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Certified Python Developer

Course Summary

Python is the most popular programming language on Today. A quick search in indeed.com reveals about 50,000 open job opportunities in the USA alone as a day average in the year 2017 for Python professionals. This trend is only going to increase exponentially in the year 2018 to 2020, aligning to 3 million Data Science job opportunities by 2020 as estimated by IBM Inc. The recent surge in popularity can be attributed to its Data Science friendliness. Python along with its Machine Learning packages is already established as the popular platform for Data Science.

This course is designed to imbibe the best practice programming skills in Certified Python Developer with real-world Python application development and Machine Learning case studies. Candidates pursuing this course will be aligned with the current market job requirements.

This course "Certified Python Developer", is designed for candidates with or without programming skills. Candidates to not only understand Python core concepts but also gain practical mastery over Certified Python Developer, which is very much in demand in today's job market.


Course Objectives

The objective of this course is to provide the fundamentals of Python programming and introduce Data Science concepts and Machine Learning.

  • Python Fundamentals: Understanding Python syntax, data types, operators, conditional statements, functions. Writing simple Python scripts for Data Science
  • Python Packages: Understand core packages exploration and application
  • Building application with Python

Course Outline

Python Programming Fundamentals

  • Introduction to Jupyter Notebook
  • Python 3.0 Installation
  • Programming Basics
  • Python Data Types
  • Structures and conditional statements
  • Loops
  • User Inputs
  • File Handling
  • Handling Date and Time
  • Standard Python functions
  • String Manuplation functions
  • Python core packages

Dealing with Programming Errors

  • Syntax Errors
  • Runtime Errors
  • Errors in Python
  • Fixing Difficult Errors
  • The Structure of a Good Programming Question
  • Exception Handling in Python

Object Oriented Python

  • Object Oriented Programming Essentials
  • Class and Class Variable
  • Data member
  • Function overloading
  • Inheritance
  • Instance
  • Method
  • Object
  • Operator overloading
  • A simple application with OOP Python concepts

Introduction to Machine Learning

  • Overview of Supervised and Unsupervised Machine Learning
  • Linear Regression
  • Clustering with K-means
  • Naive Bayes Classification
  • Introduction to Neural Networks

Overview Data Science Python Packages

  • NumPy
  • Pandas
  • SciPy
  • Seaborn
  • Plotly
  • SciKit-Learn
  • Keras
  • NLTK
  • Scrapy
  • Statsmodels

Python Project

  • Python complete application development workflow
  • Coding with Git Repo
  • Testing and deploying
  • Documentation best practices

Who can Benefit from this Course?

Any professional aspiring to become Python developer

  • Candidates wanted to pursue Data Science career, with basic or no programming skills
  • Job seekers, pursuing career as Python Developer
  • Professionals, who's job involves Data Science and Python

Prerequisite

  • This course is at beginner level, so is suitable for any one who want to learn Python

Course Objectives

The objective of this course is to provide the fundamentals of Python programming and introduce Data Science concepts and Machine Learning.

  • Python Fundamentals: Understanding Python syntax, data types, operators, conditional statements, functions. Writing simple Python scripts for Data Science
  • Python Packages: Understand core packages exploration and application
  • Building application with Python

Course Outline

Python Programming Fundamentals

  • Introduction to Jupyter Notebook
  • Python 3.0 Installation
  • Programming Basics
  • Python Data Types
  • Structures and conditional statements
  • Loops
  • User Inputs
  • File Handling
  • Handling Date and Time
  • Standard Python functions
  • String Manuplation functions
  • Python core packages

Dealing with Programming Errors

  • Syntax Errors
  • Runtime Errors
  • Errors in Python
  • Fixing Difficult Errors
  • The Structure of a Good Programming Question
  • Exception Handling in Python

Object Oriented Python

  • Object Oriented Programming Essentials
  • Class and Class Variable
  • Data member
  • Function overloading
  • Inheritance
  • Instance
  • Method
  • Object
  • Operator overloading
  • A simple application with OOP Python concepts

Introduction to Machine Learning

  • Overview of Supervised and Unsupervised Machine Learning
  • Linear Regression
  • Clustering with K-means
  • Naive Bayes Classification
  • Introduction to Neural Networks

Overview Data Science Python Packages

  • NumPy
  • Pandas
  • SciPy
  • Seaborn
  • Plotly
  • SciKit-Learn
  • Keras
  • NLTK
  • Scrapy
  • Statsmodels

Python Project

  • Python complete application development workflow
  • Coding with Git Repo
  • Testing and deploying
  • Documentation best practices

Who can Benefit from this Course?

Any professional aspiring to become Python developer

  • Candidates wanted to pursue Data Science career, with basic or no programming skills
  • Job seekers, pursuing career as Python Developer
  • Professionals, who's job involves Data Science and Python

Prerequisite

  • This course is at beginner level, so is suitable for any one who want to learn Python

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