Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

1 minute read

Python For Data Science

Python for Data Science

A brief summary of the topics covered in this course is as below. This is 30 hours course, it is suggested to complete this course in 3 weeks. After the completion of this course, you will have a solid foundation in Python programming. Following that, you need to practice it consistently for a minimum of 6 months.

Setup and Introduction

  1. Intro to Python
  2. Setup, Download, Installation Jupyter notebook
  3. Comments & Markdown in Jupyter

Basics of Python of Programming

  1. Variable & Variable Types
  2. Data Structure (list, dictionary, set, tuple)
  3. Operators
  4. Conditions
  5. Loop
  6. Break, Continue
  7. Error Handling
  8. Debugging
  9. Functions

Advance Python Programming

  1. List Comprehensions
  2. Parameter Packing
  3. Package Management
  4. File Handling
    • Working with files
    • Reading and writing files
    • Buffered read and write
    • Other file methods.
    • Logging, debugger
    • Modules and import statements
  5. Numpy
    • Numpy – ND array object.
    • Numpy – data types.
    • Numpy – array attributes.
    • Numpy – array creation routines.
    • Numpy – array from existing.
    • Data array from numerical ranges.
    • Numpy – indexing & slicing.
    • Numpy – advanced indexing.
    • Numpy – broadcasting.
    • Numpy – iterating over array.
    • Numpy – array manipulation.
    • Numpy – binary operators.
    • Numpy – string functions.
    • Numpy – mathematical functions.
    • Numpy – arithmetic operations.
    • Numpy – statistical functions.
    • Sort, search & counting functions.
    • Numpy – byte swapping.
    • Numpy – copies &views.
    • Numpy – matrix library.
    • Numpy – linear algebra
  6. Class
    • OOPS Basic concepts.
    • Creating classes
    • Pillars of oops
    • Inheritance
    • Polymorphism
    • Encapsulation
    • Abstraction
    • Decorator
    • Class methods and static methods
    • Special (magic/dunder) methods
    • Property decorators – getters, setters, and deletes
  7. Introduction to Pandas 

Project & Resources

  1. Resources for practice
  2. A Final Project in Python

Updated:

Leave a comment

You may also enjoy