Master Python programming by solving scientific projects
Master Python programming by solving scientific projects, available at $109.99, has an average rating of 4.55, with 175 lectures, 15 quizzes, based on 1302 reviews, and has 11913 subscribers.
You will learn about Python Scientific programming Data visualization Time series analysis Modeling Regular expressions Spectral analysis Filtering Data clustering Gradient descent Text processing Data projects Data animation This course is ideal for individuals who are Total beginners to Python or (optional) some experience in other languages (e.g., MATLAB or R) or Interest in using Python for data, science, engineering, physics, biology It is particularly useful for Total beginners to Python or (optional) some experience in other languages (e.g., MATLAB or R) or Interest in using Python for data, science, engineering, physics, biology.
Enroll now: Master Python programming by solving scientific projects
Summary
Title: Master Python programming by solving scientific projects
Price: $109.99
Average Rating: 4.55
Number of Lectures: 175
Number of Quizzes: 15
Number of Published Lectures: 175
Number of Published Quizzes: 15
Number of Curriculum Items: 190
Number of Published Curriculum Objects: 190
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
Unleash Your Python Skills With Real World Scientific Projects
Welcome to “Master Python Programming by Solving Scientific Projects”. If you’re searching for a course that takes a fresh, hands-on approach to learning Python while solving real-world scientific problems, you’ve found the right one. This course isn’t just about learning a list of Python functions. It’s about getting knee-deep into Python’s capabilities, understanding its quirks, and leveraging it to tackle fascinating projects.
Why Choose This Course?
Python is a dynamic language, widely used in the scientific community. But you’re probably thinking, “Why should I choose this Python course out of hundreds available on Udemy?” Let’s cut to the chase and focus on what makes this course unique:
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Problem Solving Approach: This course doesn’t just teach Python; it reinforces the learning with an array of scientific projects that you might encounter in your academic, professional, or personal life. This strong focus on project-based learning equips you with hands-on coding experience. You’ll learn how to think like a programmer and apply your skills in practical situations.
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Transparency: I’m not a Python fanatic who views it as the perfect language. I acknowledge that, like every language, Python has its idiosyncrasies. During this course, I will not shy away from Python’s annoying or confusing aspects. Instead, I’ll give you a realistic and comprehensive understanding of the language, including its strengths and weaknesses.
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Broad Spectrum of Projects: From text processing to time series filtering, from simulating a brain circuit to plotting state-space trajectories, from biomedical signal processing to cryptocurrency investing, this course brings a wide range of projects to the table. Each project is meticulously crafted to ensure you gain the maximum knowledge and practical skills from it.
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Interactive Community: In the course Q&A, you’ll have the chance to interact with me and your fellow students. Here, I discuss Python coding strategies, data types, scientific coding best practices, and more. Sharing your own clever code solutions and learning from others’ experiences will further enhance your learning journey.
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Coding with ChatGPT: Gain insights on how to use ChatGPT, a sophisticated AI language model developed by OpenAI, to assist you with boilerplate code and debug your scripts. This interactive feature makes coding more intuitive and efficient, especially when you’re stuck with bugs or need a quick solution.
What should you do now?
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Take a look at the preview videos to get a glimpse of my teaching style and the course content.
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Check out the reviews of this course. The positive feedback and experiences shared by students will give you a good sense of what to expect.
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Also, do check out the reviews of my other courses to understand my dedication and passion for teaching.
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Embark on this exciting journey and join today! Together, we will dive deep into the world of Python, solving intriguing scientific problems along the way. This course is more than just a learning experience—it’s an adventure through the realms of Python and scientific coding.
Course Curriculum
Chapter 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
Chapter 2: ———— Part 1: The basics ————
Lecture 1: Part 1: The basics
Chapter 3: Data types
Lecture 1: Variables
Lecture 2: Math operators
Lecture 3: Printing and inputting
Lecture 4: Lists
Lecture 5: Tuples
Lecture 6: Booleans
Lecture 7: Dictionaries
Chapter 4: Indexing and slicing
Lecture 1: Indexing
Lecture 2: Slicing
Chapter 5: Functions
Lecture 1: Inputs and outputs
Lecture 2: Python libraries (numpy)
Lecture 3: Python libraries (pandas)
Lecture 4: Getting help on functions
Lecture 5: Creating functions
Lecture 6: Global and local variable scopes
Lecture 7: Classes and object-oriented programming
Chapter 6: Flow control
Lecture 1: If-else statements
Lecture 2: For loops
Lecture 3: Continue
Lecture 4: While loops
Lecture 5: Initializing variables
Lecture 6: Function error checking and handling
Lecture 7: Multiple inputs with zip
Lecture 8: Single-line loops (list comprehension)
Lecture 9: Broadcasting in numpy
Chapter 7: Text and data visualization
Lecture 1: fprintf and f-strings
Lecture 2: Plotting dots and lines
Lecture 3: Subplot geometry
Lecture 4: Making the graphs look nicer
Lecture 5: Adding annotations
Lecture 6: Seaborn
Lecture 7: Images
Lecture 8: Export plots in low and high resolution
Lecture 9: Sierpinski pseudocode, part II
Chapter 8: A brief aside on sharing code
Lecture 1: Getting code from github/google-drive
Chapter 9: ———— Part 2: The projects ————
Lecture 1: Part 2: The projects
Chapter 10: Download all course materials
Lecture 1: IMPORTANT: Download course materials
Lecture 2: Strategies for solving these projects
Chapter 11: Project 1: Text search and replace
Lecture 1: Project overview and goals
Lecture 2: Import a text file
Lecture 3: Remove formatting text
Lecture 4: Replace 4-letter words and save to disk
Lecture 5: Bonus: Readability of scrambled words
Chapter 12: Project 2: The Law of Large Numbers
Lecture 1: Project overview and goals
Lecture 2: Generate a population of random numbers
Lecture 3: Monte Carlo sampling
Lecture 4: Cumulative averaging
Lecture 5: Bonus: The Central Limit Theorem
Chapter 13: Project 3: Entropy of written English
Lecture 1: Project overview and goals
Lecture 2: Import text from the web
Lecture 3: Distribution of word lengths
Lecture 4: Letter frequencies
Lecture 5: Letter entropy
Lecture 6: Conditional (sequence) entropy
Lecture 7: Bonus: Make a word cloud
Chapter 14: Project 4: State-space trajectories
Lecture 1: Project overview and goals
Lecture 2: Import and clean the data
Lecture 3: Create a channel covariance matrix
Lecture 4: Run PCA and compute components
Lecture 5: State-space trajectories
Lecture 6: Bonus: Draw time using hues
Chapter 15: Project 5: Statistics
Lecture 1: Project overview and goals
Lecture 2: Import and inspect the data
Lecture 3: T-test for acidity on wine quality
Lecture 4: Multiple regression
Lecture 5: Logistic regression
Lecture 6: Bonus: Transform to Gaussian
Chapter 16: Project 6: Spectral analysis
Lecture 1: Project overview and goals
Lecture 2: Simulate an AR process
Lecture 3: Code the Fourier transform
Lecture 4: Zero-padding the FFT
Lecture 5: Welch's method
Lecture 6: Bonus: spectrogram
Chapter 17: Project 7: The colorful rainbow of noise
Lecture 1: Project overview and goals
Lecture 2: White and brown noise
Lecture 3: Pink and blue noise
Instructors
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Mike X Cohen
Educator and writer
Rating Distribution
- 1 stars: 7 votes
- 2 stars: 10 votes
- 3 stars: 36 votes
- 4 stars: 339 votes
- 5 stars: 910 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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