Training on Data-Driven Decision Making with Tableau

Module 1: Introduction to Data Science

• What is data science?
• The role of data science in business and society
• The data science lifecycle
• Essential tools and technologies for data scientists
• Case Study: Using data science to improve business decision-making
• Practical: Identify a real-world problem and outline a data science approach

Module 2: Data Collection and Preparation

• Data sources and types
• Data quality assessment and cleaning
• Data integration and transformation
• Data exploration and visualization
• Case Study: Preparing raw data for analysis in a development project
• Practical: Clean and prepare a dataset for analysis

Module 3: Statistical Foundations

• Descriptive statistics (mean, median, mode, standard deviation)
• Probability and distributions
• Hypothesis testing
• Correlation and regression analysis
• Case Study: Analyzing survey data to draw meaningful conclusions
• Practical: Perform statistical analysis on a dataset

Module 4: Data Manipulation with Python

• Introduction to Python programming
• NumPy for numerical computations
• Pandas for data manipulation and analysis
• Case Study: Managing large datasets using Python libraries
• Practical: Use Pandas and NumPy to manipulate and analyze data

Module 5: Data Visualization

• Principles of effective data visualization
• Creating various chart types (bar charts, histograms, scatter plots, etc.)
• Interactive visualizations
• Storytelling with data
• Case Study: Communicating insights through data visualization dashboards
• Practical: Create visualizations to present key insights

Module 6: Exploratory Data Analysis (EDA)

• Techniques for exploring data
• Identifying patterns, trends, and anomalies
• Feature engineering
• Case Study: Discovering trends and anomalies in business data
• Practical: Conduct EDA on a dataset

Module 7: Machine Learning Basics

• Introduction to machine learning
• Supervised and unsupervised learning
• Model evaluation metrics
• Overfitting and underfitting
• Case Study: Applying machine learning to predict customer behavior
• Practical: Build and evaluate a simple machine learning model

Module 8: Predictive Modeling

• Linear regression
• Logistic regression
• Decision trees
• Model selection and tuning
• Case Study: Predicting outcomes using regression and classification models
• Practical: Develop and tune predictive models

Module 9: Data Ethics and Privacy

• Ethical considerations in data science
• Data privacy and security
• Bias in data and algorithms
• Case Study: Addressing bias in predictive models
• Practical: Evaluate ethical risks in a data science project

Module 10: Data Science Projects and Case Studies

• End-to-end data science project lifecycle
• Building a data science portfolio
• Case Study: Full data science project from problem definition to deployment
• Practical: Complete a capstone data science project