Advanced Certification in Data Science with Generative AI in Accounting

|| What will I learn?

Gain strong fundamentals in statistics, probability, and linear algebra required for data science, machine learning, and Generative AI applications.
Learn Python programming for data science, including hands-on experience with NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn for data manipulation, analysis, and modeling.
Perform data cleaning, data preprocessing, data transformation, and feature engineering to prepare raw datasets for machine learning models.
Conduct Exploratory Data Analysis (EDA) using statistical techniques and data visualization to identify patterns, trends, and anomalies in data.
Work with SQL databases for data extraction, joins, aggregations, and analytics, along with Python–SQL integration for real-world data analysis.
Build, evaluate, and optimize machine learning models for classification, regression, clustering, and recommendation systems.

Understand and implement supervised learning and unsupervised learning algorithms, including model validation and performance tuning.
Learn deep learning concepts, including neural networks, convolutional neural networks (CNNs), and natural language processing (NLP) using TensorFlow and Keras.
Apply Generative AI and Large Language Models (LLMs) for prompt engineering, AI-assisted coding, automated data analysis, report generation, and synthetic data creation.
Design interactive dashboards and business intelligence reports using Excel, Tableau, and Power BI for data-driven decision-making.
Understand deployment of machine learning models, real-world data science workflows, and performance monitoring in production environments.
Gain hands-on experience with real-world data science projects, capstone projects, and case studies to build a strong data science and Generative AI portfolio.

|| What will I learn?

Gain strong fundamentals in statistics, probability, and linear algebra required for data science, machine learning, and Generative AI applications.
Learn Python programming for data science, including hands-on experience with NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn for data manipulation, analysis, and modeling.
Perform data cleaning, data preprocessing, data transformation, and feature engineering to prepare raw datasets for machine learning models.
Conduct Exploratory Data Analysis (EDA) using statistical techniques and data visualization to identify patterns, trends, and anomalies in data.
Work with SQL databases for data extraction, joins, aggregations, and analytics, along with Python–SQL integration for real-world data analysis.
Build, evaluate, and optimize machine learning models for classification, regression, clustering, and recommendation systems.
Understand and implement supervised learning and unsupervised learning algorithms, including model validation and performance tuning.
Learn deep learning concepts, including neural networks, convolutional neural networks (CNNs), and natural language processing (NLP) using TensorFlow and Keras.
Apply Generative AI and Large Language Models (LLMs) for prompt engineering, AI-assisted coding, automated data analysis, report generation, and synthetic data creation.
Design interactive dashboards and business intelligence reports using Excel, Tableau, and Power BI for data-driven decision-making.
Understand deployment of machine learning models, real-world data science workflows, and performance monitoring in production environments.
Gain hands-on experience with real-world data science projects, capstone projects, and case studies to build a strong data science and Generative AI portfolio.

|| Requirements

No mandatory prior experience in Data Science, Machine Learning, Artificial Intelligence, or Generative AI.
Basic understanding of programming concepts (Python preferred) is helpful but not required.
Familiarity with basic mathematics, statistics, and logical reasoning is recommended.

Willingness to practice coding, analytics, AI tools, and real-world project work throughout the course.
Strong interest in data analysis, machine learning, deep learning, and Generative AI technologies.

|| Requirements

No mandatory prior experience in Data Science, Machine Learning, Artificial Intelligence, or Generative AI.
Basic understanding of programming concepts (Python preferred) is helpful but not required.
Familiarity with basic mathematics, statistics, and logical reasoning is recommended.
Willingness to practice coding, analytics, AI tools, and real-world project work throughout the course.
Strong interest in data analysis, machine learning, deep learning, and Generative AI technologies.

