Home DATA SCIENCE & APPLIED AI DEVELOPMENT

DATA SCIENCE & APPLIED AI DEVELOPMENT

The Data Science & Applied AI Training Program is a practical, career-focused program designed to equip students with the skills needed to collect, process, analyze, visualize, and interpret data for real-world decision-making. The program begins with Python programming, SQL, and data fundamentals, giving students a strong foundation for working with datasets and databases.

As the training progresses, students learn data cleaning, exploratory data analysis, statistics, data visualization, and dashboard development using industry-relevant tools such as Pandas, NumPy, Matplotlib, Seaborn, SQL, and Power BI. The program then moves into machine learning and applied AI, covering regression, classification, clustering, feature engineering, model evaluation, natural language processing, and the practical use of modern AI tools.

The final stage focuses on real-world projects, machine-learning application development, deployment, GitHub portfolio development, and career preparation. Students complete an end-to-end capstone project that demonstrates their ability to solve practical business problems using data. By the end of the six months, students will have developed a portfolio of practical projects and gained foundational skills relevant to careers in Data Analytics, Data Science, Business Intelligence, Machine Learning, and Applied AI.

Course Objective

The objective of this 6-Month Data Science & Applied AI Training Program is to equip students with practical, industry-relevant skills to collect, clean, analyze, visualize, and interpret data; use Python and SQL for data analysis; apply statistics and machine learning techniques; build predictive models and AI solutions; and communicate data-driven insights effectively. By the end of the program, students will be able to complete end-to-end data science projects using real-world datasets and develop a professional portfolio suitable for entry-level opportunities in Data Analytics, Data Science, Business Intelligence, Machine Learning, and Applied AI.

COURSE SYLLABUS

Beginner to Advance Level

Data Science Course Modules

Module 1: INTRODUCTION TO DATA SCIENCE

1.1 Understanding Data Science

What is Data Science?
Data Science vs Data Analytics
Data Science vs Data Engineering
Data Science vs Artificial Intelligence
Machine Learning vs Deep Learning
Role of a Data Scientist
Data Science lifecycle
Real-world applications of Data Science

1.2 Data Types & Sources

Structured data
Semi-structured data
Unstructured data
Numerical and categorical data
Internal and external data sources
APIs
Databases
CSV and Excel files
Web data

1.3 Data Science Workflow

Problem definition
Data collection
Data preparation
Exploratory Data Analysis
Feature engineering
Model development
Model evaluation
Deployment
Monitoring and improvement
Practical: Analyze and document a real-world dataset.

Module 2: PYTHON PROGRAMMING FOR DATA SCIENCE

2.1 Python Fundamentals

Installing Python
Python environments
Jupyter Notebook
VS Code
Python syntax
Variables
Data types
Operators
Input and output

2.2 Python Control Flow

Conditional statements
if, elif, else
For loops
While loops
Nested loops
List comprehensions

2.3 Python Data Structures

Lists
Tuples
Sets
Dictionaries
Strings
Working with nested data structures

2.4 Functions & Modules

Creating functions
Parameters and arguments
Return values
Lambda functions
Built-in functions
Importing modules
Creating reusable code

2.5 Advanced Python Fundamentals

Exception handling
File handling
Object-oriented programming
Classes and objects
Working with JSON
Regular expressions
Project: Build a Python-based data processing application.

Module 3: SQL & DATABASES FOR DATA SCIENCE

3.1 Database Fundamentals

What is a database?
Relational databases
Tables, rows and columns
Primary and foreign keys
Database relationships
Database normalization

3.2 SQL Fundamentals

SELECT
WHERE
ORDER BY
DISTINCT
LIMIT
INSERT
UPDATE
DELETE

3.3 SQL Aggregation

COUNT
SUM
AVG
MIN
MAX
GROUP BY
HAVING

3.4 SQL Joins

INNER JOIN
LEFT JOIN
RIGHT JOIN
FULL JOIN
Self joins
Multiple-table queries

3.5 Advanced SQL

Subqueries
Common Table Expressions
CASE statements
Window functions
Ranking
Data filtering
Date and time functions
Project: Build a SQL-based business data analysis.

Module 4: NUMPY & PANDAS

4.1 NumPy

NumPy arrays
Array creation
Indexing and slicing
Mathematical operations
Statistical functions
Array manipulation

4.2 Pandas

Series
DataFrames
Importing datasets
Reading CSV files
Reading Excel files
Data selection and filtering
Sorting
Grouping
Aggregation
Merging datasets
Joining datasets

4.3 Data Cleaning

Missing values
Duplicate records
Incorrect data types
Outliers
Inconsistent data
Data transformation
Data normalization
Project: Clean and prepare a messy real-world dataset for analysis.

Module 5: EXPLORATORY DATA ANALYSIS

5.1 Understanding EDA

What is Exploratory Data Analysis?
Importance of EDA
Understanding dataset structure
Identifying patterns
Identifying anomalies

5.2 Descriptive Analysis

Mean
Median
Mode
Range
Variance
Standard deviation
Percentiles
Quartiles

5.3 Relationship Analysis

Correlation
Covariance
Relationships between variables
Categorical analysis
Numerical analysis

5.4 Data Quality

Detecting anomalies
Detecting outliers
Data consistency
Data validation
Project: Complete an exploratory analysis of a business dataset.

