Data Science and Artificial Intelligence

Boston Institute of Analytics

Embrace the next frontier in data driven innovation with a dual certification in data science and artificial intelligence.

Part time, Online

4 Months

₹ 85000

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based	Frequency of Classes Weekends

Course overview

This Data Science and Artificial Intelligence course is being offered by the Boston Institute of Analytics (BIA), which is a globally ranked institute known for delivering practical, industry-aligned training.

This Data Science and Artificial Intelligence course is about 4 months. This Data Science and Artificial Intelligence course integrated core data science skills with cutting edge artificial intelligence techniques including generative AI. The participants engage in hands-on capstone projects, live doubt buster sessions, masterclasses by industry experts and immersive case studies.

The highlights

Dual certification
Immersive blended learning experience combining classroom and online formats
Expert-led training by industry practitioners
15+ industry case studies and assignments
Integrated modules combining data science and generative AI
Globally recognized dual certification
Real-world capstone projects
In-person 1:1 career mentorship and interview preparation
Flexible no-cost EMI options for payment

Program offerings

Real-world projects
Expert instructors
Peer & alumni community access
Doubt clearing sessions
Case studies
No cost emi options available
4 months course

Course and certificate fees

Fees information

₹ 85,000

certificate availability

Yes

certificate providing authority

Boston Institute of Analytics

Who it is for

Data scientist Business analyst Software engineer Machine learning engineer Artificial intelligence engineer

The target audience for the Data Science and Artificial Intelligence course includes:

Eligibility criteria

The participants must have completed at least a high school diploma or equivalent (degree not strictly required). Also there is no prior work experience required.

What you will learn

Knowledge of python Machine learning Knowledge of deep learning Knowledge of artificial intelligence Knowledge of data visualization

The participants will build a deep foundation in understanding data pipelines, machine learning algorithms and modern artificial intelligence systems. The participants will develop proficiency in python based data manipulation and visualisation, statistical modeling, supervised and unsupervised machine learning, deep learning architectures (e.g. CNNs, RNNs, Transformers) as well as techniques in natural language processing and model deployment.

The participants will explore generative AI paradigms - understanding transformer-based models, fine-tuning large language models, prompt engineering, and deploying generative systems such as chatbots.

The syllabus

Data Science Course and AI Foundation: Orientation

Welcome and Course Overview

Introduction to the Data Science Course
Importance of Data Science and AI

Key Concepts Overview

Fundamentals of Data Science
Introduction to Artificial Intelligence

Software Installation Guidance

Installing Anaconda
Setting up Jupyter Notebooks
Setting-up Power BI and Tableau Account
Introduction to Excel Environment

Course Expectations and Structure

Overview of the Course Modules
Brief on Assignments and Assessments

Introduction to the Learning Environment

Digital Platforms and Resources
Communication Channels

Mastering MS Excel

Fundamentals of Excel

Overview of Excel Interface
Key Formulas and Functions
Ranges and Tables
Data Cleaning – Text Functions, Dates and Times
Conditional Formatting
Sorting and Filtering

Advance Excel

Pivots
Data Analysis in Excel – Trends and Patterns
Data Visualization in Excel – Charts and Plots
Working With Multiple Worksheets
Linking and Referencing the Data Between Worksheets

Python for Data Science

Fundamentals of Python

Overview of Python
Understanding Statements, Expressions and Indentation
Overview of Identifiers, Keywords and Comments
Variables: Declaration, Assignment and Naming Conventions
Common Data Types: Integers, Floats and Strings
Type Casting and Conversion
Operators in Python
Hands-on Activity

Loops, Functions & Error Handling

Loop Control Statements: Break, Continue and Pass
Defining and Calling Functions
Function Parameters and Return Values
Scope of Variables (Global and Local)
Advanced Functions
Default Values and Variable-Length Arguments
Recursive Functions
Map, Reduce and Filter
Introduction to Exceptions
Try, Except and Finally Blocks
Handling Common Errors
Hands-on Activity

Data Structure: List and Tuple

Basic Operations on Lists
Demonstration of List Manipulation Techniques
Slicing and Indexing in Lists
List Comprehension for Concise and Readable Code
Tuples Creation
Basic Operations on Tuples
Slicing And Indexing in Tuples
Common Operations on Both Lists and Tuples
Hands-on Activity