Python Basics
Introduction to Python
Working with Data Types
Operators and Expressions
Accessing Data: Indexing & Slicing
Functions & Built-In Methods

Conditional Logic & Iterations
Statements, Indentation & Conditionals
Iterations: Loops in Action
Advanced Looping & Control Structures
Comprehensions & Smart Iteration in Python

Object Oriented Programming
Object-Oriented Programming in Python
Error & Exception Management
File Handling In Python

AI-Enhanced Coding Fundamentals
AI-Assisted Coding: Pair Programming with ChatGPT

Computational Foundations
Foundations of Data Structures
Optimized String Manipulation
Introduction to Recursion
Deep Dive into Recursive Thinking

Advanced Analysis using Python
NumPy & Pandas
Core Techniques in Data Wrangling
Advanced Methods for Data Wrangling
Data Cleaning
Handling Diverse Data Formats

Data Visualization & EDA
Data Visualization Using Matplotlib & Seaborn
Best Practices for Effective Data Visuals
Exploratory Data Analysis – Applied Case Study

AI-Enhanced Coding Fundamentals
AI-Powered Data Wrangling with PandasAI

Aggregations & Multi-Table Operations
Introduction to SQL and Query Basics

Data Cleaning & Transformation in SQL
Aggregations and Joins in SQL

Advanced Analytical SQL
Window Functions and CTEs

SQL + Python Integration
Using SQL with Python for Data Analysis

AI-Powered SQL & Optimization
AI-Assisted Querying and Optimization

Excel Essentials for Data Analysis
Excel Essentials for Data Analysis - all imp functions
Practical Dashboarding in Excel

Introduction to the Tableau
Introduction to the Tableau Ecosystem
Interactive Dashboards & Storytelling in Tableau

Introduction to PowerBI
Building Dashboards with Power BI
PowerQwery
KPI-Based Dashboarding – E-commerce Case

AI-Enhanced Analytics
AI-Enhanced Presentations with ChatGPT

Introduction to Statistics
Mathematical Foundations and Data Summarization
Measures of Central Tendency, Dispersion, and Data Visualization

Probability
Fundamentals of Probability and Event
Introduction to Discrete and Continuous Probability Distributions
Joint, Marginal, and Conditional Distributions

Sampling & Statistical Inference
Fundamentals of Sampling & Confidence Interval
Hypothesis Testing and Statistical Significance
Introduction to Central Limit Theorem

Advanced Statistical Techniques
Correlation, Regression, and Predictive Modeling Basics
Case Study

AI-Enhanced Math Fundamentals
Simplifing Math with ChatGPT

Foundations of Machine Learning
Foundations of Machine Learning

Introduction to Supervised Learning
Introduction to Supervised Learning
Supervised Algorithms & Metrics used
Implementing Supervised Algorithms

Introduction to Unsupervised Learning
Introduction to Unsupervised Learning
Unsupervised Algorithms & Metrics used
Implementing Supervised Algorithms

Data Preparation & Preprocessing for ML
Prepration & processing- scaling, encoding, outlier handling, imbalanced datasets
Model Validation & Performance Tuning
Feature Engineering & Interpretability

Ensemble Learning: Bagging & Boosting
Introduction to Bagging & Boosting

Time Series Forecasting
Introduction & Use cases, models - Moving averages, ARIMA

AI-Enhanced ML Fundamentals
AI for AI – AutoML & GenAI-assisted Modeling

Introduction to Deep Learning
Introduction to Neural Networks and Activation Functions
Optimization Algorithms in Deep Learning

Deep Learning Frameworks & Applications
Building Neural Networks with Keras & TensorFlow
Convolutional Neural Networks for Image Analysis
Transfer Learning with Pretrained Models

NLP
Text Preprocessing Techniques
Text Classification with Neural Networks

Foundations of Generative AI
What is Generative AI & How It Works
Understanding LLMs in the Data Context

Prompt Engineering & Applied AI for Data Tasks
Prompt Engineering Essentials
Using GenAI for Data Cleaning & EDA
GenAI for Code, Queries, and Reports
Synthetic Data & Image Generation

Multimodal Generation & Data Automation
Automating Analytics Reports with LLMs

Building and Integrating AI Agents
Building an AI Agent with n8n

Deployment, Ethics & Future of GenAI
Integrating GenAI into Data Science Pipelines
The Future of Generative AI in Data Science

