26 エピソード
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.
