Global leader in online professional education
Bennett Franklin presents a future-ready, 9-month online Post Graduate Program in Data Science and Artificial Intelligence, a comprehensive and immersive journey for aspiring data professionals, working individuals, and tech visionaries alike.
Rooted in a strong academic foundation and powered by practical, research-driven methodology, this program is designed to build deep expertise in data science, AI, and their real-world applications across business, industry, and society.
Learn directly from top industry leaders and seasoned academics who bring real-world insights to every class. Engage in hands-on learning through capstone projects and practical labs modeled on real-world data challenges. The curriculum covers essential tools and technologies including Python, SQL, Machine Learning, Deep Learning, NLP, and AI frameworks crafted in collaboration with industry experts.
Our flexible online format is tailored to fit the schedules of working professionals, enabling you to upskill without disrupting your career. Build a portfolio that demonstrates your capabilities, gain in-demand technical skills, and prepare for impactful roles such as Data Scientist, AI Researcher, or AI-powered Entrepreneur.
Whether you’re looking to transition into a data-centric role or elevate your current career with cutting-edge innovation, Bennett Franklin’s PGP in Data Science and AI will empower you to lead in a technology-driven future.
Data Science job demand grew by 650% recently.
61% roles open to 0–5 yrs experience professionals.
2.7M roles expected globally by year 2025.
Data Science is among 2025's top job skills.
See which benefits you can derive from joining this program.
An overview of what you will learn from this program.
| Introduction to Python and its applications in data science and AI Python basics: data types, variables, operators, and control flow |
| Working with data structures: lists, tuples, dictionaries, and sets |
| Functions and modules in Python File handling and data input/output |
| Introduction to object-oriented programming in Python |
| Introduction to relational databases and MYSQL Creating databases,tables and relationships DDL and DML commands in MYSQL |
| SQL queries with: where, having, groupby, limit, order by, operators, wildcards, etc. Managing data with INSERT, UPDATE, DELETE operations |
| Joins: Inner, Left, Right, Full-Outer Join, Cross Join Subqueries and Views |
| Connecting MySQL with Python DDL commands with Python DML commands with Python |
| Creating and Manipulating Multi-Dimesional Arrays |
| NumPy Arithmetic Functions |
| Concatenation and Stacking of Arrays |
| Eyes, Ones, Zeros arrays |
| Random Array Generation |
| Introduction to data analysis and its importance in decision-making |
| Data preprocessing: data cleaning, handling missing values, and data transformation |
| Introduction to Pandas |
| Data Manipulation & Analysis |
| Data visualization using libraries like Matplotlib and Seaborn Line Chart, Bar Chart, Stacke- Bar Chart, Histograms, Scatter Plot, Pie Chart, Box Plot, Pairplot, Heatmaps, Subplots, etc. |
| Exploratory Data Analysis (EDA) techniques |
| Performing EDA on an Industrial Dataset |
| Definition of statistics and its importance in machine learning |
| Understanding the role of statistics in data-driven decision making |
| Overview of statistical concepts commonly used in machine learning |
| Measures of central tendency: mean, median, mode |
| Measures of variability: range, variance, standard deviation |
| Data visualization techniques: histograms, box plots, and scatter plots |
| Probability rules: addition and multiplication rules |
| Applications of probability in machine learning algorithms |
| Normal & Standard Distribution and its properties |
| Binomial distribution and its applications in binary classification |
| Poisson distribution and its use in count data analysis |
| Understanding hypothesis testing and its role in machine learning |
| Null and alternative hypotheses |
| Performing a hypothesis test: z-test and t-test |
| P-values and significance leve |
| Introduction to Machine Learning and its Applications |
| Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning |
| Data preprocessing and feature engineering |
| Linear Regression: Theory and Implementation in Python |
| Model evaluation and metrics for regression |
| Use cases of Unsupervised Learning |
| K-Means Clustering: Theory and Implementation in Python |
| Elbow Curve |
| Silhouette Score |
