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Data Analysis using NumPy and Pandas

Analyze data quickly and easily with Python's powerful numpy and pandas libraries

Course Description

If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!

Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.

Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!

I call it "Excel on steroids"!

Over the course of more than 19 hours, I'll take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!

Who this course is for:

  • Data analysts and business analysts
  • Excel users looking to learn a more powerful software for data analysis

Goals

  • NumPy Introduction
  • Python Numpy Array
  • Indexing & Slicing
  • Statistical Functions, Operators & Random Numbers
  • Introduction Series & DataFrame
  • Date Range & Inspecting Data
  • Indexing & Slicing on DataFrame
  • Concatination & Descriptive Statistics
  • Merging DataFrames
  • Working with Text Data
  • Function Application

Prerequisites

  • Basic experience with the Python programming language
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Curriculum

  • Introduction
    01:09
    Preview
  • NumPy Introduction
    34:09
    Preview
  • Python Numpy Array
    22:32
    Preview
  • Indexing & Slicing - 1
    19:29
  • Indexing & Slicing - 2
    30:21
  • Statistical Functions, Operators & Random Numbers
    20:12
  • Introduction Series & DataFrame
    41:00
  • Date Range & Inspecting Data
    29:36
  • Indexing & Slicing on DataFrame - 1
    30:13
  • Loc & iloc
    31:18
  • Indexing & Slicing on DataFrame - 2
    18:24
  • Concatenation & Descriptive Statistics
    31:54
  • Merging DataFrames
    29:28
  • Working with Text Data
    18:40
  • Function Application & Loading data in Python
    40:33
  • Loading Data from CSV, Excel & URL
    21:24
  • Data Visualization using Pandas
    19:33
  • What is Data Science
    30:42
  • What is Machine Learning
    25:06
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  • Jitendra Acchelal Yadav
    Jitendra Acchelal Yadav

Data Analysis using NumPy and Pandas
This Course Includes
  • 8 hours
  • 20 Lectures
  • Completion Certificate Sample Certificate
  • Lifetime Access Yes
  • Language English
  • 30-Days Money Back Guarantee

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