Data Analytics  – Learning Path

Program Overview

The educational course will provide students with a comprehensive and solid theoretical foundation and powerful practical skills in data extraction, transformation and processing, as well as in data visualization and building models.

The sessions delve into the theoretical underpinnings of key Data Analytics domains and their practical implementation in different software and tools.

The practical segment of the course is geared toward using the high-level programming language Python, with a focus on mastering the functionality of libraries such as Numpy, Pandas, Statsmodels, Random, Scikit-learn, Regex, and Matplotlib, as well as SQL language for building queries and advanced experience in Excel.

The course content is balanced to reveal the essence of Data Analytics technological processes: data Extraction, Transformation, Load, Visualization, Modelling.

Prerequisites

• Basic Python Knowledge: Syntax (nice to have); data types and data structures; basic conditional operators; functional.
• Basic Mathematics Knowledge (nice to have): Elements of probability theory; discrete mathematics; matrix theory; function analysis; analytical geometry; trigonometry.
• Basic Excel Knowledge (nice to have): Read files, formatting, basic formulas.

Time Commitment

The time you need to allocate really depends on your knowledge level and can vary a lot. We did a very rough estimation for a career starter with a minimum knowledge level, and the minimum required time to work with the provided trainer content and additional is about 144 hours.

So, you can complete this course in 3.5 months if you can commit to studying at least 9 hours each week.

However, this course is flexible, and you can study at your own pace and spend more or less time depending on your progress. Nevertheless, finishing by a particular date might be required if you want to match the application date for the main track.

There will be 2 lessons per week. Each lesson will take 1.5 hours.The program also includes several homework tasks.

Course Breakdown

Module 1. Introduction to Data Analytics

1.1 Basic concepts, data types, and data structures. Overview of business processes requiring data analysis.

Module 2. Data Analyst Tools

2.1 Databases and SQL: Types of databases, MySQL installation and setup. Key components of databases

2.2 Fundamentals of SQL

2.3 Data filteringю Data aggregation and calculations

2.4 Data merging

2.5 Advanced queries

Module 3. Spreadsheet Tools (Excel)

3.1 Basics of Excelю Editing and formatting cells

3.2 Basic functions. Working with tables

3.3 Data formatting. Creating charts

3.4 Working with Excel sheets: settings, addressing, printing. Data import and processing

3.5 Statistical and mathematical functions in Excel. Pivot tables and charts

Module 4. BI Systems (Power BI)

4.1 Installation, setup, and interface basics

4.2 Data visualization using bar, scatter, and line charts

4.3 Other basic visualizations: building dashboards and KPI visualization. Storytelling

4.4 Fundamentals of Power Query

4.5 Additional Power Query capabilities

4.6 Basics of DAX

4.7 Advanced DAX

4.8 Fundamentals of Power BI Web Service

4.9 Dashboards, Semantic models

Module 5. Statistical Analysis and Visualization

5.1 Basics of probability theory and statistics (Part 1)

5.2 Basics of probability theory and statistics (Part 2)

Module 6. Introduction to Python

6.1 Development environment setup (Colab Notebook), basics of Python programming

6.2 Data types and structures

6.3 Numpy and Pandas (Part 1)

6.4 Pandas (Part 2)

6.5 Data visualization with Python

Module 7. Introduction to Machine Learning

7.1 Exploratory Data Analysis

7.2 Regression tasks and algorithms

7.3 Classification tasks and algorithms

7.4 Clustering tasks and algorithms

7.5 Basics of neural networks