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Data Manipulation in Python: Master Python, Numpy & Pandas

4.7

(25 students)
Last updated:November 5, 2024
Language:English

Data Manipulation in Python: Master Python, Numpy & Pandas

4.7

(25 students)

Rated Excellent on

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Overview

This comprehensive course on Data Manipulation in Python is designed to equip learners with the skills needed to handle, analyze, and transform data using Python and its powerful libraries, Numpy and Pandas. It focuses on building a strong foundation in Python programming while introducing essential data manipulation techniques required in data science. The course covers a wide range of topics, from data cleaning and preparation to performing complex transformations and aggregations. With practical, hands-on exercises, learners gain experience working with real datasets, preparing them to tackle data-driven challenges across various domains. This course is suitable for beginners and those with some Python experience who wish to deepen their understanding of data manipulation, providing skills that can be directly applied in fields like finance, healthcare, e-commerce, and beyond.

Learning Outcomes

By the end of this course, you will be able to:

  • Master Python fundamentals for data manipulation.
  • Learn essential Numpy functions for numerical operations.
  • Perform data cleaning and preparation with Pandas.
  • Conduct data aggregation and analysis efficiently.
  • Handle and transform large datasets effectively.
  • Gain practical experience with real-world datasets.
  • Develop problem-solving skills for data-driven roles.

How will I get my certificate?

You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.

Who is This course for?

This course is ideal for beginners in data science, Python enthusiasts, or professionals in fields like finance, marketing, and business analytics. It is also suited for anyone looking to enhance their data manipulation skills for career growth.

Requirements

Basic knowledge of Python programming is recommended but not mandatory. Learners should have access to a computer with an internet connection, and familiarity with basic math concepts would be beneficial. No prior experience with data manipulation or data science tools is required.

Career Path

  • Data Analyst
  • Data Scientist
  • Business Intelligence Analyst
  • Python Developer
  • Financial Analyst

Course Curriculum

  • 9 sections
  • 105 lectures
  • 3 hours, 52 minutes total length
Expand all sections
  • video Welcome to the course!
    00:01:00
  • video Introduction to Python
    00:01:00
  • video Setting up Python
    00:02:00
  • video What is Jupyter?
    00:01:00
  • video Anaconda Installation: Windows, Mac & Ubuntu
    00:04:00
  • video How to implement Python in Jupyter?
    00:01:00
  • video Managing Directories in Jupyter Notebook
    00:03:00
  • video Input/Output
    00:02:00
  • video Working with different datatypes
    00:01:00
  • video Variables
    00:02:00
  • video Arithmetic Operators
    00:02:00
  • video Comparison Operators
    00:01:00
  • video Logical Operators
    00:03:00
  • video Conditional statements
    00:02:00
  • video Loops
    00:04:00
  • video Sequences: Lists
    00:03:00
  • video Sequences: Dictionaries
    00:03:00
  • video Sequences: Tuples
    00:01:00
  • video Functions: Built-in Functions
    00:01:00
  • video Functions: User-defined Functions
    00:04:00

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This course includes:

  • Duration:
    3 hours, 52 minutes
  • Access:
    1 year access
  • Units:
    105
  • Level:
    Intermediate
  • CPD Points:
    10
  • Certificate:
    Yes