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DataDrivenInvestor
Dec 20
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Data science is a growing field with many possibilities for people interested in it. It’s not just a job title or buzzword, but rather a way of approaching problems and thinking about how data can be used to solve them. If you’re looking to get into data science, this guide will walk you through all the steps from learning what kind of work is involved in the field to getting started on your first project as an entry-level data scientist.
Data science is a broad field and can be described as the process of extracting knowledge from data to make better decisions. There are many definitions of data science, but they all have one thing in common: they define it as something that applies computer science, statistics, and mathematics to solve problems in the real world.
Data scientists work with large amounts of structured and unstructured data from various sources including databases, spreadsheets, text files, social media feeds, or web browser histories. Regarding programming languages, the most used by data scientists are R and Python.
The skills required to become a data scientist are many and varied. The ones most in demand include statistical analysis, machine learning, coding (in Python, Java, R, etc.), and business acumen.
There are a number of resources available to you that can help you learn data science.
There are many tools available for data science and machine learning, some free and some paid. Below are the ones that data scientists use most frequently:
You’ll need to learn at least one programming language. There are many options, but we recommend Python or R. Both languages are great for data science, and they’re often used together in courses and tutorials.
Python is a general-purpose programming language that’s easy to pick up and has a lot of support from the community. It’s also very popular with data scientists, so you’ll be able to find plenty of resources online if you get stuck on something.
R is an open-source statistical programming language built for statistical analysis and modeling tasks such as analyzing data sets or making predictions based on them (hence “r” being short for “regression”). Because it was designed specifically with statistics in mind, R can do things like automatically plot graphs based on the data that was entered into it — something that most other programming languages don’t offer out of the box (though this isn’t always desirable!).
Data science is a hot field that is attracting both young graduates and experienced professionals from other fields. But how do you get started?
It all starts with learning the skills for data science and then putting them into practice by working on projects, building your portfolio, and getting experience in the industry.
As we’ve shown here, there are many different ways to get started with this exciting career path, so don’t be afraid if you don’t have formal training or certification yet!
Take a look at our list of recommended resources below that will teach you more about data science.
Remember that the most important skill you can have is your ability to learn.
Thanks for reading!
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