Course “Data Science Specialist” - course 112,000 rub. from Yandex Workshop, training 8 months, date November 30, 2023.

Complete course program in Data Science
We update it regularly to ensure it meets industry and employer needs. In other words, you learn only what will definitely be useful in your work.

Basics of Python and Data Analysis: Free Introductory Course:

Learn the basic concepts of data analysis and understand what data analysts and data scientists do. Solve five cases of working with data from different areas:
- find out the reason for the massive breakdown of gadgets,
- check the payback of mobile application advertising,
- find the best location for a new store,
- help you choose a development strategy for an AI startup,
- evaluate the effectiveness of robots in the support service.

By solving cases, you will learn the basics of Python and the pandas library, learn how to build some graphs and interpret them correctly.

Introduction to the profession “Data Science Specialist”

What is a Data Science Specialist?
How we teach.

Basic Python:
Dive deeper into the Python programming language and the pandas library.

+1 project in portfolio
Compare Yandex user data. Music by city and day of the week.

Data preprocessing:
Learn to clean data from outliers, omissions and duplicates, as well as convert different data formats.

+1 project in portfolio
Analyze data about bank clients and determine the share of creditworthy ones.

Exploratory data analysis:
Learn the basics of probability and statistics. Use them to explore the basic properties of data, looking for patterns, distributions and anomalies. Get to know the scipy and matplotlib libraries. Draw diagrams and practice analyzing graphs.

+1 project in portfolio
Explore the archive of advertisements for the sale of real estate in St. Petersburg and the Leningrad region.

Probability theory. Additional course
Remember or recognize the basic terms in probability theory: independent, opposite, incompatible events, etc. Using simple examples and fun problems, you will practice working with numbers and building the logic of solutions.
This is an optional sprint. This means that each student himself chooses one of the options:

• Take an additional course of ten short lessons, brush up on theory and solve problems.
• Open only the block with interview tasks, remember the practice without theory.
• Skip the course completely or return to it when there is time and need.

Final project of the first module
Learn how to conduct preliminary data research and formulate and test hypotheses.

+1 project in portfolio
Find patterns that determine the success of the game.

Introduction to Machine Learning:
Master basic machine learning concepts. Get to know the Scikit-Learn library and use it to create your first machine learning project.

+1 project in portfolio
Develop a tariff recommendation system for a mobile operator.

Tutored training:
Dive deeper into the hottest area of ​​machine learning: supervised learning. Learn how to deal with imbalanced data.

+1 project in portfolio
Predict the likelihood of a client leaving the bank.

Machine learning in business:
Learn how machine learning (abbr. MO) helps the business on how to collect data and how product metrics relate to MO metrics. Learn to launch new service functionality using ML. Learn what business metrics, KPIs and A/B testing are.

+1 project in portfolio
Train a model that helps identify a new location for oil production with the least risk of loss.

Final project of the second module:
Prepare data for machine learning. Using the model, evaluate its quality.

+1 project in portfolio
Simulate the process of smelting gold ore to improve the operation of the enterprise.

Linear algebra:
Take a look inside some of the algorithms you've learned so far and gain a better understanding of how to use them. In practice, master the main concepts of linear algebra from scratch: linear spaces, linear operators, Euclidean spaces.

+1 project in portfolio
Use data conversion method to protect the personal information of insurance company clients.

Numerical methods:
You will analyze a number of algorithms and adapt them to solve practical problems using numerical methods. Master approximate calculations, algorithm complexity estimates, and gradient descent. Learn how neural networks are trained and what gradient boosting is.

+1 project in portfolio
Develop a model to determine the cost of a used car.

Time series:
Time series describe how parameters, such as electricity consumption or the number of taxi orders, change over time. You will learn to analyze series, look for trends and identify seasonality. Learn how to create tabular data and a time series regression problem.

+1 project in portfolio
Build a model and predict peak taxi loads.

Machine learning for texts:
Learn to make numerical vectors from texts and solve classification and regression problems for them. Learn how TF-IDF features are calculated and become familiar with word2vec and BERT language representations.

+1 project in portfolio
Speed ​​up comment moderation in your community by automating toxicity assessments.

Basic SQL:
Learn the basics of SQL query language and relational algebra for working with databases. Get acquainted with the features of working in PostgreSQL, a popular database management system (abbr. DBMS). Learn to write queries of varying levels of complexity and translate business problems into SQL.
You will work with a database of an online store that specializes in films and music.

+1 project in portfolio
Write a series of queries of varying complexity to a database that stores data on venture investors, startups, and investments in them.

Computer vision:
Learn to solve simple computer vision problems using ready-made neural networks and the Keras library. Get to know Deep learning.

+1 project in portfolio
Build a model to determine the approximate age of a person from a photograph.

Unsupervised learning:
Unsupervised learning is one of the methods of machine learning in which the system solves a problem without pre-labeled data based on its features and structure. Learn about clustering and anomaly detection problems.

Graduation project:
In the last project, confirm that you have mastered a new profession. Clarify the customer’s task and go through all stages of data analysis and machine learning. Now there are no lessons or homework - everything is like at a real job.

+1 project in portfolio
Project to choose from:
- Build a model that predicts customer churn from a telecommunications company.
- Build a model that predicts the parameters of the technological process at a metallurgical plant.