Python For Data Science Primer Training

Microtek Learning's Python for Data Science Primer technical training introduces data analysts, business analysts, and anyone interested in data science to the Python programming language.

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| Python For Data Science Primer

Course Overview

Microtek Learning's Python for Data Science Primer technical training introduces data analysts, business analysts, and anyone interested in data science to the Python programming language.

This course aims to provide a basic understanding of the core ideas of the Python program.

Acts as a bridge that connects professionals with in-depth training and actual-world practice. Acquiring the course will introduce a quick overview of Python.

Professionals will also get a demonstration of both Python types.

One is script-based Python, and the other one is notebook-based Python.

After learning the basics, the professional will jump to the essentials of pythons vital for a data scientist.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

What you will learn

  • This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.
  • How to work with Python interactively in web notebooks
  • The essentials of Python scripting
  • Key concepts necessary to enter the world of Data Science via Python

Who Should Attend This Course?

  • Business Analysts, Data Analysts, and anyone interested in data science who is comfortable working with numerical data in Excel or other spreadsheet environments.

 

Prerequisites

  • No prior programming experience is required.

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📘 Python For Data Science Primer Outline

  • Why Python?
  • Python in the Shell
  • Python in Web Notebooks (iPython, Jupyter, and Zeppelin)
  • Demo: Python, Notebooks, and Data Science
  • Using variables
  • Built-in functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • Command line parameters
  • About flow control
  • White space
  • Conditional expressions
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits
  • About sequences
  • Lists and list methods
  • Tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions, keywords, and operators
  • List comprehensions
  • Generator Expressions
  • Nested sequences
  • Working with Dictionaries
  • Working with Sets
  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Reading and writing raw (binary) data
  • Defining functions
  • Parameters
  • Global and local scope
  • Nested functions
  • Returning values
  • Sorting
  • Exceptions
  • Importing Modules
  • Classes
  • Regular Expressions
  • Math functions
  • The string module
  • Working with dates and times
  • Translating timestamps
  • Parsing dates from text
  • Formatting dates
  • Calendar data
  • Data Science Essentials
  • Pandas Overview
  • NumPy Overview
  • SciKit Overview
  • MatPlotLib Overview
  • Working with Python in Data Science

Still have questions?

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