Data Analysis and Data Science Foundation (SkillsFuture course)

COURSE DESCRIPTION

SkillsFuture credit application is in progress. Course id is CRS-N-0047529. All Singaporeans aged 25 and above can use their $500 SkillsFuture Credit from the government to pay for a wide range of approved skills-related courses. See www.skillsfuture.sg/credit for more details.

This is a face to face class, with additional self-learning practice on Solace Tech’s e-learning platform here in between the sessions, spread for 6 days before the class and the second face to face session. Participants expected to run through the e-learning portal before the course for the participants’ benefit, since usually for complete beginners it is harder to grasp the concept without repetitions.

Here is the learning flow of the course:

1. Participants spend 3 hours self-learning one week before the course, to read materials and do exercises, 7 days before the course. The goal is to get familiar with some materials that are not straightforward and need repetition to understand.
2. The first face to face session, participants will learn with instructor guidance for 7.5 hours. In this session, participants expected to have questions from self-learning session, and the instructor will help to connect the dots
3. Another one week period to be spent for self-learning, where participants expected to spend 3 hours to do exercise and to submit the exercises to be evaluated by Solace Tech’s team, to assess participants’ understanding of the first session.
4. The second face to face session, another 7.5 hours, participants will do a project guided by the instructor.

The course will start at 9 am and ends at 5.30 pm, with a 1-hour lunch break.

After this course, participants should be able to :

• acquire data by consuming an API
• use standard tools in data science project (Conda, Miniconda, Jupyter)
• Store and analyze data in a relational database, load the data from a database into python for analysis
• clean and transform data
• Familiar with standard libraries used in data science project (Numpy, Pandas, scikit-learn)
• Understand the basic concept of data visualization and standard charts (barplot, histogram, line chart, boxplot, scatterplot)
• Understand how a data science project end to end with basic linear modeling
• Understand all the activities to be done in a typical data science project
• compete on Kaggle to get recognized within a data science community

The course will be a hands-on approach where participants will be expected to write code. Participants will receive e-certificate at the end of the course to mark the completion of the course.

This course is also the pre-requisite course to join advance data science course here.

Maximum 10 participants per class. The venue would be at Depot Road.

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is canceled, we will refund 100% to you.

Note that the minimal class size to start a class is 3.

• Familiar with Python syntax, and a basic understanding of Python data structure, especially list and dictionary.
• Familiar with running python scripts from a command line or IDE

For those not familiar with the above topics, you can join the Python introduction course to prepare for this course.