1.0 Introduction
As some people rightly say, "Data is the new oil". Is does not necessarily mean one can make a lot of money by getting into data sciences, though it is one of the lucrative career options at this point in time. Data is the new oil, or any new ore, for that matter. Like an ore can be explored in many different ways right from manual excavation to the most modern mechanized and automated explorations, data can be explored in many ways, right from finding out the mean and standard deviation of a given small data set up to applying the most modern computational techniques.
In India, there is a lot of gap between the supply and demand of engineers, with many engineering colleges even at the remotest parts of the country. It is practically impossible for all the pass-outs to get jobs in their own fields of study even if there is a significant improvement in the curriculum to make the students industry-ready by the time they pass out.
Like the engineers of the last 2-3 decades had to be computer literates by the time they passed out from the colleges even to survive in their own field, the engineers of present generation and next generation need to be data literate. This data literacy can be achieved without extensive efforts from one's end unless one wishes to be highly specialized in data sciences.
Right from high school, the students of mathematics are exposed to the rudiments of probability and statistics, and so are engineering students at the undergrad level! Applying these concepts in their own field of study to study the data that they deal with, enhances their understanding of the subject matter and enables them to make data-driven decisions. This also opens an additional avenue for job opportunities.
1.1 Target readers
The target readers of this blogspot article are engineering students of non-IT streams who want to improve their data skills and faculty members and managements who wish to improve their campus placements.
1.1 Target readers
The target readers of this blogspot article are engineering students of non-IT streams who want to improve their data skills and faculty members and managements who wish to improve their campus placements.
2. Developing the skills
A non-IT engineering student has many necessary skills in data sciences as one studies advanced concepts of probability and statistics as part of engineering maths, and acquires a good amount of coding skills. Is that enough?
2.1 Courses
To understand terminology used in and to get acquainted with the state-of-the art data sciences, an engineering undergraduate can do some of the online courses. To enroll for a new course offered by IITs and get certified, one can visit www.swayam.gov.in. To listen to the archived courses, one can visit www.nptel.ac.in. Search using the keywords Data Science and Data Analytics and you will find many courses - introductory to advanced. A beginner can do introductory courses.
One can also go thru the video lectures in Coursea, Udacity etc.
2.2 Coding
One can use any programming language to write a program for solving the equations. However, to capitalize on the several readily available libraries, one can learn Python and/or R.
2.3 Software packages
Students have the privelege to freely download many software packages from the net by furnishing their credentials. Look for the below:
1. Student versions available for free
2. Free software (see https://www.predictiveanalyticstoday.com/top-free-statistical-software/ for recommendations)
3. Packages available with your academic institution
4. Excel Analysis Toolpak available as an add-in in your MS Exce. See the link below to find how to activate it:
Get in touch with experts in the field for their opinion which can include your teachers in the IT/Computer Science/Mathematics/Statistics departments.
3. Applying the skills in your project work
To apply the skills, one can do their undergraduate project work. To infuse data analytics into one's own project work, the following steps can be taken.
3.1 Define your problem
This can be as visualized by you or your project guide. You may also let your guide know that you are interested in a project that involves some sort of data analysis.
3.2 Gather data
This can be done in several ways. Could be from experimentation, could be from data generated from engineering formulas, could be from literature, or could be from databases available in records, standards, archives etc.
3.3 Visualize the data
This can be achieved by plotting. If the data is too large, you may want to do sampling.
3.4 Analyze the data
How much data? Entire data set or a sample? What is the right tool? How do we test?
Depends on the nature of your problem, no. of factors involved, size of the data etc.! For this, the knowledge gained from coursework will help.
3.5 Interpret the data
After the analysis, interpret what you have observed out of it i.e. the results of the analysis.
3.6 Develop solution
If your problem definition is to find the trend, you may not have to execute this step and conclude your project at the above step itself. If you have found what is the cause of the problem, you will have to present a solution. Note that it is generally expected in engineering profession that the person who find a problem also needs to find its solution!
3.7 Test the solution
Once you have developed a solution, you will have to test the solution by doing some iterations of reanalyzing the new data set and testing if the solution works.
3.8 Conclusion and presenting the report
Like in any other project work, this is the final step!
3.9 DMAIC - A popular technique
Define, Measure, Analyze, Improve and Control (DMAIC) is one popular technique where there is a good scope for applying the data analytics skills. For understanding and appreciating the process, visit:
- Brush up your probability and statistics basics.
- Do some good data analytics/data science courses online, preferably from swayam.gov.in and/or nptel.ac.in.
- Apply the skills in your project work.
- Interact with your peers in the IT stream who should be doing some data science courses as part of their curriculum.
- Choose your mathematics/statistics/IT faculty member as your co-guide.
5. Note for the academic institutions
Showcase the skills of your students to the prospective recruiters and invite more and more companies from both IT and non-IT streams to your campus!
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