The 10 New Year Resolutions that you would want to make in 2023 as a Data Scientist!

Arthi Rajendran
10 min readJan 6, 2023


As the new year approaches, many of us take the opportunity to reflect on the past year and set goals for the year ahead. As a data scientist, there are a variety of resolutions that you may want to consider to continue learning and growing in your field.

I am listing down the 10 resolutions that I have taken this year, as a growing Data Scientist.

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1. Stay up to date with the latest technologies and techniques

The field of data science is constantly evolving, so it’s important to stay on top of the latest trends and best practices. Make a commitment to regularly reading industry blogs and attending conferences or workshops to stay informed. This is your cue to follow me on Medium.😜 Speaking of, have you explored ChatGPT yet?

I have found Medium to be the best place for me to stay on trend about the latest as well as several old golden technologies too.

Here are some of the people/sites that I personally follow: James Briggs, Travis Tang, Cassie Kozyrkov, Khuyen Tran, Towards Data Science, etc.,

2. Build a portfolio

Having a strong portfolio of completed projects is essential for any data scientist. Consider setting a goal to complete one new project each quarter, and document your work thoroughly so you can showcase it to potential employers, and clients, or just so you can look at it whenever you have that feeling of not doing enough!

Here are some tips that I’ve gathered:

  • Include a variety of projects: Showcase a range of projects that demonstrate your skills and versatility as a data scientist.
  • Explain the process: In addition to the final results, include a description of your process and the techniques you used for each project. This will help demonstrate your understanding of the underlying concepts and how you applied them in practice.
  • Include code and visualizations: If possible, include code and visualizations that demonstrate your work. This can help employers or clients see the specifics of how you approached a problem and help them understand your thought process.
  • Write clear and concise explanations: Use clear and concise language to explain your projects, techniques, and results. This will help make your portfolio more accessible to a wider audience.
  • Use a visually appealing design: A visually appealing design can help make your portfolio more professional and engaging. Use a clean and organized layout and consider including images or visualizations to break up text and add interest.
  • Regularly update my portfolio: As you complete new projects and gain new skills, be sure to add them to your portfolio. This will help keep your portfolio current and relevant.
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Now to answer the question that has probably popped up in your mind, “Where to build my Portfolio?”.

There are several platforms you can use to build your portfolio as a data scientist:

  • Your own website: One option is to build your own website to showcase your portfolio. This can be a good option if you have some web development skills and want complete control over the design and functionality of your portfolio.
  • Online portfolio platforms: There are also many online platforms that allow you to create a professional portfolio with minimal web development skills. Some options include LinkedIn and GitHub Pages.
  • Blogs: Another option is to use a blogging platform such as Medium or WordPress to document your projects and share your work. This can be a good option if you want to write about your projects in addition to showcasing them.

Regardless of the platform you choose, it’s important to include a clear and concise explanation of your projects, as well as any relevant code and visualizations. This will help make your portfolio more informative and engaging.

3. Improve communication skills

As a data scientist, you’ll often be working with non-technical stakeholders and will need to be able to effectively communicate your findings and recommendations. Consider taking a course in data visualization or joining a public speaking group to improve your skills in this area.

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Here are some ways to improve your communication skills:

  • Practice active listening: Pay attention to what others are saying and ask questions to clarify your understanding. This will help you better understand their perspective and communicate more effectively.
  • Learn to explain technical concepts to non-technical audiences: As a data scientist, you may need to explain technical concepts to non-technical audiences. To do this effectively, use clear and concise language, provide relevant examples, and avoid jargon.
  • Write clearly and concisely: Whether you’re writing an email, report, or blog post, it’s important to communicate clearly and concisely. This means using simple language, avoiding unnecessary words, and organizing your thoughts in a logical manner.
  • Practice public speaking: If you’re nervous about speaking in front of others, consider taking a public speaking course or joining a group such as Toastmasters (PS: Click on the link to find one near you) to practice your skills.
  • Use visual aids: Visual aids such as charts, graphs, and diagrams can help make complex ideas easier to understand. Consider using them to supplement your verbal explanations.
  • Seek feedback: Ask others for feedback on your communication skills and be open to constructive criticism. This will help you identify areas for improvement and work on them.

4. Learn a new programming language

There are many programming languages that are useful for data scientists, and learning a new one can open up new career opportunities or allow you to tackle different types of problems. Some popular options for data scientists include Python, R, and SQL.

But for those of you who have already been rocking it with these languages, here are a couple more that might fire up later this year like Julia, Swift, Rust, etc.,

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5. Get involved in the data science community

Whether it’s joining a local meetup group, attending hackathons, or contributing to open-source projects, getting involved in the data science community can be a great way to network and learn from your peers.

