diff --git a/docs/html/.doctrees/class_schedule.doctree b/docs/html/.doctrees/class_schedule.doctree index ccec806..5c686cd 100644 Binary files a/docs/html/.doctrees/class_schedule.doctree and b/docs/html/.doctrees/class_schedule.doctree differ diff --git a/docs/html/.doctrees/environment.pickle b/docs/html/.doctrees/environment.pickle index 2c09962..80313db 100644 Binary files a/docs/html/.doctrees/environment.pickle and b/docs/html/.doctrees/environment.pickle differ diff --git a/docs/html/.doctrees/exercises/Exercise_dask.doctree b/docs/html/.doctrees/exercises/Exercise_dask.doctree index c8977ed..2117f78 100644 Binary files a/docs/html/.doctrees/exercises/Exercise_dask.doctree and b/docs/html/.doctrees/exercises/Exercise_dask.doctree differ diff --git a/docs/html/class_schedule.html b/docs/html/class_schedule.html index 9dfd43b..2152c3e 100644 --- a/docs/html/class_schedule.html +++ b/docs/html/class_schedule.html @@ -1 +1 @@ - Class Schedule — Practical Data Science Skip to content

Class Schedule

Class Time

Class will be Tuesdays and Thursdays from 1:25pm-2:40pm (EDT).

Date, Rm

Topic

Do Before Class

In-Class Exercise

Tues, Aug 29

  • Class Introduction

  • Welcome to VS Code

Thurs, Aug 31

  • Command Line Basics

Fri, Sep 1

SOFTWARE INSTALL DAY

A day of trouble shooting install issues

Tues, Sep 5

  • Advanced Command Line

  • Git

  • Packages

Thurs, Sep 7

  • Git Continued

  • Jupyter

Tues, Sep 12

  • Python Debugger

  • R / Python Differences

  • Packages

Thurs, Sep 14

  • Numpy Basics

Tues, Sep 19

  • Numpy Arrays

More Numpy Concepts:

Matrices:

ND Arrays:

Thurs, Sep 21

  • Pandas: Series

Tues, Sep 26

  • Pandas: DataFrames

Thurs, Sep 28

  • Pandas: Indices & Missing

Tues, Oct 3

  • Pandas: Cleaning

  • Tracebacks

Thurs, Oct 5

  • Merging

Tues, Oct 10

  • Plotting

Thurs, Oct 12

FALL BREAK

Tues, Oct 17

FALL BREAK

Thurs, Oct 19

  • Plotting

Tues, Oct 24

  • Big Data: What is it, how do I work with it?

Thurs, Oct 26

  • Defensive Programming

  • Workflow

  • Backwards Design

  • Getting Help Online

Tues, Oct 31

  • Defensive Programming

  • Groupby / Split-Apply-Combine

Thurs, Nov 2

  • Pandas: Reshaping

  • Pandas: Categoricals

Tues, Nov 7

  • Speed and Performance in Python

Thurs, Nov 9

  • Statistics with statsmodels

Tues, Nov 14

  • Machine Learning with sckikit-learn

  • JVP Chapter 5 up to “Hyperparameters and Model Validation” Section (pp 331 - 359)

Thurs, Nov 16

Tues, Nov 21

No Class

Thurs, Nov 23

THANKSGIVING

Tues, Nov 28

  • Defining Your Own Estimators

  • Regex

Thurs, Nov 30

  • Parallelism

  • Distributed Computing

(Note reading includes a 45 minute video to watch) Optional: - Setting Up Cloud Cluster

Wed Dec 13

Final Project Report and Presentation Due
\ No newline at end of file + Class Schedule — Practical Data Science Skip to content

Class Schedule

Class Time

Class will be Tuesdays and Thursdays from 1:25pm-2:40pm (EDT).

Date, Rm

Topic

Do Before Class

In-Class Exercise

Tues, Aug 29

  • Class Introduction

  • Welcome to VS Code

Thurs, Aug 31

  • Command Line Basics

Fri, Sep 1

SOFTWARE INSTALL DAY

A day of trouble shooting install issues

Tues, Sep 5

  • Advanced Command Line

  • Git

  • Packages

Thurs, Sep 7

  • Git Continued

  • Jupyter

Tues, Sep 12

  • Python Debugger

  • R / Python Differences

  • Packages

Thurs, Sep 14

  • Numpy Basics

Tues, Sep 19

  • Numpy Arrays

More Numpy Concepts:

Matrices:

ND Arrays:

Thurs, Sep 21

  • Pandas: Series

Tues, Sep 26

  • Pandas: DataFrames

Thurs, Sep 28

  • Pandas: Indices & Missing

Tues, Oct 3

  • Pandas: Cleaning

  • Tracebacks

Thurs, Oct 5

  • Merging

Tues, Oct 10

  • Plotting

Thurs, Oct 12

FALL BREAK

Tues, Oct 17

FALL BREAK

Thurs, Oct 19

  • Plotting

Tues, Oct 24

  • Big Data: What is it, how do I work with it?

