Python 3 Data Science: NumPy, Pandas, and Time Series

Python 3 Data Science: NumPy, Pandas, and Time Series, available at $49.99, has an average rating of 3.8, with 101 lectures, based on 24 reviews, and has 2456 subscribers.

You will learn about Understand the Scientific Python Ecosystem Understand Data Science, Pandas, and Plotly Learn basics of NumPy Fundamentals Learn Advanced Data Visualization Learn Data Acquisition Techniques Linear Algebra and Matrices Time Series with Pandas Time Series with Plotly, Matplotlib, Altair, and Seaborn This course is ideal for individuals who are Data Science Professionals: Data Scientists and Data Engineers or AI and Machine Learning Professionals or Scientists, Mathematicians, Physicists, and Engineers or Python Developers and Programmers or Managers and Business Professionals or Anyone who wants to learn It is particularly useful for Data Science Professionals: Data Scientists and Data Engineers or AI and Machine Learning Professionals or Scientists, Mathematicians, Physicists, and Engineers or Python Developers and Programmers or Managers and Business Professionals or Anyone who wants to learn.

Enroll now: Python 3 Data Science: NumPy, Pandas, and Time Series

Summary

Title: Python 3 Data Science: NumPy, Pandas, and Time Series

Price: $49.99

Average Rating: 3.8

Number of Lectures: 101

Number of Published Lectures: 101

Number of Curriculum Items: 101

Number of Published Curriculum Objects: 101

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the Scientific Python Ecosystem
Understand Data Science, Pandas, and Plotly
Learn basics of NumPy Fundamentals
Learn Advanced Data Visualization
Learn Data Acquisition Techniques
Linear Algebra and Matrices
Time Series with Pandas
Time Series with Plotly, Matplotlib, Altair, and Seaborn

Who Should Attend

Data Science Professionals: Data Scientists and Data Engineers
AI and Machine Learning Professionals
Scientists, Mathematicians, Physicists, and Engineers
Python Developers and Programmers
Managers and Business Professionals
Anyone who wants to learn

Target Audiences

Data Science Professionals: Data Scientists and Data Engineers
AI and Machine Learning Professionals
Scientists, Mathematicians, Physicists, and Engineers
Python Developers and Programmers
Managers and Business Professionals
Anyone who wants to learn

Become a Master in Data Acquisition, Visualization, and Time Series Analysis 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 and Time Series 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 Pandas Time Series with Python 3, this course is for you! In this course we will teach you Data Science and Time Series with Python 3, Jupyter, NumPy, Pandas, Matplotlib, and Plotly .

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

With over 100 lectures and more than 13 hours of video this comprehensive course leaves no stone unturned in teaching you Data Science with Python 3, Pandas, and Time Series Analysis!

This course will teach you Data Science and Time Series 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 Plotly 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
Dataframes and Series in Pandas
Time Series in Pandas
Time Series analysis with Matplotlib, Plotly, Seaborn, and Altair

You will get lifetime access to over 100 lectures plus corresponding PDFs and the Jupyter notebooks for the lectures!

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Objectives, Prerequisites, and Audience

Lecture 2: Course Topics Overview

Lecture 3: Please Leave your feedback

Lecture 4: Scientific Python Ecosystem

Lecture 5: Important URLs

Chapter 2: Python 3 on Windows

Lecture 1: Python 3 on Windows

Lecture 2: Verify Python 3 environment on Windows

Chapter 3: Python 3 on Raspberry Pi

Lecture 1: What is Raspberry Pi?

