Master Python Programming By Solving Scientific Projects

Learn practical coding in Python from a warm-blooded scientist. Each video includes hands-on solved practice problems!

What you'll learn

Python

Scientific programming

Data visualization

Time series analysis

Modeling

Regular expressions

Spectral analysis

Filtering

Data clustering

Gradient descent

Text processing

Data projects

Data animation

Requirements

Know how to use a computer!

Motivation to learn Python coding

Description

You're probably thinking "There are hundreds of Python courses on Udemy; why should I enroll in this one??!?"Let me skip all the blah blah blah you often read in these course descriptions, and get straight to what makes this course stand out:Strong focus on solving projects that you will encounter in your academic, work, and hobby projects.I use a problem-solving teaching style focused on getting results. The course is much more than just a list of Python functions.I'm not a member of the Python cult (you know, the people who believe Python is The Greatest Language Ever). So I'm not going to gloss over the weird or annoying parts of Python that many instructors ignore or pretend aren't a problem.The course contains a wide variety of projects, from statistics to data clustering to text processing to time series filtering. You'll also get to learn really cool things like simulating a brain circuit, plotting state-space trajectories, biomedical signal processing, and the math behind gradient descent.Access to the course Q&A, where I and your fellow students can discuss Python coding strategies, data types, best-practice in scientific coding, and so on.I encourage students to contribute their clever project solutions to the Q&A forum, so you can also learn from your colleagues. And, of course, you can post your own clever code solutions to help your fellow students!What should you do now?Check out the preview videos so you can see my teaching style.Check out the reviews of this course.You can also see the reviews of my other courses to learn that I am a dedicated and passionate teacher.

Overview

Section 1: Introductions

Lecture 1 Prerequisites and how to rock this course

Lecture 2 Code a Sierpinski triangle!

Lecture 3 Python via Google colab

Lecture 4 Local Jupyter notebooks via Anaconda

Lecture 5 Using the Q&A forum

Lecture 6 Index of functions in the course

Section 2: –––––– Part 1: The basics ––––––

Lecture 7 Part 1: The basics

Section 3: Data types

Lecture 8 Variables

Lecture 9 Math operators

Lecture 10 Printing and inputting

Lecture 11 Lists

Lecture 12 Tuples

Lecture 13 Booleans

Lecture 14 Dictionaries

Section 4: Indexing and slicing

Lecture 15 Indexing

Lecture 16 Slicing

Section 5: Functions

Lecture 17 Inputs and outputs

Lecture 18 Python libraries (numpy)

Lecture 19 Python libraries (pandas)

Lecture 20 Getting help on functions

Lecture 21 Creating functions

Lecture 22 Global and local variable scopes

Lecture 23 Classes and object-oriented programming

Section 6: Flow control

Lecture 24 If-else statements

Lecture 25 For loops

Lecture 26 Continue

Lecture 27 While loops

Lecture 28 Initializing variables

Lecture 29 Function error checking and handling

Lecture 30 Multiple inputs with zip

Lecture 31 Single-line loops

Lecture 32 Broadcasting in numpy

Section 7: Text and data visualization

Lecture 33 fprintf and f-strings

Lecture 34 Plotting dots and lines

Lecture 35 Subplot geometry

Lecture 36 Making the graphs look nicer

Lecture 37 Adding annotations

Lecture 38 Seaborn

Lecture 39 Images

Lecture 40 Export plots in low and high resolution

Lecture 41 Sierpinski pseudocode, part II

Section 8: A brief aside on sharing code

Lecture 42 Getting code from github/google-drive

Section 9: –––––– Part 2: The projects ––––––

Lecture 43 Part 2: The projects

Section 10: Download all course materials

Lecture 44 IMPORTANT: Download course materials

Lecture 45 Strategies for solving these projects

Section 11: Project 1: Text search and replace

Lecture 46 Project overview and goals

Lecture 47 Import a text file

Lecture 48 Remove formatting text

Lecture 49 Replace 4-letter words and save to disk

Lecture 50 Bonus: Readability of scrambled words

Section 12: Project 2: The Law of Large Numbers

Lecture 51 Project overview and goals

Lecture 52 Generate a population of random numbers

Lecture 53 Monte Carlo sampling

Lecture 54 Cumulative averaging

Lecture 55 Bonus: The Central Limit Theorem

Section 13: Project 3: Entropy of written English

Lecture 56 Project overview and goals

Lecture 57 Import text from the web

Lecture 58 Distribution of word lengths

Lecture 59 Letter frequencies

Lecture 60 Letter entropy

Lecture 61 Conditional (sequence) entropy

Lecture 62 Bonus: Make a word cloud

Section 14: Project 4: State-space trajectories

Lecture 63 Project overview and goals

Lecture 64 Import and clean the data

Lecture 65 Create a channel covariance matrix

Lecture 66 Run PCA and compute components

Lecture 67 State-space trajectories

Lecture 68 Bonus: Draw time using hues

Section 15: Project 5: Statistics

Lecture 69 Project overview and goals

Lecture 70 Import and inspect the data

Lecture 71 T-test for acidity on wine quality

Lecture 72 Multiple regression

Lecture 73 Logistic regression

Lecture 74 Bonus: Transform to Gaussian

Section 16: Project 6: Spectral analysis

Lecture 75 Project overview and goals

Lecture 76 Simulate an AR process

Lecture 77 Code the Fourier transform

Lecture 78 Zero-padding the FFT

Lecture 79 Welch's method

Lecture 80 Bonus: spectrogram

Section 17: Project 7: The colorful rainbow of noise

Lecture 81 Project overview and goals

Lecture 82 White and brown noise

Lecture 83 Pink and blue noise

Lecture 84 The colorful spectral rainbow

Lecture 85 Bonus: How do they sound?

