Section outline

• Select section Day 1: Foundations of Python for Data Analysis (7 hours)

  Day 1: Foundations of Python for Data Analysis (7 hours)

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  Module 1: Basic Programming

  • Using Jupyter as a development IDE
  • Introduction to Python programming
  • Understanding Python syntax and constructs

  Module 2: Data Frames & Datasets

  • Inspecting data frames with head() and tail()
  • Exploring data types and information using info()

   

  Module 3: Data Frame Methods and Computations

  • Calculating Min, Max, Sum, and Count
  • Understanding Mean, Median, & Mode
  • Describing data with numeric and object values

  Module 4: Series and Columns

  • Selecting a single column and exploring series
  • Important series methods: unique(), nunique(), nlargest(), nsmallest()
  • Using value_counts() and visualizing with plot()
  • Adding and removing columns

  Module 5: Organizing Data and Working With Dates/Times

  • Indexing and creating a MultiIndex
  • Sorting and filtering DataFrames
  • Using condition operators (AND/OR)
  • Working with dates and performing date math

  Module 6: Python for Data Analysis with NumPy

  • Introduction to NumPy and its applications in data analysis
  • Working with NumPy arrays and indexing
  • Performing operations with NumPy
• Select section Day 2: Data Processing and Analysis (7 hours)

  Day 2: Data Processing and Analysis (7 hours)

  Module 7: Python for Data Analysis – Pandas

  • Introduction to Pandas and its significance in data analysis
  • Working with DataFrames and analyzing missing data
  • Merging, joining, and concatenating raw data
  • Data input and output operations

  Module 8: Data Visualization with Matplotlib

  • Importing data into Matplotlib for visualization
  • Utilizing Matplotlib and Seaborn for plotting graphs
  • Enhancing the visual appearance of plots

  Module 9: Working With Text

  • Understanding string vs. object data types
  • Indexing string series and using text-related functions

  Module 10: Combining Series & DataFrames

  • Concatenating series and DataFrames by index
  • Exploring inner vs. outer joins

  Module 11: Introduction to Seaborn

  • Creating scatterplots, line plots, histograms, and categorical plots with Seaborn

  Final Project & Course Wrap-up

  • Hands-on project: Analyzing and visualizing a dataset
  • Discussion and participant presentations
  • Course feedback and Q&A
  • Certificate distribution