Data Analyst Bootcamp
The Data Analyst Bootcamp is an intensive, hands-on program designed to take learners from zero to job-ready data analysts. The bootcamp covers data analysis with Excel, SQL querying, building dashboards and reports with Power BI, statistical fundamentals, data visualization principles, and data-driven decision-making. Training is based on real business datasets and case studies, helping participants gain practical skills required for Data Analyst and BI Analyst roles.
Duration5 months
Start Date2026-09-16
Schedule3 times per week
FormatHYBRID
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
13
Excel Fundamentals & Data Analysis
- 1.Introduction to Excel and AI Tools: Interface, cells, basic navigation, Table Data Entry and Formatting Basics: Cell formats, number/date format, Excel Table Conditional Functions I: IF, Nested IF, AND, OR Mathematical Functions: SUM, AVERAGE, MIN, MAX, ROUND, COUNT, COUNTA Conditional Functions II: SUMIF, COUNTIF, SUMIFS, COUNTIFS Text Functions: LEFT, RIGHT, MID, CONCAT, TRIM, UPPER/LOWER Date and Time Functions: TODAY, DATE, DATEDIF, NETWORKDAYS, EOMONTH Lookup Functions: VLOOKUP, INDEX/MATCH, XLOOKUP Conditional Formatting: Color Scales, Data Bars, Icon Sets, Rules Data Cleaning and Preparation: Duplicates, Data Validation, Sort/Filter Pivot Table Fundamentals: Rows, Columns, Values, Grouping Data Visualization and Simple Dashboard: Chart types, Sparklines, Simple Dashboard
12
Power BI & Data Analysis
- 1.Power BI Desktop overview Data sources and data connections Power Query (cleaning, transforming, merging, appending) Data modeling and relationships DAX fundamentals DAX measures and calculated tables Charts and visualizations Slicers, KPIs, and maps Dashboard creation Workspace and dataset management Row-level security Incremental refresh Publishing and sharing Final Exam (Project)
16
SQL (Microsoft SQL Server) & Data Analysis
- 1.Oracle SQL environment setup Understanding relational databases and analytical data models Writing basic SELECT queries Filtering, sorting, and limiting data Understanding how analysts retrieve and control data Column calculations and expressions Conditional logic (CASE statements) Handling missing and inconsistent data String functions for data cleaning Date and time analysis functions Aggregations and grouping logic Filtering aggregated data Understanding and applying table relationships Joining multiple tables for analysis Avoiding common analytical join errors Subqueries (single-row and multi-row) When to use subqueries vs joins Common Table Expressions (CTEs) Structuring readable, step-by-step analytical queries Problem decomposition for analytical questions Common analytical query patterns Introduction to window functions Reading and interpreting SQL codes Debugging queries Case study End-to-end review Practical final exam Realistic analytical SQL scenarios
8
Python & Data Analysis
- 1.Python Introduction & Conditions: • Setting up environment (Python install, Jupyter Notebook or VS Code) • Variables, data types (int, float, str, bool), operators, input/output • if / elif / else statements • Combining conditions with logical operators (and, or, not); nested conditionals Loops: • for and while loops • break, continue, pass • Nested loops Functions & Recursion: • Defining functions, parameters, return values, default/keyword arguments • Base case vs. recursive case • Classic recursive examples: factorial, Fibonacci, sum of list • Recursion vs. iteration — when to use which Data Structures I — Lists & Tuples: • Lists: creation, indexing, slicing, methods (append, sort, pop, etc.) • List comprehensions • Tuples: immutability, use cases, packing/unpacking Data Structures II — Dictionaries & Sets: • Dictionaries: key-value pairs, methods, iterating over keys/values/items • Dictionary comprehensions • Sets: uniqueness, set operations (union, intersection, difference) • Choosing the right structure for the job NumPy Essentials + Intro to Pandas: • NumPy (half): arrays vs. lists, indexing/slicing, vectorized operations, basic stats (mean, median, std) • Pandas (half): Series & DataFrame structures, loading data (CSV/Excel/JSON), .head(), .info(), .describe() Pandas — Cleaning & Transformation: • Selecting/filtering: .loc, .iloc, boolean indexing • Handling missing data, duplicates, type conversions • GroupBy operations (split-apply-combine) • Merging and joining DataFrames Pandas Aggregation, Visualization & Capstone: • Pivot tables and cross-tabulations • Visualization with Matplotlib, Seaborn, and Plotly (static and interactive charts) • Choosing the right chart for the insight
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Market Salaries
1000-1500 AZN
Junior
1 year experience
1500-2500 AZN
Middle
1–3 years experience
3500+ AZN
Senior
3+ years experience
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.