- Lesson #01
- Outline & course directions
- Tasks: complete datacamp's courses
- Spreadsheet Basics
- Data Analysis with Spreadsheets
- Intermediate Spreadsheets for Data Science
- Introduction to Shell for Data Science
- Introduction to Git for Data Science
- Lesson #02
- How to become a data scientist
- Dev platforms for data Science
- Python crash course
- List, List of Lists
- Condicional statements
- Dictionaries
- Lesson #03
- How pandas can be combined to make working with data easier
- About the two core pandas types: series and dataframes
- How to select data from pandas objects using axis labels
- How to select data from pandas objects using boolean arrays
- How to assign data using labels and boolean arrays
- How to create new rows and columns in pandas
- Many new methods to make data analysis easier in pandas
- Lesson #04
- Select columns, rows and individual items using their integer location. Work with integer axis labels.
- How to use pandas methods to produce boolean arrays.
- Use boolean operators to combine boolean comparisons to perform more complex analysis.
- Use index labels to align data.
- Use aggregation to perform advanced analysis using loops.
- Lesson #05
- Reading CSV files with encodings
- Cleaning column names
- Converting a string column to numeric
- Extracting Values from the start/end of strings
- Correcting bad values
- Dropping missing values
- Lesson #07
- Groupy operation
- Common aggregation methods with groupby
- Aggregation with pivot table
- Lesson #08
- Combining Dataframes with the concat()
- Joining Dataframes with the merge()
- Lesson #09
- Transforming data with Series, Dataframe
- Map, apply, applymap, melt
- Lesson #10
- Using apply() to transform strings
- Vectorized string methods
- Extracting substring using regular expressions
- Lesson #11
- Project: open data ufrn
- Crash course: interactive data visualization with Bokeh
- Lesson #12
- Storytelling from geographic data
- Basemap and Matplotlib
- Lesson #13
- Folium
- Maps styles, markers, color and icon types
- Marker clusters
- Heatmap
- Popups
- Lesson #14
- Working with API
- Case study: IBGE
- Geojson Files
- Creating choropleths maps
- Lesson #15
- Introduction to NetworkX
- Construct a simple network with NetworkX
- Add attributes
- Visualize a network with matplotlib and nxviz
- Share and preserve networks
- Lesson #16
- Network visualization using Gephi
- Combine Gephi & NetworkX
- Case Study: constructing a network of Wikipedia Pages
- Lesson #17
- Create Networks from Adjacency and Incidence Matrices
- Generate Synthetic Networks
- Lesson #18
- Global measures
- Explore neighborhoods
- Think in terms of paths
- Choose the right centralities
- Lesson #19
- Use the urllib and requests packages
- Make HTTP requests (GET requests)
- Scrape web data such as HTML
- Parse HTML into useful data (BeautifulSoup)