Data Science with Python

The Data Science Using Python is a 60-hour practical and industry-oriented training program designed to develop learners' foundational and applied skills in data analysis, data visualization, statistical concepts, and data-driven problem-solving using Python. The program introduces participants to the complete data science workflow, from collecting and cleaning raw data to analyzing, visualizing, interpreting, and presenting meaningful insights. Learners will gain hands-on experience with popular Python libraries such as NumPy, Pandas, Matplotlib, and Seaborn, along with an introduction to statistical analysis and machine learning concepts. The course focuses on practical exercises, real-world datasets, coding activities, and project-based learning to help learners apply data science techniques to practical problems. Key Learning Areas: Introduction to Data Science & Data Science Lifecycle Python Fundamentals for Data Science Variables, Data Types, Operators & Control Structures Functions, Lists, Tuples, Dictionaries & Sets NumPy for Numerical Computing Pandas for Data Manipulation & Analysis Data Cleaning & Preprocessing Handling Missing and Duplicate Data Exploratory Data Analysis (EDA) Descriptive Statistics & Basic Probability Data Visualization using Matplotlib Data Visualization using Seaborn Correlation & Relationship Analysis Feature Preparation and Data Transformation Introduction to Machine Learning Supervised & Unsupervised Learning Concepts Regression and Classification Basics Model Training, Testing & Evaluation Introduction to Scikit-learn Data Interpretation & Insight Generation Practical Data Science Case Studies Git/GitHub Basics for Project Management Final Capstone Project