Data Analyst

Turn raw data into business insights with Excel, SQL, Python and dashboards.

1 month (4 weeks)
20 topics
Final exam

Book a trainer

Your learning path

4 weeks from start to certificate

1
Week 1
Analytics foundations
2
Week 2
SQL for analysts
3
Week 3
Python analysis
4
Week 4
Storytelling project
Finish
Exam & certificate

Every week

How each week works

Learn

Live class with your trainer

Practice

Guided hands-on exercises

Build

Mini project for your portfolio

Review

Feedback and Q&A

Concept lab

See how the core concepts connect

Follow the path from foundations to real workplace application. Every concept feeds the next project.

Week 1

Introduction to Data Analytics

Week 1

Business Understanding & Data Ethics

Week 2

Relational Database Concepts

Week 2

Basic SQL Queries

Week 3

Python Environment Setup

Week 3

Introduction to Pandas

Week 4

Project Planning & Data Acquisition

Week 4

Advanced Visualization & Dashboards

Topics per week

20 topics · 80 lessons

Week 15

Week 25

Week 35

Week 45

Course curriculum

Week 1 · 5 topics · 20 lessons

Analytics foundations

By the end of this week: Learners will understand data analyst roles, fundamental statistical concepts, and data lifecycle stages, preparing them for practical analysis.

1.1Introduction to Data Analytics
- What is a Data Analyst?
- Key roles and responsibilities
- Data types and data sources
- Analytic process: CRISP-DM
1.2Business Understanding & Data Ethics
- Defining business problems
- Stakeholder analysis, requirements gathering
- Data privacy laws: GDPR, CCPA
- Ethical considerations in data use
1.3Descriptive Statistics Essentials
- Measures of central tendency
- Measures of variability
- Data distribution: normal, skew
- Correlation vs. causation
1.4Data Visualization Fundamentals
- Why visualize data?
- Effective chart types: bar, line, pie
- Choosing the right visualization
- Principles of good design
1.5Spreadsheet Tools for Data
- Google Sheets/Excel basics
- Formulas: SUM, AVERAGE, COUNTIF
- Pivot tables for aggregation
- Basic charting in spreadsheets

Week 1
Analytics foundations

Introduction to Data Analytics

Business Understanding & Data Ethics

Descriptive Statistics Essentials

Data Visualization Fundamentals

Spreadsheet Tools for Data

Hands-on project

Business Problem Data Mapping

Week 2 · 5 topics · 20 lessons

SQL for analysts

By the end of this week: Learners will confidently query, filter, join, and aggregate data from relational databases using SQL for practical business insights.

2.1Relational Database Concepts
- Database schemas, tables
- Primary and foreign keys
- SQL: Structured Query Language
- SQL workbench setup (DB Fiddle)
2.2Basic SQL Queries
- SELECT statement and FROM
- Filtering data with WHERE
- Ordering results with ORDER BY
- Limiting results with LIMIT
2.3Intermediate SQL Operations
- Aggregate functions: COUNT, SUM, AVG
- Grouping data with GROUP BY
- Filtering groups with HAVING
- Aliases for tables and columns
2.4Joining Tables in SQL
- INNER JOIN explained
- LEFT JOIN for all records
- RIGHT JOIN, FULL JOIN
- Complex join conditions
2.5Advanced SQL for Analytics
- Subqueries and common table expressions (CTEs)
- Window functions: ROW_NUMBER, RANK
- CASE statements for conditional logic
- Data modification: INSERT, UPDATE, DELETE

Week 2
SQL for analysts

Relational Database Concepts

Basic SQL Queries

Intermediate SQL Operations

Joining Tables in SQL

Advanced SQL for Analytics

Hands-on project

Customer Orders SQL Report

Week 3 · 5 topics · 20 lessons

Python analysis

By the end of this week: Learners will effectively use Python with Pandas and Matplotlib for data manipulation, cleaning, and exploratory data analysis.

