26 epizod
1. The Course Overview
1. The Course Overview
This video provides an overview of the entire course.
2. The Anaconda Distribution
2. The Anaconda Distribution
Explain what Anaconda Distribution is and why we are using it in this course. Also show how to get and install the software.
3. Introduction to the Jupyter Notebook
3. Introduction to the Jupyter Notebook
Introduce the computing environment in which we will work for the rest of the course.
4. Using the Jupyter Notebook
4. Using the Jupyter Notebook
Use the Jupyter notebook for basic Python code and explain the basics of using markdown and code cells in the Jupyter Notebook.
5. NumPy: Python's Vectorization Solution
5. NumPy: Python's Vectorization Solution
Explain what Numpy is, the problem it solves and why it is important for Python's Data Stack.
6. NumPy Arrays: Creation, Methods and Attributes
6. NumPy Arrays: Creation, Methods and Attributes
Introduce arrays, the main objects in Numpy, and how to create and use them.
7. Using NumPy for Simulations
7. Using NumPy for Simulations
Introduce with an example one of the common uses of Numpy: doing simulations.
8. The Pandas Library
8. The Pandas Library
Explain what pandas is and what we can do with it. An introduction to the main objects: Series and DataFrames.
9. Main Properties, Operations and Manipulations
9. Main Properties, Operations and Manipulations
Show how to use pandas Series and DataFrames with a real-world data set.
10. Answering Simple Questions about a Dataset - Part 1
10. Answering Simple Questions about a Dataset - Part 1
Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.
11. Answering Simple Questions about a Dataset - Part 2
11. Answering Simple Questions about a Dataset - Part 2
Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.
12. Basics of Matplotlib
12. Basics of Matplotlib
Explain to the viewer what matplotlib is and the main concepts needed for using it.
13. Pyplot
13. Pyplot
Explain what pyplot is, how to use the pyplot interface, and its limitations.
14. The Object Oriented Interface
14. The Object Oriented Interface
Explain how to use the Object-Oriented Interface and how it compares with the plyplot interface.
15. Common Customizations
15. Common Customizations
Show some of the common customizations that can be done to plots.
16. EDA with Seaborn and Pandas
16. EDA with Seaborn and Pandas
Explain what Exploratory Data Analysis (EDA) is and how to perform it in a real-world dataset; in the process, introduce the Seaborn plotting library.
17. Analysing Variables Individually
17. Analysing Variables Individually
Show how to analyze and make sense of individual variables depending on their type.
18. Relationships between Variables
18. Relationships between Variables
Show how to produce the main plots used to show relationships between variables.
19. SciPy and the Statistics Sub-Package
19. SciPy and the Statistics Sub-Package
Give a quick introduction to the Scipy package and all the different sub-packages it contains.
20. Alcohol Consumption - Confidence Intervals and Probability Calculations
20. Alcohol Consumption - Confidence Intervals and Probability Calculations
Show how to perform statistical calculations with the stats package like confidence intervals and probabilities of events.
21. Hypothesis Testing - Does Alcohol Consumption Affect Academic Performance?
21. Hypothesis Testing - Does Alcohol Consumption Affect Academic Performance?
Explain how to perform one of the most common statistical tests using the stats package.
22. Hypothesis Testing - Do Male Teenagers Drink More Than Females?
22. Hypothesis Testing - Do Male Teenagers Drink More Than Females?
Show how to perform a chi-square test using the stats package.
23. Introduction to Predictive Analytics Models
23. Introduction to Predictive Analytics Models
Present an overview of the section. Discuss the concepts of Predictive Analytics and its relationship with Machine Learning and give some characteristics of ML models.
24. The Scikit-Learn Library - Building a Simple Predictive Model
24. The Scikit-Learn Library - Building a Simple Predictive Model
Introduce the Scikit-Learn library and show the workflow traditionally used to build a Predictive Model with this library.
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