Udemy

Python : Pandas & Altair Data Science & Visualization

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  • 1,265 Students
  • Updated 7/2022
4.3
(24 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
14 Hour(s) 34 Minute(s)
Language
English
Taught by
Ashwin Pajankar • 85,000+ Students Worldwide
Rating
4.3
(24 Ratings)

Course Overview

Python : Pandas & Altair Data Science & Visualization

Learn to acquire Data with NumPy and Pandas and visualize it with Matplotlib and Altair

Become a Master in Data Acquisition and Visualization with Python 3 and acquire employers' one of the most requested skills of 21st Century! An expert level Data Science Professional can earn minimum $100000 (that's five zeros after 1) in today's economy.

This is the most comprehensive, yet straight-forward course for the Data Science with Python 3 on Udemy! Whether you have never worked with Data Science before, already know basics of Python, or want to learn the advanced features of Altair with Python 3, this course is for you! In this course we will teach you Data Science with Python 3, Jupyter, NumPy, Pandas, Matplotlib, and Altair.

(Note, we also provide you PDFs and Jupyter Notebooks in case you need them)

With over 105 lectures and more than 14.5 hours of video this comprehensive course leaves no stone unturned in teaching you Data Science with Python 3, Pandas, and Altair!

This course will teach you Data Science in a very practical manner, with every lecture comes a programming video and a corresponding Jupyter notebook that has Python 3 code! Learn in whatever manner is the best for you!

We will start by helping you get Python3, NumPy, matplotlib, Jupyter, Pandas, and Altair installed on your Windows computer and Raspberry Pi.

We cover a wide variety of topics, including:

  • Basics of Scientific Python Ecosystem

  • Basics of Pandas

  • Basics of NumPy and Matplotlib

  • Installation of Python 3 on Windows

  • Setting up Raspberry Pi

  • Tour of Python 3 environment on Raspberry Pi

  • Jupyter installation and basics

  • NumPy Ndarrays

  • Array Creation Routines

  • Basic Visualization with Matplotlib

  • Ndarray Manipulation

  • Random Array Generation

  • Bitwise Operations

  • Statistical Functions

  • Basics of Matplotlib

  • Installation of SciPy and Pandas

  • Linear Algebra with NumPy and SciPy

  • Data Acquisition with Python 3

  • MySQL and Python 3

  • Data Acquisition with Pandas

  • Basics of Altair and Vega Datasets

  • Data Visualization with Altair

You will get lifetime access to over 105 lectures plus corresponding PDFs, Image Datasets, and the Jupyter notebooks for the lectures! 

So what are you waiting for? Learn Data Science with Python 3 in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Course Content

  • 10 section(s)
  • 109 lecture(s)
  • Section 1 Introduction
  • Section 2 Python 3 on Windows
  • Section 3 Python 3 on Raspberry Pi
  • Section 4 Python 3 Basics
  • Section 5 Python Package Index and pip
  • Section 6 Install Matplotlib and NumPy on Raspberry Pi
  • Section 7 Jupyter Notebook
  • Section 8 Getting Started with NumPy
  • Section 9 Creation of Arrays and Matplotlib
  • Section 10 Random Sampling

What You’ll Learn

  • Understand the Scientific Python Ecosystem
  • Understand Data Science, Pandas, and Altair
  • Learn basics of NumPy Fundamentals
  • Learn Advanced Data Visualization
  • Learn Data Acquisition Techniques
  • Linear Algebra and Matrices


Reviews

  • T
    Tegh Bir Singh
    5.0

    Only went through the Altair content. There is nice amount of detail.

  • F
    Fleming Hilskov
    3.5

    Hard to understand his English and he speeks to fast

  • A
    Anonymized User
    3.5

    Gostaria de mais PDF's com os códigos prontos para que eu pudesse compara-lo ao invés de ficar tendo que pausar o vídeo e escrevendo, principalmente quando a aula já começa com um código pré escrito.

  • E
    Edwin van Amersfoort
    3.5

    Relatively slow going; content is fine, but transcripts are not very good.

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