Get in touch

I Accept The Terms & Conditions

I Agree To Get Program-related Info On WhatsApp & SMS

||Skills to Master

Python

NumPy & Pandas

Data Cleaning & Wrangling

Exploratory Data Analysis (EDA)

Data Visualization

SQL & Database Management

SQL–Python Integration

Excel for Data Analysis

Tableau Dashboarding

Power BI Dashboarding

Descriptive Statistics

Probability & Statistical Inference

Hypothesis Testing

Regression Analysis

Linear Algebra

Machine Learning Algorithms

Supervised Learning

Unsupervised Learning

Feature Engineering

Model Evaluation & Validation

Ensemble Learning (Bagging & Boosting)

Time Series Forecasting

Deep Learning Frameworks (TensorFlow & Keras)

Neural Networks

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Natural Language Processing (NLP)

Generative AI

Prompt Engineering

AI-Powered Data Analysis

Synthetic Data Generation

AI Agents & Automation

AutoML

Project-Based Learning

||What Makes BIT Different

Industry-Aligned Curriculum

Courses designed with industry experts to match current market demands

Expert Faculty

Learn from professionals with years of industry and teaching experience

Project-Based Learning

Hands-on projects that simulate real-world challenges and solutions

Vibrant Learning Community

Collaborate with peers in an engaging and supportive environment

Lifetime Learning Access

Continuous access to updated materials even after course completion

Comprehensive Resources

Access to extensive learning materials and career support services

|| Real projects, real skills, real results.

A collection of hands-on projects created by BIT students, demonstrating their technical expertise and creative thinking.

0% Average Salary Hike

0 LPA The Highest Salary

0+ Career Transitions

0+ Hiring Partners

Online Payment Fraud Detection

Data Science

Kunj Patel

View Project

Brain Tumor Classification

Data Science

Vidit Modi

View Project

Air Quality Project

Data Science

Rutvik Panchal

View Project

Abalone Age Prediction

Data Science

Tirth Modi

View Project

Pockemon Machine Learning Project

Data Science

Vidit Modi

View Project

|| Frequently asked question

The Advanced Certification in Data Science with Generative AI is a comprehensive, industry-focused program that teaches students the full spectrum of data science skills, from programming, statistics, and SQL to machine learning, deep learning, and Generative AI applications. The course combines theoretical knowledge with hands-on projects, AI-assisted tools, and real-world case studies, preparing learners to become industry-ready data scientists and AI specialists.

This course is ideal for beginners interested in data science, AI, and machine learning, working professionals looking to upskill, and graduates or analysts seeking hands-on experience with Python, SQL, dashboards, machine learning models, and AI tools. It is designed for anyone who wants a career in data science or AI, regardless of prior programming experience.

No prior programming experience is required. The course begins with fundamental programming concepts and gradually progresses to advanced topics such as machine learning, deep learning, and Generative AI. Basic logical thinking and problem-solving skills are helpful, and students should be ready to practice coding, data analysis, and AI tools.

Students will work with industry-standard tools and software, including Python, Jupyter Notebook, SQL databases, Excel, Tableau, Power BI, TensorFlow, Keras, and AI tools such as ChatGPT and other Generative AI frameworks.

Students will gain proficiency in Python programming, computational thinking, data analysis, and visualization. They will master SQL for database analytics, statistics and probability for predictive modeling, and machine learning and deep learning techniques including supervised and unsupervised models, ensemble methods, time series forecasting, CNNs, and NLP applications. Learners will also gain practical experience with Generative AI for tasks like data cleaning, coding, report generation, AI agent building, and prompt engineering for large language models.

Yes, the course emphasizes hands-on learning through real-world projects, programming assignments, and AI-enhanced exercises. By the end of the course, students will have a strong portfolio of practical applications demonstrating their skills and readiness for industry roles.

