Data Science

Statistics, data preprocessing, SQL, exploratory analysis with Pandas and predictive modelling in Python.

• Category: AI & Data Science
• Duration: 3 Months (60 hours)
• Level: Beginner to Intermediate
• Delivery: Live + Recorded
• Projects: 3

Fees and formats

• Self-paced: ₹7,499 (regular ₹9,000), 365 days access — includes Recorded video lessons, Quizzes & assignments, Learn at your own pace, Verifiable certificate
• Mentor-led: ₹14,000 (regular ₹16,000), 365 days access — includes Everything in Self-paced, Weekly live mentor sessions, 1:1 doubt support from mentors, Project reviews & feedback, Career guidance

Enrol in Data Science

About the program

Data science turns raw data into decisions. You'll learn the statistics behind good analysis, query databases with SQL, explore and clean data with Pandas, and build predictive models in Python.

Each module ends with a hands-on exercise, and the program closes with portfolio projects reviewed by mentors.

Highlights

• Recorded lessons + weekly live mentor sessions
• 3 hands-on portfolio projects
• Doubt support from industry mentors
• Verifiable digital certificate
• Resume & interview preparation

What you will learn

• Write optimized SQL queries for extracting datasets.
• Clean, pre-process, and analyze raw unstructured data in Python.
• Apply descriptive and inferential statistics to extract metrics.
• Build basic predictive modeling pipelines.

Tools and technologies

Python, SQL, Pandas, NumPy, Statistics

Curriculum

1. Python Fundamentals: Internals, Architecture, and Best Practices
   • Installation and Setup of Anaconda Environment
   • Jupyter Notebook Overview with Modern Usage Practices
   • Essential Shortcut Keys and Productivity Tips in Jupyter Notebook
   • Understanding Python Data Types with Real-World Applications
2. Python Data Structures & File Handling
   • Best Practices for Naming Variables in Python (PEP 8 Standards)
   • Python Data Structures: List, Tuple, Set, and Dictionary with Practical Use Cases
   • Introduction to Files and Directories in Modern Development Environments
   • Working with Command Line Interfaces (CLI) and Terminal Navigation
   • File Handling in Python: Reading Text Files and Using Context Managers (with Statement)
3. Regular Doubt-Clearing & Interactive Discussion Sessions — Dedicated sessions will be held to address course-related queries and foster interactive discussions.
4. Control Flow in Python: Loops and Conditional Statements
   • Conditional Statements in Python: if, elif, and else
   • Looping Constructs: for loops and while loops
5. Data Analysis & Manipulation using NumPy
   • Introduction to Machine Learning Libraries
   • NumPy: Hands-on Implementation and Practical Applications
6. Continuous Doubt Resolution & Concept Strengthening — Regular sessions will be conducted to address course-related queries and promote interactive discussions.
7. Data Analysis with Pandas (Data Cleaning & Transformation) — Pandas: Hands-on Data Analysis and Real-World Applications
8. Exploratory Data Analysis (EDA) & Visualization
   • Learn to explore, visualize, and extract meaningful insights from data
   • Data Visualization Concepts and Techniques
   • Matplotlib: Hands-on Implementation
   • Seaborn: Hands-on Visualization for Advanced Insights
9. Statistical Thinking in Python: Core Foundations
   • Build Statistical Thinking and Learn to Understand Data Effectively
   • Measures of Central Tendency
   • Measures of Dispersion
   • IQR (Interquartile Range) Statistics – Hands-on Practice
10. Introduction to Machine Learning: Supervised & Unsupervised
    • Introduction to Classification, Regression, and Model Fine-Tuning
    • Supervised Learning Concepts
    • Unsupervised Learning Concepts
    • Linear Regression: Theory and Practical Implementation
    • Evaluation Metrics in Linear Regression – Hands-on Practice
11. Logistic Regression: Concepts & Practical Implementation
    • Logistic Regression: Fundamentals and Practical Applications
    • Performance Evaluation Metrics for Logistic Regression
    • Hands-on Implementation of Logistic Regression
12. Linear Regression: Model Building and Evaluation
    • Linear Regression: Concepts and Practical Applications
    • Evaluation Metrics for Linear Regression
13. Data Preprocessing Techniques for Machine Learning
    • Introduction to Data Preprocessing Techniques
    • Data Standardization and Normalization
    • Exploratory Data Analysis (EDA)
    • Handling Missing Values
    • Outlier Detection and Treatment
    • Feature Scaling and Feature Selection Techniques
14. Tree-Based Models: Classification & Regression Trees (CART)
    • Decision Trees: Concepts and Practical Applications
    • Bagging Techniques for Model Improvement
    • Boosting and Random Forest Methods
15. Fundamentals of Neural Networks and Deep Learning — Neural Networks: Concepts and Practical Applications
16. Capstone Project (Real-World Implementation) — Utilize Data Science Libraries for Data Analysis, Visualization, Model Building, and Data Extraction
17. Advanced Excel for Data Analysis
    • Advanced Excel Functions for Data Analysis
    • Conditional Formatting for Data Insights
    • Pivot Tables for Data Summarization
    • Interactive Dashboards for Reporting and Visualization
18. SQL for Data Extraction and Management
    • Fundamentals of Database Schema Design
    • Importance of SQL Clauses in Data Querying
    • SQL Joins for Data Integration and Analysis
19. Power BI for Data Visualization and Reporting
    • Introduction to Power BI Desktop and Its Interface
    • ETL Pipeline Implementation in Power BI (Data Extraction, Transformation, and Loading)
    • Creating Calculated Fields using DAX
    • Data Visualization and Report Building in Power BI
    • AI-Powered Features and Advanced Analytics in Power BI

Capstone projects

• Global Cohort Retention & Churn Profiler — An analytical pipeline extracting relational database tables via SQL, performing cohort analysis in Python, and modeling future client churn patterns. (built with SQL, Python, Pandas)

Eligibility

Open to all. No prior coding experience required.

Frequently asked questions

Who can join this program?

Students, fresh graduates and working professionals. No prior experience is needed unless listed under eligibility — we start from the basics.

How are classes delivered?

The program runs for 3 Months. You get recorded lessons you can watch any time, plus weekly live mentor sessions for doubt clearing and project reviews.

Will I get a certificate?

Yes. When you complete all lessons and the capstone project you receive a Wayspire certificate with a unique ID that anyone can verify on our website.

Do you provide placement?

We provide career support — resume reviews, mock interviews and guidance. We do not guarantee jobs or placements.

What if I miss a live session?

Every live session is recorded and added to your learner panel within 24 hours.

Wayspire provides training, mentorship, projects and certification. We do not guarantee jobs, internships, placements or salary packages — career support is guidance to help you in your own job search.