Complete Data Wrangling & Data Visualisation With Python

Complete Data Wrangling & Data Visualisation With Python, available at $64.99, has an average rating of 4.45, with 55 lectures, based on 956 reviews, and has 15697 subscribers.

You will learn about Install and Get Started With the Python Data Science Environment- Jupyter/iPython Read In Data Into The Jupiter/iPython Environment From Different Sources Carry Out Basic Data Pre-processing & Wrangling In the Jupyter Environment Learn to IDENTIFY Which Visualisations Should be Used in ANY given Situation Go From A Basic Level To Performing Some Of The MOST COMMON Data Preprocessing, Data Wrangling & Data Visualization Tasks In Jupyter How To Use Some Of The MOST IMPORTANT Data Wrangling & Visualisation Packages Such As Matplotlib Build POWERFUL Visualisations and Graphs from REAL DATA Apply Data Visualization Concepts For PRACTICAL Data Analysis & Interpretation Gain PROFICIENCY In Data Preprocessing, Data Wrangling & Data Visualisation In Jupyter By Putting Your Soon-To-Be-Acquired Knowledge Into IMMEDIATE Application This course is ideal for individuals who are Students Interested In Getting Started With Data Science Applications In The Jupyter Environment or Students Interested in Learning About the Common Pre-processing Data Tasks or Students Interested in Gaining Exposure to Common Python Packages Such As pandas or Those Interested in Learning About Different Kinds of Data Visualisations or Those Interested in Learning to Create Publication Quality Visualisations It is particularly useful for Students Interested In Getting Started With Data Science Applications In The Jupyter Environment or Students Interested in Learning About the Common Pre-processing Data Tasks or Students Interested in Gaining Exposure to Common Python Packages Such As pandas or Those Interested in Learning About Different Kinds of Data Visualisations or Those Interested in Learning to Create Publication Quality Visualisations.

Enroll now: Complete Data Wrangling & Data Visualisation With Python

Summary

Title: Complete Data Wrangling & Data Visualisation With Python

Price: $64.99

Average Rating: 4.45

Number of Lectures: 55

Number of Published Lectures: 55

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Install and Get Started With the Python Data Science Environment- Jupyter/iPython
Read In Data Into The Jupiter/iPython Environment From Different Sources
Carry Out Basic Data Pre-processing & Wrangling In the Jupyter Environment
Learn to IDENTIFY Which Visualisations Should be Used in ANY given Situation
Go From A Basic Level To Performing Some Of The MOST COMMON Data Preprocessing, Data Wrangling & Data Visualization Tasks In Jupyter
How To Use Some Of The MOST IMPORTANT Data Wrangling & Visualisation Packages Such As Matplotlib
Build POWERFUL Visualisations and Graphs from REAL DATA
Apply Data Visualization Concepts For PRACTICAL Data Analysis & Interpretation
Gain PROFICIENCY In Data Preprocessing, Data Wrangling & Data Visualisation In Jupyter By Putting Your Soon-To-Be-Acquired Knowledge Into IMMEDIATE Application

Who Should Attend

Students Interested In Getting Started With Data Science Applications In The Jupyter Environment
Students Interested in Learning About the Common Pre-processing Data Tasks
Students Interested in Gaining Exposure to Common Python Packages Such As pandas
Those Interested in Learning About Different Kinds of Data Visualisations
Those Interested in Learning to Create Publication Quality Visualisations

Target Audiences

Students Interested In Getting Started With Data Science Applications In The Jupyter Environment
Students Interested in Learning About the Common Pre-processing Data Tasks
Students Interested in Gaining Exposure to Common Python Packages Such As pandas
Those Interested in Learning About Different Kinds of Data Visualisations
Those Interested in Learning to Create Publication Quality Visualisations

Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!

I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science- data wrangling and visualisation.

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GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION!

This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.

To be more specific, here’s what the course will do for you:

(a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common data wrangling tasks in Python.

(b) It will equip you to use some of the most important Python data wrangling and visualisation packages such as seaborn.

(c) It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.

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(d) You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results.

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The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners

After each video you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.

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TAKE ACTION NOW 🙂You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1: Welcome to the Course

Lecture 2: Data & Script For the Course

Lecture 3: Python Data Science Environment

Lecture 4: For Mac Users

Lecture 5: Introduction to IPython/Jupyter

Lecture 6: ipython in Browser

Chapter 2: Read in Data From Different Sources With Pandas

Lecture 1: What are Pandas?

Lecture 2: Read CSV Data

Lecture 3: Read Excel Data

Lecture 4: Read in HTML Data

Chapter 3: Data Cleaning

Lecture 1: Remove NA Values

Lecture 2: Missing Values in a Real Dataset

Lecture 3: Data Imputation

Lecture 4: Imputing Qualitative Values

Lecture 5: Use k-NN for Data Imputation

Chapter 4: Basic Data Wrangling

Lecture 1: Basic Principles

Lecture 2: Preliminary Data Explorations

Lecture 3: Basic Data Handling With Conditional Statements

Lecture 4: Drop Column/Row

Lecture 5: Change Column Name

Lecture 6: Change the Column Type

Lecture 7: Explore Date Related Data

Lecture 8: Simple Date Related Computations

Chapter 5: More Data Wrangling

Lecture 1: Data Grouping

Lecture 2: Data Subsetting and Indexing

Lecture 3: More Data Subsetting

Lecture 4: Extract Information From Strings

Lecture 5: (Fuzzy) String Matching

Lecture 6: Ranking & Sorting

Lecture 7: Concatenate

Lecture 8: Merging and Joining

Chapter 6: Feature Selection and Transformation

Lecture 1: Correlation Analysis

Lecture 2: Using Correlation to Decide Which Features to Retain

Lecture 3: Univariate Feature Selection

Lecture 4: Recursive Feature Elimination (RFE)

Lecture 5: Theory Behind PCA

Lecture 6: Implement PCA

Lecture 7: Data Standardisation

Lecture 8: Create a New Feature

Chapter 7: Theory Behind Data Visualisation

Lecture 1: What is Data Visualisation?

Lecture 2: Some Theoretical Principles Behind Data Visualisation

Chapter 8: Most Common Data Visualizations

Lecture 1: Histograms-Visualize the Distribution of Continuous Numerical Variables

Lecture 2: Boxplots-Visualize the Distribution of Continuous Numerical Variables

Lecture 3: Scatter plot-Relationship Between Two Numerical Variables

Lecture 4: Barplot

Lecture 5: Pie Chart

Lecture 6: Line Charts

Lecture 7: More Line Charts

Lecture 8: Some More Plot Types

Lecture 9: And Some More

Chapter 9: Miscallaneous Information

Lecture 1: Using Colabs as an Online Jupyter Notebook

Lecture 2: Github

Lecture 3: What is data science?

Lecture 4: Different Data Types

Lecture 5: Posit On POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 34 votes
2 stars: 61 votes
3 stars: 144 votes
4 stars: 265 votes
5 stars: 452 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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