Python DS

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Course Content

[Python Programming] – Python – Home

  • IDE for python
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Python – Overview

Python – Environment Setup

Python – Basic Syntax

Python – Variable Types

Python – Basic Operators

Python – Decision Making

Python – Loops

Python – Numbers

Python – Strings

Python – built-in Functions

Python -custom Functions

Modules

Python – Files I/O

Python – Exceptions

[Advanced Topics] – Generators

List Comprehensions

Multiple Function Arguments

Regular Expressions

Exception Handling

Sets

Partial functions

Python Decorators

Closures

Decorators

Map, Filter, Reduce

Python Database access and data management

Python Lambda functions

[Data Science] – Introduction to data Science

Python For data science

[Numpy] – Introduction to NumPy in Python

NumPy Ndarray

NumPy Data Types

Creation Array From Existing Data

Arrays within the numerical range

NumPy Broadcasting

NumPy Array Iteration

Built-in Functions

NumPy String Functions

NumPy Mathematical Functions

Statistical Functions

Sorting & Searching

Matrix Library

NumPy Linear Algebra

Matrix Multiplication

NumPy Functions

numpy array()

numpy concatenate()

numpy append()

numpy reshape()

numpy sum()

numpy random()

numpy.zeros()

numpy.log()

numpy.where()

numpy.mean()

numpy.unique()

numpy.ndarray.tolist()

numpy.std()

numpy.empty()

numpy.histogram()

numpy.sort()

numpy.average()

numpy.pad()

numpy.ravel()

Numpy.save()

NumPy arccos()NumPy arcsin()NumPy arctan() NumPy tan()

Python Pandas – Introduction

Introduction to Data Structures

Panel

Basic Functionality

Iteration

Sorting

Working with Text Data

Indexing & Selecting Data

Aggregations

Statistical Functions

Missing Data

GroupBy

Merging/Joining

Concatenation

Data Visualization

Pandas Index

Matplotlib (Python Plotting Library) – Matplotlib – Introduction

Matplotlib – Environment Setup

Plotting graphs using Matplotlib

Bar Graph, Scatter plot

SciPy
Module 1: Introduction to Scientific Computing with Python 1.1 Overview of Scientific Computing Introduction to numerical computing and its applications. Role of Python in scientific computing. 1.2 Introduction to SciPy Overview of SciPy library and its functionalities. Comparison with other scientific computing libraries. 1.3 Installation and Setup Installing SciPy using different methods (pip, conda). Setting up development environment (IDEs, Jupyter Notebooks). Module 2: NumPy Fundamentals 2.1 Introduction to NumPy Basics of NumPy arrays and their advantages. Array creation and manipulation. 2.2 Indexing and Slicing Accessing elements and sub-arrays in NumPy arrays. Advanced indexing techniques. 2.3 Array Operations Mathematical operations on arrays (element-wise operations, broadcasting). Universal functions (ufuncs) in NumPy. Module 3: Linear Algebra with SciPy 3.1 Introduction to Linear Algebra Overview of linear algebra concepts and applications. Importance of linear algebra in scientific computing. 3.2 Matrix Operations Matrix creation and manipulation with SciPy. Matrix multiplication, inversion, and transpose. 3.3 Solving Linear Systems Solving systems of linear equations using SciPy. Methods for solving sparse and dense linear systems. 3.4 Eigenvalue Problems Computing eigenvalues and eigenvectors of matrices. Applications of eigenvalue decomposition. Module 4: Optimization and Root Finding 4.1 Introduction to Optimization Overview of optimization techniques and algorithms. Applications of optimization in various fields. 4.2 Optimization with SciPy Optimization functions in SciPy (minimization, root finding). Unconstrained and constrained optimization. 4.3 Non-linear Least Squares Fitting models to data using non-linear least squares. Curve fitting and parameter estimation. Module 5: Numerical Integration and Interpolation 5.1 Introduction to Numerical Integration Overview of numerical integration methods. Applications in calculus and numerical analysis. 5.2 Integration with SciPy Numerical integration functions in SciPy (quad, trapz, simps). Adaptive and non-adaptive integration methods. 5.3 Interpolation Interpolation techniques (linear, polynomial, spline interpolation). Applications in data analysis and visualization. Module 6: Statistical Computing with SciPy 6.1 Introduction to Statistics Basic concepts in statistics (mean, median, variance, standard deviation). Overview of probability distributions. 6.2 Statistical Functions in SciPy Probability distributions and random number generation. Hypothesis testing and statistical tests. 6.3 Data Analysis and Visualization Data manipulation and analysis using SciPy. Visualization of statistical data (histograms, box plots, etc.). Module 7: Advanced Topics 7.1 Sparse Matrix Operations Sparse matrix representation and storage formats. Sparse matrix operations and algorithms. 7.2 Signal Processing with SciPy Introduction to signal processing concepts. Signal processing functions in SciPy (filtering, Fourier analysis). 7.3 Image Processing Image manipulation and processing with SciPy. Image filtering, segmentation, and feature extraction. Module 8: Case Studies and Projects 8.1 Real-world Applications Case studies demonstrating the use of SciPy in various scientific and engineering fields. Applications in physics, biology, finance, etc. 8.2 Project Work Group or individual projects involving the implementation of scientific computing algorithms using SciPy. Presentation and discussion of project outcomes. This syllabus covers the key concepts and applications of SciPy, providing a comprehensive understanding of scientific computing with Python. The course may vary in duration and depth depending on the target audience and learning objectives.

