Data analytics

Data science and Data Analytics SyllabusData Science & Data Analytics Syllabus (2026 Comprehensive Roadmap)

This syllabus is designed for progressive learning taking you from beginner to advanced level with practical projects and industry tools.

PHASE 1 — COMPUTER & PROGRAMMING FOUNDATIONSModule 1: Introduction to Data Science & Analytics

Topics:

• What is Data Science?

• What is Data Analytics?

• Differences between Data Analyst, Data Scientist, and Data Engineer

• Data lifecycle

• Types of data

• Structured vs unstructured data

Applications:

• Banking

• Healthcare

• E-commerce

• Government

• Telecommunications

Module 2: Computer Fundamentals

Topics:

• Operating systems

• File systems

• Internet fundamentals

• Command line basics

• Software installation

• Version control with Git

Tools:

• Github

• Visual Studio Code

Module 3: Python Programming

Topics:

• Variables and data types

• Loops and conditions

• Functions

• Lists, tuples, dictionaries

• Object-oriented programming

• File handling

• Error handling

Projects:

• Student result processor

• Expense tracker

• Sales calculator

Tools:

• Python

PHASE 2 — MATHEMATICS & STATISTICSModule 4: Mathematics for Data Science

Topics:

• Algebra

• Functions

• Logarithms

• Linear equations

• Vectors and matrices

Important concept:

Ax⃗=b⃗A\vec{x}=\vec{b}Ax=b

Applications:

• Recommendation systems

• Machine learning

Module 5: Statistics Fundamentals

Topics:

• Mean, median, mode

• Variance

• Standard deviation

• Probability

• Sampling

• Distributions

• Correlation

Important formula:

σ=∑(xi−μ)2N\sigma=\sqrt{\frac{\sum (x_i-\mu)^2}{N}}σ=N∑(xi​−μ)2​​

Projects:

• Population analysis

• Survey analytics

Module 6: Probability & Inferential Statistics

Topics:

• Hypothesis testing

• Confidence intervals

• Bayes theorem

• Regression analysis

• Statistical significance

Important formula:

P(A∣B)=P(B∣A)P(A)P(B)P(A\mid B)=\frac{P(B\mid A)P(A)}{P(B)}P(A∣B)=P(B)P(B∣A)P(A)​

P(A)P(A)P(A)

P(B∣A)P(B\mid A)P(B∣A)

P(B∣¬A)P(B\mid \neg A)P(B∣¬A)

P(A∣B)=P(B∣A)P(A)P(B)≈0.68,  P(B)≈0.25P(A\mid B)=\frac{P(B\mid A)P(A)}{P(B)}\approx 0.68,\; P(B)\approx 0.25P(A∣B)=P(B)P(B∣A)P(A)​≈0.68,P(B)≈0.25

P(B)=0.25P(B|A)P(A)=0.17P(A|B)~0.68Posterior = useful evidence / total evidence

Applications:

• Market forecasting

• Risk analysis

PHASE 3 — DATA HANDLING & DATABASESModule 7: Data Collection & Cleaning

Topics:

• Data sources

• CSV, Excel, JSON

• APIs

• Web scraping basics

• Missing values

• Duplicate removal

• Data normalization

Projects:

• Cleaning messy datasets

• Data validation system

Libraries:

• Pandas

• NumPy

Module 8: SQL & Relational Databases

Topics:

• Database concepts

• SELECT queries

• WHERE clause

• JOINs

• GROUP BY

• Indexing

• Database normalization

Databases:

• MySQL

• PostgreSQL

• Microsoft SQL Server

Projects:

• Sales reporting database

• Student management system

Module 9: NoSQL Databases

Topics:

• Document databases

• Key-value databases

• Graph databases

Databases:

• MongoDB

• Redis basics

Applications:

• Real-time analytics

• Big data systems

PHASE 4 — DATA ANALYSISModule 10: Data Analysis with Python

Libraries:

• Pandas

• NumPy

Topics:

• DataFrames

• Filtering data

• Aggregation

• Merging datasets

• Time-series analysis

Projects:

• Financial analysis

• Student performance analysis

Module 11: Data Visualization

Topics:

• Charts and graphs

• Dashboards

• Storytelling with data

• Interactive visualizations

Tools:

• Matplotlib

• Plotly

• Power BI

• Tableau

Projects:

• Executive dashboard

• Sales visualization platform

Module 12: Excel for Analytics

Topics:

• Formulas and functions

• Pivot tables

• Data cleaning

• Dashboards

• Power Query

Projects:

• Business KPI tracker

• Payroll analysis

PHASE 5 — MACHINE LEARNING FOR DATA SCIENCEModule 13: Introduction to Machine Learning

Topics:

• Supervised learning

• Unsupervised learning

• Reinforcement learning

• Training vs testing data

Applications:

