Data Science and Analytics Using Python

This three-day course covers essential data science techniques using Python, from data cleaning and EDA to advanced data visualization and predictive analytics. Participants will gain hands-on experience with Python libraries such as Matplotlib and Seaborn and will develop predictive models to analyze business data effectively.

To equip PhD scholars and academicians with advanced skills in data science and analytics using Python. This course focuses on data preprocessing, exploratory data analysis (EDA), data visualization, and predictive analytics, culminating in a capstone project that applies these skills to solve real-world business problems.

Day 1: Data Preprocessing and Exploratory Data Analysis (EDA)

• Data Cleaning Techniques
  • Handling missing data, outliers, and data transformation
  • Using Python libraries like Pandas for data manipulation
• EDA with Python
  • Techniques for summarizing and visualizing data
  • Identifying trends, patterns, and anomalies in datasets

Day 2: Data Visualization Tools (Matplotlib, Seaborn)

• Visualizing Data Insights
  • Creating informative and aesthetically pleasing visualizations
  • Using Matplotlib and Seaborn for various types of plots
• Hands-on Visualizations
  • Interactive sessions creating visual reports
  • Best practices for data storytelling with visualizations

Day 3: Predictive Analytics with Machine Learning

• Introduction to Predictive Modeling
  • Supervised learning techniques (e.g., regression, classification)
  • Building and evaluating predictive models with Python
• Capstone Project: Solving a Business Problem with Data
  • Applying all learned skills to a real-world business problem
  • Presenting the solution with actionable insights

Data scientists, analysts, researchers, and academicians in data science and related fields.