Data Science & Analytics

Module 1: Data Fundamentals

• Data types, collection methods
• Cleaning and preprocessing data
• Exploratory Data Analysis (EDA)

Module 2: Programming for Data Science

• Python and R basics
• Libraries: Pandas, NumPy, Matplotlib, Seaborn

Module 3: Statistics & Probability

• Descriptive and inferential statistics
• Hypothesis testing
• Probability distributions

Module 4: Data Visualization

• Creating charts and dashboards
• Tools: Tableau, Power BI, Matplotlib

Module 5: Machine Learning Basics

• Supervised and unsupervised learning
• Regression, classification, clustering

Module 6: Big Data Tools

• Introduction to Hadoop, Spark
• Data storage and processing

Module 7: Advanced Analytics

• Time series analysis
• Natural Language Processing (NLP)

Module 8: Capstone Project & Ethics

• Real-world data project
• Data ethics and privacy

Experience in the Industry Gain knowledge from experts with practical Data Science & Analytics project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as HCL, TCS, Accenture, and Cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques, and best practices.