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Data Science 4.9

Data Science with Python

Data Science with Python is a comprehensive training program designed to help learners master the skills required to collect, analyze, visualize, and interpret data using Python. The course covers the complete Data Science lifecycle, including data collection, data cleaning, expl...

  • Instructor-Led Training
  • Hands-On Learning with Python
  • Real-World Data Science Projects
  • Industry-Relevant Case Studies
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Data Science with Python

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₹32,995 ₹65,990
  • Duration Flexible
  • Instructor-Led Training
  • Hands-On Learning with Python
  • Real-World Data Science Projects
  • Industry-Relevant Case Studies
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Overview

Data Science with Python is a comprehensive training program designed to help learners master the skills required to collect, analyze, visualize, and interpret data using Python. The course covers the complete Data Science lifecycle, including data collection, data cleaning, exploratory data analysis (EDA), statistical analysis, data visualization, machine learning fundamentals, and real-world business applications.

Through hands-on projects and practical exercises, learners will gain experience working with industry-standard Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn. By the end of the course, participants will be able to transform raw data into actionable insights, build predictive models, and make data-driven business decisions, preparing them for careers in Data Science, Analytics, and Artificial Intelligence

Prerequisites

Basic Computer Knowledge

Learners should be comfortable using computers, managing files, and working with software applications.

Basic Python Knowledge (Recommended)

A basic understanding of Python programming can be helpful, but many concepts are introduced from the ground up, making the course accessible to beginners.

Basic Mathematics and Statistics

Understanding concepts such as averages, percentages, probability, and basic statistics will help learners better understand data analysis and machine learning concepts.

Analytical Mindset

Curiosity, problem-solving skills, and an interest in working with data are valuable for success in Data Science

Key Features

  • Instructor-Led Training
  • Hands-On Learning with Python
  • Real-World Data Science Projects
  • Industry-Relevant Case Studies
  • Data Analysis and Visualization Techniques
  • Introduction to Machine Learning
  • Capstone Project
  • Practical Assignments and Assessments
  • Interview Preparation and Career Guidance
  • Certificate of Completion

Curriculum

Module 1: Introduction to Data Science

This module introduces the fundamentals of Data Science and explains how organizations use data to make informed business decisions.

Topics Covered:

  • What is Data Science?
  • Data Science Lifecycle
  • Applications of Data Science
  • Roles and Responsibilities of a Data Scientist
  • Industry Use Cases
Module 2: Python Programming Fundamentals

Learn the core Python programming concepts required for Data Science.

Topics Covered:

  • Python Basics
  • Variables and Data Types
  • Operators
  • Conditional Statements
  • Loops
  • Functions
  • Lists, Tuples, Dictionaries, and Sets
Module 3: NumPy for Numerical Computing

Understand how to perform efficient numerical operations using NumPy.

Topics Covered:

  • NumPy Arrays
  • Array Operations
  • Mathematical Functions
  • Indexing and Slicing
  • Statistical Operations
Module 4: Data Analysis with Pandas

Learn how to manipulate and analyze structured datasets using Pandas.

Topics Covered:

  • Series and DataFrames
  • Data Import and Export
  • Data Cleaning
  • Handling Missing Values
  • Data Filtering and Aggregation
  • Data Transformation
Module 5: Data Visualization

Create meaningful visual representations of data to uncover patterns and trends.

Topics Covered:

  • Data Visualization Fundamentals
  • Matplotlib
  • Seaborn
  • Bar Charts
  • Line Charts
  • Histograms
  • Scatter Plots
  • Heatmaps
Module 6: Exploratory Data Analysis (EDA)

Learn techniques to understand datasets and discover valuable insights.

Topics Covered:

  • Descriptive Statistics
  • Correlation Analysis
  • Outlier Detection
  • Trend Identification
  • Data Interpretation
Module 7: Statistics for Data Science

Build a strong statistical foundation required for data-driven decision-making.

Topics Covered:

  • Mean, Median, Mode
  • Standard Deviation
  • Variance
  • Probability
  • Hypothesis Testing
  • Statistical Distributions
Module 8: Data Wrangling and Feature Engineering

Prepare raw data for advanced analytics and machine learning applications.

Topics Covered:

  • Data Cleaning Techniques
  • Feature Selection
  • Feature Engineering
  • Data Encoding
  • Data Scaling
Module 9: Introduction to Machine Learning

Understand how machines learn from data and make predictions.

Topics Covered:

  • Machine Learning Fundamentals
  • Supervised Learning
  • Unsupervised Learning
  • Classification
  • Regression
  • Clustering
Module 10: Machine Learning with Scikit-Learn

Build predictive models using popular Machine Learning libraries.

Topics Covered:

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Means Clustering
  • Model Evaluation
Module 11: Business Analytics and Real-World Applications

Apply Data Science techniques to solve practical business challenges.

Topics Covered:

  • Customer Analytics
  • Sales Forecasting
  • Marketing Analytics
  • Financial Analytics
  • Business Intelligence Concepts
Module 12: Capstone Project

Implement a complete Data Science project from data collection to final insights and presentation.

Project Examples:

  • Customer Churn Prediction
  • Sales Performance Analysis
  • Healthcare Analytics
  • Financial Risk Analysis
  • Retail Demand Forecasting

Who Can Do

Students and Fresh Graduates

Individuals looking to start a career in Data Science, Analytics, Artificial Intelligence, or Machine Learning.

Data Analysts

Professionals who want to enhance their analytical skills and move toward advanced Data Science roles.

Software Developers

Developers interested in working with data-driven applications and AI technologies.

Business Analysts

Professionals who want to use data insights for strategic decision-making and business growth.

Working Professionals

IT professionals, engineers, managers, and consultants looking to transition into Data Science and Analytics careers.

Entrepreneurs and Researchers

Individuals who want to leverage data to solve business challenges, conduct research, and drive innovation.

FAQ

1. What is Data Science?

Data Science is the process of collecting, analyzing, and interpreting data to uncover meaningful insights that support decision-making and business growth.

2. Why should I learn Data Science with Python?

Python is one of the most widely used programming languages in Data Science due to its simplicity, powerful libraries, and strong industry adoption.

3. Do I need programming experience to join this course?

No prior programming experience is mandatory. Basic Python concepts are covered during the course, making it suitable for beginners.

4. What tools and technologies will I learn?

You will learn Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and other essential tools used by Data Scientists worldwide.

5. Will I work on real-world projects?

Yes. The course includes hands-on projects and case studies that simulate real business scenarios and industry challenges.

6. Is Machine Learning included in this course?

Yes. The course introduces Machine Learning concepts and teaches learners how to build basic predictive models using Python.

7. What career opportunities are available after completing this course?

You can pursue roles such as Data Scientist, Data Analyst, Business Analyst, Machine Learning Engineer, AI Associate, and Analytics Consultant.

8. Is Data Science a good career choice?

Yes. Data Science is one of the fastest-growing and highest-paying technology fields, with demand across industries such as healthcare, finance, retail, marketing, and technology.

9. How is Data Science different from Data Analytics?

Data Analytics focuses on understanding past and current data, while Data Science combines analytics, programming, statistics, and machine learning to predict future outcomes and solve complex problems.

10. What will I be able to do after completing this course?

You will be able to collect and clean data, perform analysis, create visualizations, build predictive models, generate insights, and solve real-world business problems using Python and Data Science techniques.

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