Exploring courses in Data Science

Following on my previous post

There are many programs available addressing the unique needs of different segments of learners. Typically, these can be studied using the following attributes:

·????????Full-time or weekend

·????????Delivery modes: Classroom, Online Live, E-learning, Hybrid

·????????Focus areas: Data Science, Data Engineering, Industry, Technology

·????????Eligibility or Learner profile: Fresh graduates, working professionals, executives

·????????Campus placement (Y or N)

There is demand in the industry for candidates with various profiles. Examples: Executives with data science proficiency, professionals who can participate in a new data science project and later contribute as users, data scientists, data engineers, visualisation specialists etc.

In short, this skill set will make practically everyone more valuable.

Several common concerns are voiced as learners explore data science course options. The following are among the top concerns. My responses below are based on my experiences delivering related courses in universities, Ed Tech firms and corporate settings.

“Does this require programming?”

·????????Courses are customised for the typical learner profiles to help gain basic programming and math needed to grasp the targeted data science concepts. To elaborate, an Executive program may use intuitive user interfaces offered by the likes of Power BI, Tableau or KNIME to demonstrate concepts, while others could use Python and R programming languages. E.g. recently, I mentored HR executives to execute a short project using a Logistic Regression plug-in in Excel. The same concepts and algorithms can be demonstrated using different sets of technologies. Depending on the participants, these courses also modulate the level of depth in the algorithms.

“How deep should I be in math?”

·????????One way of defining this is higher secondary (10+2) level. However, for data science courses, we need comfort with only a subset of topics - Linear Algebra, Statistics, and Calculus. When we approach a topic like Calculus for data science, it is application-oriented and turns out to be much easier to grasp. Python can be a big help here. Yes. With just a few lines of unscary code, you can differentiate functions. The same goes for Statistics and Linear Algebra. Learners with varied experience levels and comfort with math have joined these courses and done well.

Hope this helped! I will follow on with full-time program options for degree holders aspiring to enter the industry as data scientists. Feel free to add your views and questions in reply or DM.

In an era of turbulence, helping someone become more valuable in their job search or current job is the best gift we can give them. Let us.