5 Tips on Becoming a Self-Taught Data Scientist. Benefits of being a self-taught data scientist, skill sets needed, tips on self-learning, and 7 data science projects that are widely used in industry.

5 Tips on Becoming a Self-Taught Data Scientist

Data science is a booming field with excellent career opportunities for those who are passionate about it. A self-taught data scientist is someone who has learned the skills needed to be a data scientist without any formal training at universities. The skills usually include

  • Statistics
  • Machine Learning
  • Programming Languages such as Python and R
  • Data Visualization Tools such as Tableau and Power BI
  • SQL

Self-taught data scientists are most likely to find jobs with the titles of a data analyst, data scientist, research scientist, applied scientist, statistician, modeler, and machine learning engineer.

Resources for this post:

  • If you prefer the video version of the tutorial, watch the video below on YouTube
Tips on Becoming a Self-Taught Data Scientist – GrabNGoInfo.com

Benefits of Being a Self-taught Data Scientist

Many people assume that the only way to learn is if you go to school for it. However, being self-taught can always be an option. There are a number of benefits of being a self-taught data scientist:

  1. You have a lot more time to read and study than those who attend university or college. This is because you don’t need to dedicate a large portion of your time to lectures or class time. All the time saved can be used for studying the topics that directly help you land a data science job.
  2. The cost is lower. You do not need to spend money on expensive textbooks, classes, or tuition fees.
  3. You can learn data science knowledge at your own pace and set your own goals, according to your individual tastes, needs, and schedules.
  4. You can create a personalized curriculum based on the skills that you want to improve or learn, rather than those that the university requires for you to graduate in a particular course. With this freedom and flexibility, students are empowered with more opportunities and fewer restrictions when it comes to selecting courses.

This post will talk about 5 tips that will help you transform into an unstoppable data science professional who can get hired with ease, even if you don’t have a university degree in a related field.

Tip 1: Build Your Own Data Science Study Plan

As more companies start to use data-driven approaches, data scientists are in high demand, but there are many different paths that you can take in data science depending on your interests and skillsets. The first step for people who want to become a data scientist is figuring out what type of work they want to do. Marketing analytics? Finance analytics? Clinical trial? Product analytics? NLP? Computer vision? There are many possibilities and having a career goal in mind when self-studying helps you to find the focus and learn more efficiently.

Tip 2: Pick Learning Materials Appropriate To Your Level

The best way to start learning data science is by reading books and tutorials that are geared toward your level.

A good book for beginners is “Introduction to Machine Learning with Python”. It teaches you the fundamentals of machine learning and how to implement them with Python. You can also check out Youtube tutorials on the same topic if you prefer a video format.

If you are already familiar with the basics, then you can try reading this book: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”. This is my favorite book on machine learning. It covers most of the machine learning and deep learning models, when to use them, and how to use them. But do not try to fully understand all the details in the whole book, because that will take a long time. Pick out the contents you need to learn based on your study plan and focus only on those relevant contents.

Tip 3: Learning by Doing – Tackle Widely Used Machine Learning Projects

There are many ways to learn data science, but one of the most effective ways is by doing hands-on projects. You should always try to find and tackle interesting projects that you can work on during your spare time, side jobs, or even as a part-time job. This way you will be able to practice your skills in solving problems using data science methods and tools.

In this section, I will provide 7 example projects that are most widely used by data scientists.

  1. Use a binary classification model to predict the propensity of an event. Binary classification model is the most widely used model in a data scientist’s daily work. So having a deep understanding of the model algorithms, model tuning techniques, and model performance metrics are essential in landing a data scientist job. Please check out my tutorial on XGBoost Hyperparameter Tuning and LASSO vs. Ridge vs. Elastics Net to get a better understanding of the binary classification model.
  2. Use a time series model to predict sales value. Time series model is another widely used model, especially in the finance and retail industry. You can use the Walmart sales forecasting dataset on Kaggle to practice the time series project. I created a few tutorials on time series, including Multivariate Time Series Forecasting with Seasonality and Holiday Effect3 Ways for Multiple Time Series Forecasting Using Prophet, and Hyperparameter Tuning and Regularization for Time Series.
  3. Use a clustering model to do customer segmentation. Customer segmentation model is commonly used in marketing analytics, and clustering model algorithm is one of the most commonly asked data science interview questions. Please refer to my tutorial 4 Clustering Model Algorithms in Python and Which is the Best to understand the different types of clustering model algorithms’ pros and cons, and 5 Ways for Deciding Number of Clusters to learn how to decide the number of clusters for a clustering model.
  4. Use an anomaly detection model to do fraud detection. Fraud detection is mostly used in financial institutions and insurance companies. My tutorial on anomaly detection listed different algorithms for finding outliers, including one-class support vector machineisolation forest, and time series anomaly detection.
  5. Use a natural language processing (NLP) model to do sentiment analysis. Sentiment analysis is usually used by companies with customer reviews or social media platforms. My tutorial on TextBlob vs. VADER vs. Flair can get you started on this topic.
  6. Use a recommendation system to recommend products. A recommendation system is usually used by online retailers and online entertainment companies. You can learn User-Based Collaborative Filtering and Item-Based Collaborative Filtering from my tutorials.
  7. Use a computer vision model to detect objects. Computer vision is usually used in auto-driving, manufacturing, agriculture, and the medical industry.

Tip 4: Set Realistic Goals

If you want to become a self-taught data scientist, it’s important that you set realistic goals. It is crucial that you don’t go after unrealistic ones because this will make it hard for you to stay motivated and achieve your goals.

The first and most important step in setting goals is to identify the right goal. It is important to set a goal that you are passionate about and that you enjoy doing. Goals should be challenging but not impossible, achievable but not too easy.

It’s common to set goals that are too lofty and end up feeling discouraged and defeated when we can’t reach them. This is because the goal was so out of our reach and we were so intimidated by it that it was impossible for us to even think about how to get there. Instead, the way to create achievable goals is to break them down into smaller, more manageable chunks. Instead of trying to finish a machine learning book in one week, for example, you might decide that reading 30 minutes at a time is an attainable goal. You would start by turning your bedtime reading into this 30-minute goal and then move on from there.

Tip 5: Build Your Reputation as a Data Scientist

Building a reputation as a data scientist is not easy. You need to work hard to show your skills and knowledge. You can do this by writing data science blogs, sharing data science knowledge on social media, publishing code on GitHub, and getting involved with the data science community by answering questions on Quora, Stack Overflow, or Reddit. You can also join Kaggle competitions and share your knowledge in the competition discussion forum.


In summary, the 5 tips on becoming a self-taught data scientist are

  1. Build Your Own Data Science Study Plan
  2. Pick Learning Materials Appropriate To Your Level
  3. Learning by Doing – Tackle Widely Used Machine Learning Projects
  4. Set Realistic Goals
  5. Build Your Reputation as a Data Scientist

For more information about data science and machine learning, please check out my YouTube channel and Medium Page or follow me on LinkedIn.

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