English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4.5 Hours | 1.01 GB
Take your data analytics and predictive modeling skills to the next level using the popular tools and libraries in Python
The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Python’s most important tools and libraries for doing Data Science; they are known in the community as “Python’s Data Science Stack”.
This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python.
What You Will Learn
- Learn about the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution.
- Understand the basics of Numpy which is the foundation of all the other analytical tools in Python.
- Produce informative, useful and beautiful visualizations for analyzing data.
- Analyze, answer questions and derive conclusions from real world data sets using the Pandas library.
- Perform common statistical calculations and use the results to reach conclusions about the data.
- Learn how to build predictive models and understand the principles of Predictive Analytics
Table of Contents
The Anaconda Distribution and the Jupyter Notebook
The Course Overview
The Anaconda Distribution
Introduction to the Jupyter Notebook
Using the Jupyter Notebook
Vectorizing Operations with NumPy
NumPy: Python’s Vectorization Solution
NumPy Arrays: Creation, Methods and Attributes
Using NumPy for Simulations
Pandas – Everyone’s Favorite Data Analysis Library
The Pandas Library
Main Properties, Operations and Manipulations
Answering Simple Questions about a Dataset – Part 1
Answering Simple Questions about a Dataset – Part 2
Visualization and Exploratory Data Analysis
Basics of Matplotlib
Pyplot
The Object-Oriented Interface
Common Customizations
EDA with Seaborn and Pandas
Analysing Variables Individually
Relationships between Variables
Statistical Computing with Python
SciPy and the Statistics Sub-Package
Alcohol Consumption – Confidence Intervals and Probability Calculations
Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance?
Hypothesis Testing – Do Male Teenagers Drink More Than Females?
Introduction to Predictive Analytics Models
Introduction to Predictive Analytics Models
The Scikit-Learn Library – Building a Simple Predictive Model
Classification – Predicting the Drinking Habits of Teenagers
Regression – Predicting House Prices
Resolve the captcha to access the links!