This is the age of data, and in this era the data scientists are kings, they possess a set of diverse and important skills ranging from data management to machine learning, and they are mainly responsible for converting data into actionable insights using predictive models of self-analysis or using A customized analysis mechanism according to the requirements of the company ...

In other words, it is very important to be a data scientist in the current data age to the extent that an article in the Harvard Business Review described it as "the most attractive job in the twenty-first century", and that as a data scientist your average salary will be 102 2 thousand annually, this article is a complete guide to becoming a data scientist in 2020 that you can follow if you are interested in learning more about data science:

But there is still confusion about the differences between the data analyst and the data scientist, so we'll start our article:

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What is the difference between a data analyst and a data scientist?

It is clear that both the "data analyst" and "data scientist" have a job description that relates to the data. But what is it?

The data analyst uses data to solve various problems and obtain actionable insights in the company, this is done by using different tools in the specific data sets to answer companies' questions such as "Why is the marketing campaign more effective in certain regions?" Or "Why has product sales been reduced at this time?" Etc., for this, the basic skills that a data analyst possesses are data mining, R, SQL, statistical analysis, data analysis, etc., and many data analysts can acquire the additional skills required to become data scientists.

The data scientist can design new processes and algorithms to model data, create predictive models, and perform custom data analysis according to company requirements, so the main difference is that the data scientist can use sophisticated software to design data modeling processes instead of using pre-existing processes to obtain answers from data such as an analyst Data, so the basic skills the data scientist possesses are data mining, R, SQL, machine learning, Hadoop, statistical analysis, data analysis, OOPS, etc.

Educational requirements to become a data scientist

There are many paths to reaching your goal as a data scientist, but keep in mind that most of these paths pass through the university college as the four-year Bachelor's degree is the minimum requirement.

The best course is to complete a Bachelor's degree in Data Science, where it will clearly teach you the skills required to collect, analyze and interpret large amounts of data, you will learn all about statistics, analysis techniques, programming languages, etc. that will only assist in your work as a scientist Data.

Another course that you can follow is to complete any technical college that helps in your role as a data scientist, such as studying computer science, statistics, mathematics, economics, etc., and after completing your studies, you will have skills such as programming, data processing, and quantitative problem solving, after which you can find A job for beginners or masters and doctorate degrees.

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Skill Requirements to Become a Data Scientist:

1- Statistical Analysis:

As a data scientist, your primary task is to collect, analyze and interpret large amounts of data and produce useful insights for the company. It is clear that statistical analysis is a large part of the job description !! This means that you should be familiar with the basics of statistical analysis, including statistical tests, distributions, linear regression, and probability theory, and that there are many useful analytical tools in statistical analysis such as data scientist such as SAS, Hadoop, Spark, Hive, Pig ... etc. , So it's important to have an accurate knowledge of it.

2- Programming skills:

This is because it is much easier to study and understand data in order to draw useful conclusions, as you will be able to use certain algorithms according to your needs.

Python and R are the most used languages ​​for this purpose, as Python is used because of its ability to statistical analysis and ease of reading, since Python contains multiple packages for machine learning, data visualization, data analysis, etc. (such as Scikitlearn) that make it suitable for data science, and R makes it easier Solve almost any data science problem with the help of packages like e1071, rpart, etc.

3- Machine learning:

If you are in any way connected to the technology industry, you have probably heard of machine learning! It enables machines and computers to learn through training without having to specifically program them, and this is done by using different data and algorithms.

So you have to be aware of supervised and unattended learning algorithms in machine learning like linear regression, logistic regression, decision tree, K Nearest Neighborhood etc.

4- Data management:

Data plays a big role in the life of the data scientist, so you must be proficient in “data management” that includes extracting, converting and downloading data, this means that you must extract data from different sources, then convert it in the required format for analysis and finally download To a data warehouse.

You should also be familiar with Wrangling Data, this basically means that the data in the warehouse needs to be cleaned and consolidated in a coherent manner before it is analyzed to get any actionable insights.

5- Data intuition:

Do not underestimate the power of intuition in data! In fact it's the basic non-technical skill that distinguishes the data scientist from the data analyst, this is almost the same as finding the needle in a haystack that represents the actual potential in the huge unexplored data heap.

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