How to start learning Data Science?

How To Start Learning Data Science?

How to start learning Data Science?

There is no denying that Data Scientists are in demand right now. 11.5 million new jobs will be created by the year 2026, according to a report by the U.S. Bureau of Labor Statistics. As of 2020, a data scientist in the US earns an average of $113,000 annually. This is a promising and well-compensated field. So, if you are planning to start a career in the field of Data Science, now is the time. You can easily become Data Engineer by doing a few courses. Well, try your luck now.

But, how can you start learning Data Science? The usual answer to this question is a long list of books and courses. And even though these are important, there is so much more to do while learning Data Science. In this article, we have laid down 6 steps that will help you get started in the Data Science field:

  1. Get started with Python

The two most common programming languages used for Data Science are R and Python. While R is more popular for research purposes, Python is usually used in the industry. However, both languages offer packages that can support the Data Science workflow.

If you haven’t worked in any of these languages, you should focus on one and its Data Science packages ecosystem. In case you choose python, you should install the Anaconda distribution as it can simplify the process of installing and managing packages on Windows, Linux, and OSX. 

Don’t spend too much time trying to become an expert on Python. Instead, master important concepts like data types, data structures, functions, imports, comparisons, conditional statements, comprehensions, and loops. You can cover everything later or while practicing. Also, you can opt for the Python Data Science online course that will help you master Data Science Analytics using Python straightway.

  1. Learn the analysis, manipulation, and visualization of data with Pandas

If you are using Python to work with data, you should get familiar with the Pandas library. DataFrame is a high-performance data structure provided by Pandas that is suited for tabular data with different types of columns, similar to a SQL table or Excel spreadsheet. It will provide you with the tools to read and write data, handle missing data, filter data, clean messy data, merge datasets, visualize data, and so much more. Basically, through Pandas, you will be able to increase your efficiency significantly while working with data.

However, you should note that Pandas have several functionalities that can be overwhelming at times. There can be too many ways of accomplishing the same task. This can make it challenging to work with Pandas.

  1. Learn Machine Learning through scikit-learn

If you want to use Python for Machine Learning, you must have an understanding of how to use the scikit-learn library. The main and the most popular aim of Data Science is creating Machine Learning models for extracting insights automatically from data or predicting the future. The most popular Python library for Machine Learning is scikit-learn and here is why:

  • It offers a clean as well as a consistent interface for tons of models.
  • It provides several tuning parameters for every model but also selects sensible defaults.
  • Its documentation will help you in understanding the models and how you can properly use them.

However, you should note that Machine Learning is a highly complex field that is evolving rapidly. And, scikit-learn is known for its steep learning curve. What you can do is enroll in a training program that will help you in getting a thorough grasp of scikit-learn workflow and machine learning fundamentals.

  1. Dive deep into Machine Learning to get an in-depth understanding of the field

As mentioned before, Machine Learning can be a complex field. And even though scikit-learn will provide you with the tools to perform effective machine learning, it won’t be giving a direct answer to several important questions like:

  • How will you know which ML model is best for your dataset?
  • How will you be interpreting the results of your model?
  • How will you evaluate if your model can be generalized for future data?
  • How will you select what features must be included in your model?
  • And many more.

If you want to become an expert in Machine Learning, you should be able to answer all of these questions. For this, you need to study further and get experience. Here are a few books that can help you with this:

  • An Introduction to Statistical Learning’. Through this book, you will be able to gain a theoretical as well as practical understanding of important methods for classification and regression. You don’t even need an advanced mathematics background for this. The author has even released high-quality videos for supplementing the book.
  • If you want to get a refresher on statistics or probability, you can try ‘OpenIntro Statistics’.
  1. Keep practicing and learning

This is undoubtedly the best and the most important step to learn Data Science. You need to find what motivates you to learn Data Science and then use it to practice more. This can be a Data Science online course, competitions, projects, reading blogs, reading books, attending conferences or meetups, or anything else.

  • Kaggle competitions – This is the best way of practicing data without having to come up with the problem. It is important that you focus on the opportunity to learn something new in every competition, instead of how high your place is. However, competitions like these don’t allow you to practice some of the most important aspects of Data Science workflow like asking questions, collecting data, and communicating results.
  • Contribute to open-source projects – This will allow you to practice on Data Science projects while collaborating with others. It is a great way to get an idea of how real-world projects work. You can check out GitHub for open-source projects. 
  • Create your own projects – If you have created your own Data Science projects, you can share them on HitHub. Make sure that you include writeups. These projects will help show potential employers that you are familiar with reproducible Data Science.
  • Subscribe for email newsletters – Newsletters can help you stay updated with the latest happenings of the Data Science field. Some popular newsletters include Python Weekly, PyCoder’s Weekly, Data Science Weekly, and Data Elixir.

Your journey in the field of Data Science has just begun. The field is so vast that it can take you a lifetime to master it. However, it is important to remember that you don’t have to be an expert in all for launching your Data Science career. All you have to do is get started.

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About the Author: Rene Bennett

Rene Bennett is a graduate of New Jersey, where he played volleyball and annoyed a lot of professors. Now as Zobuz’s Editor, he enjoys writing about delicious BBQ, outrageous style trends and all things Buzz worthy.


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