Python Programming with Data Science
What you'll learn
- You will first learn how to Install Anaconda and Jupyter on your desktop/laptop
- You will understand and learn the basics of For Loops and Advanced For Loops
- You will have clarity on Python generators and will master the flow of your code using "If Else"
- You will understand Why foundations Modify Lists and Dictionaries and Functions
- Learn how to analyze, retrieve and clean data with Python
- Get introduced to Using API's
- Learn Concatenation (Combining Tables) with Python and Pandas and Manipulating Time and Date Data with Python Datetime
- Data Cleaning and Preparation for Machine Learning
- You will learn to Use Pandas with Large Data Sets, Time Series Analysis and Effective Data Visualization in Python
Requirements
- You don't need any prior programming experience, and by the time you finish, you'll have built a real-world data science project from the ground up using your new Python skills!
- If you're just getting started on your Python journey, the best thing you can do is give yourself a great foundation in the fundamentals.
Description
Why Learn Python For Data Science?
Before we explore how to learn Python for data science, we should briefly answer why you should learn Python in the first place.
In short, understanding Python is one of the valuable skills needed for a data science career.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, Python replaced Java as the most popular language in colleges and universities and has never looked back
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
In 2019 IEEE Spectrum (world’s largest professional organization dedicated to applied science and engineering) has ranked Python at the top of the list of ‘top programming languages in 2019’
Tiobe analysts believe that within three to four years' time, Python will "probably replace C and Java" to become the most popular programming language in the world
Data science experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
According to Indeed, the average salary for a Data Scientist is $127,918.
The good news? That number is only expected to increase. The experts at IBM predicted a 28% increase in demand for data scientists by the year 2021.
So, the future is bright for data science, and Python is just one piece of the proverbial pie. Fortunately, learning Python and other programming fundamentals is as attainable as ever.
But remember – just because the steps are simple doesn’t mean you won’t have to put in the work. If you apply yourself and dedicate meaningful time to learning Python, you have the potential to not only pick up a new skill, but potentially bring your career to a new level.
Who this course is for:
- Beginner python developers who need a solid foundation on python with data science
- Professionals with < 5 years of experience and are looking to transition to programming roles
Course content
- Preview03:07
Instructor
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- 41 Courses
We specialize in Cybersecurity, Data Science and Talent Management/Human capital management training. The USP of all our training's is the hands-on that we provide, our focus is on real-life practical knowledge sharing, and not tool-based PPT slides. All our training's are conducted by highly experienced practitioners who are dyed-in-the-wool penetration testers. The material is cutting edge and updated with even the most recent developments. We have a standard set of courses outlined in different information security domains, data analytics domains and Talent management domain. However, we also customize the training according to the clients’ requirements.