AI+ Data

Description

AI+ Data (5 Days)

Program Detailed Curriculum

Executive Summary
The AI+ Data certification equips professionals with vital skills for data science. It covers key concepts like
Data Science Foundations, Statistics, Programming, and Data Wrangling. Participants delve into advanced
topics such as Generative AI and Machine Learning, preparing them for complex data challenges. The
program includes a hands-on capstone project focusing on Employee Attrition Prediction. Emphasis is
placed on Data-Driven Decision-Making and Data Storytelling for actionable insights. Personalized
mentorship, immersive projects, and cutting-edge resources ensure a transformative learning journey,
preparing individuals for success in AI and data science.

Course Prerequisites
Basic knowledge of computer science and statistics (beneficial but not mandatory)
Keen interest in data analysis
Willingness to learn programming languages such as Python and R

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
AI+ Data™ Detailed Curriculum
Date Issued: 20/01/2024
Version: 1.1