Module 1:  Foundations of Data Science 

• 1.1 Introduction to Data Science
  • What is Data Science?: Explores the fundamentals of data science, including its methodologies, tools, and applications, providing a foundational understanding of this interdisciplinary field.
  • Importance of Data Science: Examines the significance of data science in modern society, highlighting its role in driving innovation, decision-making, and competitive advantage across industries.
• 1.2 Data Science Life Cycle
  • Understanding the Business Problem: Focuses on identifying and defining business challenges, ensuring data science projects address organizational objectives effectively for impactful solutions.
  • Data Preparation: Equips with skills to collect, clean, and preprocess data, laying the foundation for accurate analysis and effective model building.
  • Exploratory Data Analysis (EDA): Explores data visually and statistically to understand patterns, relationships, and anomalies, guiding further analysis and model development.
  • Modeling the Data: Introduces various machine learning algorithms and techniques to build predictive models, optimizing performance and accuracy for solving business problems.
  • Evaluating the Model: Covers methods to assess model performance and reliability, ensuring that developed models meet business requirements and provide actionable insights. 
  • Deploying the Model: Guides through the process of implementing models into production environments, enabling organizations to derive value from data-driven insights effectively.
• 1.3 Applications of Data Science
  • Examples of Data Science Applications Across Various Industries: Explores how data science drives innovation and optimization in diverse sectors, showcasing real-world applications and their impact on industry.

Module 2: Foundations of Statistics 

• 2.1 Basic Concepts of Statistics
  • Descriptive Statistics: Covers methods to summarize and visualize data, providing insights into its central tendencies, variability, and distribution for initial analysis.
  • Inferential Statistics: Explores techniques to draw conclusions and make predictions about populations based on sample data, enabling informed decision-making and hypothesis testing.
• 2.2 Probability Theory
  • Probability Distributions: Explores various probability distributions, including normal, binomial, and Poisson, to model uncertainty and analyze random phenomena in diverse contexts.
  • Central Limit Theorem: Examines the central limit theorem, demonstrating how it facilitates the use of sample statistics to approximate population parameters reliably.
• 2.3 Statistical Inference
  • Hypothesis Testing: Covers procedures to assess the validity of hypotheses using sample data, enabling informed decisions in research and decision-making processes.
  • Confidence Intervals: Explores methods to estimate population parameters with uncertainty, providing intervals that likely contain the true parameter value with specified confidence levels.

Module 3: Data Sources and Types 

• 3.1 Types of Data
  • Structured, Semi-structured, and Unstructured Data: Explores various types of data formats and their characteristics, enabling effective management and analysis in diverse data environments.
• 3.2 Data Sources
  • Databases, APIs, Web Scraping: Explores methods to access and retrieve data from databases, application programming interfaces (APIs), and websites, facilitating data acquisition for analysis.
• 3.3 Data Storage Technologies
  • Relational Databases (SQL), NoSQL Databases (MongoDB): Contrasts relational and NoSQL databases, covering SQL querying and MongoDB operations, enabling proficiency in managing diverse data storage systems.
  • Hands-on Exercise: Querying Structured Data and Handling Semi-structured Data: Provides practical experience in querying relational databases with SQL and manipulating semi-structured data using appropriate tools and techniques. 

Module 4: Programming Skills for Data Science 

• 4.1 Introduction to Python for Data Science
  • Basics of Python Programming: Covers Python syntax, data types, and variables, laying the foundation for programming proficiency in data science tasks.
  • Python Libraries for Data Science: Explores essential Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualization tasks in data science projects.
• 4.2 Introduction to R for Data Science
  • Basics of R Programming: Introduces R programming fundamentals, including vectors, data frames, and functions, providing a basis for statistical analysis and visualization in R.
  • R Libraries for Data Science: Examines key R libraries like dplyr and ggplot2, enhancing data manipulation and visualization capabilities for effective data analysis and presentation.
  • Hands-on Exercise: Data Manipulation and Visualization with Python and R Libraries: Provides practical experience in manipulating and visualizing data using Python and R libraries, reinforcing programming skills in data science contexts.

