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Best Way to Learn Data Science

by | Mar 14, 2024 | Data Science, Machine Learning

Whether you want to transition your career to data science or simply want to learn more about it, mastering data science can be overwhelming at times. This is partly due to the ever-increasing amount of learning resources and partly because of the different learning paths you can follow.

My journey to learning data science followed a more traditional path. I pursued my Bachelor’s degree and Master’s degree in a quantitative field, which means that I learned the underlying theories needed for data science at the university. To deepen my data science know-how, I completed several online courses and worked on personal projects on the side.

Today, there are far more learning options to choose from. Considering the current state of data science, I asked myself: how could I learn data science efficiently if I were to start learning it from scratch today?

In this article, we will try to answer this question. My hope is that this article will help you learn data science more efficiently and position yourself well in the data science job market. So, without further ado, let’s get started.


Data Science and It’s Various Roles

In essence, data science is a field with one primary goal: to extract meaningful information from data to drive impactful business decision-making. However, the process of extracting meaningful information is not as straightforward as it sounds, for various reasons. First, the volume of data keeps increasing over time. Second, each step towards information extraction requires different knowledge.

Therefore, you will typically find several roles considered part of the data science family, such as:

  • Data Scientist: The jack of all trades for all things related to data science. The job description for a data scientist can vary depending on the company, but you can expect tasks like data processing or machine learning model building for this position.
  • Data Analyst: This position deals mainly with descriptive analytics, where you need to analyze data and present your findings to stakeholders using charts and various data visualization techniques.
  • Business Intelligence Analyst: This position has similar tasks to a data analyst, but a business intelligence analyst deals mainly with the business side of a project rather than the technical side.
  • Machine Learning Engineer: This position deals with everything related to the machine learning model lifecycle, from model training to model testing and model monitoring after it’s been deployed in a production environment.
  • Data Engineer: This position deals more with data pipelines and data preprocessing, ensuring that the data are ready to be further analyzed by data analysts and data scientists.

As you can see, each position has its own typical tasks and requires different skill sets. To position yourself well for obtaining data science roles like these, you need to know a wide variety of subjects such as databases, programming, statistics, machine learning, deep learning, and software engineering.

Data science roles

The diverse skill sets that need to be learned can sometimes overwhelm beginners. Hence, you need strategies to learn data science so that you can pick the skills you need faster and more effectively. In the next section, we’ll discuss these strategies.


Strategies to Learn Data Science

The field of data science is broad and multidisciplinary. Therefore, you need some strategies to learn data science faster and more effectively.

We all learn in different ways. Maybe you prefer reading books to learn data science. Others might prefer taking courses. Regardless of your preferred content resource, the following strategies will help you maximize your learning experience.

Let’s dive in.


Specify Your Goal

The first thing you need to do before starting to learn data science is to ask yourself: which part of the data science lifecycle am I most interested in? Is it analyzing data, building data infrastructure, building sophisticated machine learning models, or something else?

Jump into learning data science without knowing what interests you; it’s like sailing a boat without a destination. Knowing what interests you most in data science is helpful to set the roadmap and narrow down the learning process.

After you find your preferred topics within the data science field, you can start thinking about the skills you’ll need. These are some of the skills you need for each path:

  • Data engineering: SQL, programming, database and big data system design, different database types, and all the tools and platforms necessary to execute data transfers and data processing automatically, such as Apache Airflow, and Apache Kafka, among others.
  • Data analysis and Business Intelligence: SQL, statistics, probability, data storytelling, data processing and transformation, data visualization tools such as Looker, Power BI, or Tableau.
  • Data science: SQL, programming language (Python), statistics and probability, calculus and linear algebra, data processing and transformation, machine learning, tools and platforms like TensorFlow and PyTorch.
  • Machine learning engineering: programming languages (Python and possibly C++), machine learning and deep learning concepts and theories, data structures, software engineering theory such as object-oriented programming, unit testing, version control, CI/CD, and web development theory such as the concept of API.

