48 Sections
95 Lessons
16 Weeks

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Numerical Computing & Advanced NumPy
Master array operations, vectorization, statistics, and basic linear algebra using NumPy to perform fast and efficient data analysis for real-world business problems.
0
Key Business Questions
3
- 2.1
  How can we quickly calculate average sales, revenue growth, and standard deviations across large datasets?
- 2.2
  How can we simulate customer purchase patterns to forecast demand?
- 2.3
  How can we use matrix operations to optimize resource allocation or pricing strategies?
NumPy Fundamentals
4
- 3.1
  Array Creation: arange, linspace, zeros, ones
- 3.2
  Indexing, Slicing, and Boolean Masking
- 3.3
  Array Operations: Element-wise math, broadcasting
- 3.4
  Aggregate Functions: mean, sum, min, max, std
Advanced NumPy
4
- 4.1
  Random numbers, normal distribution, random sampling
- 4.2
  Reshaping, Stacking & Splitting Arrays
- 4.3
  Vectorization for performance optimization
- 4.4
  Linear Algebra: dot product, matrix multiplication, inverse, determinant
Numerical Computing with NumPy
Master array operations, vectorization, statistics, and basic linear algebra using NumPy to perform fast and efficient data analysis for real-world business problems.
0
Key Business Questions
3
- 6.1
  Which products, regions, or customer segments generate the highest revenue?
- 6.2
  How can we clean and merge messy datasets to create a single source of truth?
- 6.3
  What trends can we uncover in customer behavior using grouping and time-based analysis?
Pandas Basics
4
- 7.1
  Series & DataFrames: Creation, indexing, slicing
- 7.2
  Inspecting Data head, tail, info, describe
- 7.3
  Importing Data (CSV, Excel, JSON)
- 7.4
  Selecting, Filtering, Sorting Data
Data Cleaning & Transformation
4
- 8.1
  Handling Missing Values: dropna, fillna
- 8.2
  Renaming, Replacing, and Mapping Columns
- 8.3
  GroupBy: Aggregations and transformations
- 8.4
  Merging & Joining multiple datasets
Pandas Advanced Analytics
4
- 9.1
  Pivot Tables in Pandas
- 9.2
  Time Series: parsing dates, resampling, rolling windows
- 9.3
  Cross-tabulation and contingency tables
- 9.4
  Exporting data to Excel/CSV
Projects
0
E-Commerce data analysis
1
- 11.1
  Build an interactive dashboard to track online sales, products, and customer insights.
Income Analysis of employees
1
- 12.1
  Visualize workforce income distribution and highlight pay trends across roles.
Google play store apps data analysis
1
- 13.1
  Analyze app ratings, installs, and reviews to identify market patterns.
Data Visualization & Statistical Analysis
Create clear and impactful visual insights using Matplotlib and Seaborn to analyze trends, correlations, distributions, and performance metrics across real-world business datasets, enabling data-driven decision-making.
0
Key Business Questions
3
- 15.1
  How do sales, revenue, or customer growth trends change over time?
- 15.2
  What correlations exist between customer demographics and purchasing behavior?
- 15.3
  How can we visually compare product performance across categories and regions?
Introduction to Matplotlib
4
- 16.1
  Plotting Basics: line, bar, scatter plots
- 16.2
  Customization: titles, labels, legends, colors
- 16.3
  Subplots and figure layout
- 16.4
  Saving plots as images (PNG, JPG)
Advanced Matplotlib
4
- 17.1
  Histograms, boxplots, pie charts
- 17.2
  Annotating plots with text & arrows
- 17.3
  Combining plots for multi-view analysis
- 17.4
  Styling: gridlines, markers, colormaps
Seaborn for Statistical Visualization
4
- 18.1
  Intro to Seaborn: themes, palettes
- 18.2
  Relational Plots: scatterplot, lineplot
- 18.3
  Categorical Plots: barplot, countplot, boxplot, violinplot
- 18.4
  Heatmaps & correlation analysis
Analysis with Gen AI
This module focuses on using Generative AI for data-driven decision-making. Students apply AI tools for data cleaning, analysis, structured reporting, and storytelling to transform raw data into actionable business insights.
0
Concepts to study
8
- 20.1
  AI for Data-Driven Decision-Making
- 20.2
  From Data to Decisions: Data Cleaning and Analysis With AI
- 20.3
  Generating structured reports for decision-making
- 20.4
  Data Detective: Using AI for Exploratory Data Analysis (EDA)
- 20.5
  From Data to Story: Tailoring Your Narrative
- 20.6
  Presenting Your Data Story: From Report to Impact
- 20.7
