Python For Analytics and Non-Analytics: A Full and Comprehensive Yet Easy to Understand University Course Module

#Pythonprogramming #easypython #completepython Technology is just Starting. You still have time to Learn Python Programming. Maximize this FREE Python for Analytics and Non Analytics: This is a Full, Complete and Comprehensive University Python Programming Course for Everybody.



With its unprecedented power and usefulness, Python has become of the hottest and fastest growing programming language since it was launched and is become one of the buzzwords in Digital Transformations together with other domains in the Pantheon of Technology – Data Science and Big Data.

Data Science is a multi-disciplinary skill in Statistics/ Data Mining, Computer Science and Business Management. Data Science was regarded as the solution to provide scalable insights based on Big Data. The advancement in Technology makes these domains and areas visible to almost all big industries today. Big enterprises invested huge amount of money to install Data Science team in their organization to take care and manage Big Data infrastructures and architecture.

 

Prior to Pandemic Outbreak, these companies are continuously reaping success and not being swayed down because of the successful deployment of Artificial Intelligence Technology, Machine Learning, Automation, Internet of Things (IoT), Blockchain and other areas of Data Science.

 

Skills in Statistics, Data Mining or Data Wrangling was successfully used and applied to these companies and areas because of the Computer Programming Language such as Python. Business Models was carefully curated by experts in Business Management. Thus, Data Science was regarded as the sexiest job in the 20th Century because it encapsulated a wide spectrum of knowledge, skills, and expertise.

 

Based on Stack Overflow Developer’s Survey in 2019, Python was regarded as the second and the most preferred language with 73% of the developers are choosing it over other programming languages and is expected to dominate the Marketplace for Programming Languages.

 

This course is intended for everyone who are aiming to upskills their programming skills. This is intended for all type of users from Novice to Expert. Anyone who have no prior experience in programming can take this course and for anyone who would like to learn the Python Programming Language for upskilling purposes.


Why Do We Need to Learn Python?

Python is very useful in general aside from being an open-source programming language. It was used by Amazon, Google, Reddit, Instagram, and all other big companies around the world.

  • Python is user-friendly programming language – Python was able to simplify coding process just like transforming complicated coding to make it simpler and more straightforward. This what makes Python easy to learn and understand that even kids can able to understand and use.

  • Python makes us more Productive – Instead of us taking further education to know the old school programming languages, Python makes everything simple. It makes the Programming task a lot easier compared with other programming languages.

  • Python is very Dangerous in a Positive way – It can used for anything. It is very powerful as you can generate insights based from actual sentiments of the people, It can replicate things through machine learning algorithms, It can automate things through automation, It can replicate human through the development of virtual agents and the like. Because of its powers, it comes with great responsibility. It can be dangerous if you will use its powers to connive with evil who have a lot of destruction activities which includes all forms of fraud.

  • Python is a language for creating a script – You can directly type your script to its interpretation environment such as IDLE and many other. It does not require compilation like any other languages. You can easily detect and identify errors in your scripts. This makes a programming a fan activity.

  • Python is a Cross-Platform Programming Language – Anyone can use it if you have the motivation and purpose of using it. If you are in non-analytics field but you want to learn this because you have a goal and motivation, you can easily use it. You can use and install Python on Windows, Mac, Linux and from other platform like Raspberry Pi. You can also run Python on Android and IOS tablets.

  • Python uses dynamic typing of variables – When you start programming, you do not actually need to explain the machine what the variables is supposed to be. You can just write your variables as it is.

  • Python is very collaborative language – there are so many experts have written libraries. You do not need to build and create your own library as there are available libraries in place. All you can do is to install.

  • Python is Open-Source Programming Language – It does not require you to pay for licenses. You can download and run python from different distribution channels like such as Anaconda.

Where to Use Python

Python can be use everywhere and anywhere. You can use Python to whatever you plan to do or interested to work on.

  • In Space – Python was used for the Central Command System at the International Space Station’s Robonaut 2. The European Mission to Mars was planning to use Python to collect and study soil samples.

  • In Laboratories – Python was used to generate insights from the atom smashing experiments at the CERN Large Hadron Collider.

  • In Astronomy – Python was used to control and monitor system of the MeerKat Radio Telescope Array

  • In Movie Studios – The Star Wars experts uses Python to automate movie productions. Effects Software’s computer-generated imagery program Houdini uses Python for the Programming interface and to script the engine.

  • In Games – Activision uses Python for building games, testing, and analyzing stuffs. They are also using Python to detect people cheating in game activity.

  • In the Video and Music Industry – Spotify, Netflix, and other streaming services uses Python for the recommendation engine. It understands the people’s preference when it comes to music, movies and therefore, automatically generate recommendations.

  • In Search Engines – Google uses Python all over in its early development stage. Google uses ElasticSearch

  • In Medicine – Other Medical Institution, Drugs Manufacturing company uses Python to develop and analyze the efficacy of the medicine.

  • In Virtual Agents and Robots – Python was used to developed applications for robots.

  • Internet-of-things (IoT) – You can use Python to developed application for automation and for other integrated systems in the Internet-of-things.


⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Why Program? ⌨️ (0:12:21) Why Program? - Hardware Architecture ⌨️ (0:24:24) Python 3 Windows Installation ⌨️ (0:32:34) Python 3 Mac Installation ⌨️ (0:36:41) Why Program? - Python as a Language ⌨️ (0:44:17) Why Program? - What do we say? ⌨️ (0:56:55) Variables, Expressions, and Statements ⌨️ (1:06:20) Variables, Expressions, and Statements - Expressions ⌨️ (1:26:00) Conditional Execution ⌨️ (1:39:13) Conditional Execution - More Conditional Structures ⌨️ (1:52:48) Functions ⌨️ (2:03:02) Functions - Functions of our own ⌨️ (2:15:21) Loops and Iteration ⌨️ (2:25:04) Loops and Iteration - Definite Loops ⌨️ (2:31:40) Loops and Iteration - Loop Idioms ⌨️ (2:40:07) Loops and Iteration - More Loop Patterns ⌨️ (2:58:39) Strings ⌨️ (3:09:06) Strings - More String Operations ⌨️ (3:27:33) Reading Files ⌨️ (3:35:12) Reading Files - Reading Files in Python ⌨️ (3:48:42) Python Lists ⌨️ (3:59:27) Python Lists - Loop Operations ⌨️ (4:08:52) Python Lists - Strings vs. Lists ⌨️ (4:16:42) Python Lists - Strings, Files, Lists & the Guardian Pattern ⌨️ (4:28:44) Dictionaries ⌨️ (4:36:32) Dictionaries - Counting ⌨️ (4:45:43) Dictionaries - Counting Words in Text ⌨️ (4:58:21) Dictionaries - Counting Word Frequency Using a Dictionary ⌨️ (5:22:46) Tuples ⌨️ (5:32:18) Tuples - Sorting ⌨️ (5:44:26) Tuples - Sorting a Dictionary Using Tuples ⌨️ (5:54:56) Regular Expressions ⌨️ (6:05:21) Regular Expressions - From Matching to Extracting ⌨️ (6:13:47) Regular Expressions - String Parsing ⌨️ (6:22:17) Networked Programs ⌨️ (6:29:45) Networked Programs - Application Protocols ⌨️ (6:38:56) Networked Programs - Write a Web Browser ⌨️ (6:43:10) Networked Programs - Code Example: socket1.py ⌨️ (6:48:58) Networked Programs - Characters and Strings ⌨️ (6:59:57) Networked Programs - urllib ⌨️ (7:05:10) Networked Programs - Code Example: urllib1.py, urlwords.py ⌨️ (7:08:25) Networked Programs - Parsing HTML ⌨️ (7:14:48) Networked Programs - Code Example: urllinks.py ⌨️ (7:23:43) Using Web Services ⌨️ (7:26:35) Using Web Services - XML ⌨️ (7:32:02) Using Web Services - Code Example: xml1.py, xml2.py ⌨️ (7:37:40) Using Web Services - XML Schema ⌨️ (7:51:32) Using Web Services - JavaScipt Notation ⌨️ (7:57:45) Using Web Services - Code Example: json1.py, json2.py ⌨️ (8:03:08) Using Web Services - Service Oriented Approach ⌨️ (8:04:44) Using Web Services - Web Services ⌨️ (8:11:33) Using Web Services - Code Example: geojson.py ⌨️ (8:18:49) Using Web Services - API Security & Rate Limiting ⌨️ (8:28:45) Using Web Services - Code Example: twitter1.py, twitter2.py ⌨️ (8:48:01) Python Objects ⌨️ (8:58:28) Python Objects - Sample Code ⌨️ (9:06:50) Python Objects - Object Lifecycle ⌨️ (9:13:19) Python Objects - Inheritance ⌨️ (9:20:44) Databases ⌨️ (9:35:55) Databases - SQLite Browser ⌨️ (9:45:40) Databases - Code Sample: emaildb.py ⌨️ (9:58:55) Databases - Code Sample: twspider.py ⌨️ (10:08:06) Databases - Database Design ⌨️ (10:16:29) Databases - Representing Relationships ⌨️ (10:20:37) Databases - Relationship Building ⌨️ (10:33:05) Databases - Join Operation ⌨️ (10:43:13) Databases - Code Sample: tracks.py ⌨️ (10:57:45) Databases - Many-to-Many Relationships ⌨️ (11:09:37) Databases - Code Sample: roster.py ⌨️ (11:20:40) Databases - Code Sample: twspider.py ⌨️ (11:20:40) Data Visualization ⌨️ (11:48:18) Data Visualization - Code Sample: Geodata ⌨️ (12:01:05) Data Visualization - Page Rank ⌨️ (12:12:14) Data Visualization - Code Sample: Pagerank Spidering ⌨️ (12:29:12) Data Visualization - Code Sample: Pagerank Computation ⌨️ (12:44:17) Data Visualization - Code Sample: Pagerank Visualization ⌨️ (12:44:17) Data Visualization - Mailing List Crawl ⌨️ (12:57:08) Data Visualization - Code Sample: Gmane Data Retrieval ⌨️ (13:13:42) Data Visualization - Code Sample: Gmane Data Modeling ⌨️ (13:26:04) Data Visualization - Code Sample: Gmane Data Visualization





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