• Master data preprocessing and cleaning techniques.
• Perform exploratory data analysis using Python.
• Create advanced visualizations to communicate data insights.
• Develop predictive models to solve business problems.
• Apply data science techniques to real-world datasets.

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.

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Intelligence Analyst
• Data Engineer
• Research Scientist in Data Science

• Technology companies
• Financial services
• Healthcare analytics firms
• E-commerce and retail companies
• Research institutions
• Government agencies