Module 5: Data Wrangling and Preprocessing

• 5.1 Data Imputation Techniques
  • Overview of Data Imputation: Explore the significance of data imputation in filling missing values, ensuring data completeness and integrity for robust analysis.
  • Overview of Missing Data: Provides an understanding of missing data, its implications, and methods to address it, crucial for accurate and reliable data analysis.
  • Overview of Imputation: Explore techniques for filling missing data in datasets, including mean imputation, interpolation, and advanced methods.
  • Common Imputation Techniques: Covers various data imputation methods including mean, median, mode, KNN, and regression imputation, equipping with tools to handle missing data effectively.
• 5.2 Handling Outliers and Data Transformation
  • Identifying and Handling Outliers: Explores methods to detect and manage outliers, ensuring data integrity and reliability in statistical analysis and modeling.
  • Data Transformation Techniques: Normalization, Standardization: Covers normalization and standardization methods to scale and transform data, facilitating accurate comparisons and analysis in data science tasks. 
  • Hands-on Exercise: Data Cleaning, Imputation, and Preprocessing with Python and R Libraries: Provides practical experience in cleaning, imputing, and preprocessing data using Python and R libraries.

Module 6: Exploratory Data Analysis (EDA)

• 6.1 Introduction to EDA
  • Purpose and Goals of Exploratory Data Analysis: Explores the objectives and methods of EDA, enabling effective data exploration to uncover patterns, anomalies, and insights.
  • Common Techniques: Summary Statistics, Data Visualization: Covers essential techniques such as summary statistics and data visualization to gain insights and make informed decisions from data exploration.
• 6.2 Data Visualization
  • Types of Visualizations: Histograms, Scatter Plots, Box Plots: Explores various visualization types, including histograms, scatter plots, and box plots, to represent and analyze different data distributions effectively.
  • Choosing the Right Visualization for Different Types of Data: Guides in selecting appropriate visualizations based on data types, ensuring clear communication and interpretation of insights.
  • Hands-on Exercise: Creating Visualizations using Python's Matplotlib and Seaborn, and R's ggplot2

Module 7: Generative AI Tools for Deriving Insights 

• 7.1 Introduction to Generative AI Tools
  • Overview of Generative AI Techniques: Explores autoencoders, GANs, and VAEs, enabling understanding of how these techniques generate new data samples, images, and text.
  • Hands-on Exercise for Various Gen AI Tools: Explore Gen AI tools through hands-on exercises, covering Plotly, Seaborn, and more for effective data visualization and analysis.
• 7.2 Applications of Generative AI 
  • Application in Data Synthesis, Augmentation, and Anomaly Detection: Explores how generative AI techniques are applied in creating synthetic data, augmenting datasets, and detecting anomalies effectively.

Module 8: Machine Learning Refresher

• 8.1 Introduction to Supervised Learning Algorithms 
  • Simple Linear Regression: Understand and apply the basic concepts of linear regression to analyze relationships between two variables.
  • Multiple Linear Regression: Extend regression analysis to multiple predictors, mastering techniques for modeling complex relationships in datasets.
  • Polynomial Regression: Learn to capture nonlinear relationships between variables using polynomial regression models.
  • Logistic Regression: Dive into binary classification problems, mastering logistic regression techniques for predicting categorical outcomes.
  • K-Nearest Neighbors (KNN): Explore the KNN algorithm for classification and regression tasks, leveraging proximity based learning. 
  • Decision Tree: Understand decision tree structures and algorithms, mastering techniques for classification and regression tasks.
  • Support Vector Machine (SVM): Learn to classify data points by finding the optimal hyperplane, mastering SVM for both linear and nonlinear classification problems.
  • Naive Bayes Classification: Explore the probabilistic Naive Bayes algorithm for classification tasks, mastering techniques for text and categorical data.
• 8.2 Introduction to Unsupervised Learning 
  • Types of Unsupervised Learning: Uncover various unsupervised learning methods, understanding their applications and differences in extracting patterns from data without explicit guidance.
• 8.3 Different Algorithms for Clustering
  • K – Means Clustering: Master K-Means Clustering, a popular unsupervised learning algorithm, to partition data into clusters based on similarity, ideal for segmentation and pattern recognition tasks.
  • Hierarchical Clustering: Understand hierarchical grouping methods, analyzing data structures through dendrogram visualization, and applying clustering techniques to uncover patterns in datasets.
• 8.4 Association Rule Learning
  • Hands On: Experience hands-on exercises in Association Rule Learning, delving into practical applications and techniques for deriving valuable insights from data sets.

