How to learn Data Science?

Data science may be an overwhelming profession. Many people will tell you that you cannot become a data scientist unless you have mastered statistics, linear algebra, calculus, programming, machine learning, data visualization, deep learning, natural language processing, and many more. That is just not the case. Data scientists are in high demand, there’s no denying that. In India, the average data scientist earns more than 8 lakhs per year as of 2021. If you learn data science, you could find yourself working in this exciting and well-paid industry.

As a result, how can you begin to understand data science? One of the most common answers to this question is “take linear algebra or statistics,” followed by a long list of courses and books to study. Advanced mathematics, deep learning knowledge, and many other skills listed above are not really required for data science. However, it does need familiarity with a programming language as well as the ability to interact with data in that language. True, the other specific skills stated above may one day assist you in resolving data science challenges. However, you do not need to be proficient in these skills to begin a career in data science. And, although mathematical fluency is necessary to become an expert in data science, only a fundamental understanding of mathematics is required to get started. 

Step 1: Learn Python/R

Python and R are both excellent data science programming languages. To get started, you don’t need to know both Python and R. Instead, concentrate on mastering a single language and its ecosystem of data science packages.

Although R is more prevalent in academics and Python in the industry, both languages offer many tools that assist the data science workflow. If you’ve decided on Python, you might wish to install the Anaconda distribution, which makes package installation and administration easier on Windows, OSX, and Linux.

Step 2: Learn Data Analysis & Visualization

Data scientists are frequently required to convey their findings to others. What makes a good data scientist into a great one is how well you do this. Business data analysis and Data Visualization are usually only practical if you can persuade other people in your firm to act upon your findings. Understanding the topic and theory is an essential part of conveying ideas; you’ll never be able to explain something to others if you don’t comprehend it yourself. Another aspect is knowing how to organize your results logically. The ability to effectively describe your analysis is the final component.

You should learn how to utilize the pandas library in Python to work with data. Like an Excel spreadsheet or SQL table, Pandas provides a high-performance data structure called a “DataFrame” suited for tabular data with columns of various kinds. It has tools for reading and publishing data, dealing with missing data, filtering data, cleaning up dirty data, combining datasets, visualizing data, and much more. In summary, knowing pandas will significantly improve your data-processing efficiency.

Step 3: Learn machine learning.

What makes data science exciting is creating “machine learning models” that can forecast the future or automatically extract insights from large datasets. Machine learning is made easier using Scikit-learn, Python’s most popular package for machine learning. As a result, lots of distinct models have an easy-to-use interface. It gives a wide range of tuning options for each model and selects reasonable defaults for each model. In addition to helping you understand the models, its documentation also demonstrates correct usage.

Step 4. Build Algorithms from Scratch

I propose building an algorithm from scratch once you’ve used it and understood how it works in practice—this aids in understanding the underlying arithmetic and other mechanisms that allow it to function. You will very certainly need to grasp the theory behind it as well. Learning in this way, in my opinion, is a lot more intuitive than attempting to understand the theory and then apply it.

To begin with, a linear regression algorithm is recommended. This will help you grasp gradient descent, which is an important concept to learn. As your data science career advances, the theory will become increasingly important. You make a vital contribution by matching the appropriate algorithm to the data science challenge. The algorithm’s related approach substantially aids in implementing it in the real-world.

Step 5. Learn from your peers

Having to work with people may teach you a lot of valuable lessons. As a data scientist, working in a team may be pretty beneficial. Most data scientists are part of a team, and lone data scientists at smaller businesses often collaborate with other teams at their company to tackle specific challenges. So, collaboration may be more crucial than ever for data scientists! They may shift from one team to another to address data queries for different departments inside their organization.

While working on your projects is rewarding, there are instances when you don’t know how you should proceed.  It suggested that you study the code of more experienced data scientists to learn what to study next and improve logic and syntax. There are millions of kernels on Kaggle and GitHub where individuals have released the code they used to analyze datasets. Going through this is a fantastic approach to round off your efforts.

Step 6. Never Stop Learning

What makes data science so appealing is that it is a never-ending adventure. To stay on top of new packages and developments in the sector, you’ll need to study constantly. Kaggle contests are a fantastic way to practice data science without having to create your challenge. Don’t be concerned about your ranking; instead, concentrate on learning something new with each challenge. Contributing to open-source machine learning projects will allow you to practice collaborating with others. If you build your data science projects, you should post them on GitHub and add writeups. This will assist you in demonstrating to others that you understand how to do reproducible data research.

This is only the beginning of your data science adventure. In the subject of data science, there is so much to learn that it would take a lifetime to master it. Keep in mind that you don’t have to master it all to establish a data science profession; you simply have to get going.

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