| Hierarchical Clustering: Theory and Implementation in Python |
| Agglomerative Clustering |
| Divisive Clustering |
| Logistic Regression: Theory and Implementation in Python Model evaluation and metrics for classification |
| NLP with NLTK, chatbot building, ethics in AI |
| K-Nearest Neighbours (kNN) : Theory and Implementation in Python Model Evaluation |
| Naïve Bayes: Theory and Implementation in Python Model Evaluation |
| Decision Trees: Theory & Implementation in Python |
| Parameter Tuning of Decision Trees |
| Cross Validation Technique |
| Ensemble Methods: Random Forests |
| Parameter Tuning of Random Forests |
| Handling imbalanced data in classification |
| Scaling of Data |
| Introduction to Support Vector Machines (SVM) : Theory |
| SVM implementation in Python with scikit-learn |
| Kernel methods and non-linear SVM |
| Understanding Boosting |
| Boosting vs Bagging |
| AdaBoost Technique |
| Understanding Gradient Boosting Concept |
| Implementation of Gradient Boosting |
| Use cases of Unsupervised Learning |
| K-Means Clustering: Theory and Implementation in Python |
| Elbow Curve |
| Silhouette Score |
| Hierarchical Clustering: Theory and Implementation in Python |
| Agglomerative Clustering |
| Divisive Clustering |
| Understanding the curse of Dimensionality |
| PCA: Principal Component Analysis Theory |
| Eigenvalues and Eigen Vectors |
| Implementation of PCA using Python |
| N-Components |
| Variance, Variance Ratio |
| Improving the performance of ML Models |
| Finding the best performing models |
| Randomized Search CV |
| GridSearch CV |
| ML Pipelining |
| What is Deep Learning and how it differs from traditional programming |
| Building Blocks of Neurons, Layers, and Activation Functions |
| Neural networks and their structure |
| Hands-on: Introduction to AI tools and platforms for beginners TensorFLow, Keras, PyTorch, scikit-learn, etc. |
| What is Artificial Intelligence (AI) and its applications |
| Overview of AI history and key milestones |
| Understanding AI subfields: Robotics, Computer Vision, Natural Language Processing (NLP), and more |
| Activation functions |
| Multi-layer Perceptrons and feedforward neural networks |
| Recurrent Neural Networks (RNNs) for Sequence Data |
| Introduction to TensorFLow and Keras in Deep Learning Hands-on: Building a simple neural network for image recognition |
| Understanding NLP and its applications in language understanding Tokenization, stemming, and lemmatization |
| Text representation methods: Bag-of-Words and word embeddings |
| Introduction to NLTK libraries for NLP in Python |
| Hands-on: Building a basic chatbot using NLP techniques |
| Real-world AI applications and success stories |
| Ethical considerations and challenges in AI development |
| Future trends in AI, DL, and NLP |
| Exploring Chat-GPT and Google Bard |
| Introduction to Power BI and its capabilities |
| Power BI Products: Power BI Desktop and Power BI Service |
| Navigating the Power BI interface |
| Connecting to various data sources and importing data |
| Data transformation and modeling in Power Query Editor |
| Cleaning and Shaping Data for Analysis |
| Creating interactive visualizations: charts, graphs, and maps |
| The capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you’ll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers. |
Explore real-world projects across diverse industries.
Predictive analytics for patient care
Market basket and loyalty analysis
Fraud detection and risk scoring
Claims prediction using NLP
Text mining & customer prediction
Credit scoring and risk modeling
Global leader in online professional education
Curriculum built by experienced industry experts.
Mentorship and career services for job-readiness.
Practical, hands-on approach to learning
Career Support Services
Worked at companies like Google, Amazon, etc.
Over 200+ top global hiring partners.
Highest CTC reported $250,000 per year.
Average salary hike of 87% post-course.
How to Enroll Easily
This program blends theory with real-world projects, making learners industry-ready with hands-on experience and expert mentorship.
Yes, you’ll have access to live doubt-clearing sessions, academic support, and dedicated mentor guidance throughout the program.
Absolutely. Data Science is among the top 5 future skills, with high global demand, attractive salaries, and versatile career paths.
The PGP in Data Science and AI is a 9-month online program designed to fit around your professional schedule.
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