The Data Science MicroDegree: Data Analysis & Visualization

The Data Science MicroDegree: Data Analysis & Visualization, available at $69.99, has an average rating of 4.55, with 70 lectures, 2 quizzes, based on 495 reviews, and has 30242 subscribers.

You will learn about Learn Intermediate Python Programming Skills Using the Jupyter Notebook Environment Using the NumPy Library To Create & Manipulate Arrays Using The Pandas Module To Create & Structure Data Create Data Visualizations Using Matplotlib & Seaborn Modules With Python Learn To Work With Various Data Formats Within Python, Including: JSON,HTML, & MS Excel Worksheets. This course is ideal for individuals who are Students With A Keen Interest In Data Science or Job Seekers Who Want To Leverage Their Data Skills or Python & Data Science Beginners Who Don't Know Where To Start It is particularly useful for Students With A Keen Interest In Data Science or Job Seekers Who Want To Leverage Their Data Skills or Python & Data Science Beginners Who Don't Know Where To Start.

Enroll now: The Data Science MicroDegree: Data Analysis & Visualization

Summary

Title: The Data Science MicroDegree: Data Analysis & Visualization

Price: $69.99

Average Rating: 4.55

Number of Lectures: 70

Number of Quizzes: 2

Number of Published Lectures: 70

Number of Published Quizzes: 2

Number of Curriculum Items: 72

Number of Published Curriculum Objects: 72

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $129.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Intermediate Python Programming Skills
Using the Jupyter Notebook Environment
Using the NumPy Library To Create & Manipulate Arrays
Using The Pandas Module To Create & Structure Data
Create Data Visualizations Using Matplotlib & Seaborn Modules With Python
Learn To Work With Various Data Formats Within Python, Including: JSON,HTML, & MS Excel Worksheets.

Who Should Attend

Students With A Keen Interest In Data Science
Job Seekers Who Want To Leverage Their Data Skills
Python & Data Science Beginners Who Don't Know Where To Start

Target Audiences

Students With A Keen Interest In Data Science
Job Seekers Who Want To Leverage Their Data Skills
Python & Data Science Beginners Who Don't Know Where To Start

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial, we build on what had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This comprehensive course will be your guide to learning how to use the power of Python to analyze data and create beautiful visualizations. This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

“Data Scientist” has been ranked the Number #1 Job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will still be successful in this course! I can’t wait to see you in class.

In This Course You’ll Learn:

Programming with Python
NumPy with Python
Using pandas Data Frames to solve complex tasks
Use pandas to handle Excel Files
Use matplotlib and seaborn for data visualizations

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What Will You Learn?

Chapter 2: Environment Setup

Lecture 1: Setting Up Your PC

Lecture 2: Anaconda Installation

Lecture 3: Launching Jupyter Notebook

Lecture 4: Navigating Jupyter NoteBook

Lecture 5: Markdown Cells

Chapter 3: Basics Of Python (Refresher Course)

Lecture 1: Data Types & Arithmetic Operations

Lecture 2: What Should You Do With The Attached Resource?

Lecture 3: Variables

Lecture 4: Strings & Print Function

Lecture 5: String Splicing

Lecture 6: Lists

Lecture 7: Dictionaries

Lecture 8: Tuples & Sets

Lecture 9: Relational & Logical Operators

Lecture 10: If Else

Lecture 11: For Loops

Lecture 12: While Loops

Lecture 13: In-Built Functions

Lecture 14: Creating A Function

Lecture 15: Feeling Stuck?

Chapter 4: NumPy – Data Analysis

Lecture 1: Introduction To NumPy

Lecture 2: NumPy Arrays

Lecture 3: Generating NumPy Arrays

Lecture 4: NumPy Linspace

Lecture 5: Identity Matrix

Lecture 6: Generating Arrays With Random Values

Lecture 7: Reshape, Min and Max

Lecture 8: Shape and Dtype

Lecture 9: NumPy Indexing

Lecture 10: Index Broadcasting I

Lecture 11: Index Broadcasting II

Lecture 12: 2D Indexing

Lecture 13: Extracting Submatrices

Lecture 14: Conditional Indexing

Lecture 15: NumPy Operations

Lecture 16: Universal Functions

Lecture 17: Reference – Universal Functions

Chapter 5: Pandas – Data Analysis

Lecture 1: Pandas Series I

Lecture 2: Pandas Series II

Lecture 3: Pandas Dataframes

Lecture 4: Dataframes – Adding & Dropping columns

Lecture 5: Loc and iLoc

Lecture 6: Conditional Selection

Lecture 7: Multiple Conditions

Lecture 8: Reset Index & Set Index

Lecture 9: dropna & fillna

Lecture 10: Group By

Lecture 11: Join, Merge & Concatenate

Lecture 12: Pandas Operations

Lecture 13: File Processing

Chapter 6: MatPlotLib – Data Visualization

Lecture 1: Introduction To Matplotlib

Lecture 2: Plotting A Simple Graph

Lecture 3: Multiple Plots Inside Same Canvas

Lecture 4: Object Oriented Plots

Lecture 5: Subplots Using OOP

Lecture 6: Modifying Figure Size & DPI

Lecture 7: Saving The Plot

Lecture 8: Creating A Legend

Lecture 9: Customization

Lecture 10: Plot Range

Chapter 7: Seaborn – Data Visualization

Lecture 1: Introduction To Seaborn

Lecture 2: Distribution Plots – Part 1

Lecture 3: Distribution Plots – Part 2

Lecture 4: Categorical Plots – Part 1

Lecture 5: Categorical Plots – Part 2

Lecture 6: Matrix Plots

Lecture 7: Grids

Lecture 8: Size & Color

Instructors

Abhishek Pughazh
I'm a Python Freelancer, creating cool stuff.

Rating Distribution

1 stars: 1 votes
2 stars: 5 votes
3 stars: 45 votes
4 stars: 178 votes
5 stars: 266 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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