Data Structure: Dictionary and Sets

Basic Operations on Dictionaries
Manipulating Dictionaries
Dictionary Comprehension for Concise Creation
Creation of Sets
Manipulating Sets
Common Operations on Both Dictionaries and Sets
Hands-on Activity

Introduction to Numpy

Intro To Numpy and Creating Numpy Array
Basic Operations on Arrays
Indexing and Slicing
Reshaping, Stacking and Splitting
Iteration, Filtering and Boolean Indexing
Image Processing Using Numpy and Matplotlib
Hands-on Activity

Introduction to Pandas and Data Visualization

Data Structure in Pandas
Creating Dataframe and Loading Files
Data Exploration (EDA)
Creating and Saving Basic Plots Using Matplotlib
Creating Statistical Plots Using Seaborn
Exploring Relationships in Data: Pair Plot and Heat Map
Hands-on Activity

SQL for Data Science

Introduction to SQL and Querying

SQL and Its Significance
SQL’S Role in Data Retrieval and Manipulation
Select Statement for Data Retrieval
Retrieving Specific Columns and All Columns
Using Distinct to Remove Duplicates
Data Models & ER Diagrams
Relational Vs. Transactional Models
Organizing Data in Tables
Filtering Data with Where Clause
Sorting Data with Order By
Limiting Results with Limit
Using Aliases for Column Names
Hands-on Activity

Advanced SQL Concept and Data Manipulation

Creating and Using Temporary Tables
Adding Comments to SQL Code for Documentation
Introduction to Data Modeling
Designing A Database Schema
Sorting Data with Order By (Advanced)
Advanced Filtering (With In, Or, And, Not)
Performing Mathematical Operations on Data
Introduction to Aggregate Functions (Count, Sum, Avg, Max, Min)
Grouping Data with Group By
Filtering Grouped Data with Having
Understanding Subqueries and Their Types
Performing Join Operations (Inner Join, Left Join, Right Join, Full Outer Join)
Updating and Deleting Data with SQL
Analyzing Data with Statistics
Hands-on Activity

Application of Statistics and Probability

Fundamentals of Statistics and Probability

Define Statistics and Its Importance
Explain The Types of Data: Categorical and Numerical
Inferential and Descriptive Statistics
Measure Of Central Tendency: Mean, Median, Mode
Measure Of Dispersion: Variance and Standard Deviation
Probability Basics, It’s Rules and Notation
Probability Distribution – Discrete and Continuous
Normal Distribution and Properties
Central Limit Theorem and Its Importance
Skewness and T-Distributions

Advance Statistic and Hypothesis Testing

Hypothesis Testing – Null and Alternative
Significance Level (Alpha) and P-Value
One-Sample and Two-Sample T-Test
Visualization Plots for Data Exploration
Interpretation of Visualization
Correlation and Regression
Confidence Interval
Hypothesis Testing With Z-Test
Chi-Square Test for Categorical Data
One-Way and Two-Way Anova

Explore Supervised Machine Learning

Introduction to Machine Learning (ML) and Regression

Intro to ML & Its Role in Data Analysis
Types of Machine Learning – Supervised, Unsupervised and Reinforcement
Data Pre-processing Methods
Feature Scaling
Linear Regression as Regression Technique
Simple Linear Regression
Hands-on Activity

Multiple Linear Regression and Model Evaluation

Model Evaluation Metrics for Regression
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
R-Squared (Coefficient of Determination)
Multiple Linear Regression
California Housing Dataset – Model Evaluation
Hands-on Activity

Logistic Regression and Classification Metrics

Overview of Logistic Regression
Binary Classification Problem and Logit Function and Odds Ratio
Binary & Multi-class LR
Classification Matrix: Accuracy, Precision, Recall and F1-Score
Confusion Matrix Interpretation
ROC Curves & AUC
Hands-on Activity

Decision Trees and Ensemble Methods

Decision Tree and Its Structure
Decision Nodes and Leaf Nodes, Parent/Child Node
Splitting Criteria – Gini Impurity and Entropy
Tree Pruning and Overfitting
Techniques to Prevent Overfitting
Random Forest – Ensemble Learning and Bagging
Gradient Boosting And AdaBoost Ensemble Method
Hands-on Activity

Model Evaluation and Validation Techniques

K-Fold Cross-Validation for Model Evaluation
Hyper-parameter Tuning Using Grid Search
Detailed Coverage of Classification Metrics
Precision, Recall, F1-Score, ROC Curves, AUC
Interpretation and Practical Usage
Hands-on Activity