Lecture 2: Raspberry Pi OS Setup

Lecture 3: Remotely connect to RPi with VNC

Lecture 4: Install IDLE3 on Raspberry Pi Raspbian

Lecture 5: Python 3 on Raspberry Pi

Lecture 6: Additional Software for Remote Connection

Lecture 7: Turn your Raspberry Pi 4 into a portable Tablet with Sunfounder Raspad 3

Chapter 4: Python 3 Basics

Lecture 1: Hello World! on Windows

Lecture 2: Hello World! on Raspberry Pi

Lecture 3: Interpreter vs Script Mode

Lecture 4: IDLE

Lecture 5: Raspberry Pi vs PC

Chapter 5: Python 3 and PyPI

Lecture 1: PyPI and pip

Lecture 2: pip on Windows

Lecture 3: pip3 on Raspberry Pi

Chapter 6: Installing NumPy and Matplotlib

Lecture 1: Install NumPy and Matplotlib on Windows

Lecture 2: Install NumPy and Matplotlib on Raspberry Pi

Chapter 7: Jupyter Notebook

Lecture 1: Jupyter and IPython

Lecture 2: Jupyter Installation on Windows

Lecture 3: Jupyter Installation on Raspberry Pi

Lecture 4: Remote connection with PuTTY

Lecture 5: Connect to a remote Jupyter Notebook

Lecture 6: A brief tour of Jupyter

Lecture 7: Commands used in the section

Chapter 8: Getting Started with NumPy

Lecture 1: Introduction to NumPy

Lecture 2: Ndarrays, Indexing and Slicing

Lecture 3: Ndarray Properties

Lecture 4: NumPy Constants

Lecture 5: NumPy Datatypes

Chapter 9: Array creation routines

Lecture 1: Ones and Zeros

Lecture 2: Matrices

Lecture 3: Introduction to Matplotlib

Lecture 4: Numerical Ranges and Matplotlib

Chapter 10: Random Sampling

Lecture 1: Random Sampling

Chapter 11: Array Manipulation

Lecture 1: Array Manipulation

Chapter 12: Bitwise Operation

Lecture 1: Bitwise Operation

Chapter 13: Statistical Functions

Lecture 1: Statistical Functions

Chapter 14: Plotting in Detail

Lecture 1: Single Line Plots

Lecture 2: Multiline Plots

Lecture 3: Grid Axes and Labels

Lecture 4: Color Line Markers

Chapter 15: Installing SciPy and Pandas

Lecture 1: Introduction to SciPy

Lecture 2: Install SciPy on Windows

Lecture 3: Install SciPy on Raspberry Pi

Lecture 4: Introduction to Pandas

Lecture 5: Install Pandas on Windows

Lecture 6: Install Pandas on Raspberry Pi

Chapter 16: Matrices and Linear Algebra

Lecture 1: Dot Products

Lecture 2: Vector and Dot Products

Lecture 3: Inner Products

Lecture 4: QR Decomposition

Lecture 5: Determinants and Solving Linear Equations

Lecture 6: Linear Algebra with SciPy

Chapter 17: Data Acquisition with Python, NumPy, and Matplotlib

Lecture 1: Plain Text File Handling

Lecture 2: CSV

Lecture 3: Excel File

Lecture 4: NumPy file format

Lecture 5: Read a CSV file with NumPy

Lecture 6: Matplotlib CBook

Chapter 18: Python and MySQL

Lecture 1: MySQL installation on Windows

Lecture 2: Getting Started with MySQL and SQL Workbench

Lecture 3: Install SQL Developer on Windows

Lecture 4: Connect to MySQL with SQL Developer

Lecture 5: Exploring MySQL Workbench

Lecture 6: Pymysql installation on Windows

Lecture 7: Connect to MySQL with Python 3

Lecture 8: DDL

Lecture 9: INSERT

Lecture 10: SELECT

Lecture 11: UPDATE

Lecture 12: DELETE

Lecture 13: DROP

Chapter 19: Dataframes and Series in Pandas

Lecture 1: Series

Lecture 2: Dataframe

Instructors

Ashwin Pajankar • 85,000+ Students Worldwide
Instructor | Programmer | Maker | Author | Youtuber

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 9 votes
4 stars: 10 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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