Here’s how you can actually get started with it:

  • Join online communities like Data Science Central forums, the Data Science subreddit, and LinkedIn groups.
  • Participate in online courses and MOOCs (Massive Open Online Courses) as they often have discussion forums where you can interact with other students and learn from instructors and guest speakers.
  • Contribute to open source projects as it is a great way to gain experience and make connections in the data science community. Look for projects on platforms like GitHub that are related to data science and see if you can contribute your skills.
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6. Implement end-to-end MLOps in a Project

There are a lot of Data Scientists out there who could build a solution to a massive problem within a matter of few weeks, but there are only a few who bring it to Production and make it sustainable. I haven’t had the chance to be one of them yet. But, this year, this is one of my goals!

And I plan on writing about my experiences as I move forward. Check out the article below:

7. Enhance Math skills

Data science involves a basic to medium-level understanding of the Math behind all those complex algorithms. And the best way to learn Math is by practice!

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I plan on taking a few of these to improve my understanding of these 3 Math topics:

  1. Linear Algebra for Data Science — Matrix algebra and eigenvalues. Resources: The Math Cookbook, Andrew Ng’s Linear Algebra Review Video Series
  2. Calculus for Data Science — Derivatives and gradients. Resource: Multivariable Calculus Review
  3. Gradient Descent from Scratch — Implement a simple neural network from scratch. Resource: Build a Simple Neural Network from scratch. It’s GitHub Repo that would get your hands dirty!

8. Learn more about data ethics

As data scientists, we have a responsibility to ensure that the data we work with is handled ethically and responsibly, and being in the Pharmaceutical Industry & dealing with Patient data, this is all the more conspicuous. Make a commitment to learning more about data ethics and how to apply it in your work.

A few pointers as to how to make sure we are ethical:

  • Understand the ethical considerations: Make sure you understand the ethical considerations involved in the data you are working with. This includes things like privacy, consent, and bias.
  • Follow relevant laws and regulations: Familiarize yourself with the laws and regulations that apply to the data you are working with, and make sure you follow them.
  • Obtain proper consent: If you are collecting data from individuals, make sure you obtain proper consent before collecting and using their data. This includes explaining how their data will be used and obtaining their explicit consent.
  • Protect sensitive data: If you are working with sensitive data, such as personal or financial information, make sure you take appropriate measures to protect it. This may include encrypting the data, using secure servers, and implementing access controls.
  • Be transparent: Be transparent about how you are collecting, using, and sharing data. This includes documenting your processes and making sure that others involved in the project are aware of them.
  • Continuously evaluate your practices: Regularly evaluate your data collection, use, and sharing practices to ensure that they are ethical and responsible. Make adjustments as needed.
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I will write a detailed article about Data Ethics, especially in the world of Healthcare in the future.

9. Improve Data Visualization skills

Being able to effectively visualize data is an important skill for any data scientist. Consider taking a course or workshop on data visualization, or practice creating your own charts and graphs on a regular basis.

Here are a couple of Python libraries (Not the commonly used Seaborn, Matplotlib & Plotly) to get your hands dirty:

  • PyQtGraph: PyQtGraph is a data visualization library that allows you to create interactive plots and graphics in Python using the PyQt5 toolkit. It is designed to be fast and efficient and is well-suited for real-time data visualization.
  • Gleam: Gleam is a data visualization library that allows you to create interactive plots and dashboards in Python. It is designed to be simple and easy to use and is well-suited for creating visualizations for online use.
  • Pygal: Pygal is a data visualization library that allows you to create a wide range of static, interactive, and animated charts in Python.
  • Folium: Folium is a data visualization library that allows you to create interactive maps in Python. It is particularly useful for visualizing geospatial data.
  • Altair: Altair is a declarative statistical visualization library for Python. It allows you to create a wide range of visualizations by writing simple Python code.
Photo by Clay Banks on Unsplash

10. Expand Domain Expertise

As a data scientist, you may be working with data from a variety of different domains. Consider setting a goal to learn more about a specific industry or domain that interests you, and practice applying data science techniques to problems within that domain.

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I am from a healthcare educational background, so I get my way around this. But recently, I have been too focused on the area of Clinical trials viz., patient recruitment, randomized allocation, etc. This year I plan to learn about other areas in the Healthcare Industry like Healthcare Informatics, Drug Design, Medical Devices, etc.,

Well, that’s my 10 Resolutions for the year 2023! There is one more bonus resolution that I have set for myself is to post frequently on Medium! This way, I get to share my knowledge with the world & I learn new things in detail before posting them here! Well, it’s a win-win!

So, Stay tuned & Subscribe to me via email to receive my future posts delivered to your inbox.

We will revisit this post in 2024 to see how many of them I have actually achieved/followed consistently.

Did any of them inspire you enough to add to your set of resolutions? Let me know in the comments.

Happy New Year!! ✨

Thanks for sticking around until the end. Visit me on my Linkedin to have a more in-depth conversation or any questions.



Arthi Rajendran

A Data Scientist, who is passionate about Healthcare, Data & Machine Learning.