Exploratory Data Analysis using Python

Exploratory Data Analysis using Python, available at $19.99, has an average rating of 3.35, with 33 lectures, based on 10 reviews, and has 1077 subscribers.

You will learn about Hands on skills in Data Insights Visualization Basic Python- Creating Identifiers, Operators, Decision Controls, Loops Collections – List, Set, Dictionaries, Tuples Numerical Python – 1 to Multidimensional array and Operations and more. Pandas DataFrames – Intruduction and Operations and more Visual plots such as line, bar, scatter, histogram etc. Matplotlib for basic visualizations to retrieve meaningful insights Seaborn for basic visualizations to retrieve meaningful insights Case Study to visualize meaningful insights Installing anaconda and Launching jupyter and more ! This course is ideal for individuals who are Anyone who want to start with Data Science in short period of time. or Anyone who want to up skill then into Data Science or Managers who want to up skill them for high paying data jobs or Production Support Engineers who want to up skill them or Beginner python developer curious about data visualization or Data analysts, Business Analysts or Anyone who want to up skill them for high paying data jobs It is particularly useful for Anyone who want to start with Data Science in short period of time. or Anyone who want to up skill then into Data Science or Managers who want to up skill them for high paying data jobs or Production Support Engineers who want to up skill them or Beginner python developer curious about data visualization or Data analysts, Business Analysts or Anyone who want to up skill them for high paying data jobs.

Enroll now: Exploratory Data Analysis using Python

Summary

Title: Exploratory Data Analysis using Python

Price: $19.99

Average Rating: 3.35

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $149.99

Quality Status: approved

Status: Live

What You Will Learn

Hands on skills in Data Insights Visualization
Basic Python- Creating Identifiers, Operators, Decision Controls, Loops
Collections – List, Set, Dictionaries, Tuples
Numerical Python – 1 to Multidimensional array and Operations and more.
Pandas DataFrames – Intruduction and Operations and more
Visual plots such as line, bar, scatter, histogram etc.
Matplotlib for basic visualizations to retrieve meaningful insights
Seaborn for basic visualizations to retrieve meaningful insights
Case Study to visualize meaningful insights
Installing anaconda and Launching jupyter
and more !

Who Should Attend

Anyone who want to start with Data Science in short period of time.
Anyone who want to up skill then into Data Science
Managers who want to up skill them for high paying data jobs
Production Support Engineers who want to up skill them
Beginner python developer curious about data visualization
Data analysts, Business Analysts
Anyone who want to up skill them for high paying data jobs

Target Audiences

Anyone who want to start with Data Science in short period of time.
Anyone who want to up skill then into Data Science
Managers who want to up skill them for high paying data jobs
Production Support Engineers who want to up skill them
Beginner python developer curious about data visualization
Data analysts, Business Analysts
Anyone who want to up skill them for high paying data jobs

Lets learn basics to transform your career.

I promise not to exhaust you with huge number of videos.

Welcome to the most comprehensive Data Analysis and Insights Visualization course! An excellent choice for beginners and professionals looking to expand their knowledge on one of the most popular Python libraries in the world such as collections, numerical pyhton, matplotlib, seaborn and pandas data frames. This course includes case study for drawing meaningful insights out of given business data.

Start Data Science with Numpy, Pandas, Matplotlib & Seaborn courseoffers video tutorials on the most powerful data analysis toolkit available today.

Why learn Data Analysis and Insights Visualization using Python?

If you’ve spent time in a spreadsheet software like Microsoft Excel, Google Sheets or any form of tabular data such as database tables, delimited files or csv files and are eager to take your data analysis skills to the next level using python, this course is for you!

Numerical Python is a powerful library which efficiently performs matrix operations faster and exceed the python capabilities of data processing.

Pandasis a powerhouse tool that allows you to do anything and everything with tabular or columnar data sets — analyzing, organizing, sorting, filtering, aggregating, cleaning, calculating, and more!

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc.

Seaborn is a library for making statistical graphics in Python. It is built on top of matplotlib and closely integrated with pandas data structures.

Whether you’re a new data analyst, planning to transit yourself to data analyst role or have spent years in Excel, Data Analysis and Meaningful insights courseoffers you an incredible introduction to one of the most powerful data tool kits available today!

Course Curriculum

Chapter 1: Course Outline

Lecture 1: Course Outline

Chapter 2: Python Basics

Lecture 1: Anaconda or Python Installation

Lecture 2: Creating variables

Lecture 3: Variable Value Assignment

Lecture 4: Data Types and Memory Address

Lecture 5: Type Conversions

Lecture 6: Indentation, Comments and Doc Strings

Lecture 7: Input Output

Lecture 8: Operators

Lecture 9: Decision Control

Lecture 10: While Loop

Lecture 11: For Loop

Chapter 3: Collections

Lecture 1: Collections

Lecture 2: Collections Exercise

Chapter 4: Numpy Array

Lecture 1: Numpy Array – Topic 1

Lecture 2: Numpy Array – Topic 2

Lecture 3: Numpy Array – Topic 3

Lecture 4: Numpy Array – Topic 4

Lecture 5: Numpy Array – Topic 5

Lecture 6: Numpy Array – Topic 6

Chapter 5: Pandas DataFrame

Lecture 1: Pandas DataFrame – Topic 1

Lecture 2: Pandas DataFrame – Topic 2

Chapter 6: Matplotlib Visualizations

Lecture 1: Matplotlib – Topic 1

Lecture 2: Matplotlib – Topic 2

Chapter 7: Seaborn Visualizations

Lecture 1: Seaborn Library – Topic 1

Lecture 2: Seaborn Library – Topic 2

Lecture 3: Seaborn Library – Topic 3

Lecture 4: Seaborn Library – Topic 4

Lecture 5: Seaborn Library – Topic 5

Chapter 8: Case Study – EDA or Insights Visualization

Lecture 1: Dataset and Goal of Analysis

Lecture 2: Exploratory Data Analysis

Chapter 9: Exercise

Lecture 1: Dataset and Goal of Exploratory Data Analysis

Chapter 10: Why Python?

Lecture 1: Why to choose Python Programming?

Instructors

Pradeep D
Programmer, Data Engineer and Instructor

Rating Distribution

1 stars: 2 votes
2 stars: 2 votes
3 stars: 2 votes
4 stars: 0 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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