As we can there is a demand for a data scientist, and as we can see side by side training programs are also growing with some training programs for teaching the people of data science within the companies and many institutions. So how can we easily meet the demand for the jobs, the once facilitating the classrooms for data science must need more of the resources. So here you can see some real-life instructors just to make sure that there is a proper effective environment of learning for students there.
1. Participation of every individual is necessary for every individual
Create an atmosphere where every single student is involved with the things you are teaching, always interact with every single student of your class so they feel that they are involved in the learning. Prioritize people of color, women, LGBTQIA+, and folks with disabilities when assessing participation rates in the class.
2. The students having no knowledge of technical background should be kept up
In data science, every individual student has various levels of expertise. Check the material on a daily basis so that students are alert for bringing their study material. Every student should get a chance to reach out to the teacher if they feel that they are lacking behind.
3. You should always be an engaging speaker
Data science is not an easy topic to comprehend with all the coding, math, and analysis that goes into this type of work. When teaching data science lessons, ensure that you are an engaging and confident speaker. Pace yourself and speak clearly. Ensure that your students are engaged in your material and understanding each step of your process well.
4. Lessons should be started with the answers to your problem sets.
Data science offers a set of tools to answer and analyze real-life questions. Sometimes those tools can get overly complicated and it's easy to lose students in the process of explaining a concept. Always begin lessons with the answer to the data science question you're asking. For example, if the problem set is about predicting outcomes, begin the lesson showing what the predicted outcomes might look like. It's easier for students to follow along with the minutiae of data science processes if they clearly understand why they are conducting each step.
5. Always translate numerical outputs and data visualizations into clear sentences.
This one is similar to the step above but places emphasis on how you state your conclusions. Always translate numerical outputs of your code and your data visualizations into clear sentences. What do the numbers you have calculated signify? What do your data visualizations reveal about your data? Go back to your original data science question and state the answers clearly.
6. Introduce new and/or alternate coding methods in separate cells.
Most data science courses use Jupyter Notebook for instruction. If you are showing multiple methods to code a concept or if you are making edits to an original block of code that alters an output, use a new cell. Do not keep iterating on one block of code and changing your output within one cell. Teach your code in steps, and showcase variations to your code in new cells.
7. Tie previous lessons to the current one.
When introducing new concepts, demonstrate and contextualize how the current lesson connects to what students have learned already. One way of doing this is through using tools like mindmaps to illustrate how the topics are related to each other. This empowers students with context and tools to communicate what they have learned so far.
8. Use inclusive datasets.
Data science is a powerful tool to help uncover hidden truths and unknown stories about the world. Use the potential of data science to explore datasets that are overlooked. For example, if you are using sports data to illustrate a concept, why not use WNBA datasets instead of the NBA? To determine how inclusive your datasets are, make a list of all the datasets you use in the entirety of the course you are teaching. Who and what do your datasets represent? And who and what is not being represented?
9. Use real datasets.
And while we're on the topic of datasets, make sure to use real-life datasets in your lessons. Yes, it's still extremely useful to use the iris, wine, breast cancer, and Boston toy datasets. However, whenever possible, supplement your teaching material with actual and attainable datasets. Get students used to answering real-life questions using data about the real world that they can take with them beyond the classroom. There are tons of places to acquire open source data that you can use.
10. Have fun!
Create a learning environment that's fun! This might be the most important step to teaching data science well. Have fun in your classroom, whether its an in-person or remote setting. Data science is an exciting and revolutionary topic to be learning and sharing. As an instructor, you should feel proud of your ability to democratize knowledge and make data science accessible to all interested students.