Data Science Course Overview The Data Science course is designed for students, professionals, and freshers who want to build a career in data-driven technologies. Data Science combines statistics, programming, and business knowledge to extract insights from raw data. This course covers the complete data science pipeline including data collection, cleaning, analysis, visualization, machine learning, and deployment. You will learn popular tools and languages like Python, NumPy, Pandas, Matplotlib, SQL, and libraries for machine learning such as Scikit-learn and TensorFlow. With a strong focus on practical projects, the course ensures you can apply concepts to real-world business problems in domains like finance, healthcare, retail, and technology. By the end of the course, you will have the skills to analyze datasets, build predictive models, and present insights that support decision-making. Module 1: Introduction to Data Science Understand what data science is, its applications, career opportunities, and the lifecycle of a data science project. Module 2: Python for Data Science Learn Python basics, data types, functions, loops, and libraries like NumPy and Pandas for data manipulation. Module 3: Data Collection and Cleaning Work with structured and unstructured data. Learn data preprocessing, handling missing values, outliers, and normalization techniques. Module 4: Exploratory Data Analysis (EDA) Use descriptive statistics and visualization tools like Matplotlib and Seaborn to explore and understand data patterns. Module 5: SQL and Databases Learn SQL queries for data extraction, joins, aggregations, and integrating databases with Python. Module 6: Probability and Statistics for Data Science Understand probability, hypothesis testing, correlation, regression, and statistical inference for data analysis. Module 7: Machine Learning Basics Learn supervised and unsupervised learning techniques including linear regression, logistic regression, decision trees, clustering, and recommendation systems. Module 8: Advanced Machine Learning Explore ensemble methods (Random Forest, XGBoost), neural networks, and introduction to deep learning with TensorFlow/Keras. Module 9: Data Visualization and Storytelling Learn to create dashboards and reports using tools like Power BI or Tableau. Master techniques to present insights effectively. Module 10: Big Data and Cloud in Data Science Introduction to big data tools like Hadoop and Spark. Learn how cloud platforms (AWS, Azure, GCP) support data science workflows. Module 11: Real-Time Projects and Case Studies Work on projects such as customer churn prediction, sentiment analysis, fraud detection, or sales forecasting.