Data Science & Machine Learning Course

• Lesson 1

  • Motivation
  • Provocations: hw, data, internet, AI, cloud computing
  • Tendencies
  • Platforms
  • References

• Lesson 2

  • Conda essentials
  • Platforms (jupyter, jupyterlab, colab)
  • Python Crash Course

• Lesson 3

  • Modules, Iterations, List Comprehesion
  • String and date operations
  • Introducting to Object-Oriented Programming (OOP)

• Lesson 4

  • Introduction to Numpy
  • Introduction to Pandas

• Lesson 5

  • Data Cleaning Basic

• Lesson 6

  • Exploratory Data Analysis I
  • Matplotlib
  • Line, Bar and Scatter Plots

• Lesson 7

  • Exploratory Data Analysis II
  • Histogram and Box Plots
  • Wrapper from Pandas to Matplotlib

• Lesson 8

  • Exploratory Data Analysis III
  • Case study: gender gap
  • Aesthetics
  • Colors, Lines width
  • Annotations

• Lesson 9

  • Exploratory Data Analysis IV
  • Case study: titanic
  • Visualizing missing values
  • Aggregate data using pivot table
  • Storytelling from Seaborn

• Lesson 10

  • Exploratory Data Analysis V
  • Visualizing geographical data
  • Working with basemap
  • Customizing the plot
  • Folium
  • Maps with markers
  • Maker clusters -Heatmap

• Lesson 11

  • Exploratory Data Analysis VI
  • Case Study #1 - Jonh Snow Map
  • Case Study #2 - Open Data Natal-RN

• Lesson 12

  • Exploratory Data Analysis VII
  • Case study: IBGE
  • Geojson
  • Importing files
  • Creating maps
  • Choropleths maps