Explore Unsupervised Machine Learning

Unsupervised Learning

K-Means Clustering and Its Applications
K-Means Algorithm
Choosing the Number of Clusters (K)
Introduction to Hierarchical Clustering
Agglomerative Hierarchical Clustering
Hands-on Activity

Support Vector Machines (SVM) and K-Nearest Neighbors (KNN)

Classification and Regression with SVM
The Concept of Margin and Support Vectors
Kernel Trick for Non-Linear Data
Introduction to KNN
Predictions of KNN Based on Nearest Neighbors
Euclidean Distance, Manhattan Distance and Other Distance Metrics
Choosing the Value of K
Hands-on Activity

Time Series Modeling with ARIMA And SARIMA

Understanding Time Series Data
ARIMA Model and Its Components
Building ARIMA Models
Forecasting with ARIMA
Seasonal ARIMA (SARIMA) Model and Its Components
Building and Forecasting with SARIMA
Model Evaluation and Tuning
Hands-on Activity

Explore Deep Learning

Introduction to Deep Learning

Overview of Artificial Neural Networks (ANNs)
Neural Network Basics
Model Representation in Deep Learning
Deep Learning Applications
Training Deep Learning Models
Building A Simple Artificial Neural Network
Hands-on Activity: ANN
Convolutional Neural Networks (CNNs)
Hands-on Activity: CNN

Deep Learning Architectures and Training

Recurrent Neural Networks (RNNs)
Recurrent Neurons
Vanishing Gradient Problem
LSTM and GRU
Building and Training RNN
Overfitting and Regularization Techniques
Dropout and Normalization
Model Evaluation, Metrics and Hyper-parameter Techniques
Hands-on Activity: RNN, LSTM, GRU

Discover Natural Language Processing (NLP)

Introduction to Natural Language Processing (NLP)

Overview of NLP
Challenges in NLP
Key NLP Tasks
Text Preprocessing in NLP
NLP Libraries and Frameworks
Feature Extraction and Representation
Building A Text Classification Model
Hands-on Activity

Advanced NLP Techniques

Advanced Word Embeddings
GLOVE (Global Vectors for Word Representation)
N-Grams
Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
GRU
Hands-on Activity

Class Project: Application of ML, Deep Learning and NLP

Data Science Project – 1

Introduction – Data Science Workflow
Data Collection
Exploratory Data Analysis (EDA) and Visualization
Data Preprocessing
Machine Learning Model Development
Introduction to Model Deployment
Model Deployment Using Streamlit

Data Science Project – 2

Introduction to Problem Statement
Dataset Overview
NLP Model Development
Deep Learning Model Development
Model Evaluation
Model Deployment Using Streamlit

Mastering Data Visualization

Mastering Power BI

Introduction to Power BI, Key Features, Installation and Setup
Understanding the Power BI Desktop Interface
Exploring the Workspace: Ribbons, Panes and Menus
Data Transformation
Data Modeling: Relationships, Keys and Hierarchies
Data Analysis Expressions (DAX), DAX Functions and Calculations
Advanced DAX Calculations: Time Intelligence, Filters and Measures
Charts and Page Layouts
Creating A Power BI Dashboard
Publishing and Sharing Reports and Dashboards
Hands-on Activity

Mastering Tableau

Overview of Tableau Prep
Data Connections, Cleaning and Transformation
Introduction to Tableau Desktop
Data Source Connection and Navigation
Visual Analytics – Sorting and Filtering Data Interactivity
Working with Calculated Fields
Aggregations and Level of Detail (LOD) Expressions
Creating Charts and Dashboards in Tableau
Hands-on Activity

Mastery in Generative AI (Gen AI)

Introduction to Generative AI, Transformers and LLMs

Overview of Generative AI
Definition and Key Features of Generative Models
Applications of Generative AI Across Various Industries
Ethical Considerations and Potential Biases in Generative AI
Architecture Overview: Transformers and Their Key Components
Pre-Training and Fine-Tuning of LLMs
Comparison of Different LLM Models (GPT-3, T5, Jurassic-1 Jumbo)
Introduction to Hugging Face and Text Generation/Summarization
Setting Up the Environment and Accessing Hugging Face
Exploring Pre-Trained LLM Models and Functionalities
Implementing Text Generation Tasks Using Transformers and LLMs
Experimenting With Text Summarization Techniques with LLMs
Analyzing the Strengths and Limitations of Different Approaches
Hands-on Activity

