This Data Science & Statistics for Beginners Course is designed to provide a strong foundation in data analysis, statistical thinking, and practical data science tools. The course follows a structured, step-by-step learning path that helps beginners understand core concepts while gaining hands-on experience with real-world datasets.

Ideal for students, freshers, working professionals, and career switchers, this program covers essential skills including Python programming, statistics, SQL, data visualization, Power BI, and introductory machine learning. The course emphasizes practical learning through projects, case studies, and guided exercises.

Delivered through live online sessions, learners benefit from interactive teaching, clear explanations, and continuous support throughout the training.

What Students Will Learn

By the end of this course, learners will develop expertise in:

Module 1 — Introduction (4 hrs)

This module introduces the core concepts of data science and prepares learners for the journey ahead.

• Data Science vs ML vs AI explained

• Data types table

• CRISP-DM lifecycle

• Python setup step-by-step

• Excel functions guide

• Statistics (mean/median/mode/SD) with examples

Module 2 — Python (6 hrs)

Learn the essential programming skills required for data analysis.

• Variables, lists, loops, conditions with full code examples

• NumPy arrays

• Pandas (load/filter/groupby/clean)

• Handling missing values

• Outliers using IQR method

• Full EDA project

Module 3 — Statistics (5 hrs)

Understand the statistical foundations needed for data-driven decision making.

• Probability rules

• Bayes Theorem with travel example

• Normal/Binomial/Poisson distributions

• Hypothesis testing step-by-step

• 6 common tests table

• t-test in Python with code

Module 4 — SQL (4 hrs)

Learn how to extract and analyze data from databases.

• SELECT/WHERE/ORDER BY

• Aggregates + GROUP BY + HAVING

• All JOIN types with code

• Window functions (ROW_NUMBER, RANK, Running Total)

Module 5 — Visualization (4 hrs)

Master the art of presenting data visually.

• Chart selection guide table

• Matplotlib + Seaborn full code (bar, line, heatmap, boxplot)

Module 6 — Power BI (5 hrs)

Build interactive dashboards for business insights.

• Interface guide

• Power Query transformations table

• DAX formulas

• Full dashboard project instructions

Module 7 — Machine Learning (6 hrs)

Learn how to build predictive models using real-world datasets.

• Linear & Logistic Regression with code

• Evaluation metrics table

• Random Forest + feature importance

• K-Means customer segmentation

Module 8 — Capstone + Career (4–8 hrs)

Prepare for real-world data science roles with hands-on projects and career guidance.

• 3 project options

• Deliverables checklist with weightage

• Resume tips

• 12 real interview questions

Teaching Method

The course is conducted through live online interactive sessions focusing on practical implementation and concept clarity. Teaching methods include:

• Step-by-step concept explanations

• Real-time coding demonstrations

• Hands-on assignments and mini projects

• Real dataset practice sessions

• Interactive discussions and doubt clearing

• Career guidance and interview preparation

This approach ensures learners gain both theoretical knowledge and practical skills.

Benefits & Outcomes

After completing this course, learners will:

• Develop strong foundations in data science and statistics

• Gain practical experience using Python, SQL, and Power BI

• Learn how to analyze and visualize real-world datasets

• Build basic machine learning models

• Create a professional project portfolio

• Prepare for entry-level roles in data analytics and data science

This course serves as an ideal starting point for anyone aiming to build a career in data-driven fields.