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What I will learn?

  • NumPy - This library provides the ndarray object for efficient storage and manipulation of dense data arrays in Python.
  • Pandas - This library provides the DataFrame object for efficient storage and manipulation of labeled/columnar data in Python.
  • Matplotlib - This library provides capabilities for a flexible range of data visualizations in Python.
  • Scikit-Learn - This library provides efficient and clean Python implementations of the most important and established machine learning algorithms.

Course Curriculum

Python for Data Science

  • Introduction to Python
  • Variables and Expressions
  • Conditional Statements
  • Loops
  • Lists
  • Strings
  • Tuples
  • Dictionary

Data Analysis for Python – NumPy

Data Analysis for Python – pandas

Data Visualization with Python

Introduction to Machine Learning

Case Studies

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Free
Free access this course
  • Level
    Expert
  • Certificate
    Certificate of completion

A course by Coursemate

Material Includes

  • Hundreds of Illustrations to make you understand each concept.
  • Built-in code editor to write and execute the code that you'll learn in the lessons.
  • Numerous challenge problems to test your knowledge of the learned concepts.

Requirements

  • Familiarity with the Python language, including defining functions, assigning variables, calling methods of objects, controlling the flow of a program, and other basic tasks.
  • Embark Course will be a good start.

Target Audience

  • Anyone who wants to learn to use Python’s data science stack—libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools—to effectively store, manipulate, and gain insight from data.

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