Module 9: Advance Machine Learning 

• 9.1 Ensemble Learning Techniques
  • Overview of Ensemble Learning: Introduces ensemble learning techniques, combining multiple models for improved prediction accuracy and robustness in various machine learning tasks.
  • Bagging (Bootstrap Aggregating): Explores bagging, a technique to reduce variance by training multiple models on bootstrap samples of the dataset.
  • Random Forest with Code and Real-life Example: Dives into Random Forest, a popular ensemble method, with implementation and real-life case studies, demonstrating its effectiveness.
  • Boosting (AdaBoost and Gradient Boosting): Covers boosting algorithms such as AdaBoost, Gradient Boosting, and XGBoost, enhancing model performance through sequential model training and correction.
  • XGBoost with Code and Real-life Example: Focuses on XGBoost, a powerful gradient boosting algorithm, with hands-on coding exercises and real-world applications to illustrate its capabilities.
  • Stacking: Examines stacking, a meta-ensemble technique, combining predictions from multiple base models to improve overall performance.
  • Ensemble Learning Applications and Case Studies: Explores practical applications of ensemble learning across domains, showcasing case studies to illustrate its effectiveness in solving real-world problems.
• 9.2 Dimensionality Reduction
  • Introduction to Dimensionality Reduction: Provides an overview of dimensionality reduction techniques, aiming to reduce the number of features while preserving important information.
  • Principal Component Analysis (PCA): Explores PCA, a linear dimensionality reduction technique, to transform high dimensional data into a lower-dimensional space while preserving variance.
  • Linear Discriminant Analysis (LDA): Covers LDA, a supervised dimensionality reduction technique, focusing on maximizing class separability for classification tasks.
  • t-Distributed Stochastic Neighbor Embedding (t-SNE): Investigates t-SNE, a non-linear dimensionality reduction technique, emphasizing visualization of high-dimensional data clusters in lower-dimensional space.
  • Autoencoders for Dimensionality Reduction: Explores autoencoders, neural network-based models, for unsupervised dimensionality reduction, learning efficient representations of input data.
  • Applications of Dimensionality Reduction in Machine Learning: Examines real-world applications of dimensionality reduction techniques, demonstrating their role in improving model performance and interpretability in machine learning tasks.
• 9.3 Advanced Optimization Techniques
  • Gradient Descent Optimization Algorithms (SGD, Adam, RMSprop): Explores gradient descent variants, including SGD, Adam, and RMSprop, enhancing understanding of optimization techniques in machine learning model training.
  • Learning Rate Schedulers: Investigates learning rate schedulers, dynamically adjusting learning rates during training to improve model convergence and performance.
  • Momentum-Based Optimization: Covers momentum-based optimization algorithms, enhancing gradient descent efficiency by introducing momentum terms to accelerate convergence and reduce oscillations.
  • Second-Order Optimization (Newton's Method, Quasi-Newton Methods): Explores second-order optimization methods like Newton's method and quasi-Newton methods, offering faster convergence and robustness compared to first-order methods.
  • Meta-Learning and Hyperparameter Optimization: Examines meta-learning and hyperparameter optimization techniques, automating the process of tuning model parameters for improved performance.
  • Practical Tips for Efficient Model Training and Optimization: Provides practical insights and strategies for efficient model training and optimization, optimizing workflows and avoiding common pitfalls in machine learning projects.

