Introduction

Course Objectives 

• Learn how to define and structure data analysis questions
• Understand how to clean, prepare, and wrangle raw data into usable formats
• Develop skills to explore datasets and identify meaningful patterns
• Perform statistical analysis to support insights and predictions
• Build visual understanding of data using charts and plots
• Communicate findings effectively for business or technical audiences
• Gain hands-on experience with Python, NumPy, Pandas, and MySQL for data analysis tasks

Target Audience

• Beginners and professionals interested in starting a career in data analysis
• Data analysts who want to strengthen their skills in Python-based data analysis tools
• Students and graduates in computer science, business, or related fields
• Business professionals who need to analyze and interpret data for decision-making
• Developers and engineers who want to transition into data analysis roles
• Anyone interested in learning how to work with data using Python, NumPy, Pandas, and MySQL

Course Outlines 

Day 1: Problems Solved by Data Analysts

•  Questions about Student Data Solution
•  Investigating the Data
•  Problems in the Data 
•  Missing Engagement Records

Day 2: Debugging Data Analysis Code

• Making Histograms
• Predicting Based on Many Features
• Improving Plots and Sharing Findings

Day 3: NumPy

• One-Dimensional Data in NumPy
• Two-Dimensional NumPy Arrays
•  NumPy Arrays
•  Vectorized Operations
•  Multiplying by a Scalar
• Standardizing Data

Day 4: Pandas

• Pandas Series
• Filling Missing Values
•  Plotting in Pandas

Day 5: Statistics

• Mean, Median and Mode
• Standard Variation, Error mean
• Calculating Correlation