Data Analysis Using NumPy and Pandas | Master Data Manipulation and Analysis

Data Analysis using NumPy and Pandas.

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

Created by : Rated :4.5 9437 views

Last Updated: 2025-01-14 05:01:34 English Course Type: Self Learning

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.

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!
Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!

Basic experience with the Python programming language.

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

Section 1 : Introduction. 1 Lectures 00:01:09
Lecture 1 :
Introduction. Preview
Section 2 : NumPy Introduction. 1 Lectures 00:34:09
Lecture 1 :
NumPy Introduction.
Section 3 : Python Numpy Array. 1 Lectures 00:22:32
Lecture 1 :
Python Numpy Array.
Section 4 : Indexing & Slicing - 1 1 Lectures 00:19:29
Lecture 1 :
Indexing & Slicing - 1
Section 5 : Indexing & Slicing - 2 1 Lectures 00:30:21
Lecture 1 :
Indexing & Slicing - 2
Section 6 : Statistical Functions, Operators & Random Numbers. 1 Lectures 00:20:12
Lecture 1 :
Statistical Functions, Operators & Random Numbers.
Section 7 : Introduction Series & DataFrame. 1 Lectures 00:41:00
Lecture 1 :
Introduction Series & DataFrame.
Section 8 : Date Range & Inspecting Data. 1 Lectures 00:29:36
Lecture 1 :
Date Range & Inspecting Data.
Section 9 : Indexing & Slicing on DataFrame - 1 1 Lectures 00:30:13
Lecture 1 :
Indexing & Slicing on DataFrame - 1
Section 10 : loc & iloc. 1 Lectures 00:31:18
Lecture 1 :
loc & iloc.
Section 11 : Indexing & Slicing on DataFrame - 2 1 Lectures 00:18:24
Lecture 1 :
Indexing & Slicing on DataFrame - 2
Section 12 : Concatenation & Descriptive Statistics. 1 Lectures 00:31:54
Lecture 1 :
Concatenation & Descriptive Statistics.
Section 13 : Merging DataFrames. 1 Lectures 00:29:28
Lecture 1 :
Merging DataFrames.
Section 14 : Working with Text Data. 1 Lectures 00:18:40
Lecture 1 :
Working with Text Data.
Section 15 : Function Application & Loading data in Python. 1 Lectures 00:40:33
Lecture 1 :
Function Application & Loading data in Python.
Section 16 : Loading Data from CSV, Excel & URL. 1 Lectures 00:21:24
Lecture 1 :
Loading Data from CSV, Excel & URL.
Section 17 : Data Visualization using Pandas. 1 Lectures 00:19:33
Lecture 1 :
Data Visualization using Pandas.
Section 18 : What is Data Science. 1 Lectures 00:30:42
Lecture 1 :
What is Data Science.
Section 19 : What is Machine Learning. 1 Lectures 00:25:06
Lecture 1 :
What is Machine Learning.

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Datahill Solutions,

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Having 10+ Years of Experience in Software Industry which includes Development, Support & Training. My Experience Includes Managing, Processing, Predicting and Analyzing of Large volume of Business Data. Expertise in Data Management, BI Technologies & Data Science with Data Analytics, Machine Learning, Deep Learning & Artificial Intelligence using R Programming, Python Programming, WEKA and EXCEL. Having publications and patents in various fields such as machine learning, data security, and data science technologies. I received my Masters of Technology in Computer Science & Engineering from JNTU. Professionally, I am a Data Science management consultant with over 8 years of experience in finance, retail, transport and other industries.