FULL STACK DIPLOMA IN DATA SCIENCE WITH AI

Master Data Science, Artificial Intelligence, Machine Learning and Full-Stack Data Technologies

The Full Stack Diploma in Data Science with AI is a comprehensive, career-oriented program designed for students, graduates, working professionals, aspiring data scientists, and technology enthusiasts who want to build strong skills in Data Science, Artificial Intelligence, Machine Learning, Python, Data Analytics, SQL, Data Visualization, Deep Learning, and AI applications.

In today’s technology-driven world, organizations generate enormous amounts of data through websites, applications, transactions, customers, social media, financial systems, and business operations. The ability to collect, process, analyze, and interpret this data has become increasingly important for organizations across industries.

At the same time, Artificial Intelligence is transforming the way businesses operate. AI-powered systems are being used for automation, prediction, recommendation, natural language processing, computer vision, customer support, fraud detection, and many other applications.

The Full Stack Diploma in Data Science with AI is designed to provide learners with an end-to-end understanding of the data science ecosystem. Instead of focusing on only one tool or technology, the program covers the complete journey from data collection and preparation to analysis, machine learning, AI model development, visualization, deployment, and real-world applications.

At SRC Education, the course follows a practical and career-oriented learning approach, helping students develop technical knowledge as well as problem-solving and analytical skills.

What is Full Stack Data Science with AI?

Full Stack Data Science refers to the complete set of skills required to work on a data science project from beginning to end.

A typical data science project can involve:

Data Collection → Data Cleaning → Data Analysis → Data Visualization → Machine Learning → Model Evaluation → AI Applications → Deployment → Monitoring

The Full Stack Diploma in Data Science with AI introduces learners to each of these stages.

Students can learn programming with Python, work with databases using SQL, analyze data using popular libraries, create dashboards and visualizations, build machine learning models, explore deep learning and generative AI, and understand how data science solutions can be deployed.

The objective is to help learners develop a broader understanding of how data science is applied to real-world problems.

Why Choose Full Stack Diploma in Data Science with AI?

Complete Data Science Learning

The program covers multiple areas of data science instead of focusing on a single software tool.

Python-Based Data Science

Python is widely used in data analysis, machine learning, AI, and automation. Students develop Python programming skills for data-related applications.

Artificial Intelligence Integration

Learners explore AI concepts and understand how AI can be applied to modern business and technology problems.

Machine Learning

Students learn how machines can identify patterns in data and make predictions using machine learning algorithms.

Data Analytics

The program covers data cleaning, analysis, visualization, and interpretation.

Database Skills

SQL and database concepts help learners understand how structured data is stored and retrieved.

Practical Projects

Projects allow students to apply technical concepts to realistic datasets and business problems.

Career-Oriented Skills

The program can prepare learners for entry-level opportunities in data analytics, data science, machine learning, AI, and related technology areas.

Who Should Join This Course?

The Full Stack Diploma in Data Science with AI can be suitable for:

Students interested in Data Science
Graduates from technical and non-technical backgrounds
BCA students
B.Sc. students
B.Tech students
MCA students
Working professionals
Software professionals
Data analysts
Business professionals
Mathematics and statistics students
Technology enthusiasts
Aspiring AI professionals
Individuals interested in Machine Learning
Entrepreneurs interested in data-driven business

A basic understanding of computers and mathematics can be helpful, while programming concepts can be learned progressively during the course.

Full Stack Data Science with AI Course Curriculum

Module 1: Introduction to Data Science

Students begin by understanding the complete data science ecosystem.

Topics include:

Introduction to Data Science
What is data?
Types of data
Data Science lifecycle
Data-driven decision-making
Role of a Data Scientist
Data Analyst vs Data Scientist
Machine Learning vs AI
Business applications of Data Science
Data Science project workflow

Students understand how data science is used to solve real-world problems.

Module 2: Python Programming

Python is one of the most widely used programming languages for Data Science and AI.

Students learn:

Python fundamentals
Variables
Data types
Operators
Conditional statements
Loops
Functions
Lists
Tuples
Sets
Dictionaries
Strings
File handling
Exception handling
Modules and packages
Object-oriented programming fundamentals

The objective is to develop a strong programming foundation before moving into advanced data science concepts.

Module 3: Python for Data Science

After learning Python fundamentals, students apply Python to data-related tasks.

Topics include:

NumPy
Arrays
Numerical operations
Pandas
Series
DataFrames
Data filtering
Data sorting
Data grouping
Data transformation
Data merging
Data cleaning

Students learn how Python can be used to work with real datasets.

Module 4: Data Cleaning and Preprocessing

Real-world datasets are rarely perfect.

