/data-science-one

Materials and exercises from IMD905 (data science one) classes

Primary LanguageJupyter Notebook

IMD0905 - Data Science I - 2019.1

  • 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)