Data Science and Artificial Intelligence

BY
Boston Institute of Analytics

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

Mode

Part time, Online

Duration

4 Months

Fees

₹ 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

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

Module 1 - 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

Module 2 - MS Excel & Google Sheets

Spreadsheet Fundamentals with Excel
  • Spreadsheet Interface and Navigation
  • Formulas and Common Functions
  • Data Cleaning and Formatting
  • Sorting and Filtering Data
  • Lookup Functions and References
Excel for Analysis, Pivots & Dashboards
  • Advanced Formulas and Functions
  • Pivot Tables and Charts
  • Data Analysis Techniques
  • Interactive Dashboard Creation
  • Google Sheets Collaboration

Module 3 - SQL for Data Science

SQL Foundations & Relational Data
  • Database and SQL Basics
  • Tables, Rows and Columns
  • Select and Filtering Queries
  • Sorting and Limiting Results
  • Relational Database Concepts
Joins, Aggregations & Analytical SQL
  • SQL Joins and Relationships
  • Grouping and Aggregate Functions
  • Subqueries and Common Expressions
  • Window Functions for Analysis
  • Business Data Analysis
SQL in the Cloud: Warehouses, BigQuery & Managed Databases
  • Cloud Database Fundamentals
  • Data Warehouse Concepts
  • Google BigQuery Basics
  • Querying Cloud Datasets
  • Managed Database Workflows

Module 4 - Python Programming for Data Science

Programming Thinking & Python Basics
  • Python Syntax and Variables
  • Data Types and Operators
  • Input and Output
  • Programming Logic Fundamentals
  • Writing Basic Python Programs
Control Flow, Functions & Program Structure
  • Conditional Statements and Loops
  • Creating Reusable Functions
  • Function Arguments and Returns
  • Error Handling Basics
  • Structured Program Development
Data Structures, Files & JSON
  • Lists, Tuples and Sets
  • Dictionaries and Operations
  • Reading and Writing Files
  • Working with JSON
  • Data Manipulation Basics
Modular Python, OOP & Version Control with Git
  • Modules and Packages
  • Object Oriented Programming
  • Classes and Objects
  • Git and GitHub Basics
  • Version Control Workflows
NumPy & Numerical Computing
  • NumPy Arrays and Operations
  • Array Indexing and Slicing
  • Mathematical Array Operations
  • Statistical Functions with NumPy
  • Numerical Data Processing
Pandas, EDA, Visualization & Working with APIs
  • Pandas DataFrame Operations
  • Data Cleaning with Pandas
  • Exploratory Data Analysis
  • Data Visualization Fundamentals
  • Working with Web APIs

Module 5 - BI & Dashboarding: Tableau & Power BI

Dashboarding Principles & Tableau
  • Data Visualization Principles
  • Tableau Interface and Workflow
  • Charts and Visualizations
  • Filters and Calculated Fields
  • Interactive Tableau Dashboards
Power BI: From Data Model to Dashboard
  • Power BI Fundamentals
  • Data Transformation with Power Query
  • Data Modeling and Relationships
  • DAX Calculations and Measures
  • Interactive Power BI Dashboards

Module 6 - Statistics & Math for ML

Statistics, Probability & Sampling
  • Descriptive Statistics Fundamentals
  • Probability Concepts and Distributions
  • Sampling Methods and Techniques
  • Measures of Central Tendency
  • Variance and Standard Deviation
Statistical Inference & A/B Testing for Business Decisions
  • Hypothesis Testing Fundamentals
  • Confidence Intervals and Significance
  • A/B Testing Methodology
  • Statistical Decision Making
  • Business Experiment Analysis
Math Foundations for Machine Learning
  • Linear Algebra Fundamentals
  • Vectors and Matrices
  • Calculus for Machine Learning
  • Optimization Concepts
  • Mathematical Foundations for ML

Module 7 - Machine Learning

ML Workflow, Data Preparation & Validation
  • Machine Learning Workflow
  • Data Preprocessing Techniques
  • Feature Engineering Fundamentals
  • Train Validation Test Split
  • Cross Validation Techniques
Regression Models & Evaluation
  • Linear Regression Fundamentals
  • Multiple Regression Models
  • Regression Model Evaluation
  • Regularization Techniques
  • Regression Performance Metrics
Classification: Logistic Regression & Decision Metrics
  • Classification Problem Fundamentals
  • Logistic Regression Models
  • Confusion Matrix Analysis
  • Precision Recall and F1
  • ROC and AUC Metrics
Trees, Ensembles & Model Explainability
  • Decision Tree Algorithms
  • Random Forest Models
  • Boosting and Ensemble Methods
  • Feature Importance Techniques
  • Model Explainability Fundamentals
Time Series Forecasting
  • Time Series Fundamentals
  • Trend and Seasonality
  • Time Series Preprocessing
  • Forecasting Model Techniques
  • Forecast Evaluation Metrics

Module 8 - Deep Learning

Neural Networks & Deep Learning Fundamentals
  • Neural Network Architecture
  • Activation Functions and Layers
  • Forward and Backward Propagation
  • Training Neural Networks
  • Deep Learning Applications
Sequence Models: RNN, LSTM & the Bridge to Attention
  • Sequential Data Fundamentals
  • Recurrent Neural Networks
  • LSTM Network Architecture
  • Sequence Modeling Applications
  • Introduction to Attention

Module 9 - Natural Language Processing (NLP)

Text Processing & Classical NLP
  • Text Cleaning and Preprocessing
  • Tokenization and Normalization
  • Bag of Words
  • TF-IDF Text Representation
  • Classical NLP Techniques
Context, Attention & Transformers
  • Word Embeddings and Context
  • Attention Mechanism Fundamentals
  • Transformer Architecture Basics
  • Contextual Language Representations
  • Modern NLP Applications

Module 10 - Generative AI

Generative AI & Modern LLM Foundations
  • Generative AI Fundamentals
  • Large Language Model Basics
  • Transformer Based Language Models
  • LLM Capabilities and Limitations
  • Generative AI Applications
LLM Customization, Evaluation & Responsible Applications
  • LLM Customization Techniques
  • Fine Tuning Fundamentals
  • LLM Evaluation Methods
  • Responsible AI Principles
  • LLM Application Development

Module 11 - Agentic AI

Agentic AI Fundamentals: Tools, Reasoning & Workflows
  • AI Agent Fundamentals
  • Tool Calling and Integration
  • Agent Reasoning Patterns
  • Multi Step Agent Workflows
  • Building Autonomous Agents
MCP, Reliable Agents & Human-in-the-Loop
  • Model Context Protocol
  • MCP Tools and Servers
  • Reliable Agent Design
  • Human in the Loop
  • Agent Safety and Evaluation

Module 12 - End-to-End Projects

From Problem Statement to Production: Project Approach & Shipping
  • Business Problem Definition
  • Data Collection and Preparation
  • Model Development Workflow
  • Application Deployment Basics
  • Production Project Presentation

Module 13 - 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

Module 14 - 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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