They may contain:

Missing values
Duplicate records
Incorrect formats
Outliers
Inconsistent information
Unnecessary columns
Data entry errors

Students learn techniques for:

Handling missing values
Removing duplicates
Data transformation
Data normalization
Outlier detection
Data validation
Feature preparation

Data preprocessing is an important stage before building analytical or machine learning models.

Module 5: Exploratory Data Analysis

Exploratory Data Analysis, or EDA, helps data professionals understand datasets before developing models.

Students learn:

Data exploration
Statistical summaries
Distribution analysis
Correlation
Trend analysis
Pattern identification
Outlier analysis
Relationship analysis
Data interpretation

EDA helps professionals identify useful patterns and potential problems within a dataset.

Module 6: Data Visualization

Data visualization helps transform complex information into understandable visual formats.

Students can learn tools and concepts related to:

Matplotlib
Seaborn
Charts
Graphs
Bar charts
Line charts
Scatter plots
Histograms
Heatmaps
Interactive visualization
Dashboard concepts

Students learn how to communicate analytical findings visually.

Module 7: Statistics for Data Science

Statistics plays an important role in data analysis and machine learning.

Topics include:

Mean
Median
Mode
Range
Variance
Standard deviation
Probability
Distribution
Sampling
Correlation
Regression
Hypothesis testing fundamentals
Statistical interpretation

The goal is to help students understand the mathematical concepts behind data analysis.

Module 8: SQL and Database Management

Data scientists and analysts frequently work with databases.

Students learn:

Database fundamentals
Relational databases
Tables
Rows and columns
Primary keys
Foreign keys
SQL fundamentals
SELECT
WHERE
ORDER BY
GROUP BY
Aggregate functions
JOIN operations
Subqueries
Data filtering
Data retrieval

SQL helps learners access and manipulate structured business data.

Module 9: Advanced SQL for Data Analysis

Students move beyond basic queries and explore analytical SQL concepts.

Topics include:

Advanced JOINs
Subqueries
Common Table Expressions
Window functions
Ranking
Aggregation
Data segmentation
Analytical queries
Performance concepts

These skills can be useful when working with larger business datasets.

Module 10: Machine Learning Fundamentals

Machine Learning is a core component of modern Data Science.

Students learn:

Introduction to Machine Learning
Supervised learning
Unsupervised learning
Training data
Testing data
Features
Labels
Model training
Model evaluation
Overfitting
Underfitting
Model selection

Students understand how machine learning models learn patterns from data.

Module 11: Supervised Machine Learning

Supervised learning is used when historical data contains known outcomes.

Students can explore algorithms such as:

Linear Regression
Logistic Regression
Decision Trees
Random Forest
K-Nearest Neighbors
Support Vector Machines
Classification
Regression

Applications may include:

Sales prediction
Customer classification
Risk assessment
Price prediction
Customer churn prediction

Module 12: Unsupervised Machine Learning

Unsupervised learning works with datasets where predefined outcomes may not be available.

Students learn concepts such as:

Clustering
K-Means
Hierarchical clustering
Customer segmentation
Pattern discovery
Dimensionality reduction fundamentals

These techniques can help organizations discover hidden patterns within data.

Module 13: Feature Engineering

Feature engineering involves preparing and creating useful variables for machine learning models.

Students learn:

Feature selection
Feature transformation
Encoding categorical variables
Scaling
Normalization
Feature extraction
Handling missing values
Creating meaningful features

Good features can significantly influence the performance of machine learning models.

Module 14: Model Evaluation

Building a model is only one part of machine learning.

Students learn how to evaluate model performance using concepts such as:

Accuracy
Precision
Recall
F1 Score
Confusion Matrix
Mean Absolute Error
Mean Squared Error
Root Mean Squared Error
Cross-validation
Model comparison

Students learn why selecting the right evaluation metric is important for different problems.

Module 15: Deep Learning Fundamentals

Deep Learning is a specialized area of Artificial Intelligence based on neural networks.

Students are introduced to:

Neural networks
Artificial neurons
Layers
Activation functions
Training concepts
Loss functions
Optimization
Deep learning applications

Students can understand how neural networks are used for complex AI problems.

Module 16: Natural Language Processing

Natural Language Processing allows computers to work with human language.

Students learn fundamentals such as:

Text processing
Tokenization
Stop words
Stemming
Lemmatization
Text classification
Sentiment analysis
Text representation
NLP applications

Applications include chatbots, sentiment analysis, document classification, and text-based AI systems.

Module 17: Computer Vision

Computer Vision focuses on enabling machines to interpret visual information.