Let’s clarify this with an example. Let’s say that you’re interested in kickstarting your data science career because of the surge in generative AI. You want to become a machine-learning engineer. Then, you might want to focus on improving your programming skills, as well as understanding various machine learning and deep learning concepts, rather than focusing on SQL or database design.

Best way to learn data science


Find the Right Learning Resources

Once you have defined your goals and the topics you want to learn, the next step is to find the right resources. This step can overwhelm many people, given the abundance of learning materials available. Unfortunately, not all of them are of high quality, and investing time in poor resources can be counterproductive. So, where do you start?

Consider your availability. If you’re currently working full-time and want to begin learning data science, you might prefer learning paths that offer flexibility, such as online courses, certification programs, or books. If you have ample time to dedicate to learning data science, you might consider enrolling in a data science bootcamp or pursuing a data science or computer science degree.

For those of you who want to learn by yourselves, I have compiled several resources that teach various topics, catering to all the data science roles that we’ve discussed.


Online Courses

If you’re more of a visual learner, online data science courses would be a perfect resource for you. Moreover, if you’re a beginner in data science and don’t know where to start, online courses can guide your learning journey in a more structured way. Another plus is that online courses are highly flexible, and you can complete them according to your own schedule and pace.

Here are several online courses that I recommend you take as part of your data science learning journey, depending on your interest:


Data Science Specialization from IBM (General)

This specialization would be a perfect introduction for everyone who wants to start learning data science from scratch. In this specialization, you’ll get a general overview of the end-to-end data science lifecycle, from data ingestion to machine learning model building.


Data Engineering Specialization from IBM (Data Engineering)

If data engineering is something that you would like to learn, then this specialization would be perfect for you. This specialization will teach you everything you need to know about data engineering, from the foundational concepts of databases to how to use popular tools such as Kafka and Airflow to create data pipelines.


Mathematics for Machine Learning and Data Science (Data Analysis, Business Intelligence, Data Science, and Machine Learning Engineering)

Before diving into data science and machine learning concepts, you need to first learn foundational knowledge, particularly mathematics. I know that it might be daunting to learn math at the beginning, but trust me, it will pay off in your journey of learning data science later on. You’ll be able to pick up all of the data science and machine learning concepts faster, hence speeding up your learning process.

This specialization from offers a very good overview of all the math you need for data science. The learning process is beginner friendly, interactive, and fun to ensure that you can understand every concept easily.


Google Data Analytics (Data Analysis and Business Intelligence)

Everyone who wants to become a data analyst or a business intelligence analyst should take this specialization as a starting point. This specialization will teach you every skill that you need to become job-ready for these two positions. Specifically, you’ll learn how businesses use data to make actionable decisions and techniques to clean, analyze, and visualize data.


Machine Learning Specialization from (Data Science and Machine Learning Engineering)

If you wish to become a data scientist or machine learning engineer, this specialization is a must. In this specialization, you will learn common machine learning algorithms and concepts applied in the real world with easy-to-understand explanations.


Advanced Machine Learning from Train in Data (Data Science and Machine Learning Engineering)

Once you are familiar with different machine learning algorithms, you can take more advanced courses. Being a data scientist or a machine learning engineer isn’t just about training a machine learning model; you also need to understand its behavior, interpret its results, and learn how to further optimize its performance.

Advanced machine learning courses at Train in Data

With Train in Data’s Advanced Machine Learning courses, you’ll learn all of these things and put yourself in a strong position to earn a data scientist or machine learning engineer job. Specifically, you’ll learn how feature selection and hyperparameter optimization can help optimize the performance of machine learning algorithms; how to interpret machine learning results with popular techniques like LIME and SHAP, and how to deal with imbalanced datasets when you want to train a machine learning model.


Deep Learning Specialization from (Data Science and Machine Learning Engineering)

Once you’ve got a good understanding of machine learning, this specialization will take your knowledge to the next level. In this specialization, you’ll learn more about deep neural networks, which are the foundational blocks behind cool AI stuff that you’ve seen recently.



Books are excellent resources for delving deeper into specific topics in data science. If you find certain concepts important to remember, you can easily highlight the text or create notes directly within the book. Learning data science through books is also highly flexible, as you can read each chapter at your own pace.

