In the recent market, predicting the behavioral patterns of consumers has been an ongoing trend. Distance e-Learning has always been a supportive option to boost-up one’s career and growth. At times, it fails in delivering the exact requirement as to why the course should be studied. It is a fact that data-driven decisions are more precise and it helps in mining out the exact areas of improvement. Collection of historical data and further analyzing puts firms at a competitive advantage of others. Now, the question arises as why and when an individual decides to focus in learning Data Science i.e. what compels them to search and look for courses over the internet. Data Science as a full time degree is yet to be implemented for undergrads in the top institutes of our country therefore this research paper discusses the similar needs at the root level. It also explores the various scopes of Data Science like Data Analysis, Data Visualization, Business Intelligence, Business Analysis and similar. It is necessary to understand the perception of the consumers as in professionals for figuring out the motive behind studying the same. According to National Association of Software and Services Companies (NASSCOM), the data analytics sector in India is expected to witness eight-fold growth to reach $16 billion by 2025 from the current level of $2 billion. It is not clear whether this forecast drives professionals towards Data Science whether as an upgrade to their profession, as a sudden trend or certain factors like “employability”, “Emergence of AI” or linking “Innovation to Entrepreneurship” through various start-ups. The present study intends to investigate on these important segments.

The field of Data Analytics in business to enhance the education sector is at nascent stage. ‘eLearning’ is just now in its infancy [1] Lot of work in the form of research models and methods are being proposed. In addition to helping to enhance the students experience, the ‘engagement analytics’ are providing increased opportunities for cross-institutional development  [2] Yet, most of the time it is rather difficult to avail such opportunities, choices left are to grab the internet to exploit its resources. Today’s in short ‘e-Learning’ education traces its history where it all began some time ago, when ‘Open CourseWare’ was introduced, and this enabled the promotion and growth of open and free online learning which everywhere seems to heard of or known. These types of courses are called “Massive Open Online Courses,” more commonly known as “MOOCs” [3] The expansion of MOOCs in the 21^st Century of online learning is utilized by the millions of participants from all over the globe who enroll themselves for the same. MOOCs bring revolutionary innovation to elementary education as well as to higher education.The broad availability of data has led to increasing interest in methods for exploring useful knowledge relevant to education—the realm of data science [4] .Data is accessible through any of the electronic devices available today, some companies even allow offline readings i.e. download the chapters and study according to one’s own free time. 

Further, with the improvement of bandwidth, video, and storage technology, the demand for e-Learning products and service will increase exponentially. The process starts with an individual going to any search engine and looking for courses. Smart algorithms are present to track their usage patterns and starts recommending choices one by one through videos, posts on social media and similar. Google Analytics is used to gather information about a site, such as the number of visits, pages visited, the average duration of each visit, and demographics [5]. The past couple of years saw a rapid increase in demand for learning data science, analytics business intelligence and similar. In an article in the New Yorker, the President of Stanford, John Hennessy said, “There’s a tsunami coming” and Daphne Koller, a professor of Computer Science at Stanford University and the co-founder of ‘Coursera’, responded by saying “The tsunami is coming whether we like it or not. . . You can be crushed or you can surf and it is better to surf” [6] Various start-ups are coming forward and contributing to this by providing lucrative courses and career options through this. Pioneering institutions are extending and diversifying their reach through collaboration with ‘e-Learning’ firms by offering diplomas and certificates for industry absorption. Learning Analytics emerges as a fast-growing and multi-disciplinary area of TEL [7] which forms its own domain  [8] It offers an array of courses like in the field of Mathematics, Science, Social Sciences, Art to name a few; from a wide variety of Universities to choose from. The anticipated success of MOOCs vary between business purposes such as saving costs and scenarios of improvement involving the pedagogical and educational concepts of online learning  [9] Mentionable features offered are where participants go with their own sweet pace, complete the course and once a course has been registered it stays with the participant for lifetime. Another advantage of MOOCs is their long-term ability to contribute to lifelong learning as well as Technology Enhanced Learning (TEL) contexts [8] In India, prime institutions like IIT’s and IIM’s including some IIIT’s and BITS Pilani are offering online courses collaborating with platforms through international providers edX and domestic UpGrad. Academicians, undergraduates, post graduates and teachers are all benefiting from this service. Among them high-level of awareness is evidenced among Engineering and Management students in India [11]. Another dimension of e-Learning widely used by managers is to inculcate training amongst the employees to make them up to date with latest changes and trends in technology and innovation. E-learning is less expensive than traditional classroom instruction). All these factors amalgamated together have just ignited the craze towards shift in data science, analytics and business intelligence. Being such a new phenomenon, one must wonder if all this hype is generated from their substantial contribution to the intellectual development, or if it is a result of a promise of new emerging technologies .In other words, data-driven decision-making through the collection and analysis of educational data is increasingly used to inform policy and practice, and this trend is only likely to grow in the future [12]

