Introduction to Data Science - free course from Skillbox, training, Date: November 29, 2023.
Miscellaneous / / December 06, 2023
For beginners
Master Python and SQL from scratch, learn how to collect and analyze data, and get the necessary theoretical minimum in mathematics, probability theory and statistics. Consolidate your knowledge in practice - prepare and defend your thesis, which will become the first case in your future portfolio.
For programmers
Improve your mathematics, statistics, analytical and algorithmic thinking, and learn to identify business needs. Gain experience working with machine learning models and use Python to solve data problems. You will go through the process from data collection to model deployment.
For beginning analysts
You will learn to formulate hypotheses and draw conclusions based on data. You will be able to write efficient code in Python, turn raw data into useful information for the company, understand mathematics based on statistics, train machines and predict results. You will polish your knowledge, increase the speed of your work and achieve a promotion.
Author of the Machine Learning course. Senior Data Scientist, Team Lead at SberData, Sber. 5+ years in the profession
Course speaker, R&D Director, UBIC Tech. More than 15 years of experience in development
Data Scientist at Sberbank, mathematician at the Computing Center of the Russian Academy of Sciences. Block “Fundamentals of Mathematics for Data Science”. More than 4 years of experience in teaching higher mathematics
Introduction to the course
Get acquainted with the main areas of Data Science, find out what problems data analysts, data engineers and machine learning specialists solve.
Business Understanding
Learn to communicate with customers, identify needs, collect and document requirements, and conduct interviews.
Python Basics
Master the basics of Python at a sufficient level to confidently work with data.
Data Understanding
Learn to download data from various sources, master Excel, SQL and Power BI tools. Learn how to describe and evaluate the quality of source data.
Data Preparation
Master exploratory data analysis: learn to find, clean and prepare data sets so that the output is a dataset ready for further work.
Modeling
Learn to formulate and test hypotheses. You will go through the basics of modeling in machine learning and analytics, create your first ML model, and try yourself as a product and marketing analyst.
Evaluation
Learn how to compare models and evaluate their quality. Prepare the model for industrial use.
Deployment
Turn the model into a finished product. Learn to automate data flows, run models on servers, and monitor the operation of the model.
Basic Mathematics for Data Science
Gain basic knowledge of mathematics to work with machine learning. You will understand what approximation, interpolation, functions, regressions, matrices and vectors are. Learn to work with mathematical entities in the SymPy Python library.
Fundamentals of statistics and probability theory
You will understand the principles of working with random variables and events. Become familiar with some types of distributions and statistical tests that are useful in constructing models and testing hypotheses.
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Masha Busha
17.01.2022 G.
It turns out that you can grow your career even in a startup.
Pros: Clear tutorial. Disadvantages: None. At first I was a project manager, then I plunged into analytics and now I am doing machine learning. Simply, a great boss who is ready to invest in the development of employees) It was he who brought me and my colleague to the Data Science course from skillbox. I was also inspired by feedback from graduates who are already working in a new specialty. Now...
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wladislove888
18.05.2021 G.
I like training at SkillBox
Advantages: Brevity, consistency, lack of water, information integrity. Disadvantages: There are no cons for me. For two years I wanted to train as an Internet marketer. I started training at Skillbox by purchasing a course on sale. I studied for two months, 2-3 hours a day, and realized that it was not for me. It doesn't work and that's it. Elena, a Skillbox employee, went to a meeting and they exchanged a course for Data-Science. And then n...
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Alexanders M
18.10.2022 G.
Good driver for beginners
Pros: Great start for beginners. Disadvantages: There are no team competitions At the age of 28, I decided to study Data Science. Before that, I tried to learn Python on my own, but I didn’t have enough strength or motivation. I decided to buy an expensive DS course (luckily I got a good discount on free intensive courses from Skillbox). I thought that if I paid the money, I would definitely study. Sp...
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gurauld
03.11.2022 G.
A good course to learn the basics of Data Science
Advantages: Your assignments are checked by a live person. Availability of a final project based on real data for the portfolio. Bonus mini-courses. Disadvantages: Only one block out of three is completely ready for study; the rest of the lessons are added rather slowly. Therefore, it will not be possible to quickly master a profession using the course. The course is divided into blocks: basic, Junior and Advanced, and the block is divided into modules. Most of the modules I...