Python Data science

In today’s world, the ability to code continues to grow in importance. Coding is no longer the sole domain of computer scientists and programmers, but rather a useful skill to have in any career.

Tens & Kids with an eye to their future know that learning to code is important, but figuring out which one to learn can be an intimidating task. Some languages are easier to learn, while others have a wider application. But one language sits right in the sweet spot.

As you strike out on your programming adventure, you’ll learn how to:

  • Use fundamental data structures like lists, tuples, and maps
  • Organize and reuse your code with functions and modules
  • Use control structures like loops and conditional statements
  • Draw shapes and patterns with Python’s turtle module
  • Create games, animations, and other graphical wonders
  • Free eBooks for starters, beginners

For software installation we might need parents supervision

  • Software like Python development environment ebook Readers etc
  • Need 3 GB of hard disk space with i3 or better processor with 4GB Minimum RAM

Trainer profile

  • Total-experience 27+ years.
  • Bachelors in Computer, MBA (finance), LLB (copyright, patent, cybersecurity)
  • 18 years of corporate training in 16 countries.
  • PMI, Agile, PCI, Cybersecurity, IoT, Robotics, PMO, QA, QC, Legal, Business Incubation

Syllabus

Special table of contents Python (not bound by hours/days, more focus on understanding )

  • Overview of Python- Starting with Python
  • Python & data science
  • Anaconda vs. python
  • Introduction to the installation of Python
  • Introduction to Python IDE’s (Jupyter,/Ipython)
  • Concept of Packages – Important packages
  • Python Development
  • NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels – Date & Time Values
  • Basic Operations – Mathematical/string/date
  • Control flow & conditional statements
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent
  • Decision Tree