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Generative AI with PDCloudEx
Master Program for Working Professionals

Integrated with AI Co-Lab Experience and BYOP

Learn to integrate GenAI into your existing software stack.

Build Autonomous
AI Agents

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Program Highlights

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Learning Approach

Practice-Oriented Learning Approach

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Learning Approach

Industry-Ready AI Curriculum (2026 Focus)

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Learning Approach

Transparent and Fundamental Learning

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Learning Approach

Transparent and Fundamental Learning

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Course Modules

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Module 1: Python Fundamental for Data Science

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Fundamentals & Querying

  • Command Mastery: Master DDL (Definition), DML (Manipulation), and DQL (Query) types. Core Querying: Write SELECT statements with filters, logical operators, and wildcards.
  • Organization: Apply sorting, limits, and define data types and constraints.
  • Architecture: Manage Normalization (1NF–3NF) and optimize performance with Indexes. Joins & Data Relationships


Master the art of connecting datasets while maintaining data integrity.

  • Join Types: Expertly handle Inner, Left, Right, Full, Cross, and Self joins. Manage missing data and establish complex table relationships.


Advanced SQL Analytics

  • Transition into high-level data engineering and complex logic.
  • Aggregations: Use aggregate functions with GROUP BY and HAVING clauses. Analytic Logic: Implement CASE WHEN logic, subqueries, and CTEs (Common Table Expressions).
  • Window Functions: Perform time-series and ranking tasks using ROW_NUMBER, RANK, LEADS & LAGS Function

Probability & Distributions

  • Fundamentals: Master Random Variables and core Probability Rules (addition & multiplication Discrete & Continuous: Differentiate between countable outcomes and continuous data ranges.
  • Non-Gaussian Models: Model arrivals and successes with Binomial, Poisson, Exponential, and Uniform distributions.
  • The Normal Curve: Master Normal and Standard Normal distributions as the foundation for modeling.
  • Foundations of Inference : Bridge the gap between sample data and population reality.
  • Central Limit Theorem: Understand why sample means converge toward a Normal
  • distribution. Sampling Theory: Master sampling distributions and calculate Confidence Intervals for population parameters.


Hypothesis Testing & Decision Making

  • Rigorous Framework: Define Null (H0) and Alternate (H1) hypotheses to make data-driven decisions using p-values and Python.
  • Statistical Tests: Deploy t-Tests, ANOVA, and Chi-Square to validate group relationships and compare means or ratios across datasets.
  • Optimization: Design and evaluate A/B Testing experiments to drive product improvements and feature enhancements with mathematical certainty.

Core Objects & Operations

  • Data Structures: Master the hierarchy of Scalars, Vectors, and Matrices to represent complex datasets.
  • Matrix Calculus: Execute Transpose, Inverse, and Determinant operations to manipulate data dimensions.
  • Equations: Solve Systems of Linear Equations to find optimal intersections in data models. Geometry of Data
  • Vector Spaces: Understand Linear Independence to identify and remove redundant features. Multiplication: Master the Dot Product and Matrix Multiplication for similarity scoring and transformations.
  • Spectral Theory: Grasp Eigenvalues and Eigenvectors to unlock advanced dimensionality reduction (PCA).


Machine Learning Integration

  • Algorithmic Role: Discover how linear algebra drives Weights in Neural Networks and Linear Regression.
  • Vectorization: Optimize performance by replacing slow loops with high-speed NumPy vectorized operations.
  • Industry Execution: Power ML algorithms by translating mathematical structures into optimized Python code.

Data Sourcing & Preparation

  • Sourcing: Acquire and integrate data from Public & Private datasets to build robust analytical foundations.
  • Cleaning: Master essential techniques to handle missing values, fix invalid/inconsistent data, and standardize values.
  • Processing: Apply advanced filtering and restructuring to prepare raw data for high-level modeling. Univariate & Bivariate Analysis
  • Univariate: Analyze single variables through Categorical (Ordered/Unordered) and Numerical lenses, including Segmented analysis.
  • Bivariate: Explore relationships by comparing Continuous vs. Continuous and Categorical vs. Continuous variables.


Advanced Metrics & Business Logic

  • Derived Metrics: Generate value-added insights using Type-driven, Business-driven, and Data-driven metrics.
  • Analytical Reasoning: Transform statistical observations into actionable business strategies.
  • Foundations of Visual Communication : Master the art of selecting the right visual for the right data story.
  • Core Principles: Understand the role of visualization in decision-making and how to choose the optimal chart for specific data types.
  • Matplotlib Fundamentals: Master Python’s core plotting library to create, plot, and modify diverse graph types for maximum clarity
  • Layout Design: Implement Subplots and advanced layout designs to present multi-dimensional data effectively.


Statistical & Advanced Visualization
Create statistically rich visuals to uncover hidden data patterns.

  • Seaborn Integration: Build high-level, aesthetically pleasing plots like Pair Plots and Heatmaps with minimal code.
  • Distribution Analysis: Utilize Histograms for frequency patterns and Box Plots for rigorous outlier detection.
  • Relationship Mapping: Deploy Scatter Plots for correlations and Line Charts to track trends over time.


