Who is Data Scientist?

Data Science is working with Big Data. Big data is a huge amount of unstructured information: for example, weather 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 good book to read is Data science from scratch PDF.

The specialist who does this kind of work is called a Data Scientist (or 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.

Are these predictions really useful?

Yes. A lot of services that we are already accustomed to have been created by data scientists. And you see the results of their work every day. For example, these are weather forecasts, chat bots, voice assistants ... And also algorithms that recommend music and video to the taste of a particular user. The list of possible friends on social networks is also the result of Data Science. Search engines and face recognition software are also based on algorithms written by data scientists.

So Data Science is the same as regular business intelligence?

No, they are not the same. The main difference lies in the result. The Data Scientist looks in the datasets for connections and patterns that will allow him to create a model that predicts the result - that is, you can say that the Data Scientist is working for the future. He uses software algorithms and mathematical statistics and solves the problem in the first place as a technical one.

The business analyst is focused not so much on the technical, software side of the problem as on the commercial performance of the company. He works with statistics and can evaluate, for example, how effective an advertising campaign was, how many sales were in the previous month, and so on. All this information can be used to improve the business performance of the company. If there is a lot of data and some kind of forecast or estimate is needed, then a business analyst can engage data scientists to solve the technical side of this problem.

Let us explain with an example we found here Python workout 50 ten-minute exercises pdf. Let's say the program analyzes the client's financial transactions and recommends to issue him a loan or refuse. That is, the task of the program is to assess the client's solvency. The creation of such a software algorithm is the job of a data scientist.

And a business analyst does not deal with such technical tasks. He is not interested in working with a specific client, but he can analyze all the bank's statistics on loans, for example, over the last three months - and recommend that the bank reduce or increase the volume of lending. This is a business task: actions are proposed that will increase the bank's profitability or reduce financial risks.

The work of a business analyst and a data scientist often overlaps, it is just that each is engaged in his own part of the task.

If I don't have a technical education, is it better not to dream of working in Data Science?

Let's be frank - it can be difficult for humanities to master this profession: to work in Data Science, you need a good knowledge of mathematics and programming. And the humanities scholar most often does not have this knowledge. And vice versa: the more confident you feel about it already at the start, the easier it will be to learn.

However, do not give up: a lot depends on motivation, on how willing you are to fill the gaps in your education. Now people come to Data Science with different backgrounds and at different ages. Here is an example of one such story - perhaps it will support you.

Where is the best place to start?

Better to start with math. You won't need very complicated math, but you should be fluent in concepts such as derivative, differential, matrix determinant, and what is related to them. Books and lecture courses will help you master this. For example, the book "Mathematical Analysis" by Lipman Bers, written in rather simple language.

What about programming?

Learning Python is the next step. Now this programming language is perhaps the main tool in Data Science. Its strengths include relative simplicity and flexibility. It is quite possible to master Python for a beginner who has not previously programmed. It is no coincidence that this language is often recommended for beginners.

There are many courses on Python, both free and paid. Here is one of the free courses. And one more: "Pythontyutor".

Here Python RealPath is a course called Profession Python Developer. The course is paid, lasts a year, and during this time, students actually master a new profession from scratch (both theory and practice) and collect a personal portfolio - with the help of a mentor.