This eLearning bundle consists of these 4 courses:
- Begin Python Programming in 7 Days
- Python for Everyday Life
- Learning Python for Data Science
- Learn R programming
Begin Python Programming in 7 Days – 4 hours
We get you started setting up your environment and the tools you need to start programming in Python. You will be learning about variables and operators and how to make use of them in Python programs. You will learn all about control flow statements and loops in Python and you will be using them in your programs to solve your coding problems.
Then you will learn to use Python’s advanced data structures such as lists and dictionaries. You will be able to organize in functions and save time coding by writing code that can be reused. Then, you will learn about Python modules and how to make use of them. On the last day, you will start interacting with files using Python code.
The course will give you a strong entry point into programming in general and programming in Python in particular.
The code bundle for this course is available here.
Get started with Python from scratch by setting up a code editor and executing code from the command line
Python variables and operators and how to use them well to simulate a simple app such as a calculator
Execute program control flows and loops in Python to create your own simple mini-games
Implement the List and Dictionary data types to take text as input and produce a word count
Program efficiently in Python by organizing your code in functions to code a game such as rock, paper, and scissors
Work with Python Modules to create your first web-scraping app in Python
Handle files using your Python code to build your own Python-based text editor
Python for Everyday Life – 14 hours 22 minutes
Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.
This course is about leveraging the Python programming language and its thriving ecosystem to save yourself time and money when doing common routine tasks. Nobody wants to do boring and time-consuming tasks: days have 24 hours and you should squeeze out the most of this time for yourself – automating the boring tasks gives you back time to focus on what you really like to do. Moreover, this is also the chance for you to learn a great general-purpose language such as Python, with which you can build very cool applications both at work and in your spare time.
The course is structured as an incremental learning path: you will start with a deep-dive into Python software development basics, then move on to write scripts to automate file system operations and file contents processing on your local host, then you will learn how to interact with web-based services such as websites and APIs in order to robotize the cool things that we do everyday – such as tweeting, posting to social networks, and reading RSS feeds. Moreover, you will practice how to set up a web-based services yourself in the form of web applications and in the end you will learn how to analyze and visualize datasets in order to extract knowledge.
By the end of this course, you will have learned how to proficiently write structured Python code in a wide range of applications – from one-liner scripts to complex web applications – aiming at the automation of lots of common everyday life tasks.
Automate the execution of lots of common everyday life tasks using Python
Write Python code proficiently in a structured fashion
Identify the boundaries of a coding problem and spot the best libraries to solve it
Design and Implement a wide range of applications from simple stand-alone one-liner scripts to complex web applications depending on external services
Manipulate efficiently and visualize data as a way to make informed decisions
Learning Python for Data Science – 3 hours and 39 minutes
Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science.
In this course you will learn all the necessary libraries that make data analytics with Python a joy. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.
By the end of this course, you’ll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science.
Learn R programming – 1 hour and 29 minutes
R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. This solution-based video will be your guide, taking you through different programming aspects with R.
Beginning with the basics of R programming, this video provides step-by-step resources and time-saving methods to help you solve programming problems efficiently. Starting with the installation of R, each recipe addresses a specific problem with a discussion that explains the solution and offers insight into how it works.
You will learn to work with powerful R tools and techniques. You’ll be able to boost your productivity with the most popular R packages and tackle data structures such as matrices, lists, and factors. You’ll see how to create vectors, handle variables, and perform other core functions. You’ll be able to tackle issues with data input/output and will learn to work with strings and dates.
Moving forward, we’ll look into more advanced concepts such as metaprogramming with R and functional programming. Finally, you’ll learn to tackle issues while working with databases and data manipulation.