Module 6: DATA VISUALIZATION & DATA STORYTELLING

6.1 Matplotlib

Line charts
Bar charts
Histograms
Scatter plots
Pie charts
Customizing charts

6.2 Seaborn

Statistical visualization
Distribution plots
Box plots
Heatmaps
Relationship plots
Categorical visualization

6.3 Business Data Visualization

Choosing the right chart
KPI visualization
Trends
Comparisons
Patterns
Data storytelling

6.4 Power BI

Power BI fundamentals
Importing data
Data transformation
Relationships
Measures
Calculated columns
Interactive dashboards
Filters and slicers
Publishing dashboards
Project: Develop a professional business intelligence dashboard.

Module 7: STATISTICS FOR DATA SCIENCE

7.1 Probability

Basic probability
Events
Conditional probability
Independent events
Bayes' theorem

7.2 Probability Distributions

Normal distribution
Binomial distribution
Poisson distribution
Uniform distribution

7.3 Statistical Concepts

Population vs sample
Sampling techniques
Central Limit Theorem
Confidence intervals
Standard error

7.4 Hypothesis Testing

Null hypothesis
Alternative hypothesis
p-values
Significance levels
Type I and Type II errors
t-tests
Chi-square tests
Project: Perform statistical analysis on a real-world dataset.

Module 8: MACHINE LEARNING FUNDAMENTALS

8.1 Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning

8.2 Machine Learning Workflow

Problem definition
Data preparation
Feature selection
Training data
Testing data
Model training
Model evaluation

8.3 Data Preparation

Feature engineering
Encoding categorical variables
Feature scaling
Train-test split
Handling imbalanced datasets

Module 9: SUPERVISED MACHINE LEARNING

9.1 Regression

Linear Regression
Multiple Linear Regression
Polynomial Regression
Regression evaluation

9.2 Classification

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

9.3 Model Evaluation

Accuracy
Precision
Recall
F1-score
Confusion matrix
ROC-AUC
Cross-validation
Project: Build a predictive machine-learning model.

Module 10: UNSUPERVISED MACHINE LEARNING

10.1 Clustering

Introduction to clustering
K-Means
Hierarchical clustering
DBSCAN concepts

10.2 Dimensionality Reduction

Feature reduction
Principal Component Analysis
Visualization of high-dimensional data

10.3 Customer & Business Segmentation

Customer segmentation
Behavioral analysis
Market segmentation
Project: Develop a customer segmentation solution.

Module 11: ADVANCED MACHINE LEARNING

Ensemble learning

Bagging

Boosting

Random Forest

Gradient Boosting

XGBoost

Feature selection

Hyperparameter tuning

Grid Search

Random Search

Model optimization

Model interpretability

Feature importance

Introduction to SHAP

Project: Optimize an existing machine-learning model and compare multiple algorithms.

Module 12: TIME SERIES & FORECASTING

Introduction to time-series data

Time-series components

Trend

Seasonality

Moving averages

Time-series visualization

Time-series preprocessing

Forecasting concepts

Regression-based forecasting

Introduction to ARIMA

Forecast evaluation

Project: Build a sales or demand forecasting model.

Module 13: NATURAL LANGUAGE PROCESSING

13.1 NLP Fundamentals

What is NLP?
Text data
Text preprocessing
Tokenization
Stop-word removal
Stemming
Lemmatization

13.2 Text Analysis

Word frequency
Text classification
Sentiment analysis
Feature extraction
TF-IDF

13.3 Applied NLP

Spam detection
Sentiment analysis
Document classification
Project: Build a sentiment-analysis or text-classification application.

Module 14: APPLIED AI & GENERATIVE AI

14.1 Introduction to Applied AI

AI concepts
Machine Learning vs AI
Generative AI
Large Language Models
AI applications in business

14.2 Generative AI for Data Science

Using AI assistants for data analysis
AI-assisted Python programming
AI-assisted SQL
Dataset exploration with AI
Generating analytical summaries
Prompt engineering fundamentals

14.3 AI APIs

Introduction to AI APIs
Sending data to AI models
Processing AI responses
Building simple AI-powered applications

14.4 Responsible AI

AI bias
Data privacy
Security
Responsible AI
Hallucinations
Human oversight
Project: Build an AI-powered data analysis application.

Module 15: DATA SCIENCE APPLICATION DEVELOPMENT

15.1 Streamlit

Introduction to Streamlit
Building data applications
Creating interactive interfaces
Charts and dashboards
User inputs
Deploying applications

15.2 APIs

What is an API?
REST API fundamentals
Consuming APIs with Python
JSON data
Integrating external data sources

15.3 Application Deployment

Preparing applications for deployment
Environment management
Requirements files
Basic cloud deployment concepts
Application testing
Project: Deploy an interactive Data Science/AI application.

Module 16: DATA SCIENCE WITH CLOUD & MODERN TOOLS

Introduction to cloud computing

Cloud data concepts

Data storage

Cloud databases

Cloud analytics

Introduction to AWS/Azure data services

Cloud-based machine learning concepts

Data security

Identity and access management

Cost awareness

Module 17: GIT, GITHUB & DATA SCIENCE PORTFOLIO

Git fundamentals

Creating repositories

Commits

Branches

Pull requests

GitHub

README documentation

Managing Data Science projects

Publishing projects

Building a professional GitHub portfolio

Module 18: DATA SCIENCE PROJECT MANAGEMENT

Understanding business requirements

Translating business problems into data problems

Defining project objectives

Selecting datasets

Data quality assessment

Project documentation

Model selection

Measuring business impact

Presenting results

Working with stakeholders

Module 19: FINAL PROJECT

Final project details will be announced by the instructor.

COURSE BRIEF

Course Title:Data Science & Applied AI Development
Duration: 6 months instructor-led Training
Level: Beginner to Advance Level
Format: Online Training with Series of Projects
Regular Assignments & Case Studies