Unlike courses, live training sessions are designed to be more open-ended, with a lot more room to create a training that replicates 2 to 3-hour real-life data science projects. Accordingly, they are easier and faster to author as the amount of content expected to be created is considerably lower than an average DataCamp course. The following describes the README document in each live training repository, make sure to read up on how to set up your repository here.

README Outline

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 ... Here’s a list of questions that will help you think about what your live training session could look like.

Step 1: Foundations

A. What problems will users learn how to solve?

B. What technologies, packages or functions you want to use?

  • It’s okay if you don’t have a thorough list of functions you’re going to use now as you’ll build that list of syntax as you build your live training.

C. What are terms or jargon you want to define?

  • Whether during your opening and closing talk or your live training, you might have to define some terms and jargon to walk learner through a problem you’re solving.

D. What mistakes and 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 learners might encounter along the way.

E. What are the dataset(s) you will use?

  • Live training sessions are designed to walk learners 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 out our list of datasets to avoid.

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 learners have above and beyond what's included in the persona.

Click here to learn more about Learner Roles and Choosing Roles

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 learners 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: 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 introductory and ending slides and at least 3 Q&A sessions) with a hard-limit at 3h30m.

You can check out our live training content guidelines here.

Example from Python for Spreadsheet Users:

Example Session Outline:

Introduction Slides

  • Introduction to the webinar and instructor (led by DataCamp TA)
  • Introduction to topics
  • Discuss need to become data fluent (eg; 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 and use .describe(), .info()


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()


Ending slides

  • Recap of what we learned
  • The data science mindset
  • Call to action and course recommendations

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