Python Coding
Tags: Python
Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization
Last updated 2022-01-10 | 4.6
- Learn to program in Python at a good level- Learn how to code in Jupiter Notebooks
- Learn the core principles of programming
What you'll learn
* Requirements
* No prior knowledge or experience needed. Only a passion to be successful!Description
Learn Python Programming by doing!
There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!
This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.
After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.
This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!
I can't wait to see you in class,
What you will learn:
Learn the core principles of programming
Learn how to create variables
How to visualize data in Seaborn
How to create histograms, KDE plots, violin plots and style your charts to perfection
Learn about integer, float, logical, string and other types in Python
Learn how to create a while() loop and a for() loop in Python
And much more....
Sincerely,
Kirill Eremenko
Who this course is for:
- This course if for you if you want to learn how to program in Python
- This course is for you if you are tired of Python courses that are too complicated
- This course is for you if you want to learn Python by doing
- This course is for you if you like exciting challenges
- You WILL have homework in this course so you have to be prepared to work on it
Course content
8 sections • 80 lectures
Installing Python (Windows & MAC) Preview 08:55
Here you will learn how to install Anaconda, Python and Jupyter Notebook
BONUS: Learning Paths Preview 00:34
Get the materials Preview 00:05
Some Additional Resources!! Preview 00:13
FAQBot! Preview 01:28
Your Shortcut To Becoming A Better Data Scientist! Preview 02:05
Updates on Udemy Reviews Preview 01:09
Types of variables Preview 08:44
Using Variables Preview 08:58
Boolean Variables and Operators Preview 06:03
The "While" Loop Preview 09:56
The "For" Loop Preview 07:57
The "If" statement Preview 12:29
Code indentation in Python Preview 02:40
Section recap Preview 03:08
HOMEWORK: Law of Large Numbers Preview 12:51
Core Programming Principles
What is a List? Preview 03:15
Let's create some lists Preview 08:42
Using the [] brackets Preview 06:28
Slicing Preview 09:27
Tuples in Python Preview 06:17
Functions in Python Preview 05:37
Packages in Python Preview 13:39
Numpy and Arrays in Python Preview 07:08
Slicing Arrays Preview 04:32
Section Recap Preview 03:06
HOMEWORK: Financial Statement Analysis Preview 10:11
Fundamentals of Python
Project Brief: Basketball Trends Preview 08:16
Matrices Preview 03:31
Building Your First Matrix Preview 16:50
Dictionaries in Python Preview 14:20
Matrix Operations Preview 08:34
Your first visualization Preview 11:04
Expanded Visualization Preview 09:37
Creating Your First Function Preview 11:09
Advanced Function Design Preview 11:15
Basketball Insights Preview 11:17
Section Recap Preview 04:07
HOMEWORK: Basketball free throws Preview 08:43
Matrices
Importing data into Python Preview 08:25
Exploring your dataset Preview 10:51
Renaming Columns of a Dataframe Preview 02:56
Subsetting dataframes in Pandas Preview 16:31
Basic operations with a Data Frame Preview 09:49
Filtering a Data Frame Preview 18:52
Using .at() and .iat() (advanced tutorial) Preview 09:01
Introduction to Seaborn Preview 10:47
Visualizing With Seaborn: Part 1 Preview 10:05
Keyword Arguments in Python (advanced tutorial) Preview 10:42
Section Recap Preview 04:30
HOMEWORK: World Trends Preview 06:57
Data Frames
What is a Category data type? Preview 10:29
Working with JointPlots Preview 07:38
Histograms Preview 07:52
Stacked histograms in Python Preview 18:29
Creating a KDE Plot Preview 07:59
Working with Subplots() Preview 14:05
Violinplots vs Boxplots Preview 08:55
Here you will see the difference between violinplots and boxplots, will know what they used for and what executives prefer in their analytics!