Learning Path · Data Analytics

Data Analytics Course Syllabus: Full Curriculum Breakdown

AK Ankit Kaushik · Lead Trainer, Data Science & Analytics | Updated 28 August 2026 | 17 min read Free · Syllabus

Data analytics syllabus — from foundation to advanced topics and career readiness.

Quick summary — data analytics course syllabus

A comprehensive data analytics course covers everything from Python and SQL to statistics, visualization, and machine learning. This guide breaks down the complete curriculum so you know exactly what you'll learn.

In this guide you will learn:

Foundation Modules — Python, Excel, SQL, and data handling.
Core Analytics — statistics, EDA, data visualization, and dashboards.
Advanced Topics — machine learning, big data, cloud analytics.
Career Readiness — projects, portfolio, and placement preparation.

Module 1Python for Analytics

Python is the most important language for data analytics. This module covers everything you need to get started.

Module 1: Python for Analytics

Topics:
1. Python basics: Data types, variables, loops
2. Functions and modules
3. NumPy: Arrays and mathematical operations
4. Pandas: DataFrames, series, data manipulation
5. Data cleaning: Missing values, outliers
6. File I/O: CSV, Excel, JSON

Sample project:
- Load a dataset, clean it, and perform basic analysis

Time: 3-4 weeks

Tools: Jupyter Notebook, VS Code, pandas, numpy

Skills gained:
- Data manipulation with pandas
- Basic Python programming
- Data cleaning and preparation

module1-python.md

Key insight: Pandas is the most important library for data analytics. Master DataFrames, groupby, and merge operations.

Module 2Excel

Excel is still a crucial tool for data analytics. You'll learn how to use it effectively for data analysis and reporting.

Module 2: Excel for Analytics

Topics:
1. Data entry and formatting
2. Formulas and functions (VLOOKUP, SUMIF, COUNTIF)
3. Pivot tables and PivotCharts
4. Data validation and conditional formatting
5. Advanced functions: INDEX-MATCH, XLOOKUP
6. Power Query for data transformation
7. Creating professional dashboards

Sample project:
- Create a sales dashboard with pivot tables and charts

Time: 2-3 weeks

Skills gained:
- Data analysis with Excel
- Pivot table mastery
- Dashboard creation
- Business reporting

module2-excel.md

Pro tip: Excel is often the first tool analysts use. Make sure you're comfortable with pivot tables and advanced formulas.

Module 3SQL

SQL is the language of databases. You'll learn to query, filter, aggregate, and join data.

Module 3: SQL

Topics:
1. SELECT, FROM, WHERE (basic queries)
2. ORDER BY, LIMIT, DISTINCT
3. Aggregate functions: COUNT, SUM, AVG, MIN, MAX
4. GROUP BY and HAVING
5. JOINs: INNER, LEFT, RIGHT, FULL
6. Subqueries and CTEs
7. Window functions (ROW_NUMBER, RANK)
8. Database design (normalization)

Sample project:
- Query a sales database to find top customers and revenue

Time: 3-4 weeks

Tools: PostgreSQL, MySQL, SQLite

Skills gained:
- Writing complex SQL queries
- Database analysis
- Data extraction and reporting

module3-sql.md

Key insight: SQL is used in almost every data analytics role. Practice writing complex queries with multiple joins and subqueries.

Module 4Statistics

Statistics helps you make sense of data and draw meaningful conclusions. This module covers the essential concepts.

Module 4: Statistics for Analytics

Topics:
1. Descriptive statistics: Mean, median, mode
2. Measures of spread: Variance, standard deviation
3. Data distributions (normal, binomial)
4. Probability and Bayes theorem
5. Hypothesis testing (t-test, chi-square)
6. Correlation and covariance
7. A/B testing concepts

Sample project:
- Analyze a marketing campaign with hypothesis testing

Time: 3-4 weeks

Skills gained:
- Statistical reasoning
- Data interpretation
- Hypothesis testing
- Business decision-making

module4-stats.md

Pro tip: You don't need to be a mathematician, but understanding statistical concepts is essential for making data- driven decisions.

Module 5Exploratory Data Analysis (EDA)

EDA is the process of exploring, cleaning, and understanding data before formal modeling.

Module 5: Exploratory Data Analysis (EDA)

Topics:
1. Data profiling and summary statistics
2. Handling missing data
3. Outlier detection and treatment
4. Feature engineering (creating new variables)
5. Univariate and bivariate analysis
6. Correlation analysis
7. Data storytelling

Sample project:
- Perform EDA on a real dataset and present insights

Time: 2-3 weeks

Skills gained:
- Data exploration
- Insight generation
- Data cleaning
- Business storytelling

module5-eda.md

Key insight: EDA is where most of your time as a data analyst will be spent. It's the most important skill to develop.

Module 6Data Visualization

Visualization helps you communicate insights effectively. You'll learn to create compelling charts and dashboards.