Section 18: Project 8: Awesome mathy stuff

Lecture 86 Project overview and goals

Lecture 87 Pascal's triangle

Lecture 88 Euler's identity

Lecture 89 Parameterized Gaussian

Lecture 90 Time dilation in special relativity

Lecture 91 Eigenvalues on the complex circle

Lecture 92 Bonus: I heart math

Section 19: Project 9: Denoising noisy signals

Lecture 93 Project overview and goals

Lecture 94 Smoothing via running-mean filter

Lecture 95 Smoothing via Gaussian convolution

Lecture 96 Despeckling via median filter

Lecture 97 Denoise these biomedical data!

Lecture 98 Bonus: Highlight plot areas

Section 20: Project 10: Time series filtering

Lecture 99 Project overview and goals

Lecture 100 Generate a signal with random noise

Lecture 101 Notch out line noise

Lecture 102 High-pass FIR filter

Lecture 103 Low-pass IIR (Butterworth) filter

Lecture 104 Bonus: Desert landscape

Section 21: Project 11: Descriptive stats without numpy

Lecture 105 Project overview and goals

Lecture 106 Mean and median

Lecture 107 Frequencies table

Lecture 108 Mode

Lecture 109 Standard deviation

Lecture 110 Bonus: Create a csv report file

Section 22: Project 12: Clustering: PCA, t-SNE, and k-means

Lecture 111 Project overview and goals

Lecture 112 Import and normalize the cloud data

Lecture 113 Compute and inspect covariance matrices

Lecture 114 Determine the number of components using PCA

Lecture 115 Cluster the data using t-SNE and k-means

Lecture 116 Bonus: Make a 2D likelihood density plot

Section 23: Project 13: Index your Python code

Lecture 117 Project overview and goals

Lecture 118 Import multiple ipynb files

Lecture 119 Identify function calls

Lecture 120 Create an alphabetized function index

Lecture 121 Alphabetize the function list

Lecture 122 Create an alphabetized file list

Lecture 123 Bonus: Which file has the most points?

Section 24: Project 14: Local minimum via gradient descent

Lecture 124 Project overview and goals

Lecture 125 The function and its derivative

Lecture 126 Gradient descent

Lecture 127 Repeat in 2D

Lecture 128 Bonus: Visualize in 3D

Section 25: Project 15: Data curve fitting

Lecture 129 Project overview and goals

Lecture 130 Fit a Gaussian

Lecture 131 Fit an exponential decay

Lecture 132 Use a user-defined function (sigmoid)

Lecture 133 Conjunctive model fitting

Lecture 134 Multivariate model-fitting

Lecture 135 Bonus: The eye of Sauron!

Section 26: Project 16: Time-frequency analysis of EEG

Lecture 136 Project overview and goals

Lecture 137 Real-valued Morlet wavelets

Lecture 138 Complex-valued Morlet wavelets

Lecture 139 Create a wavelet family

Lecture 140 Import and visualize the data

Lecture 141 Wavelet convolution

Lecture 142 Create a time-frequency map

Lecture 143 Bonus: Phase map with cyclic colormap

Section 27: Project 17: Interpolation and extrapolation

Lecture 144 Project overview and goals

Lecture 145 Down/upsample a time series

Lecture 146 1D interpolation

Lecture 147 1D extrapolation

Lecture 148 Resampling revisited

Lecture 149 Fix corrupted image with interpolation

Lecture 150 Bonus: Draw a Necker cube

Section 28: Project 18: Simulate a brain circuit

Lecture 151 Project overview and goals

Lecture 152 Simulate one brain cell

Lecture 153 Create a circuit of 1000 neurons

Lecture 154 Simulate the neural circuit

Lecture 155 Visualize population activity

Lecture 156 Run some experiments!

Lecture 157 Bonus: Separate excitation and inhibition

Section 29: Project 19: Animate data

Lecture 158 Project overview and goals

Lecture 159 Wavey wavelets in plotly, part 1

Lecture 160 Wavey wavelets in plotly, part 2

Lecture 161 Wavey wavelets in matplotlib

Lecture 162 Mobius transform in matplotlib

Lecture 163 Bonus: the wandering primes

Section 30: Project 20: Cryptocurrency investing

Lecture 164 Project overview and goals

Lecture 165 Import and average data from one coin

Lecture 166 Create a dataframe of selected coins

Lecture 167 Data dimensionality via PCA

Lecture 168 Simulating DCA investments (instructions)

Lecture 169 Simulating DCA investments (code)

Lecture 170 Bonus: Which coin should you have bought?

Section 31: Bonus

Lecture 171 Bonus

Total beginners to Python,(optional) some experience in other languages (e.g., MATLAB or R),Interest in using Python for data, science, engineering, physics, biology