Python Programming

Python is an object oriented programming language created by Guido Rossum in 1989. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Most automation, data mining, and big data platforms rely on Python for scientific computation. This is because it is the ideal language to work with for Computer Graphic, Machine Learning and Artificial Intelligence. Python also has various modules, libraries and platforms that support development of games.

Main Features

  • Install Anaconda Navigator and write your first Python program on Spyder
  • Get acquainted with the Jupyter Notebook Environment
  • Use variables to store, retrieve and calculate information
  • Utilize core programming tools such as functions and loops
  • Skillfully handle syntax errors and exceptions in python.
  • Use the numpy library to create and manipulate arrays and dataframes.
  • Use the pandas module with Python to create, structure and import data.
  • Create data visualizations using matplotlib and the seaborn modules with python.

Getting Started with Python Programming

1
Introduction to Excel
2
General formatting Options
3
Basic command line functions and GUI
4
Simple calculations
5
Numeric functions
6
String functions

Introduction to Anaconda

1
Why Anaconda
2
IDE Spyder
3
Getting started with Spyder
4
Basic Variables
5
Numeric Operations
6
Types of data
7
Isinstance
8
Logical Operators

Data Structure and Conditional Executions

1
Sequence Structures
2
List
3
Tuple
4
Dictionaries in Python
5
Key Value Pairs
6
Different types of operators
7
Mutability
8
Different mutability for different sequence structures
9
String Functions
10
Indexing and slicing
11
Control Flow tools
12
If, Else, Elif
13
The Continue and Break statement
14
While Loop

Working with Different Functions

1
Built in functions
2
Different functions and their uses
3
Using different functions
4
Built in modules
5
Executing functions using libraries
6
User defined functions
7
Lambda Keyword
8
Map() function
9
Filtering
10
Reduce()
11
Default Parameters
12
Multiple Parameters
13
Local Variables
14
Global Variables
15
List Comprehension

Expressions and Exceptions

1
Range
2
File input output
3
Types of errors
4
Exception Handling

Introduction to the Data Science Library

1
Introduction to data science
2
Why Python for data science
3
Different editors of Python
4
Jupyter Notebook
5
Spyder
6
Starting off with Spyder
7
Working with libraries
8
Numpy
9
Scipy

Graphs and Plotting

1
More on libraries
2
Charting with the matplotlib library Histograms
3
Scatter plots
4
Line diagrams
5
Bar charts
6
Working on a combination of matplotlib and scipy libraries
Faq Content 1
Faq Content 2

Productivity Hacks to Get More Done in 2018

— 28 February 2017

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Enrolled: 34 students
Duration: 60 hours
Lectures: 62
Video: 9 hours
Level: Advanced

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