3.1Python Environment Setup
- Anaconda installation, Jupyter Notebook
- Variables, data types in Python
- Control flow: if/else, loops
- Functions for code reusability
3.2Introduction to Pandas
- DataFrames and Series explained
- Loading data: CSV, Excel
- Inspecting data: .head(), .info()
- Selecting data by rows, columns
3.3Data Cleaning with Pandas
- Handling missing values: .dropna(), .fillna()
- Duplicate data removal
- Data type conversion: .astype()
- Renaming columns, indexing
3.4Data Manipulation with Pandas
- Filtering and sorting DataFrames
- Grouping data with .groupby()
- Merging and concatenating DataFrames
- Applying custom functions (.apply())
3.5Basic Visualization with Matplotlib/Seaborn
- Creating basic plots: scatter, bar
- Customizing plots: titles, labels
- Histograms for distribution
- Box plots for outliers

Week 3
Python analysis

Python Environment Setup

Introduction to Pandas

Data Cleaning with Pandas

Data Manipulation with Pandas

Basic Visualization with Matplotlib/Seaborn

Hands-on project

Sales Data Cleaning & EDA

Week 4 · 5 topics · 20 lessons

Storytelling project

By the end of this week: Learners will synthesize their skills to conduct a full data analysis project, developing a compelling narrative and presenting insights effectively.

4.1Project Planning & Data Acquisition
- Defining project scope, objectives
- Identifying relevant data sources
- Data extraction strategies
- Setting up project workspace
4.2Advanced Visualization & Dashboards
- Tableau/Power BI introduction
- Interactive dashboard design principles
- Building calculated fields, parameters
- Story points and guided analytics
4.3Developing a Data Narrative
- Structuring a compelling story
- Identifying key insights
- Crafting a clear message
- Visual hierarchy and emphasis
4.4Effective Presentation Skills
- Audience analysis and tailoring
- Designing impactful slides
- Verbal communication techniques
- Handling Q&A sessions
4.5Capstone Project: Presentation Workshop
- Full project analysis lifecycle
- Review of SQL and Python code
- Developing a full presentation deck
- Peer feedback and refinement

Week 4
Storytelling project

Project Planning & Data Acquisition

Advanced Visualization & Dashboards

Developing a Data Narrative

Effective Presentation Skills

Capstone Project: Presentation Workshop

Hands-on project

End-to-End Data Analysis Presentation

Final exam & certificate

Finish the 4 weeks, then take the online exam. Score 70% or more to pass.

Job-ready syllabus

Learn it. Build it. Explain it. Use it.

The syllabus goes beyond watching lessons. You practice the tasks employers expect, produce evidence of your skills, and prepare to discuss your work clearly.

Workplace skills

Apply Introduction to Data Analytics, Business Understanding & Data Ethics, Descriptive Statistics Essentials, Data Visualization Fundamentals in guided business scenarios.

Portfolio proof

Complete 4 practical projects, including Business Problem Data Mapping.

Professional practice

Present your work, respond to feedback, document decisions, and improve the final result.

Interview readiness

Review common role questions and explain your process, tools, trade-offs, and project outcomes.

Your completion pack

Weekly projects, trainer feedback, final assessment, and a certificate you can add to your résumé and LinkedIn profile.

Why learn from us?

Six reasons students pick M - IT Solutions

One-month programs built around live teaching, real projects and support that actually shows up.

Live online classes

Learn with a real instructor in real time — not pre-recorded videos you watch alone.

Hands-on projects

Every week ends with practical work you can show employers on day one.

1-on-1 trainer time

Stuck or want to go deeper? Book private sessions with a trainer whenever you need.

A real learning area

Lessons, progress tracking and exams all live in your account, on any device.

Trusted by companies

We train individuals and whole teams — from small businesses to enterprise programs.

Verified & supported

Verified accounts, real certificates and a support team that answers when you write.

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