AI+ Data™
About Course
Executive Summary
The AI+ Data™ program equips professionals with essential data science skills, covering statistics, programming, data wrangling, machine learning, and generative AI.
Learn to analyze, model, and visualize data for actionable insights.
Apply advanced techniques to solve real-world problems using Python, R, and cloud tools.
Complete a capstone project on Employee Attrition Prediction.
Gain expertise in Data-Driven Decision Making and Data Storytelling, enabling effective communication of insights to stakeholders.
Learning Outcomes
Participants will be able to:
Understand the fundamentals and lifecycle of data science projects.
Apply statistical concepts and probability for informed analysis.
Manipulate, clean, and preprocess structured and unstructured data.
Develop data visualization and storytelling skills to convey insights effectively.
Build predictive models using machine learning and generative AI tools.
Optimize model performance and apply advanced ML techniques like ensemble learning and dimensionality reduction.
Make data-driven decisions using open-source tools (Power BI, Apache Superset, Pentaho, Redash).
Communicate findings effectively through dashboards, reports, and narratives.
Course Modules
Module 1 – Foundations of Data Science
Introduction to Data Science: concepts, importance, and applications
Data Science Life Cycle: business problem, data preparation, exploratory analysis, modeling, deployment, evaluation
Real-world data science applications
Module 2 – Foundations of Statistics
Descriptive & inferential statistics
Probability distributions & central limit theorem
Hypothesis testing & confidence intervals
Module 3 – Data Sources and Types
Structured, semi-structured, unstructured data
Accessing data: databases, APIs, web scraping
Data storage: SQL & NoSQL databases
Hands-on: querying and handling different data types
Module 4 – Programming Skills for Data Science
Python and R basics
Key libraries: NumPy, Pandas, Matplotlib, Seaborn, ggplot2, dplyr
Hands-on: data manipulation and visualization
Module 5 – Data Wrangling & Preprocessing
Handling missing values: imputation techniques
Outlier detection & data transformation: normalization & standardization
Hands-on: cleaning, preprocessing, and preparing data
Module 6 – Exploratory Data Analysis (EDA)
Summary statistics and data visualization
Selecting the right visualization: histograms, scatter plots, box plots
Hands-on: visualizations with Python (Matplotlib, Seaborn) and R (ggplot2)
Module 7 – Generative AI Tools for Insights
Introduction to generative AI: autoencoders, GANs, VAEs
Applications in data synthesis, augmentation, anomaly detection
Hands-on exercises with Gen AI tools
Module 8 – Machine Learning Refresher
Supervised learning: regression, KNN, logistic regression
Unsupervised learning: clustering, decision trees, SVM, hierarchical clustering
Association rule learning
Hands-on exercises with ML tools
Module 9 – Advanced Machine Learning
Ensemble learning: Random Forest, Bagging, Boosting, Stacking, XGBoost
Dimensionality reduction: PCA, t-SNE
Advanced optimization: SGD, Adam, RMSprop, LDA, momentum-based, learning rate schedulers
Practical tips for model training and optimization
Module 10 – Data-Driven Decision Making
Importance of data-driven decision making
Tools: Apache Superset, Pentaho, Redash, Power BI
Case study: Adidas sales dataset for predictive modeling, segmentation, and insights
Module 11 – Data Storytelling
Crafting compelling narratives with data
Identifying use cases, business relevance, and audience
Visualizing data for impact: charts, graphs, maps, dashboards
Interactive and engaging presentation techniques
Module 12 – Capstone Project: Employee Attrition Prediction
Problem statement, data collection, and preparation
Exploratory data analysis and feature engineering
Predictive modeling: logistic regression, decision trees, random forests, gradient boosting
Model evaluation: accuracy, precision, recall, F1-score
Data storytelling: dashboards, visualizations, and actionable business insights
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