What is Data Scientist ?

 As a Data Scientist, you have to take your decision depending on the problem. Are you looking for quantitative analysis or statistical tools? Do you want to understand how machine learning does it? In this post, we would discuss all these questions and finally let’s make some conclusion in response to those questions.

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What is data scientist?

Data scientists are people who work with raw data to uncover hidden trends to give useful feedback to business strategy. This can be done through various analytical methods such as predictive modeling, statistics, etc.

Data science also includes programming and software techniques to discover correlations between variables and their relationships. These insights from data are used to inform decisions. The term “data scientist” means any person who works with large volumes of disparate data. It is very common that there will always be different types of people working on different projects. That’s why Data Science has become a multidisciplinary field.

What do I learn by using data science?

As a Data Scientist, you have to take your decision depending on the problem. Are you looking for quantitative analysis or statistical tools? Do you want to understand how machine learning does it? In this post, we would discuss all these questions and finally let’s make.

It doesn’t matter whether you use Python or R, if you use SQL or SPSS, you would still get answers from me. At first, you need to define what a Data Scientist actually do and then you need to know how to become one of them. There’s no formula or formula, but there are some steps you should follow before getting into the field of data science.

Here is what you need:

Analytics skills: Business analysts usually need at least basic analytics skills. They may consider themselves statisticians. You need to know about Bayesian analysis — how to perform various kinds of inferences when data is incomplete or incomplete and so on.

Python (at least 2 years): If you want to explore more, you can look for open source libraries like PySpark, Matplotlib and others. And even you need a course in Stanford. Or else you can try coursera or other online courses. I recommend you the Hadoop course.

Python scripting (code or algorithm): A good tool is Jupyter Notebook where you need to write code. Most companies have an IDE for R like Google Colab. Even Python needs IDEs to run in OS like Mac OS and Windows. So you need to learn C, Python, R and IDEs to be able to write in the cloud environment.

Statistics (at least MATLAB or Excel): In many cases, you don’t need to know much about statistics. But data science requires a lot of computing power; therefore, you should know advanced math.

Machine learning: Since data science also involves coding and algorithms, you will find ML techniques in most data science tutorials. In addition to writing your own codes and model, you will need to create and train and test.

Deep Learning: Many companies try to teach deep learning but there is too little support from academia and they need experts. Moreover, if you need help with training then you can check my site which has a great community of resources just for beginners.

SQL+DataScience: When I started out, there was only the MySQL. With time, newer databases came up. Then python came along and every company started adopting its framework. Now there are APIs for SQL. Also, you also need to figure out how to build models with MongoDB or whatever else is needed. As a result, the majority of job descriptions include Python and SQL.

Do you have SQL experience?

Yes! No? Good question. On top of fundamental knowledge, you need to know about the database you want to work with. Because there are several different programming languages now and lots of options are available to develop SQL-based API.

If you come to a point where you need to deal with terabytes, then you need to hire expert. Though it might seem difficult, it is not that hard. You can do it. Just remember the famous proverb “If you have 10 hands, then you can do anything you want.”

So keep reading and enjoy your journey. Hope you can explore your career in the realm of data science.

Originally published on wix.com

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