Analyze Features With Python And Seaborn In Google Colab

Beginners python data analytics : Data science introduction : Learn data science : Python data analysis methods tutorial

What you'll learn
Learn how to work with different types of data
Calculate the measures of central tendency, asymmetry, and variability
Distinguish and work with different types of distributions
Perform hypothesis testing
Requirements
Absolutely no experience is required. We will start from the basics and gradually build up your knowledge. Everything is in the course.
Description
Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & MoreTHIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS:It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python!HERE IS WHY YOU SHOULD TAKE THIS COURSE:First of all, this course a complete guide to practical data science using Python…That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python-based data science.In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level!NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:You’ll start by absorbing the most valuable Python Data Science basics and techniques…I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real life.After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python.You’ll even understand deep concepts like statistical modelling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques).I will even introduce you to deep learning and neural networks using the powerful H2o framework!

Overview

Section 1: 00 Course Overview

Lecture 1 Course Overview

Section 2: 01 Mammoth Interactive Courses Introduction

Lecture 2 00 About Mammoth Interactive

Lecture 3 01 How to Learn Online Effectively

Lecture 4 source files

Section 3: 02 What is Machine Learning (Prerequisite)

Lecture 5 01 What is Machine Learning

Lecture 6 02 Types of Machine Learning Models

Lecture 7 03 What is Supervised Learning

Section 4: 03 Introduction to Python (Prerequisite)

Lecture 8 00. Intro To Course And Python

Lecture 9 01. Variables

Lecture 10 02. Type Conversion Examples

Lecture 11 03. Operators

Lecture 12 04. Collections

Lecture 13 05. List Examples

Lecture 14 06. Tuples Examples

Lecture 15 07. Dictionaries Examples

Lecture 16 08. Ranges Examples

Lecture 17 09. Conditionals

Lecture 18 10. If Statement Examples

Lecture 19 11. Loops

Lecture 20 12. Functions

Lecture 21 13. Parameters And Return Values Examples

Lecture 22 14. Classes And Objects

Lecture 23 15. Inheritance Examples

Lecture 24 16. Static Members Examples

Lecture 25 17. Summary And Outro

Lecture 26 Source files

Section 5: 04 Perform Feature Analysis and Data Science

Lecture 27 01 Load And Create Data

Lecture 28 02 Perform Exploratory Data Analysis

Lecture 29 03 Visualize Data With Different Plots

Lecture 30 04 Analyze Features With More Plots

Lecture 31 05 Build Plots With Seaborn

Lecture 32 06 Build A Bokeh Plot

Lecture 33 07 Build A 3D Scatter Plot

Lecture 34 08 Rank Feature Importance

Lecture 35 09 Compare Positive And Negative Returns

Lecture 36 Source Files

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