SciPy
Module 1: Introduction to Scientific Computing with Python 1.1 Overview of Scientific Computing Introduction to numerical computing and its applications. Role of Python in scientific computing. 1.2 Introduction to SciPy Overview of SciPy library and its functionalities. Comparison with other scientific computing libraries. 1.3 Installation and Setup Installing SciPy using different methods (pip, conda). Setting up development environment (IDEs, Jupyter Notebooks). Module 2: NumPy Fundamentals 2.1 Introduction to NumPy Basics of NumPy arrays and their advantages. Array creation and manipulation. 2.2 Indexing and Slicing Accessing elements and sub-arrays in NumPy arrays. Advanced indexing techniques. 2.3 Array Operations Mathematical operations on arrays (element-wise operations, broadcasting). Universal functions (ufuncs) in NumPy. Module 3: Linear Algebra with SciPy 3.1 Introduction to Linear Algebra Overview of linear algebra concepts and applications. Importance of linear algebra in scientific computing. 3.2 Matrix Operations Matrix creation and manipulation with SciPy. Matrix multiplication, inversion, and transpose. 3.3 Solving Linear Systems Solving systems of linear equations using SciPy. Methods for solving sparse and dense linear systems. 3.4 Eigenvalue Problems Computing eigenvalues and eigenvectors of matrices. Applications of eigenvalue decomposition. Module 4: Optimization and Root Finding 4.1 Introduction to Optimization Overview of optimization techniques and algorithms. Applications of optimization in various fields. 4.2 Optimization with SciPy Optimization functions in SciPy (minimization, root finding). Unconstrained and constrained optimization. 4.3 Non-linear Least Squares Fitting models to data using non-linear least squares. Curve fitting and parameter estimation. Module 5: Numerical Integration and Interpolation 5.1 Introduction to Numerical Integration Overview of numerical integration methods. Applications in calculus and numerical analysis. 5.2 Integration with SciPy Numerical integration functions in SciPy (quad, trapz, simps). Adaptive and non-adaptive integration methods. 5.3 Interpolation Interpolation techniques (linear, polynomial, spline interpolation). Applications in data analysis and visualization. Module 6: Statistical Computing with SciPy 6.1 Introduction to Statistics Basic concepts in statistics (mean, median, variance, standard deviation). Overview of probability distributions. 6.2 Statistical Functions in SciPy Probability distributions and random number generation. Hypothesis testing and statistical tests. 6.3 Data Analysis and Visualization Data manipulation and analysis using SciPy. Visualization of statistical data (histograms, box plots, etc.). Module 7: Advanced Topics 7.1 Sparse Matrix Operations Sparse matrix representation and storage formats. Sparse matrix operations and algorithms. 7.2 Signal Processing with SciPy Introduction to signal processing concepts. Signal processing functions in SciPy (filtering, Fourier analysis). 7.3 Image Processing Image manipulation and processing with SciPy. Image filtering, segmentation, and feature extraction. Module 8: Case Studies and Projects 8.1 Real-world Applications Case studies demonstrating the use of SciPy in various scientific and engineering fields. Applications in physics, biology, finance, etc. 8.2 Project Work Group or individual projects involving the implementation of scientific computing algorithms using SciPy. Presentation and discussion of project outcomes. This syllabus covers the key concepts and applications of SciPy, providing a comprehensive understanding of scientific computing with Python. The course may vary in duration and depth depending on the target audience and learning objectives.

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