Interactive Dashboards & Real-World Practice

  • Interactive with Plotly: Build dynamic charts and plots that allow for real-time user-friendly visual exploration.
  • Complex Formatting: Master Stacked Bar Charts, Pie Charts, and custom styling themes for professional presentations.
  • Practical Execution: Apply skills to Real-World Case Studies and visualization-based problem solving to simulate industry workflows.
  • Linear Regression: From Statistical Foundations to Predictive Mastery
  • This module covers simple and multiple linear regression, regression lines and best-fit lines, and the strength of linear relationships using correlation, R², and adjusted R². Learners focus on reading and understanding data, core assumptions of linear regression, hypothesis testing, and interpreting coefficients, p-values, and confidence intervals.
  • You will follow an end-to-end model-building workflow including residual analysis, model diagnostics, prediction, and result interpretation. Practical implementation is done using both statsmodels and Scikit-learn (SKLearn), with a clear comparison between the two approaches, handling multiple predictors, multicollinearity, and categorical variables using appropriate encoding techniques.


Regularization Techniques

  • This section explains overfitting and the bias-variance tradeoff, followed by Ridge Regression (L2 regularization) and its Python implementation. Lasso Regression (L1 regularization) is covered with a focus on feature selection, along with regularization demos and model comparisons using Python. Optimization Techniques
  • Learn gradient descent for linear regression, including learning rate selection and convergence concepts, to understand how models optimize performance during training.
  • Real-World Use Cases & Outcome Prediction
  • Explore real-world use cases and understand how linear regression models are applied to predict outcomes using data-driven decision-making.

Foundations of Logistic Regression

  • This module introduces logistic regression for binary classification problems, covering univariate and multivariate logistic regression.
  • You will learn odds, log odds, probability interpretation, the sigmoid function and curve, likelihood function, and how to find the best-fit sigmoid curve.
  • Model building is done in Python using both stats models and Scikit-learn, with clear interpretation of coefficients and probabilities, handling multiple predictors, and feature elimination using manual techniques and RFE (Recursive Feature Elimination).


Model Evaluation & Performance Metrics

  • Learn how to evaluate classification models using confusion matrix and accuracy, along with metrics beyond accuracy such as sensitivity (recall), specificity, and precision.
  • Practical implementation of sensitivity and specificity in Python is also covered.
  • Advanced Model Evaluation
  • ROC Curve & AUC, ROC Curve implementation in Python, Finding the Optimal Classification Threshold, Model evaluation metrics comparison.

Foundations of Naive Bayes

  • This section covers the introduction to Naive Bayes, conditional probability and its intuition, Bayes’ Theorem with practical interpretation, and Naive Bayes with one feature.
  • Learners develop comprehension and intuitive understanding of Naive Bayes, including the conditional independence assumption and how Naive Bayes works internally.
  • The module explains types of Naive Bayes models, including Bernoulli Naive Bayes and Multinomial Naive Bayes, along with use cases and differences between Bernoulli and Multinomial models.


Python Labs & Hands-on Practice

  • Hands-on Python labs include education vs cinema text classification, SMS Spam–Ham classifier using Multinomial Naive Bayes, and SMS Spam–Ham classifier using Bernoulli Naive Bayes, ensuring strong practical exposure.


Naive Bayes for Text Classification

  • This section introduces Naive Bayes for text classification, explains the document classifier workflow, and covers text pre-processing steps including tokenization, stopword removal, and text normalization.
  • A worked-out document classifier example is included to build full comprehension of Naive Bayes for text classification.

Core Concepts of SVM

  • Learn Support Vector Machines, a powerful supervised learning algorithm used for classification and regression, especially effective in high-dimensional data.
  • Topics include the introduction to SVM, linear vs non-linear classification, support vectors, margins, and decision boundaries, hard margin vs soft margin, and the kernel trick with linear, polynomial, and RBF (Gaussian) kernels. Real-world SVM applications are also discussed.


SVM for Classification & Regression

  • This section focuses on SVM for binary classification and Support Vector Regression (SVR). Model Building & Evaluation
  • Learners build SVM models using Scikit-learn, perform hyperparameter tuning (C, kernel, gamma), apply feature scaling for SVM, and carry out model evaluation using accuracy, precision, recall, and F1-score, supported by hands-on Python implementation with real datasets.

Enquire Now

Industry Recognized Certification Course

Assessment & Certification Criteria

  • Upon completion of each module, comprehensive revision sessions and assessments will be conducted.
  • Learners must achieve a minimum score of 60% in every classroom assessment.
  • Candidates who meet the eligibility criteria and attain at least 60% will be awarded the 7-Month Industrial Hands-On Project Certification from PDCloudEX.

Program Details

Program Details

  • Training Mode: Online
  • Duration: 7 Months
  • Seats: Limited (First Come, First Served)

Program Fee

  • Course Fee : ₹ 89,000
  • Early Bird offer - ₹ 84,000
2 flexible installments plan available

Why Choose PDCloudEx?

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Frequently Asked Questions

What is Data Science and why is it in demand?
Data Science is the process of analyzing data using statistics, programming, and machine learning to extract meaningful insights. With companies relying heavily on data-driven decisions, skilled data scientists are in high demand across industries.
Data Science is the process of analyzing data using statistics, programming, and machine learning to extract meaningful insights. With companies relying heavily on data-driven decisions, skilled data scientists are in high demand across industries.
Data Science is the process of analyzing data using statistics, programming, and machine learning to extract meaningful insights. With companies relying heavily on data-driven decisions, skilled data scientists are in high demand across industries.
No prior coding experience is required. The course starts from the basics of programming and gradually moves toward advanced machine learning concepts.
Yes, we provide career guidance, resume building support, interview preparation, and placement assistance to help you secure a job in the data science field.

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