Depending on your topic of interest, below are several books that I recommend for your data science learning journey:


Fundamentals of Data Engineering: Plan and Build Robust Data Systems (Data Engineering)

This book is undoubtedly the best choice if you want to learn everything about data engineering. You’ll dive into the end-to-end data engineering lifecycle, from data generation to data storage. Additionally, you’ll gain practical experience in planning and building scalable data storage systems that meet the needs of both the company and its customers.


Data Analytics Made Accessible (Data Analytics, Business Intelligence, Data Science)

Using easy-to-understand language, this book is incredibly helpful for anyone looking to learn about data analytics. It provides a good overview of data analytics theory and includes a wealth of knowledge about the tools you can use for analysis, along with their pros and cons.


Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics (Data Analytics, Business Intelligence, Data Science, and Machine Learning Engineering)

Before diving into data science and machine learning concepts, it’s essential to grasp the fundamental math behind these concepts. This book offers an intuitive approach to learning the math behind data science, teaching not only the theories and equations but also how they are relevant to machine learning algorithms.

Machine learning books for beginners.

The Hundred-Page Machine Learning Book (Data Science and Machine Learning Engineering)

With approximately 130 pages, this compact book provides explanations of various machine learning algorithms and concepts. It’s a useful resource for quickly gaining insight into some of the most important machine learning concepts.


Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Data Science and Machine Learning Engineering)

For a deep understanding of machine learning concepts, this comprehensive book is highly recommended. It covers all the essential theories and algorithms you need to know, providing a thorough overview.

Although you can choose between books and online courses as your learning resources, I recommend you combine both resources to reinforce your understanding of key concepts. For example, after learning about linear algebra from an online course, you can read a specific book on linear algebra to test and reinforce your understanding of that topic.

You can also explore and learn data science through other less conventional resources, such as blogs, podcasts, and videos. If you want to practice your Python skills for tackling data science problems, you can find freeCodeCamp videos available on YouTube.


Learn and Practice the Concepts Deliberately

Now that you found resources to learn the skills, the next step is to actually learn data science concepts from those resources. Although each person has different learning methods, what I always find helpful is using examples, diagrams, or analogies to make a specific concept easier to grasp.

For example, understanding how a k-means algorithm arrives at the clustering result will be easier if we use diagrams to illustrate each step. We can use an analogy to explain the idea behind linear regression, such as correlating house size with price or taxi fare with distance.

Learning the concepts is just the start. You also need to put those concepts into practice. In fact, I would dare to say that learning by practice is the best way to learn data science and machine learning.

You don’t need to start practicing with something big and complicated. Practicing data science concepts can be as simple as working on assignments you’d typically find in a book or an online course, or following a step-by-step tutorial for a basic problem found online.

Later on, proactively work on bigger personal projects. This will allow you to test your understanding of specific topics and hone your problem-solving skills. The good news: all you need to do this is an internet connection.

You now might have questions: What personal project should I work on? Where do I get the data? How should I proceed? Fret not. I’ll share some guidelines on how to conduct your own data science project in the following section.


Step 1: Pick the Goal of the Project

Choosing which personal project to work on can be overwhelming. There are a plethora of choices out there. One simple tip that I have for you is: pick a project according to your passion or interests.

Once you know the domain of the project you want to work on, the next step is defining the goal of your project. If data analytics is what interests you, then you can do a project related to descriptive data analysis. If machine learning engineering is your passion, then you can do a project related to predictive modeling.

Let’s say that you’re passionate about football and you want to become a machine learning engineer. A possible project that you can undertake is, for example, building a machine learning model to predict the outcome of a football game. Since you know a lot about football, you have a good understanding of which features would make sense to be included as part of the training data. In addition, it’ll be easier for you to judge the validity of the prediction of your machine learning project and the choice of metrics to assess the performance of your model.


Step 2: Get the Data

At this point, you already know the topic that you want to work on as well as the goal you want to achieve with it. However, you can’t work on a project if you don’t have the data, and collecting data on your own is most of the time not feasible.

