Let’s face it, there are SO many online and offline Data Science courses these days. Plus, these courses do not come cheap. Even though most of the material is freely available on the Internet, finding them in one curated place is a challenge.
Here, I am outlining some of the steps I take before I join a class.
- Make your intention clear – Why am I learning this course?
- Check your eligibility – Do you have basic computer skills and communication skills?
- Check the experience of the course provider- How long has the institute been giving the courses?
- Look for reviews – Look for reviews online and offline from alumni.
- Talk to instructors regarding the syllabus – Both in terms of depth and breadth.
- Experience of the course instructor -Check the education and work experience of the instructor and match them with the course syllabus.
- Timelines – Check the number of hours of access you get for the class and the number of weeks/months you have access to the course materials.
- Projects and Capstone – Ensure the course involve either multiple hands-on projects or Capstone or both.
- Interaction with faculty – How often will you be able to directly interact with faculty and for how long? What is the minimum turnaround to get your doubts cleared?
- Statistics and mathematics – Check if modules have sufficient levels of mathematics and statistics.
- Labs and Frameworks – Do the course provide you access to the necessary software frameworks through virtual labs for your practice?
- Exams – How does the grading system work?
- The validity of the certificate – Check in what domains the certificates are valid.
- Check if the placement services, both internal and external are legitimate by speaking to staff and alumni.
Thank you Nigar Yusuf for helping us improve this post by giving us your insights!
Other than these points, if you have anything more to add, do put them in the comments section or write to firstname.lastname@example.org.