attention to detail. It tells you to focus on your design, your language, and your projects.

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5) Jessie-Raye Bauer

Our last data analyst portfolio website example is that of Jessie-Raye Bauer. Jessie works for Apple as a data scientist. She holds a Ph.D. in cognitive psychology and statistics from The University of Texas in Austin.

Highlights of Her portfolio:

The portfolio of Jessie-Raye Bauer is an intriguing illustration of where a career in data analytics may take you. Jessie’s portfolio emphasizes her abilities while also injecting individuality. Her portfolio, on the other hand, has a somewhat scholarly air to it. Jessie doesn’t need to flaunt her skills since her data scientist experience at Apple mainly speaks for itself.

Unlike many other data science portfolios, Jessie-Raye hasn't provided standard project links. Jessie-Raye has opted to exhibit her work through her blog rather than directly referring to GitHub projects or adopting typical case studies. This is a better fit for her skill set. The intricate nature of her art lends itself beautifully to the detailed format of a blog. It also corresponds to her academic background.

This particular template-based data analyst portfolio website shows you that employers aren't particularly concerned with your portfolio's flashiness. It's all about the content in the end.

Build your Data Analyst Portfolio with ProjectPro!

The easiest method to land a job in data science is to exhibit your expertise by putting together a portfolio of data analytics projects. Data analytics projects will not only help you land your first job, but they will also provide you with additional experience with data science. Many valuable projects will upskill you while also enhancing your résumé.

Check out these big data and data science projects if you're seeking some interesting data analytics portfolio project ideas. The project repository is updated with new end-to-end real-world analytics and data science projects every month that include documentation, guided videos, a downloadable dataset, one-on-one mentor sessions, mock interviews, and reusable code.

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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