Artificial IntelligenceData ScienceProgramming languages

What should you learn to become a good data scientist?

Data science is a very alluring profession now, primarily because of its particularly high salaries. Still, data science demands wide knowledge on various topics if you really want to become successful in this job.

Here are some suggestions for your future studies to get on the carrier path of data science.

Programming languages

Programming languages are absolutely crucial for a future data scientist. With their help, one can not just perform standard statistical analysis but also apply machine learning techniques the knowledge of which differs data scientists from regular statisticians or business analytics.

The most wanted languages for data science are Python and R. Note that Python can take your carrier to a whole new level due to large ready-to-use libraries on machine learning, analysis of big data as well as data visualisation.

Machine learning techniques

Machine learning techniques is a one of the most crucial subjects for data scientists who can derive a lot of information with the help of the algorithms of artificial intelligence. Many of them are actually available to data scientists and do not require being created for scratch. Still, to be able to navigate through them and apply them correctly, you will need the knowledge of programming languages mentioned above.

At the same time, in case you want to be more creative about machine learning algorithms, you should know math for data science which includes such topics as vectors and matrices.

Math

As it has been mentioned above, the knowledge of math will help you to be more flexible with machine learning algorithms. Having certain knowledge, you will be able to alternate ready-to-use algorithms and create your own ones.

Tools for presentation

Apart from the analysis itself, data scientists have to create reports and presentations on their research and the results of this research. In order to make it effective, they need some tools for data representation. One of the most popular services to use is Jupiter Notebooks which is offering a possibility of representation code in pieces with the outcome of these pieces rather than the entire result of a large programme.

Data bases

The work with data bases is very typical for data scientists at the stage of retrieving data and using it in the research. That is why it is recommended to have a grasp of SQL and one of the most popular data base management systems such as MySQL, Postgre SQL or Oracle SQL.

Statistics

Even though data science relies on machine learning which is going beyond statistics, the knowledge of this subject will be very useful. Actually, statistical tools are also often used in data science as well.

Writing

One of the least obvious things a data scientist should know is writing. Even though many people are not aware of it at the beginning, writing for data scientists is as crucial as the ability to analyse data and get information out of it.

As it has already been mentioned above, writing reports is a part of the job of data scientists and they should know how to present the information they have found with their research. Certainly, a lot will depend on the way they present this information.

The knowledge on the subject of the analysis

Even though it is quite popular for data scientists to be focused on all of the subjects mentioned above which requires a lot of time to be perfected. Yet, it will be great if the subject of your research is within the topic you actually know.

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