Module 10: Data-Driven Decision-Making

• 10.1 Introduction to Data-Driven Decision Making
  •  Understanding the Importance of Data-Driven Decision Making: Explores the significance of leveraging data to inform decision-making processes, driving organizational efficiency and effectiveness.
  • Overview of the Decision-Making Process: Provides a comprehensive understanding of the decision-making process, from problem identification to evaluation and implementation of solutions.
  • Role of Data in Decision Making: Investigates how data serves as a crucial factor in informing decisions, enabling organizations to make informed choices based on empirical evidence.
  • Benefits and Challenges of Data-Driven Decision Making: Examines the advantages and obstacles associated with adopting a data-driven approach to decision-making, highlighting opportunities for improvement and innovation.
• 10.2 Open-Source Tools for Data-Driven Decision Making
  • Apache Superset: Apache Superset is an open-source data exploration and visualization platform developed by Airbnb. It offers a rich set of visualization options, interactive dashboards, and the ability to connect to various data sources.
  • Redash: Redash is an open-source data visualization and dashboarding tool that allows users to connect to various data sources and create interactive dashboards. It supports SQL queries and offers a wide range of visualization options.
  • Pentaho: Pentaho is an open-source business intelligence suite that offers data integration, analytics, and reporting capabilities. It provides a visual interface for creating reports and dashboards and supports integration with big data platforms.
• 10.3 Deriving Data-Driven Insights from Sales Dataset
  • Introduction to Case Study: Introduces a case study analyzing Adidas sales data, providing context for subsequent courses in data analysis.
  • Data Collection and Preparation: Understanding the Adidas Sales Dataset: Covers techniques for collecting and preprocessing the Adidas sales dataset, ensuring data quality and readiness for analysis.
  • Exploratory Data Analysis (EDA): Identifying Key Patterns and Trends in Adidas Sales: Explores exploratory data analysis methods to uncover insights and patterns in Adidas sales data, informing subsequent analysis.
  • Building Predictive Models: Using Machine Learning Algorithms for Sales Forecasting and Customer Segmentation: Applies machine learning algorithms to predict sales and segment customers based on Adidas sales data.
  • Visualization and Interpretation: Creating Interactive Dashboards in Power BI to Derive Insights from Adidas Sales Data: Demonstrates creating interactive dashboards in Power BI to visualize and interpret Adidas sales data effectively.
  • Decision Making: Using Data-Driven Insights to Make Informed Business Decisions for Adidas: Explores using data-driven insights from the Adidas sales dataset to make informed business decisions, optimizing operations and strategies.
  • Case Study Discussion: Facilitates discussion and hands-on exercises based on the Adidas sales case study, reinforcing learning and practical application of data analysis techniques.

Module 11: Data Storytelling 

• 11.1 Understanding the Power of Data Storytelling
  • Introduction to Data Storytelling: Introduces the concept of data storytelling, exploring techniques to effectively communicate insights derived from data analysis.
  • Importance of Storytelling in Data Analysis and Communication: Examines the role of storytelling in data analysis, emphasizing its power to engage and persuade audiences effectively.
  • The Psychology of Storytelling: Why Stories Resonate with Audiences: Explores the psychological principles behind storytelling, highlighting why narratives resonate and influence audience perceptions.
  • Real-Life Examples of Successful Data Stories and Their Impact on Decision Making: Analyzes real-world data stories, demonstrating their impact on decision-making processes and organizational outcomes.
• 11.2 Identifying Use Cases and Business Relevance
  • Identifying Data-Driven Use Cases in Business: Teaches methods for recognizing opportunities where data can drive business value, fostering innovation and strategic decision-making.
  • Understanding the Business Context: Goals, Challenges, and Opportunities: Explores the business landscape to identify objectives, obstacles, and potential areas for data-driven solutions.
  • Selecting Relevant Data Sources and Metrics for the Use Case: Guides in selecting appropriate data sources and metrics aligned with business goals, ensuring meaningful analysis and actionable insights.
  • Defining the Audience: Stakeholders, Decision Makers, and End Users: Examines the stakeholders involved in the data-driven initiative, ensuring alignment of insights with user needs and organizational objectives.
• 11.3 Crafting Compelling Narratives
  • Structuring a Data Story: Beginning, Middle, End: Guides in structuring data narratives effectively, ensuring coherence and engagement through a well-defined storyline.
  • Developing a Clear Message and Storyline: Teaches crafting a compelling message and storyline, conveying data insights in a coherent and impactful manner.
  • Incorporating Data Insights into the Narrative Flow: Explores integrating data insights seamlessly into the narrative flow, enhancing storytelling effectiveness and audience understanding.
  • Engaging the Audience: Emotion, Connection, and Call to Action: Focuses on engaging the audience emotionally, fostering connection and motivating action through data storytelling techniques. 
• 11.4 Visualizing Data for Impact
  • Choosing the Right Visualizations for the Story: Guides in selecting appropriate visualizations to effectively convey data insights and enhance storytelling impact.
  • Data Visualization Best Practices: Clarity, Simplicity, and Relevance: Covers principles for creating clear, simple, and relevant visualizations to communicate data insights effectively.
  • Creating Engaging Visuals: Charts, Graphs, Maps, and Infographics: Explores techniques for designing visually appealing charts, graphs, maps, and infographics to engage and inform audiences.
  • Using Interactive Elements to Enhance Understanding and Engagement: Demonstrates incorporating interactive elements into visualizations to facilitate exploration, understanding, and audience engagement with data stories.

