Overview
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Learn Python programming for data science. Discover how to clean, transform, analyze, and visualize data, as you build a practical project: an automated web scraper.
Syllabus
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Introduction
- Data science life hacks
- What you should know
- Introduction to the data professions
- The four flavors of data analysis
- Why use Python for analytics?
- High-level course road map
- Filtering and selecting
- Treating missing values
- Removing duplicates
- Concatenating and transforming
- Grouping and aggregation
- The three types of data visualization
- Selecting optimal data graphics
- Communicating with color and context
- Creating standard data graphics
- Defining elements of a plot
- Plot formatting
- Creating labels and annotations
- Visualizing time series
- Creating statistical data graphics
- Simple arithmetic
- Basic linear algebra
- Generating summary statistics
- Summarizing categorical data
- Parametric correlation analysis
- Non-parametric correlation analysis
- Transforming dataset distributions
- Extreme value analysis for outliers
- Multivariate analysis for outliers
- BeautifulSoup object
- NavigableString objects
- Data parsing
- Web scraping in practice
- Introduction to NLP
- Cleaning and stemming textual data
- Lemmatizing and analyzing textual data
- Introduction to Plotly
- Create statistical charts
- Line charts in Plotly
- Bar charts and pie charts in Plotly
- Create statistical charts
- Next steps
