Fundamentals of Data Science with Python

Fundamentals of Data Science with Python
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