Below is a detailed overview of all the topics that will be covered in the course:
Data Science Foundations and Applications: Data science lifecycle, end-to-end workflows, business use cases, and core data science concepts.
Programming and Statistical Methods: Python, R, SQL, statistical modeling, regression analysis, inferential statistics, and hypothesis testing.
Big Data Analytics and Engineering: Big data ecosystems, Apache Hadoop, HDFS, YARN, MapReduce, and distributed data processing.
Database Systems for Data Science: Relational databases, MongoDB fundamentals, data modeling, database design, and ETL processes.
Data Analysis and Exploration: Environment setup, NumPy, Pandas, exploratory data analysis (EDA), data cleaning, and transformation.
Data Visualization and Business Intelligence: Matplotlib, Seaborn, Tableau, Power BI, dashboards, and storytelling with data.
Cloud and Industry Tools: Amazon Web Services (AWS), SAS, Apache Spark, and scalable analytics platforms.
Machine Learning and Artificial Intelligence: Supervised and unsupervised learning, predictive modeling, neural networks, deep learning fundamentals, and applied AI use cases.