Statistics in Python: Import an online dataset and inspect
This video is part of a full-length course on Python programming, including 32+ hours of video instruction and 80+ hours of exercises. The course starts from the absolute basics (no prior knowledge of Python is necessary!) and builds up towards intermediate-level competency.
Check it out here: https://www.udemy.com/course/python-scientific-x/?couponCode=202008
Course topics include:
- Data types
- Indexing and slicing
- Functions
- Flow control
- Text and data visualization
Here's a list of all the projects you can complete:
- Project 1: Text search and replace
- Project 2: The Law of Large Numbers
- Project 3: Entropy of written English
- Project 4: State-space trajectories
- Project 5: Statistics
- Project 6: Spectral analysis
- Project 7: The colorful rainbow of noise
- Project 8: Awesome mathy stuff
- Project 9: Denoising noisy signals
- Project 10: Time series filtering
- Project 11: Descriptive stats without numpy
- Project 12: Clustering: PCA, t-SNE, and k-means
- Project 13: Index your Python code
- Project 14: Local minimum via gradient descent
- Project 15: Data curve fitting
- Project 16: Time-frequency analysis of EEG
- Project 17: Interpolation and extrapolation
- Project 18: Simulate a brain circuit
Visit this site for more information: https://www.udemy.com/course/python-scientific-x/?couponCode=202008
All of my courses are listed here: http://sincxpress.com/
Видео Statistics in Python: Import an online dataset and inspect канала Mike X Cohen
Check it out here: https://www.udemy.com/course/python-scientific-x/?couponCode=202008
Course topics include:
- Data types
- Indexing and slicing
- Functions
- Flow control
- Text and data visualization
Here's a list of all the projects you can complete:
- Project 1: Text search and replace
- Project 2: The Law of Large Numbers
- Project 3: Entropy of written English
- Project 4: State-space trajectories
- Project 5: Statistics
- Project 6: Spectral analysis
- Project 7: The colorful rainbow of noise
- Project 8: Awesome mathy stuff
- Project 9: Denoising noisy signals
- Project 10: Time series filtering
- Project 11: Descriptive stats without numpy
- Project 12: Clustering: PCA, t-SNE, and k-means
- Project 13: Index your Python code
- Project 14: Local minimum via gradient descent
- Project 15: Data curve fitting
- Project 16: Time-frequency analysis of EEG
- Project 17: Interpolation and extrapolation
- Project 18: Simulate a brain circuit
Visit this site for more information: https://www.udemy.com/course/python-scientific-x/?couponCode=202008
All of my courses are listed here: http://sincxpress.com/
Видео Statistics in Python: Import an online dataset and inspect канала Mike X Cohen
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Modern statistics: Intuition, Math, Python, R :|: Chapter 12 exercise solutions and discussionsModern statistics: Intuition, Math, Python, R :|: Chapter 07 exercise solutions and discussionsLinear algebra for data science, chapter 10 exercise 3 (determinant via LU diags w/ permutations)Common statistical tests: regressionEssentials of Neuroscience with MATLAB: Module 5-2 (Ca+ imaging)Linear algebra for data science, chapter 2 exercise 1 (vector arithmetic)Linear algebra for data science, chapter 15 exercise 7 (demonstrate eigenvector orthogonality)Data and data visualizations: Types of data: categorical, numerical, etc.Graphing the complex roots of unity in PythonThe stationarity assumption of wavelet convolutionIdentifying empirical frequency boundaries in multichannel dataLinear algebra for data science, chapter 7 exercise 7 (image feature extraction)Essentials of Neuroscience with MATLAB: Module 5-3 (Ca+ imaging)Introduction to statisticsCluster-based multiple comparisons correction"Hubness" from graph theoryIntroduction to the audio version of the Modern Statistics bookModern Statistics by Mike X Cohen, chapter 11Can ChatGPT catch and correct math mistakes?Intro to connectivity, volume conduction, and time- vs. trial-based connectivityCompute Pythagorean triplets in Python via Euclid's method