How to Become a Data Scientist in Three Easy Steps

 Top Machine Learning Methods are a Must-Know

Because there is a new algorithm every year or so for which course developers have not yet produced material for you to study in school, it is up to you to go out of your way to learn the newest, most up-to-date, and best algorithms and libraries. This topic has two sides: one is mastering the code, and the other is mastering the theory. After some schooling, you may find yourself delving deeper and deeper into the code, attempting to get away from what makes the algorithms function. As a result, it's critical to virtually describe all of the top 10–20 algorithms in a descriptive rather than a programmatic manner. As you progress toward being a data scientist, you'll notice that most of the libraries for the algorithms work the very same way, and the actual code for them is fairly simple. You'll begin a trial and error process only to discover that you've forgotten some of the theories behind what distinguishes one algorithm from another and how it works on a conceptual level. Click here to learn more Data Science In Chennai



So, once you've mastered the fundamental algorithms, you'll see that there's still enough to learn once you're a skilled data scientist. As a result, now is the perfect time to learn them as a data analyst. To go deeper, many courses, tutorials, and instructional materials will begin with the same few fundamental algorithms, although a statistics book or a beginner's machine learning course may not cover updated and much more prominent algorithms such as XGBoost and CatBoost.


Complete a case study from beginning to end

The objective of this experience is to not only complete an end-to-end process that resembles how a typical procedure would function if you win the position, but also to share your case study with hiring managers, recruiters, and prospective coworkers so that they can see some of what you're capable of. You may already be familiar with the process of establishing a business problem and studying the data that surrounds it as a data analyst. You'll have an advantage over those who aren't as familiar with the procedure.

The following is what you should do:

  • Discover a common problem, such as stock market forecasting.

  • Get some free dummy data.

  • Identify the essential aspects that you believe should be included in the model.

  • On the same data, try about ten algorithms and compare how they perform.

  • Visualize your results to describe your findings, as you will in a professional environment.


The procedure could be demonstrated in several ways. You may either display everything in your Jupyter Notebook or a similar tool and store your plots and comments in the notebook's markdown, or you can generate separate summary visualizations in Tableau, Excel, or Google Data Studio. The much more popular approach to share your case study is on GitHub; most engineers, scientists, and managers are familiar with this format and technology, so publishing it here is preferable. You may have an advantage as a data analyst in terms of data organization, establishing business measurements or KPIs (Key Performance Indicators), and displaying outcomes.


To master data processing, use data analytics

The preparation or processing of data is perhaps the most painful aspect of data science. This is usually the most time-consuming phase. As a data analyst, you may utilize your data abilities to guarantee that the dataset you'll be using for your model is in the best possible shape. Understanding which algorithms to employ, as we described above, may save you a lot of time because missing data, for example, might be frustrating, but some algorithms manage it automatically.


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