Data Science and Analysis

The Data Science and Analysis course provides a comprehensive introduction to the core tools, techniques, and methodologies used in data analysis and science. Designed for professionals and aspiring data scientists, the course integrates foundational and advanced concepts in Excel, SQL, Power BI, Tableau, Python, and Machine Learning. Participants will gain practical experience in manipulating data, creating visualizations, and building analytical models, culminating in a real-world capstone project.

Key Learning Objectives

Module 1: Excel and SQL

• Data manipulation in Excel: Using functions and pivot tables.
• Introduction to SQL: Querying, filtering, and joining tables.
• Advanced Excel techniques: VBA scripting and data analysis.
• Advanced SQL techniques: Stored procedures and database management.

Module 2: Power BI and Tableau

• Introduction to Power BI: Data loading and transformation.
• Creating dashboards in Power BI: DAX basics.
• Introduction to Tableau: Data visualization principles.
• Advanced visualizations in Tableau: Integrating with SQL.

Module 3: Introduction to Data Science (Including Mathematics)

• Overview of Data Science: Importance in various industries, basic statistics, and probability.
• Introduction to Python for data science: Basic syntax and operations.
• Data types and structures in Python: Introduction to Numpy and Pandas.
• Basic data visualization: Using Matplotlib and Seaborn; introduction to linear algebra for data science.

Module 4: Python for Data Science

• Advanced Python programming: Functions and modules.
• Data cleaning and manipulation: Exploratory Data Analysis (EDA) with Pandas.
• Introduction to machine learning: Building simple models in Python.
• Capstone project: Applying all skills learned to a real-world dataset.

This course equips learners with practical skills to transform raw data into actionable insights, preparing them for a career in data-driven industries.