# Python For Engineers and Professionals

## Intro to Numeric Computing with Python

• Basics and Plotting — Notebook 1 — Video
• Arrays — Notebook 2 — Video
• For loops and If/Else statements — Notebook 3 — Video
• Functions — Notebook 4 — Video
• Finding the Zeros of a Function — Notebook 5
• Systems of linear equations — Notebook 6
• Bugs — Notebook 7
• Pandas and Time Series — Notebook 8 — Video (Python 2)
• Discrete Random Variables — Notebook 9 — Video
• Continuous Random Variables — Notebook 10 — Video
• Distribution of the Mean and Hypothesis Tests Theorem — Notebook 11 — Video (Python 2)
• Object oriented programming — Notebook 12 — Video (Python 2)
• Regression I — Notebook 13

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## Intro to Python and Programming - Yuov Ram

• Recitation 1: variables, operators, flow control
• Recitation 2: input, Collatz Conjecture, lists, functions
• Recitation 3: diviors, timing operations, binary system, base conversion, Python's memory model
• Recitation 4: time complexity, Big O notation, primality testing, Diffie-Hellman key exchange
• Recitation 5: object-oriented programming, recursion
• Recitation 6: recursion, quicksort
• Recitation 7: lambda expression, high-order functions, Matrix class, file handling
• Recitation 8: hash functions, hash tables, finding repeating substrings
• Recitation 9: linked list, iterators, generators
• Recitation 10: Rabin-Karp algorithm for string matching
• Recitation 11: Huffman code
• Recitation 12: LZ compression
• Recitation 13: image processing, denoising algorithms
• Recitation 14: error detection and correction

## [HUHA] Applied Python: A course in numerical methods with Python for engineers and scientists:

Currently 5 learning modules, with student assignments

• The phugoid model of glider flight.
• Space and Time—Introduction to finite-difference solutions of PDEs.
• Riding the wave: convection problems.
• Relax and hold steady: elliptic problems. Laplace and Poisson equations

## Data Science with Python - Yuov Ram

• Numerical Python with NumPy: notebook | solution
• Plotting with Matplotlib: notebook | solution
• Data analysis with Pandas and Seaborn: notebook | solution
• Machine learning with Scikit-learn: notebook | exercise: linear model | solution | solution: linear model | exercise: logistic model | solution: logistic model