Data Science using Python

Data Science using Python


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SKU: cid_72338 Category:
About the course

Computing has always played an important role in science and engineering. Sometimes it is also called the "third pillar" along with experiments and theory. In this course, you will learn computational science and engineering with the help of the Python programming language. This course covers the fundamental concepts of python variables, functions, and packages. Then it introduces control structures and basic numerical algorithms. In this course, you will also learn about interpolation, integration, differentiation, ODE and PDE solvers, and basic linear algebra.
The course mainly focuses on the practical approach and expects you to try hands-on with the exercises to get completely proficient in working with.

Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science / Information Technology.

Teachers/Faculties: All computer science teachers/faculties who wish to acquire new skills.

Professionals: All working professionals, who wish to enhance their skills.

Course Features
  • 24X7 Access: You can view lectures as per your own convenience.
  • Online lectures: ~30 hours of online lectures with high-quality videos.
  • Hands-on practice: Includes source code files for hands-on practice.
  • Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Test & Evaluation

Each lecture will have a quiz containing a set of multiple-choice questions. Apart from that, there will be a final test based on multiple-choice questions.

Your evaluation will include the overall scores achieved in each lecture quiz and the final test.


Certification requires you to complete all the lectures, quizzes, and the final test.

Topics to be covered

Module 01: About computer, python: Variables and Array

  • Introduction to computers
  • Python variables
  • Numpy arrays and its functions

Module 02: Python: Control structures, Programming style

  • Basic python control structures
  • Python packages and programming style
  • Some suggestions on programming

Module 03: Plotting, Errors, Data input/output

  • Plotting functions in Python
  • Errors analysis & Nondimensionalization
  • Data I/O & Mayavi

Module 04: Interpolation

  • Lagrange Interpolation scheme
  • Interpolation two dimensions

Module 05: Numerical Integration

  • Integration using Newton-Cotes
  • Integration using Gaussian quadrature
  • Gaussian quadrature continued

Module 06: Differentiation, ODE solvers

  • Numerical differentiation of functions
  • Ordinary differential equations solver
  • ODE solver continued

Module 07: Forier transforms, PDE solvers

  • DiscreteFourier transform
  • PDE solver: Diffusion equation in spectral method
  • PDE solver: Diffusion equation using finite difference
  • PDE solver: Wave equation using finite difference

Module 08: Linear Algebra, Summary

  • Ax = b using the Gaussian elimination method
  • Conclusion
1. Introduction 2. The Programming Cycle for Python 3. Getting started
4. Variables and simple data types 5. Elements of Python 6. Type Conversion
7. Expressions 8. Assignment Statement 9. Arithmetic Operators
10. Operator Precedence 11. Boolean Expression 12. Introducing lists
13. Working with lists 14. For Loop 15. Nested Loops
16. Tuples 17. Unpacking Sequences 18. Lists
19. Mutable Sequences 20. List Comprehension 21. Sets
22. If statements 23. Conditionals 24. Conditionals (Continued)
25. Expression Evaluation 26. Float Representation 27. Dictionaries
28. User input and loops 29. Break and Continue 30. Function
31. Parts of A Function 32. Execution of A Function 33. Keyword and Default Arguments
34. Scope Rules 35. Strings 36. Indexing and Slicing of Strings
37. More Slicing 38. Higher Order Functions 39. Sieve of Eratosthenes
40. Abstract Data Types 41. Classes 42. Modules
43. Importing Modules 44. Classes 45. Special Methods
46. Class Example 47. Inheritance 48. Inheritance and OOPS
49. Files and Exceptions 50. File I/O 51. Exceptions
52. Testing your code 53. Assertions 54. Iterators
55. Recursion 56. Simple Search 57. Estimating Search Time
58. Binary Search 59. Estimating Binary Search Time 60. Recursive Fibonacci
61. Tower Of Hanoi 62. Sorting 63. Selection Sort
64. Merge List 65. Merge Sort 66. Higher Order Sort