Embark on a comprehensive one-hour tutorial that introduces both Python and data science fundamentals. Learn to predict student grades based on study hours using real data. Begin with setting up Visual Studio Code and Anaconda, then dive into Jupyter Notebooks, Pandas for data manipulation, and Matplotlib for data visualization. Explore key data science concepts including central tendencies, distributions, statistical outliers, and correlation. Master practical skills like creating various charts, handling null values, and performing data normalization. Conclude with an introduction to regression analysis, providing a solid foundation for further data science exploration.
Overview
Syllabus
Getting started with Visual Studio Code and Anaconda
Installing Jupyter Notebooks and Python
Creating a Jupyter Notebook
Working with data
Introduction to Pandas
Working with Data Frames
Read a CSV file into a Pandas Data Frame
Remove Null values in data
Adding a column to a Data Frame
Grouping data using 'groupby'
Creating charts from data
Rainbow CSV
Using Matplotlib to make charts
Customizing charts with Matplotlib
Creating a pie chart with Matplotlib
Creating a chart directly from Data Frame
Data Science, Samples, and Distributions
Creating a histogram with Matplotlib
Central Tendencies, Mean, Median, and Mode
Creating a boxplot with Matplotlib
Visualizing probability density with Matplotlib
Creating a Jupyter Notebook
Statistical Outliers
Variance, Range, and Standard Deviation
68% Rule
Analyzing box plots
Normalizing ranges
Calculating Correlation value
Using regression lines
Least squares regression
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Taught by
Visual Studio Code
