Introduction to Data Science using Python
The online platform Siragify offers an interactive study of Python: one page explains brief theoretical information and a code interpreter. The course is designed for novice users and talks about the basic commands of a programming language.
The course is conditional-free: access to control tasks and work on projects is possible only for a paid subscription. Free lessons are appropriate to learn simple constructs and understand the syntax of the language.
Students learn three basic topics: the use of functions, the creation and use of classes. The last lesson is about creating your own projects. The training is based on the work on mini-projects and the study of important concepts. The course is aimed at those who want to become a programmer or plans to work with them.
The average salary of a data analyst, according to HeadHunter, is 120 thousand rubles. The material highlights the main requirements in the vacancies of data analysts and places where you can get skills in this area for free.
Who are the data analysts
Experts working with big data are able to extract useful information from various sources and analyze it for making business decisions. As a rule, analysts are faced with fragmented information, so it is important to be able to extract the necessary data.
Now the profession of data analytics is considered one of the most attractive and promising in the world. To become a good analyst, you need to understand statistics more than programming. Because during the work it is necessary to build mathematical models that describe the problem and the actual data.
Data analyst works with random variables and probabilistic models, its task is to find unexpected patterns. Therefore, knowledge of probability theory and mathematical statistics is one of the main requirements for applicants.
You also need to know R or Python programming languages and have an idea about big data processing technologies. This knowledge is enough to claim the initial position of the data analyst.
To become a good analyst, you must not only understand programming or statistics, but also know the product well, and most importantly, be able to test and propose hypotheses. Big data, when properly applied, contains a huge amount of cool insights and ideas on how to improve a product or determine what is important.
But most problems do not have a unique solution or algorithm: in this regard, data analysis is a very creative work. The ability to choose the right visualization is also important. The same data may look like a random set of points or tell a lot of interesting things with the right approach.
In VKontakte we work with huge amounts of data – more than 20 billion measurements per day. We collect information on the Hadoop cluster and use various processing tools: Hive gives answers to simple questions, and Spark, Pandas, Sklearn help us with more complex analytics.
We also use the data acquisition, aggregation and visualization system developed by our team to analyze product and technical metrics and A / B experiments. Thanks to data analysis, we daily test dozens of product hypotheses and conduct hundreds of experiments that allow us to constantly improve the product and make our services more convenient and personalized.
For example, in 2015 we began to analyze the activity of users in the news feed and see what can be improved. After a lot of research, we came to the conclusion that we can make everything much more convenient, and in 2016 we launched the “smart” tape, which was as interesting and useful for each user as possible.
We constantly continue to analyze the activity of the audience. At some point we found out in practice that users want to expand their circle of interests and get acquainted with new authors. Therefore, in 2017, the “Recommendations” section was launched. And now, analyzing the growing activity in the new service, we see that this was the right decision.