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Empowering Data Vision: Data Science Course

Week 1: From Python to Exploring Data with Pandas.

Day 1: Course Methodology, Tools, Setup, and Review.

Session 1: Development Environment

  1. Operating Systems

  2. Terminal

  3. Python version

  4. PIP or Anaconda

  5. Visual Studio Code

    a. Accessibility options

    b. Helpful settings

    c. Interpreter settings

    d. Python version check

    e. Extensions

    f. Internal or external Terminals

Session 2: Python Setup and or review

  1. Using the Python REPL (Read, Evaluate, Print, Loop)

  2. Basic Data types

    a. Integer

    b. Float

    c. Strings

  3. Inspecting data types

    a. Type function

    b. id function

    c. isinstance function

    d. Dir function

    e. helpful dir filter

    f. help function

  4. Grouping data types

    a. Lists

    b. Dictionaries

    c. Sets

    d. Tuples

  5. Modules

    a. NumPy

    b. Pandas

    c. Readline

  6. Numpy Review or Introduction

  7. Pandas Hello world

Day 2: From Spread sheet to Pandas

Session 1: Excel and Pandas terminology

  1. Introduction

  2. Excel verses Pandas Terminology and example.

    a. Series (rows and columns)

    b. Indexes

    c. DataFrames (Work sheets)

    d. macros / functions

Session 2: Introduction to Pandas

  1. Panda data types

  2. Series

  3. Data Frames

Session 3: Putting it all together

  1. Review of Excel sheet

  2. Steps to Reproduce with pandas.

  3. Saving the data in different forms.

  4. Check output in Excel.

Day 3: Data Manipulation with Pandas

Session 1: Loading Data and Exploring data in Pandas

  1. Reading Simple created XLS into Pandas

  2. Getting started with Exploratory Data Analysis (EDA)

    a. Head

    b. Tail

    c. Describe

    d. Info

    e. Shape.

    f. index

    g. columns

    h. data (numpy array)

Session 2: Large dataset EDA

  1. Getting data set Penguins.

  2. Exploring data set

  3. clean up data functions

    a. drop

    b. Rename

    c. Type change

    d. Filter

Manipulating data

  1. Sorting

  2. filter

  3. creating new columns

  4. Group by

Day 4: Data Manipulation with Pandas

Session 1: Advanced EDA

  1. Getting GapMinder.

  2. Basic EDA and Cleanup.

  3. Join

  4. Merge

  5. Data transformation

  6. Mutation

session 2: Class EDA of different Data sets.

Week 2 Visualization of Data

Day 1: Bar Plots and Histograms

Session 1: Bar Plots

  1. Definitions, description, and components.

    a. Bars

    b. X-axis

    c. Y-axis

    d. Labels

    e. Title

  2. Use cases for bar charts.

  3. Load data

  4. Explore and choose the data

  5. Create a Bar plot

Practical Exercise bar Plots

  1. Hands on Exercise

    a. Participants create and review bar plots with Seaborn and an Accessible library

Session 2: Histograms

  1. Definitions, description and components.

    a. Bins

    b. Bars

    c. X-axis

    d. Y-axis

    e. Frequency

  2. Use case for Histograms.

  3. Load data

  4. Explore and choose the data

  5. Create a Histogram

Practical Exercise histogram

  1. Hands on Exercise

    a. Participants create and explore Histograms with Seaborn and an Accessible library

Day 2: Line Plots and Scatter Plots

Session 1: Line Plots

  1. Definitions and Components:

    a. Lines

    b. x-axis

    c. y-axis

    d. Markers

    e. title.

  2. Use cases for line plots.

  3. EDA and Setup of data.

  4. Create Line plot

Practical Exercise

  1. Hands on Exercise

    a. Participants create and explore Line plots with Seaborn and an Accessible library

Session 2: Scatter Plots

  1. Definitions, description, and Components

    a. points

    b. axis's

    c. labels

    d. title

  2. Use cases for scatter plots.

  3. EDA and Setup of data.

  4. Create Scatter plot

Practical Exercise on Scatter Plots

  1. Hands-on Exercise:

    a. Participants create and explore scatter plots with Seaborn and an Accessible library

Day 3: Box plots and Heat Maps

Session 1: Box Plots

  1. Definitions, description, and Components:

    a. Box:

    b. Median Line

    c. Whiskers

    d. Outliers

    e. Minimum

    f. Maximum

    g. Quartiles

    h. Range

    i. Notches

    j. Axes both X and Y

    k. Title and Labels

  2. Use cases for box plots.

  3. EDA and Setup of data.

  4. Create Box plot

Practical Exercise on Box Plots

  1. Hands-on Exercise:

    a. Participants create and explore box plots with Seaborn and an Accessible library

Session 2: Heat Maps

  1. Definitions, description, and Components:

    a. Data matrix

    b. Axis both X and Y.

    c. Color Scale

    d. Titles and lables

    e. Dendrograms

    f. Ticks and grid lines

    g. Figure background

  2. Use cases for Heat Maps

  3. EDA and Setup of data.

  4. Create Heat map

Practical Exercise for Heat Map

  1. Hands-on Exercise:

    a. Participants create and explore Heat map with Seaborn and an Accessible library

Day 4: Advanced Visualizations and other data sets

Session 1: Anscombe's Quartet

  1. Definitions and Significance:

    a. Understanding Anscombe's Quartet.

    b. Importance in illustrating the importance of data visualization.

  2. Plotting Anscombe's Quartet.

Session 2: Using live data

  1. EDA and selecting data

  2. Picking the right Plot

Session 3: Questions, Comments, and Wrapup