  From Data to Story: Visual Storytelling
- 20.8
  AI for Problem Solving: The SOLVE Framework
Projects
0
Udemy course analysis
1
- 22.1
  Explore course popularity, pricing, and enrollment trends across categories.
Top 5000 youtube channel analysis
1
- 23.1
  Visualize channel performance by subscribers, views, and content type.
IMDB movie dataset analysis
1
- 24.1
  Analyze movie ratings, genres, and box office trends with interactive dashboards.
Foundations of Machine Learning & Predictive Modeling
This core phase equips you with industry-relevant skills in data science, machine learning, programming, analytics, and foundational AI concepts.
0
Foundations of Machine Learning
Train industry-standard machine learning models to automate insight generation and predict business metrics behaviour
0
Machine Learning Paradigms
1
- 27.1
  Understand supervised, unsupervised, and reinforcement learning approaches.
Linear and Logistic Regression
1
- 28.1
  Build predictive models for continuous outcomes and classification problems.
K Nearest Neighbors (KNN)
1
- 29.1
  Learn instance-based classification and regression for pattern recognition.
Regularisation and Hyperparameter Tuning
1
- 30.1
  Optimize models to prevent overfitting and improve performance.
Decision Trees and Ensembles
1
- 31.1
  Use tree-based models, Random Forests, and Boosting for robust predictions.
Clustering Models
1
- 32.1
  Apply unsupervised techniques like K-Means and Hierarchical clustering for data segmentation.
Naive Bayes
1
- 33.1
  Apply probabilistic classification based on Bayes’ theorem for text and categorical data.
Support Vector Machines (SVM)
1
- 34.1
  Build models that separate classes using optimal hyperplanes for high accuracy.
Decision Tree
1
- 35.1
  Use tree-based logic to split data and make interpretable predictions.
Random Forest
1
- 36.1
  Leverage ensemble learning with multiple decision trees for robust classification and regression.
K-Means Clustering
1
- 37.1
  Group unlabeled data into meaningful clusters for segmentation and pattern discovery.
Streamlit App Development
This module focuses on building interactive data applications using Streamlit, enabling rapid development of AI and data-driven web apps without complex frontend coding.
0
Streamlit Fundamentals
5
- 39.1
  Introduction to Streamlit Architecture
- 39.2
  Widgets: sliders, buttons, inputs, file uploads
- 39.3
  App Structure & Layout Design
- 39.4
  Displaying DataFrames, Charts, and Metrics
- 39.5
  Handling User Inputs & State Management
Data & Model Integration
5
- 40.1
  Integrating Pandas, NumPy & Visualization Libraries
- 40.2
  Connecting ML Models (Scikit-learn, TensorFlow, PyTorch)
- 40.3
  Real-time Predictions & Interactive Controls
- 40.4
  Uploading & Processing External Data
- 40.5
  Performance Optimization Techniques
Deployment & Scaling
5
- 41.1
  Streamlit Cloud & Local Deployment
- 41.2
  App Configuration & Secrets Management
- 41.3
  API Integration
- 41.4
  Authentication Basics
- 41.5
  Sharing & Hosting Data Apps
Projects
4
- 42.1
  Interactive Data Dashboard
- 42.2
  ML Prediction Web App
- 42.3
  AI-Powered Analytics Tool
- 42.4
  Real-Time Data Monitoring Application
Capstone Projects
0
Customer Churn Prediction System
2
- 44.1
  Build a machine learning model to predict which customers are likely to leave a service.
- 44.2
  Includes EDA, feature engineering, classification models, and business impact analysis.
Sales Forecasting & Demand Prediction
2
- 45.1
  Develop time-series models to forecast future sales using historical retail data.
- 45.2
  Apply regression, ARIMA/LSTM, and performance evaluation metrics.
Fraud Detection Model
2
- 46.1
  Design a classification system to detect fraudulent transactions.
- 46.2
  Focus on handling imbalanced datasets, feature selection, and model optimization.
Customer Segmentation & Recommendation Engine
2
- 47.1
  Use clustering (K-Means/Hierarchical) to segment customers and build a basic recommendation system.
- 47.2
  Translate insights into actionable marketing strategies.
End-to-End Business Intelligence Dashboard
2
- 48.1
  Perform data cleaning, advanced SQL queries, EDA, and predictive modeling.
- 48.2
  Deploy insights through an interactive dashboard (Streamlit/Power BI) with real-world business KPIs.

AI & Data Science Professional Course

Curriculum

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