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How to Become a Data Scientist
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.