Students are introduced to:

Image processing
Image classification
Object detection concepts
Image recognition
Computer vision applications
AI-based image analysis

Computer vision is used in areas such as healthcare, manufacturing, security, retail, and autonomous systems.

Module 18: Generative AI

Generative AI has become an important area of modern Artificial Intelligence.

Students explore:

Generative AI fundamentals
Large Language Models
AI-generated text
Prompt engineering
AI-assisted coding
AI-assisted data analysis
AI content generation
AI productivity
Responsible AI usage

Students learn how generative AI can support modern data science and technology workflows.

Module 19: Prompt Engineering

Effective interaction with AI systems requires clear instructions.

Students learn:

Prompt fundamentals
Prompt structure
Context
Role-based prompting
Instruction design
Structured outputs
Prompt optimization
AI-assisted analysis
AI-assisted programming

The objective is to help learners communicate effectively with AI tools.

Module 20: AI and Data Science Automation

AI can assist with repetitive and time-consuming data-related tasks.

Students explore AI applications in:

Data exploration
Code assistance
Data cleaning
Documentation
Data summaries
Visualization ideas
Report generation
Analytical workflows
Productivity automation

AI-generated results should always be reviewed and validated before being used for important decisions.

Module 21: Power BI and Business Intelligence

Data science professionals often need to communicate findings to business teams.

Students can learn:

Power BI fundamentals
Data import
Data transformation
Data modeling
Relationships
Measures
Visualizations
Filters
Slicers
Dashboards
Reports
Business KPIs

Power BI can help convert analytical information into interactive business dashboards.

Module 22: Data Science Projects

Practical projects form an important part of the program.

Students can work on projects such as:

Sales Prediction

Analyze historical sales data and develop a model to estimate future sales.

Customer Churn Prediction

Analyze customer behavior and identify customers who may be likely to leave a service.

Customer Segmentation

Use clustering techniques to divide customers into meaningful groups.

House Price Prediction

Build a regression model using property-related features.

Sentiment Analysis

Analyze text data and classify customer opinions or reviews.

Recommendation System

Explore how recommendation systems can suggest relevant products or content.

Business Dashboard

Create an interactive dashboard to monitor important business KPIs.

Module 23: Model Deployment Fundamentals

A machine learning model becomes more useful when it can be made available for practical applications.

Students are introduced to:

Model deployment concepts
APIs
Web applications
Model serving
Basic deployment workflows
Application integration
Model monitoring fundamentals

Students understand the basic journey from developing a model to making it accessible through an application.

Module 24: Full Stack Data Science Workflow

The final stage combines the skills learned throughout the program.

Students understand an end-to-end workflow:

Data Collection

↓

Data Storage

↓

Data Cleaning

↓

Exploratory Data Analysis

↓

Data Visualization

↓

Feature Engineering

↓

Machine Learning

↓

AI Model Development

↓

Model Evaluation

↓

Deployment

↓

Monitoring and Improvement

This complete workflow helps learners understand how different Data Science technologies work together.

Skills You Can Develop

After completing the Full Stack Diploma in Data Science with AI, learners can develop skills in:

Python
Data Science
Data Analytics
SQL
Database management
NumPy
Pandas
Data cleaning
Data preprocessing
Exploratory Data Analysis
Data visualization
Statistics
Machine Learning
Deep Learning fundamentals
Natural Language Processing
Computer Vision fundamentals
Generative AI
Prompt Engineering
Power BI
Business Intelligence
Model evaluation
AI-assisted analytics
Model deployment fundamentals
Problem-solving
Analytical thinking

Career Opportunities After the Course

Depending on their qualifications, practical skills, projects, experience, and employer requirements, learners can explore roles such as:

Data Scientist

Data Scientists analyze large datasets, build predictive models, identify patterns, and develop data-driven solutions.

Junior Data Scientist

Entry-level Data Scientists support data preparation, analysis, visualization, and machine learning projects.

Data Analyst

Data Analysts collect, clean, analyze, and visualize data to support business decisions.

Machine Learning Engineer

Machine Learning Engineers work on developing, testing, and deploying machine learning solutions.

AI Engineer

AI Engineers work with Artificial Intelligence technologies to develop intelligent applications and systems.

Business Intelligence Analyst

BI Analysts create dashboards, reports, and analytical solutions to support business decision-making.

Python Developer

Python Developers build software, automation solutions, data applications, and backend systems using Python.

Data Science Associate

Data Science Associates support data preparation, research, analysis, reporting, and model development.

Machine Learning Analyst

These professionals work with machine learning workflows, datasets, model evaluation, and analytical tasks.

