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Data Science specialist bootcamp - course 112,000 rub. from Yandex Workshop, training 8 months, Date: December 21, 2023.
Miscellaneous / / November 29, 2023
Quick dive into IT
Bootcamp is a short but intensive course. In just 5 months you will be ready to work as a data analyst.
Intensive load
About 8 hours of study awaits you daily: regular meetings and webinars with a mentor, theory, homework, a lot of practice and projects.
Quick Feedback
You will study in a small group, so we can pay a lot of attention to each student. The mentor answers all questions within an hour.
Personal approach
The teacher and mentor will help with personal requests via chat, and the mentor will also conduct individual consultations and weekly webinars.
Data Science specialists work with data in the same way as scientists - they use mathematical statistics, logical principles and modern visualization tools to get results. For example, a biologist conducts experiments to test hypotheses: he must generalize particular observations, exclude accidents, and draw correct conclusions.
You will have to analyze data and build models on their basis that help make decisions in science, business and everyday life.
You will analyze large volumes of data and apply machine learning for various tasks. A Data Scientist builds data-based models that help make decisions in science, business, and everyday life. With machine learning, you will predict events, forecast values, and look for unobvious patterns in data.
Free Part 20 Hours Introductory Course: Basics of Python and Data Analysis
Learn the basic concepts of data analysis and understand what data analysts and data scientists do
1 sprint 1 week Basic Python
Dive deeper into the Python programming language and the pandas library
2 sprint 1 week Data preprocessing
Learn to clean data from outliers, omissions and duplicates, as well as convert different data formats
Sprint 3 1 week 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. Create charts and practice analyzing graphs.
4 sprint 1 week Statistical data analysis
Learn to analyze relationships in data using statistical methods. Learn what statistical significance, hypotheses, and confidence intervals are.
5 sprint 1 week Final project of the first module
Learn how to conduct preliminary data research, formulate and test hypotheses
6 sprint 1 week 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.
Sprint 7 Week 1 Supervised Learning: Classification and Regression
Dive deeper into the hottest area of machine learning: supervised learning. Learn how to deal with imbalanced data.
8 sprint 1 week Machine learning in business
Learn how to conduct preliminary data research, formulate and test hypotheses
9 sprint 1 week Final project of the second module
Simulate the process of smelting gold ore to improve the operation of the enterprise
10 sprint 1 week 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.
11 sprint 1 week Numerical methods
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.
12 sprint 1 week Time series
Learn to analyze time series. Learn how to create tabular data from time series and solve a regression problem on it.
Sprint 13 1 week 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.
Sprint 14 1 week Basic SQL
Learn the fundamentals of structured query language SQL and relational algebra operations. Get to know PostgreSQL, a popular database management system (DBMS). Learn to write queries of varying levels of complexity and translate business problems into SQL.
You will also be introduced to PySpark, an open source library that is used for distributed processing of large volumes of data.
15 sprint 1 week Computer vision
Learn to solve simple computer vision problems using ready-made neural networks and the Keras library. Take a look at Deep learning.
16 sprint 1 week Graduation project
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.
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