• Fraud detection

• Recommendation systems

Module 14: Supervised Learning Algorithms

Algorithms:

• Linear regression

• Logistic regression

• Decision trees

• Random forests

Important formula:

y=mx+by=mx+by=mx+b

mmm

bbb

-10-8-6-4-2246810-10-5510y-interceptx-intercept

Projects:

• House price prediction

• Loan approval prediction

Libraries:

• Scikit-learn

Module 15: Unsupervised Learning

Topics:

• Clustering

• Dimensionality reduction

• Customer segmentation

Algorithms:

• K-Means

• PCA

Projects:

• Market segmentation

• Product recommendation grouping

Module 16: Model Evaluation

Topics:

• Accuracy

• Precision

• Recall

• F1-score

• ROC curve

Important formula:

F1=2⋅Precision⋅RecallPrecision+RecallF1=2\cdot\frac{Precision\cdot Recall}{Precision+Recall}F1=2⋅Precision+RecallPrecision⋅Recall​

PHASE 6 — BIG DATA & CLOUD ANALYTICSModule 17: Big Data Technologies

Topics:

• Hadoop ecosystem

• Spark

• Distributed computing

• Streaming analytics

Applications:

• Telecom analytics

• Social media analysis

Module 18: Cloud Platforms for Analytics

Platforms:  AWS, Google Cloud, Microsoft Azure

Topics:

• Cloud databases

• Data warehouses

• Data lakes

• ETL pipelines

PHASE 7 — ADVANCED DATA SCIENCEModule 19: Deep Learning Basics

Topics:

• Neural networks

• Activation functions

• Backpropagation

Important formula:

a=σ(wx+b)a=\sigma(wx+b)a=σ(wx+b)

Libraries: TensorFlow, PyTorch

Module 20: Natural Language Processing (NLP)

Topics:

• Text preprocessing

• Sentiment analysis

• Chatbots

• Language models

Projects:

• Review sentiment analyzer

• AI chatbot

Libraries:

• NLTK

• spaCy

Module 21: Time Series & Forecasting

Topics:

• Trend analysis

• Seasonality

• Forecasting models

Applications:

• Stock prediction

• Demand forecasting

Projects:

• Sales forecasting system

PHASE 8 — DATA ENGINEERINGModule 22: Data Pipelines & ETL

Topics:

• ETL processes

• Data warehousing

• Workflow automation

Tools:

• Apache Airflow

• Talend

Projects:

• Automated reporting pipeline

Module 23: API Integration & Data Extraction

Topics:

• REST APIs

• JSON processing

• API authentication

• Web data extraction

Projects:

• Weather analytics app

• Social media analytics collector

PHASE 9 — BUSINESS & PROFESSIONAL SKILLSModule 24: Business Intelligence

Topics:

• KPIs

• Business metrics

• Reporting systems

• Decision support systems

Projects:

• Company performance dashboard

Module 25: Data Ethics & Governance

Topics:

• Data privacy

• Responsible AI

• GDPR basics

• Data security

Case studies:

• Data leaks

• Bias in analytics

Module 26: Communication & Storytelling

Topics:

• Presentation skills

• Dashboard storytelling

• Reporting techniques

Projects:

• Executive-level analytics report

PHASE 10 — CAPSTONE PROJECTSBeginner Projects

• Student result analytics

• Expense tracker

• Sales dashboard

Intermediate Projects

• Fraud detection system

• Customer segmentation

• Recommendation system

Advanced Projects

• Real-time analytics platform

• AI-powered business intelligence system

• Predictive healthcare analytics

PHASE 11 — CAREER DEVELOPMENTModule 27: Portfolio Building

Topics:

• GitHub portfolio

• Kaggle competitions

• Resume building

• LinkedIn optimization

Platforms:  Kaggle, LinkedIn

Module 28: Interview Preparation

Topics:

• SQL interview questions

• Python interview questions

• Statistics interview questions

• Data case studies

RECOMMENDED CERTIFICATIONS

• Google Data Analytics Certificate

• Microsoft Data Analyst Certification

• IBM Data Science Professional Certificate

• AWS Data Analytics Certification

RECOMMENDED DURATION

CAREER PATHS

Possible careers:

• Data Analyst

• Data Scientist

• Business Intelligence Analyst

• Machine Learning Engineer

• Data Engineer

• AI Analyst

• Analytics Consultant

MOST IMPORTANT TOOLS TO MASTER

FINAL LEARNING ADVICE

To become strong in Data Science and Analytics:

1. Learn SQL very well

2. Practice with real datasets

3. Build dashboards regularly

4. Learn statistics deeply

5. Create projects and portfolios

6. Participate in Kaggle competitions

7. Focus on solving business problems, not only coding

Consistency and practical experience are what separate beginners from professional data experts.

Close