Data science is in high demand and growing quickly. One job in data science leads to three jobs outside of it, and we're talking about 13 million jobs in total. The question is what you can do to get a job and yield results, as well as how you can become qualified for one of the 4 million jobs available currently in the global market.

OK, so what can someone do to become a data scientist if they can't afford or get into the costly and competitive programs? What can someone do - who is looking to improve their chances of finding work in this extremely important industry. How can they use their advanced skills to make their own surroundings, communities, and countries better? Here's what you can do to become a data scientist:

Understand data

Data without context is absolutely worthless and can (and should) be ambiguous. To tell a story, data needs a story. Data is like a colour that needs a surface to show that it exists. You won't find a "data scientist" who can talk to you about "data" without bringing up Hadoop, Tableau, NoSQL, or other buzzwords and technologies. You need to know your data by thoroughly understanding it; you need to know it inside and out. If you ask someone else about strange things in "your" data, it's clear that you don't know how your data is made, and recorded or why it needs to be analyzed.

Choose a programming language

It is best to pick one programming language and stick to it. Python and R are two of the most popular programming languages. Python is a good choice for people who are just starting and have nothing to work with. This is because Python is a simple programming language that can be used for many different things.

Start simple

You must take the time out to practice on a dataset that is easier to work with. It's best to practice on a simpler dataset because it helps you to learn and get used to the machine learning libraries. You can learn good habits like cross-validation to avoid overfitting and split data sets into separate testing and training sets by practicing on easier datasets. Any programming language that you pick will have training datasets that help you get a feel for the real project.

Data exploration

Exploratory analysis is a crucial step in data science because it lets you figure out what decisions are taken during the model training process. In the process, you will learn about various functionalities, the statistical distribution of value systems, and null and missing values. Users who want to explore data are advised to use the Seaborn library. It gives you high-level functions for plotting and displaying the data.