Python with Data Science (Beginning to Advanced Level)

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

Choose the learning style that best fits your schedule and preferences:

Online Instructor-Led Training – Learn in real time with live expert-led sessions.
Online Self-Paced Learning – Study anytime, anywhere with flexible recorded modules.
Onsite/Classroom Training – Experience in-person, hands-on learning guided by certified trainers.

What’s Included in the Training Program:

Our industry-leading Training Program is designed to provide a complete learning experience through a perfect blend of theory and practical exposure. Here’s what you’ll get:

Live, Instructor-Led Online Training Sessions
Hands-on Lab Exercises
Certified and Experienced Trainers
Post-Training Support & Mentorship
Comprehensive Learning Resources including eBooks, guides, and reference materials.
Real-World Case Studies & Application Scenarios
Networking Opportunities
Group Projects and Collaborative Learning Activities
Best Practices and Industry Insights.
Access to Recorded Sessions (available for online learners, based on client permissions).
Follow-up Review Sessions.
Tool Demonstrations and Guided Walkthroughs

This training helps you gain the knowledge and hands-on experience needed to advance your career in today’s competitive IT landscape.

Course Content

Introduction to Python

Setting Up Python Environment
Python IDEs: Jupyter Notebook, PyCharm, VS Code
Python Syntax and Data Types
Variables, Operators, and Expressions
Basic Input and Output

Control Structures

Functions and Modules

Working with Lists and Tuples

Dictionaries and Set

Introduction to Object-Oriented Programming

Introduction to NumPy

Introduction to Pandas

Data Visualization with Matplotlib and Seaborn

Exploratory Data Analysis (EDA)

Data Preprocessing

Handling Date/Time Data

Scikit-learn for Machine Learning

Clustering and Dimensionality Reduction

Advanced Data Visualization with Plotly

Introduction to Neural Networks

Deep Learning Libraries: TensorFlow and Keras

Convolutional Neural Networks (CNNs)

Text Data Preprocessing

Sentiment Analysis

Text Classification with Machine Learning

Time Series Forecasting

Recommendation Systems

Big Data Tools for Data Science

Project Planning and Requirements Gathering

Building the Data Science Pipeline

Project Deployment and Presentation

Python with Data Science (Beginning to Advanced Level)

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

What’s Included in the Training Program:

Course Content

Introduction to Python

Setting Up Python Environment

Python IDEs: Jupyter Notebook, PyCharm, VS Code

Python Syntax and Data Types

Variables, Operators, and Expressions

Basic Input and Output

Control Structures

Conditional Statements (if, elif, else)

Loops (for, while)

Break, Continue, and Pass Statements

List Comprehension

Functions and Modules

Defining Functions in Python

Arguments and Return Value

Lambda Functions

Working with Modules and Packages

Importing Libraries and Functions

Working with Lists and Tuples

Creating and Manipulating Lists

List Slicing and Indexing

Tuples and Their Uses

Dictionaries and Set

Creating and Using Dictionaries

Set Operations and Methods

Introduction to Object-Oriented Programming

Classes and Objects

Instance Variables and Methods

Inheritance and Polymorphism

Encapsulation and Abstraction

Introduction to NumPy

NumPy Arrays and Operations

Array Indexing and Slicing

Array Manipulation: Reshape, Transpose, etc.

Mathematical Operations with NumPy

Introduction to Pandas

Working with Pandas DataFrames and Series

Data Import and Export (CSV, Excel, etc.

Data Cleaning: Handling Missing Values

Data Transformation and Grouping

Data Visualization with Matplotlib and Seaborn

Introduction to Matplotlib

Plotting Basic Charts: Line, Bar, Scatter

Customizing Plots

Seaborn for Advanced Visualizations

Exploratory Data Analysis (EDA)

Descriptive Statistics and Summary Methods

Distribution Analysis and Histograms

Correlation and Heatmaps

Identifying Outliers

Data Preprocessing

Handling Missing Data

Feature Scaling: Normalization and Standardization

Encoding Categorical Data (One-Hot, Label Encoding)

Data Transformation Techniques (Log Transformation, Box-Cox)

Handling Date/Time Data

Working with DateTime in Pandas

DateTime Functions and Manipulation

Time Series Analysis Introduction

Scikit-learn for Machine Learning

Introduction to Machine Learning Algorithms

Linear Regression and Logistic Regression

Classification Algorithms: k-NN, Decision Trees, etc.

Model Evaluation: Accuracy, Precision, Recall, F1 Score

Clustering and Dimensionality Reduction

k-Means Clustering

Principal Component Analysis (PCA)

t-SNE for Data Visualization

Advanced Data Visualization with Plotly

Interactive Visualizations with Plotly

Plotly Dash for Building Web-Based Visualizations

Introduction to Neural Networks

Understanding Neural Networks Basics

Activation Functions and Loss Functions

Introduction to Backpropagation

Deep Learning Libraries: TensorFlow and Keras