Module 6: Data Visualization

Topics:
1. Matplotlib: Basic plots (line, bar, scatter)
2. Seaborn: Statistical visualizations
3. Plotly: Interactive charts
4. Tableau: Creating dashboards
5. Power BI: Business intelligence
6. Dashboard design principles

Sample project:
- Create an interactive sales dashboard in Tableau

Time: 3-4 weeks

Tools: Matplotlib, Seaborn, Plotly, Tableau, Power BI

Skills gained:
- Data visualization
- Dashboard creation
- Storytelling with data
- Tool proficiency

module6-viz.md

Pro tip: Tableau and Power BI are industry standards. Learn at least one of them to be job-ready.

Module 7Machine Learning Basics

Machine learning is increasingly important for data analysts. This module covers the fundamentals.

Module 7: Machine Learning Basics

Topics:
1. Supervised vs unsupervised learning
2. Linear regression
3. Logistic regression
4. Classification (KNN, SVM basics)
5. Clustering (K-Means)
6. Model evaluation (accuracy, precision, recall)
7. Feature scaling and selection

Sample project:
- Predict customer churn using logistic regression

Time: 4-5 weeks

Tools: scikit-learn, pandas, matplotlib

Skills gained:
- ML fundamentals
- Model building
- Predictive analytics
- Business forecasting

module7-ml.md

Key insight: Even as a data analyst, understanding ML basics helps you work with data scientists and build predictive models.

Module 8Big Data & Cloud Analytics

Big data tools and cloud platforms are essential for working with large datasets in modern analytics.

Module 8: Big Data & Cloud Analytics

Topics:
1. Introduction to big data
2. Apache Spark (PySpark basics)
3. AWS Analytics services (S3, Redshift, Athena)
4. Google BigQuery
5. Data warehousing concepts
6. ETL pipelines
7. Cloud deployment of analytics

Sample project:
- Analyze a large dataset using Spark and BigQuery

Time: 3-4 weeks

Skills gained:
- Big data processing
- Cloud analytics
- ETL pipeline development
- Scalable analysis

module8-bigdata.md

Pro tip: Cloud analytics is one of the fastest-growing areas in data. Learning AWS or GCP will set you apart.

Module 9Projects & Career Readiness

The final module focuses on building your portfolio and preparing for the job market.

Module 9: Projects & Career Readiness

Topics:
1. End-to-end analytics projects
2. Portfolio building (GitHub, personal website)
3. Resume and LinkedIn optimization
4. Mock interviews
5. Case study practice
6. Industry trends and continuous learning

Sample Projects:
1. Sales analysis dashboard (Tableau/Power BI)
2. Customer segmentation (Python)
3. Churn prediction model (ML)
4. SQL data analysis report

Time: 4-6 weeks

Outcome:
- Job-ready portfolio
- Interview skills
- Professional network

module9-career.md

Key insight: Your portfolio is your most powerful tool in the job search. Focus on quality projects that demonstrate your ability to solve real business problems.

SECTION 10Interview Q&A — data analytics syllabus

Q1What are the most important topics in a data analytics course?

Python (pandas), SQL, statistics, data visualization (Tableau/ Power BI), and EDA are the most important. Also learn Excel and ML basics.

Q2How long does it take to complete a data analytics course?

A comprehensive course typically takes 4-6 months with consistent daily practice (2-3 hours). Shorter courses may take 2-3 months.

Q3Do I need a degree to become a data analyst?

No — many data analysts are self-taught or have completed certification programs. A strong portfolio and practical skills are more important.

Q4What is the difference between data analytics and data science?

Data analytics focuses on interpreting existing data to answer business questions. Data science uses advanced algorithms and ML to build predictive models.

Q5What is the average salary for a data analyst in India?

Entry-level data analysts earn ₹4-7 LPA. Experienced analysts with 3-5 years can earn ₹8-15 LPA or more.

SECTION 11Test yourself — data analytics syllabus quiz

Five questions. No sign-up.

0 / 5

Pick an answer to see why it is right or wrong.

SECTION 12Frequently asked questions

What is the best order to learn data analytics?

Start with Excel and Python, then learn SQL, then statistics and EDA, then visualization, and finally ML and big data tools.

Which tool is most important for data analytics?

Python (pandas) and SQL are the most important. Tableau/Power BI are also essential for visualization.

Can I learn data analytics for free?

Yes — there are many free resources like Kaggle, YouTube, and freeCodeCamp. However, a structured course with mentorship can accelerate your learning.

What projects should I include in my portfolio?

Include an EDA project, a dashboard (Tableau/Power BI), a SQL analysis project, and a predictive modeling project (ML).

What I would do in the first 30 daysLayoff · 14 min Why sending 500 resumes won't workJob Search · 14 min Rebuild your resume for the AI job marketResume · 15 min Which skills to learn before applyingSkills · 14 min 90-day career recovery roadmapRecovery · 16 min How AI can help you prepare for interviewsAI · 11 min

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Data Analytics Course Syllabus Full Curriculum Breakdown