The good news is: most of the time you can get the data that you need from the internet — and it’s completely free to use. Below is a list of sources where you can find data that suits your interest.


Step 3: Pick the Tools and Do the Project

Once you get the dataset you want, then you’re basically ready to work on a data science project. One last thing that you need to think about is the tools and environment, and this also depends on your area of interest in data science.

If you’re interested in becoming a data analyst, these are some examples of tools and environments you can use to achieve your project goal:

  • Use popular data visualization platforms such as Tableau, Looker, or Microsoft PowerBI to visualize the data.
  • Install one of the popular database systems such as Postgres on your computer and then load your data there, so that you can practice your SQL skills to extract meaningful information from your data.
  • Use Jupyter notebook or Google Colab to show the step-by-step approach of the exploratory data analysis that you’ve conducted.

If you’re interested in becoming a data scientist or machine learning engineer, you might want to do something like this:

  • Use Jupyter notebook and Google Colab to show the step-by-step approach of data preprocessing using Pandas as well as machine learning model training and testing with scikit-learn.
  • Implement a state-of-the-art artificial intelligence model on GitHub with your own dataset using Python and popular frameworks like PyTorch or TensorFlow.
  • Use PyTorch Forecasting library to solve a time-series use case by leveraging neural networks.

By working on a personal data science project, you will build a portfolio that you can show to potential employers, and you’ll grow your technical skills. Your experience on how to tackle common challenges related to data science projects, such as choosing the right method to solve a specific use case and debugging code, will skyrocket.

Completing a personal project and having hands-on experience will also lead to massive benefits during your interview for a data science position. You’re likely to give more elaborate answers to each question due to your experience working on your personal data science project. In fact, demonstrating the initiative and willingness to work on personal projects is seen as a strong plus when you’re applying for a data science job.


Join and Contribute to a Community

The power of a good community, especially when you’re diving into something as vast as data science, can’t be underestimated. Learning data science might be exciting at the beginning of your journey, but when that excitement wears off a little bit, having a community around can be a real game-changer.

A supportive community can provide you with valuable resources, encouragement, and motivation when you hit roadblocks or encounter challenges, and this is something that I can vouch for. I still remember how listening to smart people talk about their projects or recent advancements in data science at local meetups really fired up my motivation to continue learning data science.

The good thing is that the data science community is vast, and you can find it everywhere, both online and offline. For online communities, you can normally find them on popular sites such as Kaggle, GitHub, Stack Overflow, Coursera, Medium, or social media like LinkedIn and Reddit. If you prefer offline communities, you can try to find an on-site data science gathering event nearby using apps like

Once you join a community, there are a lot more benefits that you can get besides maintaining your motivation for learning data science. You may discover job opportunities, find mentors, or even collaborate on research projects that could significantly impact your career path trajectory.

You could also consider actively contributing to give back to the community. This can be done in various forms, such as writing a blog post about data science concepts that interest you, open-sourcing a data science project on GitHub, answering FAQs on community forums like Stack Overflow or Reddit, and giving talks at data science gathering events, among others.

Contributing to a community has at least a couple of significant benefits:

  • It can enhance your learning experience and deepen your understanding of data science concepts. It will reinforce your data science skills, and also help others learn and grow.
  • It can enhance your online presence, and this can play a pivotal role when you’re looking for a data science position. Recruiters and hiring managers often look for candidates who demonstrate a genuine passion for data science and a willingness to engage with the broader community.


In this article, I’ve highlighted strategies to learn data science faster and more efficiently, regardless of your topic of interest. First, specify the goal of your learning process. Second, find the learning resources that suit your learning style, whether it’s online courses or books. Third, learn and practice the concepts deliberately by working on personal project to reinforce your understanding and build portfolio. Last, try to join a data science community whether it’s online or offline, and don’t forget to contribute to them by sharing your project via open-source code, blogs, or talks in an event.

I hope that this article was useful to help you get started in your data science journey. Remember, learning is a continuous process, and each step you take brings you closer to mastery. Happy learning!