Steps to Learn Data Science as a Beginner

Data science is the hot new skill that’s in demand at companies all over the world. It allows you to make sense of all that data that businesses are collecting and make predictions based on the information. Data is everywhere, from social media websites to smartphones to GPS devices. Data science is the process of extracting information from massive amounts of data, then turning it into useful knowledge for businesses.

According to the recent Glassdoor survey report, the average base pay of a data scientist is ₹10 lacs in India which is a lot higher than various other similar jobs. It has become one of the most lucrative job choices out there and it’s easier to learn too.

In fact, the majority of self-proclaimed ‘data scientists’ have no formal training at all! Even for seasoned mathematicians and statisticians, internet-scale data has made many areas of traditional statistics redundant.

The idea of learning data science may seem intimidating at first, but it’s actually very simple. In fact, you don’t even have to have any programming skills or prior knowledge of data science to start learning the basics.

In this blog post, we’ll teach you everything you need to know in order to learn data science from scratch.

Learn data science as a beginner

1. Start embracing data

If you’ve ever used the web, you have contributed data — and you have data. We all do. Data is the currency of the digital age. The value of information is increasing, as data becomes more and more ubiquitous. Big companies harvest our data and use it to sell us stuff; governments use it to track our behavior; analytics companies use it to predict our future behavior.

Learning to love data can be difficult for people who are not comfortable with numbers. It’s easy to become overwhelmed by the sheer amount of data we have access to, but the key is to learn how to find insights into the data we have.

To practice, we recommend playing with the sample data from FiveThirtyEight.com, you will find tons of interesting data sets to experiment with (as shown in the screenshot above). Also, don’t forget to check their Github.

2. Choose a programming language

We are not saying any of these languages are bad, instead it is going over each language’s strengths and weaknesses.

Python is considered by many to be the best language for getting into data science because of its simple syntax. However, R is considered a more advanced language and therefore it may be harder to learn for beginners.

However, we do incline towards Python because it has an extensive library for statistics and machine learning, which makes it easy to perform analyses on datasets without having to write code from scratch. Python also has a large community of users, which means there is a lot of support available online if you run into trouble using the language.

And, if you’re not into Python or R, there are tons of other options too. To name a few:

JavaScript — D3.js, Math.js, Brain.js, etc.
Java — Hadoop, Scala, etc.
SQL — Easy to learn and added bonus even if you know other languages
MATLAB
Haskell
Perl, etc.

👉 NextStacks has an excellent course to learn Python that you can take as a beginner.

3. Learn to love mathematics

Mathematics has an undeserved reputation as being hard to learn. This reputation is not entirely deserved, but it is true that some people feel intimidated by mathematics. If you feel this way, our advice is to stop letting it intimidate you, and instead learn to love it! First of all, mathematics is a part of our everyday lives. Many people do not realize how much mathematics they use in their daily lives.

And, a basic understanding of mathematics is essential to make your data science learning process easier and better.

Data science teaches logical thinking and helps your brain work better. It improves your ability to solve problems and think outside the box. It also makes you more perceptive and helps you understand complex relationships between different data points.

It gives you the power to extract knowledge from raw data and make predictions based on that knowledge. Data scientists need a good understanding of probability theory, statistics, and machine learning algorithms to build accurate statistical models.

TensorFlow, the machine-learning framework that Google uses to power everything from voice recognition to search, has been made available for free under an open-source license. It’s a powerful tool that can help anyone get into machine learning and AI.

There are many good resources out there to help you get started with TensorFlow, but one of my favorites is Google’s own getting started guide. It covers everything from setting up your development environment to building your first neural network.

4. Learn about various data science libraries

Tons of Python data science packages are available to help with tasks like data visualization, machine learning, statistical analysis, and natural language processing. Here’s a quick look at some of the most popular ones:

NumPy: A numerical library for Python that offers advanced functions for manipulating arrays and matrices in place.
SciPy: A collection of modules used to do number crunching in Python. SciKit-Learn: A machine learning library based on SciPy.
Pandas: A data analysis library that makes importing, managing, and manipulating large datasets easier.
Matplotlib: An open-source package for making 2D plots and charts in Python.
Statsmodels: A Python module that makes it easy to do statistics like linear regression and descriptive stats.
TensorFlow: Google’s library for machine learning applications based on neural networks and deep learning with flexible numerical computation using data flow graphs.
PyBrain: A neural networks library built with the intention of being a high-level neural networks API for research, experimentation, and education.

NumPy by itself is powerful but limited in functionality; however, it can be extended by using packages like Pandas.

5. Work on smaller projects — learn by doing

Learning by doing is the best way to learn something new.

We’ve picked out a few of the most popular data science projects for you to try. These are not just random projects, but also real-world problems that require you to collect and analyze data.

Scraping WikiHow: WikiHow is one of the most popular websites on the Internet, and it’s also an invaluable source of information for aspiring web developers. The site has thousands of articles on various subjects, but what do they all have in common? If you have done any scraping before, you know that it can be a bit of a pain. Thankfully, this tutorial by FreeCodeCamp will show you everything that you need to scrape using Python and BeautifulSoup.
Scraping IMDb Movie Data: This is another interesting tutorial that shows you how to collect movie information from IMDb using Python and BeautifulSoup. It’s an interesting project because it requires more than just programming skills, but some knowledge of the movie industry as well.
Building a Simple Stock Tracker: This project shows you how to build a simple stock tracker with D3.js, which is one of the most powerful tools for data visualization available today. You’ll learn about data visualizations along with basic D3 concepts such as scales and selections. All in all, this is a great project

Final words

In short, these are the steps you need to take to learn data science. Don’t be intimidated by the amount of learning that you have to endure. You can do it. And even if you run into obstacles, there are tons of resources available to help answer questions and clear up any confusion.

So get started today, take small steps each day, and do your best!

If you have any related queries, feel free to let us know in the comments below.

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