Data Analytics
The Data Analytics course is designed for students without technical backgrounds and provides a practical introduction to the complete data analytics workflow — data collection, cleaning, analysis, visualization, and presentation of results — across five modules: Statistics, Excel, SQL, Power BI, and Python. Each new module actively reinforces the knowledge gained in previous modules, while keeping theory to a minimum and maximizing hands-on practice.
Üstünlüklərimiz
Professional Mentors
Guidance and support from experienced specialists
Practical Learning
Not just theory — hands-on experience through real projects
Modern Technologies
Python, AI, web development and other trending fields
Career Support
CV preparation, interview simulations and access to job opportunities
Curriculum
Statistics
- 1.What is data? Data types Data collection: survey design, population vs sample Central tendency: Mean, Median, Mode Min, Max, Range, and Outliers; selecting the right metric Charts: Bar, Pie, Line, Histogram Basic probability, Correlation vs Causation, misleading graphs Mini project: conducting and analyzing a real survey in class Mock Exam
Excel
- 1.Excel inter face, data entry rules Formulas and statistical functions (SUM, AVERAGE, MEDIAN, MODE, MIN/MAX, COUNT) Relative and absolute cell re ferences Data cleaning: duplicates, TRIM, empty cells, Text to Columns Sort, Filter, Format as Table Conditional Formatting Logical functions: IF, COUNTIF, SUMIF, AVERAGEIF Lookup functions: VLOOKUP, HLOOKUP, Index+Match (combining tables) Quiz 1 Pivot Table: creation, calculations, grouping, Pivot Chart What If Analysis: Goal Seek, Data Table, Scenario Manager Charts: column, line, pie, histogram, scatter; formatting rules Quiz 2 + Mini project wor k session Mini project presentations + Mock Exam
SQL
- 1.Database concepts + SELECT, FROM, DISTINCT, LIMIT WHERE clause: comparisons, AND/OR/NOT, IN, BETWEEN, LIKE ORDER BY + NULL concept + AS (alias) Quiz 1 Aggregate Functions: COUNT, SUM, AVG, MIN, MAX GROUP BY HAVING + CASE WHEN INNER JOIN - joining two tables LEFT JOIN + RIGHT JOIN + FULL JOIN Quiz 2 Subqueries String Functions + Date Functions + COALESCE UNION + CTE Window Functions: ROW_NUMBER, RANK, LAG Real Case Study + Mini project work session Mini project presentations + Mock Exam
Power BI
- 1.Data cleaning with Power Query: data types, null values, duplicates Introduction to Power BI Desktop; initial visuals (column, pie, card); slicers Data Modeling: relationships REPORT 1: Building a report from scratch DAX: explicit measures (SUM/AVERAGE/COUNT), CALCULATE, basic time intelligence REPORT 2: Sales report breakdown and creation Quiz 1 Dashboard design, KPIs, and data storytelling: layout, drill-down, tooltip Advanced DAX syntax Power BI Service: publishing, workspaces, sharing dashboards REPORT 3: Independent report building & publishing to Power BI Service Quiz 2 + Mini project work sessions & individual feedback Mini pro ject completion + presentation prep Mini pro ject presentations + Mock Exam
Python for Data Analytics
- 1.Introduction to Python: variables, data types, print/input, basic operations Conditionals (if), loops (for), and lists Revision 1 + Quiz 1 Data cleaning with Pandas: missing values, duplicates, type conversion NumPy basics + Pandas: DataFrames, reading CSV/Excel files Groupby and aggregations (Pivot/GROUP BY bridge); merging DataFrames (merge = JOIN) Revision 2 + Quiz 2 Data visualization with Matplotlib: column, line, scatter, histogram; formatting End to end analysis: import -> clean -> analyze -> visualize (full workflow) Revision 3 + Mini project work session Mini pro ject presentations + Mock Exam
AI for Data Analytics
- 1.AI in data analytics: capabilities & limitations; intro to Claude; prompt engineering basics Data analysis with Claude: generating SQL/DAX/Python code, verifying results Working with Claude Code: setup, basic commands, project context, script execution & automation Practical session: solving past module tasks using AI vs non-AI comparison; Mock Exam
Portfolio & Final Project
- 1.Project briefing: real dataset selection, defining business questions Work sessions: applying full toolset (Excel + SQL + Power BI + Python + AI); GitHub documentation & portfolio setup Final project defense & presentation
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Frequently Asked Questions
idtech is a modern learning ecosystem providing education in technology, programming, and digital skills.
You can register by filling out the form in the 'Apply' section on our website.
Our courses are suitable for both beginners and those looking to deepen their expertise.
Yes, lessons are available both online and in classrooms.