Data Science: 2021 everyone can code!

Data Science is about working with big data. Big data is a huge amount of unstructured information: for example, meteorological data for a certain period, statistics of queries in search engines, sports results, databases of microorganisms' genomes, and much more. The key words here are "huge volume" and "unstructuredness". To work with such data, they use mathematical statistics and machine learning methods.

A specialist who does this kind of work is called a data scientist. It analyzes Big Data to make predictions. What kind of forecasts - depends on what problem needs to be solved? The result of the data scientist's work is a predictive model. To simplify, this is a software algorithm that finds the optimal solution to the problem.

Things you will learn:

What is Data Science?

What Career Options are there for a data scientist?

Where Can You Train to Become a Data Scientist?

What Are Some Important Factors to Consider when Choosing an Organization for a Data Science or Big Data Project?

How can Data Science be improved?

What Is The Best way to network as a data scientist?

What Should I do for Practical Training in Data Science as an Undergraduate and how do I get in touch with a mentor?

What is a transformation function and how can it be performed?

How can you solve a problem in Data Science?

How Can One Find The Right Career in Data Science?

What are the best online resources for self-study of data science and machine learning?

How do I learn data science (machine learning + python) from scratch?

Where can I learn data science for free?

What types of organizations need data analysts/scientists and how should they hire one?

How many data scientists work in an organization?

How can one become a data engineer?

How much does a data scientist make?

How do I start my career as a big data analyst or data scientist?

What is a typical day in a life of a big data engineer/analyst/scientist like?

What are the different types of Data Scientists and what do they do?

and much more!

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