Thurs, Oct 26

  • Defensive Programming

  • Workflow

  • Backwards Design

  • Getting Help Online

Tues, Oct 31

  • Defensive Programming

  • Groupby / Split-Apply-Combine

Thurs, Nov 2

  • Pandas: Reshaping

  • Pandas: Categoricals

Tues, Nov 7

  • Speed and Performance in Python

Thurs, Nov 9

  • Statistics with statsmodels

Tues, Nov 14

  • Machine Learning with sckikit-learn

  • JVP Chapter 5 up to “Hyperparameters and Model Validation” Section (pp 331 - 359)

Thurs, Nov 16

Tues, Nov 21

No Class

Thurs, Nov 23

THANKSGIVING

Tues, Nov 28

  • Defining Your Own Estimators

  • Regex

Thurs, Nov 30

  • Parallelism

  • Distributed Computing

(Note reading includes a 45 minute video to watch) Optional: - Setting Up Cloud Cluster

Wed Dec 13

Final Project Report and Presentation Due
\ No newline at end of file diff --git a/lesson_plan_notes/2023/2023_10_24.md b/lesson_plan_notes/2023/2023_10_24.md index 6585fbf..66d0809 100644 --- a/lesson_plan_notes/2023/2023_10_24.md +++ b/lesson_plan_notes/2023/2023_10_24.md @@ -9,3 +9,19 @@ Pros of class: Could be improved: - Pairing MIDS people with non MIDS people can be quite tough as non MIDS people have different availability and may have different working styles that can start to be detrimental rather than useful especially when the differences create a communication barrier with your randomly assigned pair and you have a hard time working with them. +- Group assignments should be more than individual ones. It's time effective as discussions solve most of the doubts. +- In addition to the assignments, I would really appreciate if we had mini-projects/projects due every two weeks or so that holistically incorporates every concept we've learnt so far in those two weeks. It would also really help to solidify the learning process, and understand how two concepts work with each other. +- More time to work in class + +good self: + +- Giving the readings time +- I am thinking critically about the libraries by using the offical documentation, this helps me see ALL the options for some of these commands and opens more doors than just doing the exercsies. + +improve self: + +Better Time management +Actually I am not sure if I should focus on applying jobs more or assignments. +Time management is the most important thing that can help me maximise learning. +Sometimes I need more time to finish all of the readings. When giving feedback to my partner, there are some things that I feel other people can improve, but I was so shy and did not point it out in the feedback. +Spend more time with the readings diff --git a/source/class_schedule.csv b/source/class_schedule.csv index db76f4e..976875a 100644 --- a/source/class_schedule.csv +++ b/source/class_schedule.csv @@ -79,7 +79,8 @@ - Backwards Design - Getting Help Online","- `Workflow Management `_ - `Backwards Design `_ -- `Getting Help `_", +- `Getting Help `_ +- `Opioid Project `_", "Tues, Oct 31","- Defensive Programming - Groupby / Split-Apply-Combine","- `Defensive Programming `_ - `Iceberg Principle `_ @@ -88,11 +89,11 @@ - Pandas: Categoricals","- WM 8.3 - `Pandas reshaping (with pics!) `_ - `What is goal of reshaping? `_ -- Categoricals: WM 12.1 -- **Project Strategy Plan Due**", +- Categoricals: WM 12.1", "Tues, Nov 7",- Speed and Performance in Python,"- `Understanding Performance `_ - `Improving Performance `_ -- `Code Reviews `_", +- `Code Reviews `_ +- **Project Strategy Plan Due**", "Thurs, Nov 9",- Statistics with statsmodels,"- WM, 3rd Edition, Chapter 12, 12.1, 12.2, 12.3 (Patsy and statsmodels, not scikit-learn) - `Skim ""Linear Models"" `_ - `Skim ""Discrete Dep Var Models"" `_