Training and Fine-tuning LLMs

Fine-Tuning LLMs for Specific Tasks
Dataset Preparation and Pre-Processing Techniques
Fine-Tuning Hyper-parameter Optimization
Evaluating the Performance of Fine-Tuned Models (Bleu and Rouge)
Introduction to Retrieve, Augment and Generate (RAG) for Fine-Tuning
Hands-On: Fine-Tuning LLM with Custom Data
Selection of LLM Models and Dataset
Fine-Tuning with Hugging Face Libraries
Evaluating and Analyzing the Fine-Tuned Model’s Performance
Comparison of Results with The Pre-Trained Model
Hands-on Activity

Advanced Fine-tuning and Model Evaluation

Advanced Fine-Tuning Techniques
Prompt Engineering and Its Impact on Generated Text
Exploring Techniques Like Beam Search and Nucleus Sampling
Conditional Text Generation Based on Specific Contexts
Text-To-Speech and Speech-To-Text Integration with Hugging Face
Model Evaluation Techniques
Going Beyond Bleu and Rouge: Exploring Advanced Metrics for Different Tasks
Qualitative Analysis of Generated Text and Summarization Outputs
Importance of Human Evaluation in Generative Models
Hands-on: Fine-Tuning with Advanced Techniques and Text-To-Speech/Speech-To-Text
Experimenting with Prompt Engineering and Advanced Generation Techniques
Implementing Conditional Text Generation Based on Specific Contexts
Integrating Text-To-Speech and Speech-To-Text Functionalities
Evaluating the Performance of Fine-Tuned Models Using Advanced Metrics

Building a Real-Life Chatbot with Gradio Deployment

Real-World Applications of Generative AI
Case Studies of Successful LLM Applications in Various Industries
Identifying New Opportunities for Generative AI Solutions
Ethical Considerations and Responsible Deployment Practices
Designing and Developing a Chatbot
Defining the Chatbot’s Functionalities and Target Audience
Integrating Fine-Tuned LLM Models for Text Generation, Dialogue, and Text-To-Speech/Speech-To-Text
Building the Chatbot Interface and User Interaction Flow
Implementing and Deploying the Chatbot With Gradio
Testing and Evaluating the Chatbot

Capstone Project

Capstone Project Allocation, Mentorship and Presentation

Project and Dataset Assignment by Capstone Mentor
Orientation Session by Capstone Mentor – Project Expectations
Mentorship Session by Capstone Mentor – Doubt Resolutions
Project Presentation

Career Enhancement

Soft Skills Training

Presentation Skills
Email Etiquettes
LinkedIn Profile Building
Personality Development and Grooming

Interview Preparation

Interview Do’s and Don’ts
Mock Interviews
HR And Technical Interview Prep
One-On-One Feedback

Admission details

Follow the steps below to join the Data Science and Artificial Intelligence course.

Step 1 - Click on the link below: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Step 2 - Click on “Enquire Now”. Submit the form and your contact details and a representative will contact you.

Step 3 - Once the fees are paid, you are enrolled in the course.

How it helps

After earning this Data Science and Artificial Intelligence certification, the participants can validate their expertise in both foundational analytics and advanced AI. The participants can enhance credibility with explorers, strengthen their resume and unlock access to specialised career opportunities. This Data Science and Artificial Intelligence certification will help participants to stand out in data driven job markets, increase their chances of high paying roles and provide industry-vetted skills that align with current demand.

FAQs

What is the duration of this Data Science and Artificial Intelligence course?

The duration of this Data Science and Artificial Intelligence course is 4 months.

Who can enroll in this Data Science and Artificial Intelligence certification course?

Anyone with a basic high school qualification and strong enthusiasm for data science. There is no prior experience or programming background required.

Do I need to know programming or statistics before joining?

No, the Data Science and Artificial Intelligence course includes foundational modules that bring beginners up to speed.

What tools & technologies will I work with?

The participants will work with Python, Pandas, NumPy, TensorFlow/PyTorch, scikit-learn, Power BI, Tableau, SQL, LLM frameworks (like Hugging Face), and more.

Do you provide placement support?

Yes, the Data Science and Artificial Intelligence course includes 360° career support, resume & interview prep, and access to a job portal.

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