Industries Using Data Science and AI

Data Science and AI are being applied across a wide range of industries, including:

Information Technology
Banking and Finance
Healthcare
E-commerce
Retail
Education
Manufacturing
Telecommunications
Logistics
Insurance
Marketing
Real Estate
Automotive
Media
Travel and Hospitality
Cybersecurity
Consulting
Startups

The exact career opportunities vary according to industry, employer requirements, qualifications, and experience.

Practical Learning Approach

Data Science is a highly practical field. Reading concepts alone is not enough to develop strong analytical and technical skills.

At SRC Education, students can gain hands-on exposure through:

Python programming exercises
SQL assignments
Data cleaning activities
Data analysis projects
Visualization exercises
Machine learning projects
AI experiments
Dashboard development
Business case studies
Capstone projects
Model evaluation
AI-assisted workflows

Working with datasets and practical problems helps students understand how theoretical concepts are applied in real-world situations.

Capstone Project

A final capstone project can bring together multiple skills learned during the program.

Students can work through a complete project involving:

Problem identification
Data collection
Data cleaning
Exploratory analysis
Data visualization
Feature engineering
Model development
Model evaluation
AI integration
Dashboard or application development
Project documentation
Presentation

This type of project can help students demonstrate their technical and analytical capabilities.

Why Choose SRC Education?

Full-Stack Curriculum

The program covers programming, analytics, databases, machine learning, AI, visualization, and deployment fundamentals.

Practical Training

Students can work with datasets, coding exercises, projects, and real-world business scenarios.

AI-Focused Learning

Artificial Intelligence and Generative AI concepts are integrated into the learning journey.

Industry-Relevant Technologies

Learners can gain exposure to Python, SQL, Power BI, machine learning libraries, and other data technologies.

Project-Based Learning

Practical projects help students demonstrate what they have learned.

Career-Oriented Approach

The course focuses on building technical, analytical, and professional skills relevant to modern data careers.

Frequently Asked Questions

What is a Full Stack Diploma in Data Science with AI?

It is a comprehensive professional program covering the complete Data Science workflow, including Python, SQL, data analytics, visualization, machine learning, AI, deep learning fundamentals, Generative AI, and deployment concepts.

Is this course suitable for beginners?

Yes. The program can begin with Python and data fundamentals before progressing toward machine learning and Artificial Intelligence.

Do I need programming knowledge?

Previous programming experience can be helpful but is not necessarily required. Python fundamentals can be learned as part of the program.

Is Mathematics required for Data Science?

Basic mathematics and statistics are useful for understanding Data Science. The course can introduce learners to the statistical concepts required for analytics and machine learning.

Which programming language is used?

Python is a major programming language used throughout Data Science and AI training.

Is SQL included?

Yes. SQL and database concepts are important parts of the Data Science workflow and can be included in the program.

Is Machine Learning included?

Yes. The curriculum covers supervised and unsupervised learning, algorithms, feature engineering, and model evaluation.

Is Artificial Intelligence included?

Yes. AI fundamentals, Generative AI, NLP, Computer Vision fundamentals, and AI-assisted workflows are included.

Can I become a Data Scientist after completing the course?

The program can help build foundational and practical skills relevant to Data Science. Becoming job-ready also depends on your projects, technical ability, qualifications, experience, portfolio, and employer requirements.

Can I work as a Data Analyst?

Yes. The program includes data analysis, Excel/Power BI concepts, SQL, Python, visualization, and reporting skills that are relevant to Data Analyst roles.

Are projects included?

Practical projects can include sales prediction, customer segmentation, churn prediction, sentiment analysis, dashboards, and other real-world Data Science applications.

Build Your Future with Data Science and AI

Data Science and Artificial Intelligence are changing the way organizations understand information, solve problems, automate processes, and make decisions.

The Full Stack Diploma in Data Science with AI is designed to give learners an end-to-end understanding of the technologies involved in this rapidly evolving field.

From Python and SQL to Data Analytics, Machine Learning, Deep Learning, Generative AI, Data Visualization, and deployment, students can develop a broad technical foundation and apply their knowledge through practical projects.

Whether your goal is to become a Data Analyst, Data Scientist, Machine Learning Professional, AI Professional, Business Intelligence Analyst, or Python Developer, this program can provide a strong foundation for building your career in data and Artificial Intelligence.

Start Your Data Science Journey

Learn how to work with data, develop intelligent models, understand AI technologies, and solve real-world problems through practical Data Science.

Learn Data. Build AI. Create Intelligent Solutions.

Contact SRC Education to learn more about the Full Stack Diploma in Data Science with AI, eligibility, duration, fees, batch timings, and admission process.