Course Overview:

In today’s data-driven world, Python has become the most widely used language for data science. This course is designed to equip you with the practical skills needed to harness the power of Python for data analysis, visualization, and machine learning.

From basic Python syntax to working with large datasets, you will learn to use popular libraries like Pandas, NumPy, and Scikit-learn. With hands-on exercises and real-world projects, this course ensures that you not only understand Python but can apply it to solve complex data challenges efficiently.

Whether you are an aspiring data scientist or a professional looking to upskill, this course offers a comprehensive learning experience to kickstart or enhance your career in data science.

After Completing the course, students will be able to:

  • Understand Python fundamentals, data structures, and control flows.
  • Manipulate and analyze data with Pandas and NumPy.
  • Visualize data using Matplotlib and Seaborn.
  • Implement statistical techniques for hypothesis testing.
  • Build machine learning models using Scikit-learn.
  • Work with large datasets and perform data cleaning and transformation.
  • Apply Python tools in real-world data science projects.

Upcoming Batches

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2025-01-06

2025-01-05

2025-01-13

2025-01-12

2025-01-20

2025-01-19

2025-01-27

2025-01-26

2025-02-03

2025-02-02

Key Features:-

  • Hands-on exercises with real-world datasets and problems.

  • Extensive coverage of Python’s data science libraries.

  • Practical experience in machine learning model building and evaluation.

  • Industry-relevant case studies and projects.

  • Expert instructors with industry experience.

  • Access to post-training support and community.

  • Flexible learning options: instructor-led or self-paced.

  • Certification upon course completion.

Who should attend this Training Course?

  • Aspiring Data Scientists and Data Analysts.
  • Professionals looking to transition into data science roles.
  • Developers interested in expanding their data manipulation skills.
  • Anyone keen on working with large datasets and machine learning.

What are the prerequisites for the training?

    Basic programming knowledge (preferably in Python).
    Familiarity with fundamental mathematics (algebra, probability).
    Understanding of basic statistical concepts.
    Interest in data analytics and problem-solving.

Learning objective of the course:

  • Develop a solid foundation in Python programming with an emphasis on data analysis and manipulation.
  • Learn how to clean, transform, and explore data using Pandas and NumPy libraries.
  • Create informative and aesthetically pleasing visual representations of data to communicate insights effectively.
  • Perform hypothesis testing, statistical analysis, and draw meaningful inferences from your data.
  • Build, train, and validate machine learning models using Scikit-learn, applying them to real-world problems.
  • Discover techniques for managing and analyzing large datasets, optimizing your workflow for scale.

Python for Data Science Course Modules: Download Course Outline

  • Python basics and essential libraries for data science.
  • Data types, functions, and control structures.

  • Loading, cleaning, and transforming data.
  • Working with data frames and arrays.

  • Creating visual representations of data with Matplotlib and Seaborn.
  • Plotting graphs, charts, and statistical distributions.

  • Descriptive statistics and data distributions.
  • Performing hypothesis testing on datasets.

  • Introduction to machine learning algorithms.
  • Building and evaluating models like regression, classification, and clustering.

  • Tools and techniques for handling big data.
  • Best practices for scaling data workflows.

Course Fee

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Course ID: 22730

Course Price at

$1399 + 0% TAX
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Frequently Asked Questions

The average salary of a Python Data Scientist in India is approximately ₹8 to ₹10 lakhs per annum.

Basic programming knowledge is recommended, but we also cover Python fundamentals in the course.

You’ll work with libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, widely used in data science.

After completing the course, you can pursue roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and more.

Absolutely! We focus on hands-on learning with real-world datasets to prepare you for practical applications.

The demand for Data Scientists proficient in Python is skyrocketing, with companies across industries looking for skilled professionals.

Python offers powerful libraries like Scikit-learn that allow you to build machine learning models for regression, classification, and clustering tasks.

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