**Fundamentals of Data Science with Python**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 38m | 437 MB

Implement powerful data science techniques with Python using NumPy, SciPy, Matplotlib, and scikit-learn

Python has grown into a key language that can be used to develop solutions for a variety of data science challenges. This course will teach you the fundamentals of data science using Python and its growing collection of libraries that focus on particular elements of data science.

In this course, we will get hands-on with a variety of data science tasks. After a quick primer on Python, you will start with a quick task: sourcing, processing, and cleaning a dataset. Then, you will use Python to mine data from its source and analyze available data via statistical and probability analysis techniques by using NumPy and pandas. You will also look at modeling data in order to perform Artificial Intelligence prediction by using the SciPy, scikit-learn, and statsmodels libraries. The course also covers visualization methods using the Matplotlib library to display this analysis and visually demonstrate patterns in the data.

By the end of this course, you will be able to work on data science tasks in a practical way with different Python libraries and achieve your goals.

Learn

- Use Python for data mining, loading, and manipulation
- Understand simple statistics and probability using NumPy
- Work with Bayesian statistical analysis with NumPy library
- Perform statistical modeling and fitting using the NumPy, SciPy, and statsmodels libraries
- Use Python’s graphics libraries to plot data with the Matplotlib library
- Work with the scikit-learn library to build AI models

**Table of Contents**

**Primer on Python**

1 Course Overview

2 Introduction to Python

3 Installing Python and Creating a First Jupyter Notebook

4 Overview of the Different Variable Types

5 Manipulating Variables with Operators

6 Writing Functions with Python

7 Conditions and Loops

8 Object-Oriented Programming with Python

**Python for Data Science**

9 Introduction to the NumPy Array

10 Manipulating NumPy Arrays with Operators and Aggregate Functions

11 Making Your First Steps with Pandas

12 Performing Common Operations with Pandas

**Getting Your Dataset Ready for Processing**

13 Sourcing the Data

14 Getting Familiar with Our Two Datasets

15 Loading the Datasets into Your Program

16 Getting a Global Overview of the Data

17 Finding Missing Values in Data

18 Cleaning the Data for Use

**How Visualizations Work**

19 Using the Simple Bar Graph

20 Exploring Histogram

21 Working with Boxplots

22 Detecting Correlations with Scatter Plots

23 Extending Matplotlib’s Possibilities Thanks to Seaborn

24 Dealing with Several Plots

**Working with Statistics and Probability**

25 Finding Patterns with Descriptive Statistics

26 Finding Patterns with Python – Univariate Analysis

27 Finding Patterns with Python – Bivariate Analysis

28 Working with Probability and Distribution

29 Inferential Statistics – Testing Hypothesis with the T-Test and the Chi² Test

**Statistical Modelling and Fitting**

30 Exploring Statistical Modelling

31 Using Data to Test a Statistical Model

32 Exploring Linear Regression

33 Analysis of Variance (ANOVA)

34 Working with Logistic Regression

**Explore Machine Learning**

35 Getting Started with Machine Learning and AI

36 Differentiating Types of Learning

37 Training a Model with Scikit-Learn

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