Data Science

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

Choose the training format that best fits your schedule and learning style:

Online Instructor-Led Training – Learn directly from certified experts in live interactive sessions.
Online Self-Paced Learning – Study anytime, anywhere with flexible access to course materials.
Onsite / Classroom Training – Experience hands-on, instructor-led sessions in a collaborative environment.

What’s Included:

Our comprehensive training program is designed to ensure a complete, career-ready learning experience. You’ll get:

Onsite Instructor-Led Sessions
Live Role
Certified Trainers
Post-Training Support
Resource Materials including eBooks, eGuides, and reference documentation.
Real-World Scenarios & Case Studies
Networking Opportunities
Group Projects and Collaborative Activities
Best Practices and Industry Insights
Access to Recorded Sessions (available only for online programs) for flexible review.
Follow-Up Sessions
Tool Demonstrations and Walkthroughs

Empower your career with expert-led, practical, and flexible training designed to help you master real-world skills and stay ahead in the industry.

Course Content

What is Data Science?

Overview of Data Science and Its Role in the Industry
Key Areas of Data Science: Data Exploration, Machine Learning, and Big Data
Data Science Workflow: From Data Collection to Insights

Essential Tools for Data Science

Setting Up Your Data Science Environment

Types of Data and Data Sources

Data Collection Techniques

Exploratory Data Analysis (EDA)

Data Cleaning Fundamentals

Data Transformation Techniques

Data Preprocessing for Machine Learning

Fundamentals of Data Visualization

Advanced Visualization Techniques

Communicating Results

Overview of Machine Learning

Supervised Learning: Regression and Classification

Unsupervised Learning: Clustering and Dimensionality Reduction

Decision Trees and Random Forests

Support Vector Machines (SVM)

K-Nearest Neighbors (KNN)

Model Evaluation and Tuning

Introduction to Deep Learning

Building Neural Networks with Keras

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

Evaluating Deep Learning Models

Introduction to Big Data Concepts

Using Apache Spark for Data Science

Working with NoSQL Databases (MongoDB)

End-to-End Data Science Project

Case Study 1: Data Science in Healthcare

Case Study 2: Data Science in Finance

Case Study 3: Data Science in Marketing

Final Project: Comprehensive Data Science Challenge

Building a Portfolio and Resume for Data Science Careers

Job Search Strategies and Networking

Conclusion and Continuing Education

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

What’s Included:

Course Content

What is Data Science?

Overview of Data Science and Its Role in the Industry

Key Areas of Data Science: Data Exploration, Machine Learning, and Big Data

Data Science Workflow: From Data Collection to Insights

Essential Tools for Data Science

Introduction to Python for Data Science

Overview of Jupyter Notebooks and Anaconda

Working with Libraries: NumPy, Pandas, Matplotlib, Seaborn

Setting Up Your Data Science Environment

Installing Python and Key Packages

Setting Up Jupyter Notebooks for Data Science

Writing Your First Python Script

Types of Data and Data Sources

Structured vs Unstructured Data

Sources of Data: APIs, Web Scraping, Databases

Data Collection Techniques

Web Scraping with BeautifulSoup

Using APIs for Data Retrieval (e.g., Twitter, Google Maps)

Loading and Reading Data from CSV, Excel, and Databases

Exploratory Data Analysis (EDA)

Introduction to EDA and Its Importance

Descriptive Statistics: Mean, Median, Mode, Variance

Identifying Patterns and Anomalies in Data

Data Cleaning Fundamentals

Handling Missing Data: Imputation, Dropping, or Filling

Removing Duplicates and Outliers

Standardizing Data Formats (e.g., Date/Time, Categorical)

Data Transformation Techniques

Normalization and Scaling Data

Encoding Categorical Variables (One-Hot Encoding, Label Encoding)

Data Reshaping: Pivoting, Melting, Merging Data

Data Preprocessing for Machine Learning

Feature Engineering: Creating New Features

Data Pipeline and Feature Selection

Fundamentals of Data Visualization

The Importance of Visualization in Data Science

Basic Visualization Techniques: Bar Charts, Line Graphs, Histograms

Using Seaborn and Matplotlib for Creating Visualizations

Advanced Visualization Techniques

Heatmaps, Pair Plots, and Correlation Matrices

Creating Interactive Visualizations with Plotly

Best Practices for Data Visualization and Storytelling

Communicating Results

Writing Reports and Creating Dashboards

Visualizing Insights for Stakeholders and Decision Makers

Overview of Machine Learning

What is Machine Learning and Its Types? (Supervised, Unsupervised, Reinforcement)

Key Machine Learning Concepts: Training, Testing, Overfitting, and Underfitting

Supervised Learning: Regression and Classification

Linear Regression: Theory, Implementation, and Evaluation

Logistic Regression: Binary Classification

Evaluating Models: Accuracy, Precision, Recall, F1 Score

Unsupervised Learning: Clustering and Dimensionality Reduction

K-Means Clustering and Hierarchical Clustering

Principal Component Analysis (PCA) for Dimensionality Reduction

Decision Trees and Random Forests

Understanding Decision Trees: Building and Evaluating Models

Random Forests: How They Work and Why They Are Powerful

Support Vector Machines (SVM)

Introduction to Support Vector Machines

Hyperparameter Tuning and Model Evaluation

K-Nearest Neighbors (KNN)

Understanding KNN for Classification and Regression

Evaluating KNN Model Performance

Model Evaluation and Tuning

Cross-Validation, Grid Search, and Random Search

Hyperparameter Tuning with Scikit-learn

Introduction to Deep Learning

What is Deep Learning and How It Differs from Machine Learning

Neural Networks: Neurons, Layers, and Activation Functions

Building Neural Networks with Keras

Setting Up Keras for Deep Learning Projects

Training Neural Networks for Image and Text Classification

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

CNNs for Image Recognition