How to Learn Data Science for Real, Not Just Playing with Tricks

Contents:

• We solve problems in their entirety

The path to a career in Data Science, as presented by bloggers and popularizers, often looks like a sequence of simple steps and techniques. First, we learn the basics of Python, then master the functions of the Pandas library, and finally train a linear regression model. It would seem that becoming a data scientist is a given. However, in reality, this process requires a much deeper understanding. Data science involves not only technical skills, but also analytical thinking, knowledge of statistics, and the ability to work with big data. It's important not only to be able to apply tools, but also to understand how and when to use them to solve real-world problems.

Collective industry experience and common sense indicate that there is a significant difference between a beginner with basic skills and a true data science professional. A good data scientist not only knows a few tricks but is also able to apply complex data analysis methods to solve complex problems.

Learning Data Science in the context of classical pedagogy is a complex educational task, and Data Science itself is a multifaceted and complex skill. Mastering this area is not limited to just a set of simple skills. Successful study of Data Science requires three key requirements: a basic understanding of mathematics and statistics, programming skills, and proficiency in data analysis tools. These elements form the foundation for a deep understanding and practical application of Data Science methods, enabling specialists to effectively solve problems in various fields. Ancillary knowledge: When and in what situations to apply certain simple skills. This knowledge can be represented as heuristic "if-then-else" schemes. Describing a complex skill requires entire forests of such schemes. Procedural knowledge: Explains how exactly to apply the skills. Most often, this is instructions, guides, or checklists. Practice working on tasks piecemeal. It helps reinforce simple skills not individually, but in the right combinations and sequences—from short chains of techniques to complete action cycles.

To learn data science more effectively, it's important to consider all four components: the three core elements and a set of practical techniques. A full list of necessary skills and methods can be found in our recent article on the data scientist career path.

Solving Whole Problems

Paul Hiemstra, an experienced data science educator and practitioner, shares three recommendations for those seeking a deeper understanding of data science. These tips will help you optimize your learning process and successfully master key data science concepts and tools. By following these recommendations, you can become more effective in learning and applying data analytics.

Work on projects in their entirety. Many aspiring data scientists often work on only small parts of larger projects. This problem can be solved by creating a pet project that can be completed in parallel with your main work. Such a project will help you maintain a holistic view of the task and avoid working on individual stages in isolation. While you'll certainly need to master specific skills, such as using certain Python libraries, it's important to return to the overall task to strengthen your skills and understanding of the process. Creating a pet project begins with selecting a dataset from your area of ​​interest. Then, analyze it using the CRISP-DM methodology. It's important to describe each step in detail and logically link the analysis steps. Platforms such as Google Colab or Jupyter Notebooks are convenient for ease of use. A prepared report on your pet project will not only showcase your skills but also become a valuable addition to your portfolio, attracting the attention of potential employers. Find an experienced data science mentor. Discussing your work with a professional will help you develop the metacognitive skills needed to effectively solve complex problems. When communicating with a mentor, focus on the problem-solving process—on approaches and ideas, not just on specific solutions such as code, models, or libraries. Ask questions that begin with "how..." to gain a deeper understanding and learn from your mentor's experience. This will not only improve your analytical skills but also help you more quickly address data analytics challenges.

Find like-minded people. By explaining your solutions to others, you not only share your experience, but also deepen your own understanding of the work. This is reminiscent of the situation with a teacher who, by the third explanation, realizes his own thought, while the students remain perplexed. This example isn't just a joke, but an important lesson. When participating in discussions, try to understand the other person's motivations: why they chose a particular model or approach. This will allow you to expand your knowledge and look at the problem from different angles.