Module 12: Capstone Project – Employee Attrition Prediction

• 12.1 Project Introduction and Problem Statement
  • Introduction to the Capstone Project: Employee Attrition Prediction: Introduces the capstone project focusing on predicting employee attrition, applying data analysis techniques to HR datasets.
  • Overview of the Problem Statement: Predicting Employee Attrition Rates: Provides an overview of the project goal, predicting employee attrition rates, and its significance in organizational management.
  • Understanding the Business Context: Impact of Employee Turnover on Organizational Performance: Explores the impact of employee turnover on organizational performance, emphasizing the importance of reducing attrition rates.
  • Identifying Data Sources: HR Records, Employee Surveys, Performance Metrics: Guides in identifying relevant data sources such as HR records, surveys, and performance metrics for the project.
  • Formulating Hypotheses: Factors Influencing Employee Attrition and Their Impact: Helps in formulating hypotheses about factors influencing employee attrition and their potential impact on organizational outcomes.
• 12.2 Data Collection and Preparation
  • Collecting and Acquiring Relevant Data: HR Databases, Survey Responses, Performance Reports: Guides in gathering pertinent data sources including HR databases, surveys, and performance reports for analysis.
  • Data Cleaning and Preprocessing Techniques: Handling Missing Values, Outliers, and Inconsistencies: Covers methods for preparing data by addressing missing values, outliers, and inconsistencies to ensure accuracy.
  • Exploratory Data Analysis (EDA): Understanding Employee Demographics, Job Roles, and Attrition Patterns: Analyzes employee data to identify patterns in demographics, job roles, and attrition, guiding subsequent analysis.
  • Feature Engineering: Creating Relevant Variables such as Employee Tenure, Satisfaction Scores, and Performance Ratings: Explores techniques for creating new variables like tenure and satisfaction scores to enhance predictive modeling accuracy.
• 12.3 Data Analysis and Modeling
  • Choosing Suitable Data Analysis and Modeling Techniques: Classification Algorithms for Predictive Modeling
  • Building Predictive Models: Employing Machine Learning Models like Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting Machines
  • Evaluating Model Performance: Using Metrics like Accuracy, Precision, Recall, and F1-Score
  • Interpreting Model Results: Identifying Key Factors Contributing to Employee Attrition and Their Relative Importance
• 12.4 Data Storytelling and Presentation
  • Crafting a Compelling Narrative Around the Project Findings: Highlighting the Business Impact of Employee Attrition
  • Visualizing Key Insights Using Data Storytelling Techniques: Creating Interactive Dashboards and Visualizations 
  • Creating Interactive Dashboards and Reports to Showcase the Project Results: Displaying Attrition Trends, Predicted Attrition Rates, and Impact Analysis
  • Presenting the Capstone Project to Peers and Stakeholders: Communicating Findings, Recommendations, and Actionable Insights for Mitigating Employee Attrition