Yes, students will receive the Advanced Certification in Data Science with Generative AI upon successful completion. This certification is recognized by industry professionals and validates proficiency in data science, AI, and Generative AI applications.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

This course is unique in offering full-stack data science training combined with Generative AI expertise. It provides end-to-end learning, from programming and analytics to AI-powered solutions, with a focus on hands-on projects, live assignments, and AI-assisted coding. Graduates are prepared for careers as Data Scientists, Data Analysts, Machine Learning Engineers, AI Specialists, and Generative AI Developers.

After completing the course, students continue to receive post-course support, which may include career guidance, portfolio review, project support, access to learning resources, and alumni networking opportunities. BIT aims to support learners even after certification to help them apply their skills in real-world roles and grow in their careers.

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad.

Related courses

Full Stack Data Science Course

Data Structures & Algorithms Course

Data Science using Python Course

Machine Learning Course

Deep Learning Course

Artificial Intelligence Course

Computer Vision Course

Generative AI Course

AgenticAI and GenAI with Cloud

Agentic AI with n8n – Automate Smarter with AI-Powered Workflows

Visit our latest webinars

Mrs. Ujwala Mohane

The Future of Backend Development: Spring Boot Meets Generative AI

Rajesh Rathwa

Build an AI Code Explainer App using Next.js & Tailwind CSS v4

Mr. Jeetu Singh

The Next Era of AWS Security: Where Cyber Threats Face Cloud Protection

Ranvijay Sir

Future Engineers Go 3D — Discover the Power of E3D in Industry Design!

-->

Advanced Certification in Data Science with Generative AI

4.6 (2,000+)

4.8 (400+)

4.7 (3,600+)

4.8 (21,636) reviews

|| What will I learn?

|| What will I learn?

|| Requirements

|| Requirements

Advanced Certification in Data Science with Generative AI content

Foundations of Programming Logic

Python for Computational Thinking

Introduction to SQL

Fundamentals of Analytics & Dashboarding

Practical Statistics

Introduction to Machine Learning

Deep Learning Fundamentals

Generative AI

Get in touch

||Skills to Master

||What Makes BIT Different

Our student projects

|| Real projects, real skills, real results.

Online Payment Fraud Detection

Kunj Patel

Brain Tumor Classification

Vidit Modi

Air Quality Project

Rutvik Panchal

Abalone Age Prediction

Tirth Modi

Pockemon Machine Learning Project

Vidit Modi

|| Frequently asked question

What is the Advanced Certification in Data Science with Generative AI Course?

Who should enroll in this course?

Do I need prior programming or technical experience to join?

What tools and software will I use during the course?

What skills and knowledge will I gain from this course?

Will I get hands-on experience and projects?

Is certification provided upon completion?

What support services are available for enrolled students?

What makes this course different from other data science programs?

What ongoing support or resources are available after completing the course?

What programs does BIT offer?

What sets BIT apart from other institutions?

How do I contact the admissions office for further assistance?

Related courses

Full Stack Data Science Course

Data Structures & Algorithms Course

Data Science using Python Course

Machine Learning Course

Deep Learning Course

Artificial Intelligence Course

Computer Vision Course

Generative AI Course

AgenticAI and GenAI with Cloud

Agentic AI with n8n – Automate Smarter with AI-Powered Workflows

Related courses

Full Stack Data Science Course

Data Structures & Algorithms Course

Data Science using Python Course

Machine Learning Course

Deep Learning Course

Artificial Intelligence Course

Computer Vision Course

Generative AI Course

AgenticAI and GenAI with Cloud

Agentic AI with n8n – Automate Smarter with AI-Powered Workflows

Visit our latest webinars

Mrs. Ujwala Mohane

The Future of Backend Development: Spring Boot Meets Generative AI

Rajesh Rathwa

Build an AI Code Explainer App using Next.js & Tailwind CSS v4

Mr. Jeetu Singh

The Next Era of AWS Security: Where Cyber Threats Face Cloud Protection

Ranvijay Sir

Future Engineers Go 3D — Discover the Power of E3D in Industry Design!

Useful links

Categories