Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
13 Hour(s) 28 Minute(s)
Language
English
Taught by
Ashwin Pajankar • 85,000+ Students Worldwide
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Learn NumPy, SciPy, Matplotlib, Jupyter Notebook, Pandas, and Scikit-image in a single course
Become a Master in Scientific Python and acquire employers' one of the most requested skills of 21st Century! A great Scientific Python programmer earns more than $150000 per year.
This is the most comprehensive, yet straight-forward course for the Scientific Python on Udemy! Whether you have never used SciPy before, already know basics of Python, or want to learn the advanced features of NumPy with Python 3, this course is for you! In this course we will teach you NumPy, SciPy, Matplotlib, Jupyter Notebook, Pandas, and Scikit-image.
With over 100 lectures and more than 13 hours of video this comprehensive course leaves no stone unturned in teaching you Scientific Python!
This course will teach you Scientific Python in a very practical manner, with every lecture comes a full Python 3 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, and SciPy installed on your Windows computer and Raspberry Pi.
We cover a wide variety of topics, including:
Basics of Scientific Python Ecosystem
Basics of SciPy, 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
Ndarrays
Array Creation Routines
Basic Visualization with Matplotlib
Ndarray Manipulation
Installation of SciPy
Constants and Linear Algebra
Integration
FFTs
Signal Processing
Interpolation
Image Processing with NumPy, SciPy, Matplotlib, and Scikit-image
Pandas and Data Science
K-Means clustering with SciPy
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 SciPy in a way that will advance your career and increase your knowledge, all in a fun and practical way!
Course Content
- 32 section(s)
- 104 lecture(s)
- Section 1 Introduction
- Section 2 Python 3 on Windows
- Section 3 Raspberry Pi and Python
- Section 4 Python 3 Basics
- Section 5 Python Package Index and pip
- Section 6 Install NumPy and Matplotlib
- Section 7 Jupyter Notebook
- Section 8 Introduction to NumPy
- Section 9 Create and Visualize Ndarrays
- Section 10 Random Sampling
- Section 11 Ndarray Manipulation
- Section 12 Bitwise Operations
- Section 13 Statistical Functions
- Section 14 Install SciPy
- Section 15 Constants and Linear Algebra
- Section 16 Integration
- Section 17 Signal Processing
- Section 18 K-Means Clustering
- Section 19 FFT and IFFT
- Section 20 Audio Processing
- Section 21 Image Processing
- Section 22 Interpolation
- Section 23 Advanced Matplotlib
- Section 24 Optimization
- Section 25 SymPy
- Section 26 Install Scikit-image
- Section 27 Scikit-image Basics
- Section 28 Transformations, Thresholding, and Histogram Equalization
- Section 29 Getting Stared with Pandas
- Section 30 Series and Dataframes in Pandas
- Section 31 Downloadable Section
- Section 32 BONUS SECTION
What You’ll Learn
- Understand and explain the Scientific Ecosystem
- Work with Ndarrays in NumPy
- Mathematical and Statistical Functions
- Signal and Image Processing with NumPy, SciPy, and Scikit-image
- Basic and Advanced Visualizations using Matplotlib
- Introduction to Data Science with Pandas
- K-Means Clustering
Skills covered in this course
Reviews
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WWoody12453
strong learning opportunity. Good chunk sized info
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NNathaniel Sailor
It was alright to get an idea of the different libraries used in science, engineering and so on. My biggest problem was the authors lack of comments and use of Jupyter's markdown feature to add notes to understand how certain functions worked or what did each type of input do for a particular function. A few homework assignments would of been helpful, but not necessarily needed.
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GGeorge Reitsma
Explanation is poor, especially on subjects like Linear algebra, signal processing, FFT, he runs over the commands without explaining what they really do. I created my own exercises, and read the manual in order to figure it out.
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JJose Fernandez
Very good course I have learned many mathematical tools from this course using numpy, scipy and mayplotlib. Also how to use the Raspberry Pi to get a Jupyter server.
