Python Basics for Math and Data Science 1.0: Numpy and Sympy

Learn to know how to use two interesting libraries in Python named Numpy and Sympy and solve mathematical problems in Py
Rating: 4.1 out of 5 (202 ratings)
21,904 students
English
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Mathematical calculations using Python 3

Requirements

  • Yes, A basic knowledge in python is preferred

Description

Hey there! I welcome you all to my course - Python Basics for Mathematics and Data Science 1.0 : Numpy and Sympy . This course mainly focuses on two important libraries in python called as Numpy and Sumpy. If you're someone who know the basics of Python and looking forward to develop a project or kickstart your career in Data Science and Machine Learning, this course will highly motivate you to learn further.

After completing this course, you'll be able to

1. Create 2D Matrices (numpy arrays) in Python

2. Access the elements, rows and columns of a numpy array

3. Do matrix addition, multiplication, transpose operations in Python in a single line code

4. Inbuilt functions for statistical operations

5. Solve linear equation with one unknown in python

6. Solve linear equations with two unknowns in python

7. Solve Quadratic and cubic equations in python

8. Differential Calculus in Python

9. Integral Calculus in Python -  Definite and Indefinite Integrals

and a lot more stuff.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.  The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors.

Nothing more to write here! I'll see you there in my lectures!

Who this course is for:

  • Beginner Python developers

Course content

3 sections16 lectures1h 55m total length
  • A run through the course
    07:24
  • Installing the Libraries and Setting our environment
    06:33
  • Numpy Matrices
    12:16
  • Indexing and Slicing the Numpy Arrays
    11:33
  • Accessing a column of a matrix
    03:26
  • Useful functions and methods in numpy
    11:02
  • Matrix Transformations and Operations using numpy
    10:32
  • nditer in numpy
    04:48
  • Concatenate and arange in numpy
    07:16
  • Summary - Numpy
    01:33

Instructor

Engineer | Course Instructor
Sujithkumar MA
  • 4.4 Instructor Rating
  • 2,864 Reviews
  • 175,340 Students
  • 20 Courses

Self motivated budding electronics and communication engineer who can work on multiple roles. Interested in Modelling digital circuits using hardware description languages. Have a strong grasp of Verilog, Computer Architecture, C, C++, Java, Embedded C, Python, Data Structures, Algorithms, Machine Learning and Deep Learning. Loves to teach and so being a course instructor in Udemy, Learnfly and Guruface Inc. Cross Platform Application developer specialized in Google Flutter with Dart Programming and using Firebase as the backend. Skilled to work in tools such as MATLAB, Simulink, Xilinx Vivado, TinkerCAD, Proteus Design Suite, Camtasia and Altium Designer for PCB Design