Data & Analytics Course

Data Science with Python
Course

Learn to analyze data, uncover insights, and build data-driven solutions using Python and modern data science techniques.

⏳ Duration: 3 – 5 Months

🖥️ Mode: Online / Offline

📈 Level: Beginner to Intermediate

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Who Can Join?

Students & Freshers
Aspiring Data Scientists
Python Developers
Data & Business Analytics learners
Career switchers interested in data roles

Course Overview

The Data Science with Python course focuses on extracting meaningful insights from data using Python-based tools and techniques.

You will learn how to collect, clean, analyze, visualize, and interpret data to support data-driven decision-making in real-world scenarios.

This course is hands-on and project-driven, designed to build a strong foundation for data science, analytics, and machine learning paths.

What You Will Gain

Strong foundation in data science concepts
Hands-on experience with Python for data analysis
Ability to clean and prepare real-world datasets
Skills in data visualization and exploratory analysis
Understanding of basic machine learning concepts
Practical project experience
Job & interview readiness for data roles

Course Syllabus

Structured learning path covering core data science concepts, analytics techniques, and hands-on projects using Python.

Data Science Fundamentals

What is Data Science, data science lifecycle, types of data, data-driven decision making.

Python for Data Science

Python fundamentals review, NumPy for numerical computing, Pandas for data manipulation.

Data Cleaning & Preprocessing

Handling missing values, data transformation, data normalization.

Exploratory Data Analysis (EDA)

Data analysis techniques, statistical summaries, pattern identification.

Data Visualization

Visualization principles, Matplotlib & Seaborn, creating meaningful charts.

Statistics for Data Science

Descriptive statistics, probability basics, correlation & regression.

Introduction to Machine Learning

What is Machine Learning, supervised vs unsupervised learning, ML use cases.

Basic ML Algorithms

Linear Regression, Logistic Regression, K-Means clustering.

Data Science Projects

Data analysis project, visualization dashboard, end-to-end data science case study.

Career Objective

To build a successful career in data science by analyzing data and generating insights using Python-based tools and techniques.

Career Opportunities

Data Scientist (Junior)
Data Analyst
Business Analyst
Machine Learning Engineer (Foundation Level)
Research Analyst

Data Science with Python Course