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.
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.
- Explore a dataset in a SQL database by examining table structure and data types
- Converting or casting common data types
- How to manipulate date time data using built-in date and time functions
- How to manipulate string data using built-in string and character functions
- Create temporary tables and use them to simplify complex queries
- Learn how to use temporary tables to create new fields from unstructured string data
- Create user defined functions that can be used to simplify data manipulation tasks
- EXTRACT, DATE_PART, DATE_TRUNC
- SUBSTRING, LEFT, RIGHT
- array()
- split_part()
- CHAR_LENGTH, POSITION
- CAST
- CREATE TABLE, DROP TABLE
- CREATE FUNCTION
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.
- Data types
- Built-in functions
- User-defined functions
- Date / time characteristics like "date parts"
- Normalization
- TEMP tables
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:
Short Term Rentals in Cambridge, MA - https://data.cambridgema.gov/Inspectional-Services/Short-Term-Rentals/wxgv-w968
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.
Check all that apply.
List one or more industries that the content would be appropriate for.
List three or more examples of skills that you expect learners to have before beginning the live training
- Can use SELECT queries to retrieve data from PostgreSQL database
- Can use PostgreSQL built-in functions in SQL queries
- Understands what a user-defined function is and how to use one in SQL queries
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.
Functions for Manipulating Data in PostgreSQL Intermediate SQL Exploratory Data Analysis in SQL
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.
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
Exploring the Dataset
- What Have We Learned About the Short Term Rentals Data
- Q&A
Using built-in functions to mainpulate string and character data
- Converting data from one type to another using
CAST()
- Extracting string data using
SUBSTRING()
andPOSTIION()
- Extracting string data using `split_part()
- Using
ARRAYS
to manipulate strings stored as comma-separated-values- What Have We Learned About the Short Term Rentals Data
- Q&A
Using temporary tables to simplify complex queries
- Creating a temporary table with our rental services
User-defined functions to create reusable code
Putting it all together!
Q&A
Recap and closing
To get yourself started with setting up your live session, follow the steps below:
data
folder.assets
folder.notebooks
folder, and keep the template you want for your session while deleting all remaining ones.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.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
.