MODULE CONTENT

1. Strings
2. Lists
3. The for loop
4. Nested lists
5. Conditional statement. While loop
6. Functions
7. Data Structures in Python: List and Tuples, Dictionary and Sets
8. Pandas for data analysis
9. Data preprocessing
10. Analyzing data and organizing results in Jupyter Notebook
11. Sorting, searching, regular expressions.
12. Iterators and generators
13. Numpy
14. Introduction to OOP.

PROJECT
Creating a chatbot for Telegram on any topic of interest with the content of parsing and processing information from a third-party resource. A chatbot that will simply respond with different phrases embedded in the code is not enough.
The goal of the project is to develop a chatbot capable of interacting with users and providing useful information.

ACQUIRED SKILLS

• Python programming basics
• Code writing and debugging
• Working with third-party libraries and reading documentation
• Ability to find relevant information and apply it to your projects

MODULE CONTENT

1. Database basics, data structure
2. table creation and DDL commands
3. SELECT, WHERE
4. All kinds of join
5. Group by, Having
6. Window functions, window function frames
7. Indexes and query optimization

ACQUIRED SKILLS

• Reading data from tables using SELECT queries with required conditions
• Creating tables, writing data to a table
• Calculation of aggregate values
• Joining data from different tables

MODULE CONTENT

1. An introduction to exploratory data analysis. Algorithms and methods
2. Mathematical statistics in the context of EDA. Types of traits
3. Linear algebra in the context of Linear Methods.
4. Mathematical analysis in the context of an optimization problem
5. Probability theory and mathematical statistics
6. Probability theory in the context of Naive Bayesian classifier
7. Data analysis

PROJECT
Kaggle EDA protection:
A team research project to examine data, develop and test hypotheses, and present in front of fellow students.

ACQUIRED SKILLS

• Ability to work with exploratory data analysis
• Ability to build and work with graphs
• Ability to fix problems in the data

MODULE CONTENT

1. Algorithms and data structures
2. Linear and binary search
3. Complexity of the algorithm
4. How to estimate the execution time
5. How to solve algorithmic problems
6. How to test your program

ACQUIRED SKILLS

• Mastering popular algorithms: linear and binary search
• Ability to estimate the execution time of an algorithm
• Ability to solve algorithmic problems

MODULE CONTENT

1. Degrees and the logarithm. Limits and sequences.
2. Functions and their graphs
3. Differentiation
4. Integration
5. Vectors, matrices and operations with them
6. Singular decomposition of matrices

ACQUIRED SKILLS

• Mastering the basic concepts of math, matrices, functions, operators and the ability to work with them.
• Mastering mathematical statistics and probability theory.

MODULE CONTENT

1. Why you need statistics and A/B tests
2. Confidence intervals
3. Statistical distributions, interrelationships of random variables, correlation indices
4. Hypothesis testing, parametric statistical criteria
5. Non-parametric statistical criteria
6. Maximum likelihood method and kernel density estimation
7. A/B experiment design
8. A/A-experiments and validation of experimentation methodology
9. Mistakes when conducting A/B tests
10. Increasing sensitivity of A/B tests
11. Failure to perform A/B tests
12. Roll-out test
13. Causal Inference

PROJECT
Analysis of hypotheses to improve the performance of the marketplace or logistics company (optional)

ACQUIRED SKILLS

• Ability to hypothesize, plan experiments, and evaluate results based on statistics.

MODULE CONTENT

1. Hypotheses and working with them
2. Metrics and working with Amplitude
3. From strategy to tactics through metrics management

ACQUIRED SKILLS

• Mastering the basics of product analyst work
• Mastering product metrics, how to select, count and evaluate them

MODULE CONTENT

1. How visualization works and why you need it
2. How to cheat with charts
3. Principles of a good schedule
4. Basics in report building, breakdown of different BI tools
5. We'll spend a lot of time practicing and getting to grips with the Tableau interface together.

Description:

PROJECT
Portraitist type visualization. You will make a visualization of the "Portrait Man" type. Where the entity will be your client. Data familiar - with whom you have worked in practice.

ACQUIRED SKILLS

• Ability to turn information into a clear and understandable presentation using charts, graphs, tables, and other visual elements, to easily comprehend and analyze large amounts of data, and to identify relationships and trends.

Included in the module:

• Introduction to MO: what it is and what are the main components of the metric and the linear regression problem
• Gradient descent
• Generalizability of the model: deferred sampling method / Cross Validation
• Useful techniques for working with data
• Linear classification: probability estimation
• Error matrix and basic classification metrics
• ROC, PR curves
• AUC-ROC, AUC-PR
• Gradient Busting
• Bias-variance trade-off
• Clustering
• Recommendation systems
• Machine learning: classical problems and algorithms
• Creating an ML pipeline from scratch
• Development of the ML pipeline.

Description:
In this module, you will explore the relationship between independent variables and the dependent variable and predict values of the latter based on the former, as well as categorize observations into predetermined categories or classes based on their attributes.

You will understand how several developers can work on one project at the same time, improving the quality and speed of its development. You will learn what clustering and data preparation are.

Program:

• Regressions
• Classification
• Pair programming - ML algorithms
• Clustering
• Data preparation
• Advanced ML approaches.

Included in the module:

• The neuron and the activation function. The simplest neural network
• Neural network training (epochs, mini-batch gradient descent, loss function, cross-entropy loss)
• Batchnorm, dropout, weight regularization and early stopping
• Backpropagation
• Transfer learning
• Learning rate, adaptive LR
• Softmax
• MLP: perceptrons, device
• Data processing: augmentation
• Data processing: normalization of batches
• Architecture, principle of operation of convolution
• Layer types
• Filter hyperparameters.

Description:
This module introduces you to data analysis using Python: learning basic data analysis techniques, working with libraries for data processing, visualization, and analysis, such as Pandas, NumPy, and Matplotlib.
Explore deep learning and neural networks: understanding the basic principles and concepts of deep learning, exploring different types of neural networks and their application to machine learning tasks.

Understand how to deploy and monitor deep learning models: learn how to deploy and monitor deep learning models, including acquiring and preprocessing data, tuning and optimizing models, deploying them on production environments, and monitoring their performance.
Create a simple neural network: a hands-on exploration of the basic steps of creating and training simple neural networks, including setting up the network architecture, defining the loss function and optimizer, processing input data, training and evaluating the model.

Implement deep learning models for image classification: exploring different methods and approaches to image classification using deep learning, including the use of convolutional neural networks, pre-trained models and transfer learning.

Program:

• Using Python to analyze data. Introduction to Deep Learning and Neural Networks
• Deep learning using TensorFlow and Keras. Advanced topics in deep learning
• Advanced topics in deep learning. Deploying and monitoring deep learning models.
• Creating and training a simple neural network
• Realization of deep learning models for image classification
• Implementation of GANs and transformer-based models.