Machine Learning with Scikit-learn
by Adel Nehme

Live training sessions are designed to mimic the flow of how a real data scientist would address a problem or a task. As such, a session needs to have some “narrative” where learners are achieving stated learning objectives in the form of a real-life data science task or project. For example, a data visualization live session could be around analyzing a dataset and creating a report with a specific business objective in mind (ex: analyzing and visualizing churn), a data cleaning live session could be about preparing a dataset for analysis etc ...

As part of the 'Live training Spec' process, you will need to complete the following tasks:

Edit this README by filling in the information for steps 1 - 4.

Step 1: Foundations

This part of the 'Live training Spec' process is designed to help guide you through session design by having you think through several key questions. Please make sure to delete the examples provided here for you.

A. What problem(s) will students learn how to solve? (minimum of 5 problems)

Here's an example from the Python for Spreadsheeets Users live session

Key considerations to take in when transitioning from spreadsheets to Python.

The Data Scientist mindset and keys to success in transitioning to Python.

How to import .xlsx and .csv files into Python using pandas.

How to filter a DataFrame using pandas.

How to create new columns out of your DataFrame for more interesting features.

Perform exploratory analysis of a DataFrame in pandas.

How to clean a DataFrame using pandas to make it ready for analysis.

Apply common spreadsheets operations such as pivot tables and vlookups in Python using pandas.

Create simple, interesting visualizations using matplotlib.

B. What technologies, packages, or functions will students use? Please be exhaustive.

pandas

matplotlib

seaborn

C. What terms or jargon will you define?

Whether during your opening and closing talk or your live training, you might have to define some terms and jargon to walk students through a problem you’re solving. Intuitive explanations using analogies are encouraged.

Here's an example from the Python for Spreadsheeets Users live session.

Packages: Packages are pieces of software we can import to Python. Similar to how we download, install Excel on MacOs, we import pandas on Python. (You can find it at minute 6:30)

D. What mistakes or misconceptions do you expect?

To help minimize the amount of Q&As and make your live training re-usable, list out some mistakes and misconceptions you think students might encounter along the way.

Here's an example from the Data Visualization in Python live session

Anatomy of a matplotlib figure: When calling a matplotlib plot, a figure, axes and plot is being created behind the background. (You can find it at minute 11)

As long as you do understand how plots work behind the scenes, you don't need to memorize syntax to customize your plot.

E. What datasets will you use?

Live training sessions are designed to walk students through something closer to a real-life data science workflow. Accordingly, the dataset needs to accommodate that user experience. As a rule of thumb, your dataset should always answer yes to the following question:

Is the dataset/problem I’m working on, something an industry data scientist/analyst could work on?

Check our datasets to avoid list.

Step 2: Who is this session for?

Terms like "beginner" and "expert" mean different things to different people, so we use personas to help instructors clarify a live training's audience. When designing a specific live training, instructors should explain how it will or won't help these people, and what extra skills or prerequisite knowledge they are assuming their students have above and beyond what's included in the persona.

Please select the roles and industries that align with your live training.
Include an explanation describing your reasoning and any other relevant information.

What roles would this live training be suitable for?

Check all that apply.

What industries would this apply to?

List one or more industries that the content would be appropriate for.

What level of expertise should learners have before beginning the live training?

List three or more examples of skills that you expect learners to have before beginning the live training

Can draw common plot types (scatter, bar, histogram) using matplotlib and interpret them

Can run a linear regression, use it to make predictions, and interpret the coefficients.

Can calculate grouped summary statistics using SELECT queries with GROUP BY clauses.

Step 3: Prerequisites

List any prerequisite courses you think your live training could use from. This could be the live session’s companion course or a course you think students should take before the session. Prerequisites act as a guiding principle for your session and will set the topic framework, but you do not have to limit yourself in the live session to the syntax used in the prerequisite courses.

Step 4: Session Outline

A live training session usually begins with an introductory presentation, followed by the live training itself, and an ending presentation. Your live session is expected to be around 2h30m-3h long (including Q&A) with a hard-limit at 3h30m. You can check out our live training content guidelines here.

Example from Python for Spreadsheet Users

Introduction Slides

Introduction to the webinar and instructor (led by DataCamp TA)

Introduction to the topics

Discuss need to become data fluent

Define data fluency

Discuss how learning Python fits into that and go over session outline

Set expectations about Q&A

Live Training

Exploratory Data Analysis

Import data and print header of DataFrame pd.read_excel(), .head()

Glimpse at the data to

Get column types using .dtypes

Use .describe(), .info()

Q&A

Data Cleaning and making it ready for analysis

Convert date columns to datetime pd.to_datetime()

Change column names

Extract year, month from datetime .strftime()

Drop an irrelevant column .drop()

Fill missing values with .fillna()

Creating a report

First report question: What is our overall sales performance this year? .groupby(), .plt.plot()

Second report question: What is our overall sales performance this year? .merge(), .groupby(), plt.plot()

Third report question: What is our overall sales performance this year? .merge(), .groupby(), plt.plot()

Q&A

Ending slides

Recap of what we learned

The data science mindset

Call to action and course recommendations

Authoring your session

To get yourself started with setting up your live session, follow the steps below:

Download and install the "Open in Colabs" extension from here. This will let you take any jupyter notebook you see in a GitHub repository and open it as a temporary Colabs link.
Upload your dataset(s) to the data folder.
Upload your images, gifs, or any other assets you want to use in the notebook in the assets folder.
Check out the notebooks templates in the notebooks folder, and keep the template you want for your session while deleting all remaining ones.
Preview your desired notebook, press on "Open in Colabs" extension - and start developing your content in colabs (which will act as the solution code to the session). ⚠️ Important ⚠️ Your progress will not be saved on Google Colabs since it's a temporary link. To save your progress, make sure to press on File, Save a copy in GitHub and follow remaining prompts. You can also download the notebook locally and develop the content there as long you test out that the syntax works on Colabs as well.
Once your notebooks is ready to go, give it the name session_name_solution.ipynb create an empty version of the Notebook to be filled out by you and learners during the session, end the file name with session_name_learners.ipynb.
Create Colabs links for both sessions and save them in notebooks 🎉

AnthonyGachuru/machine-learning-with-scikit-learn-live-training

Machine Learning with Scikit-learnby Adel Nehme