Objective

• To study the awareness level of respondents regarding Data Science.

• To identify the demographic determinants affecting respondents perception towards data science

Data Collection

The study is based on both primary and secondary data. The required secondary data were collected through journals, books, industry reports, blogs and other electronic sources. In order to collect the primary data, structured questionnaires on Five point Likert scale has been used.

Sampling Technique

The present study is based on Purposive sampling which is a technique of Non-probability sampling. The target population of the sample consists of a mixture of age groups starting from 18 to 39 and above. It also includes other amalgamation of demographic determinants like gender age, currently involved, specialization. On the students of Assam a comparative study among the youth who constitute the future. For analyzing the data, a sample of 80 has been taken. Pearson correlation has been used to study the relation among the perception and demographic determinants using IBM SPSS statistics

Data Analysis and Presentation

All questions were measured on s Likert scale of Five Points. “Pearson’s Chi-sq” test on demographic constraints: Gender and Marital Status and “one way ANOVA” on demographic constraints: occupation, income and age have been used to test hypothesis. Coding criteria as follows:

Highly Aware =    5              Highly think = 5

Often Aware =      4              Often Think = 4

Usually Aware =  3              Usually Think =   3

Sometimes Aware = 2        Sometimes Think = 2

Not Aware = 1                      Not Think = 1

There are two sections in the questionnaire, basically divided into two sections “Awareness” and “Perception”. Each section is further grouped upon the questions pattern. Consumer’s responses were recorded and further assessed to prove the hypothesis.

• H1: There exists a significant relationship between gender and respondents’ awareness and knowledge regarding data science and related terminology. 

Fig.1

Pearson’s Chi-sq test has been done on both to find out whether a significant relationship exists or not and to prove the hypothesis. On comparing the two ‘p’ or significant values we find that the p value is a0.399 much greater than 0.05. Since the significant p value needs to be ≤ 0.05. We can conclude that there exists no significant relationship with awareness in terms of gender.

• H2: There is a significant relationship between gender and respondents’ awareness towards career prospects of Data Science.

Fig.2

The above figure shows the significant relation between gender and the career prospects of awareness i.e. whether the respondents are aware about the future scope Data Science might provide. The significant value obtained is 0.332 thereby we reject out null hypothesis. There exists no significance with gender, both females and males are quite aware of it.

• H3: Gender plays a significant role in acknowledging the availability and utility of data science.

Fig.3

From the above results the p value calculated is 0.732 which is quite greater than the significant value; it contradicts our null hypothesis that gender plays a significant role is acknowledging the availability and utility of data science. Respondents had clear idea regarding the upcoming prospects this field had to offer.

• H4: There exists a significant relationship between Age and respondents’ awareness and knowledge regarding data science and related terminology

Fig.4

Pearson’s Chi-sq test was performed to find out relationship of significance between Age and Awareness. Questions were grouped into clusters of two and asked, to check whether knowledge had any significance on data science and related terminology. From the tests the Chi-sq p value came out to be 0.563 and 0.405, It was then and there concluded Age played no such significance on these two.

• H5: There is a significant relationship between Age and respondents’ awareness towards career prospects of Data Science

Fig.5

Noted p values from the observations are p= 0.708 and p= 0.332 for the two clubbed question about career opportunities by data science. These significant values are no way near to 0.05 so we can reject our null hypothesis that age holds a significant relationship with career prospects with data science.

• H6: Age plays a significant role in acknowledging the availability and utility of data science as a course to learn.

Fig.6

Similarly two questions on the utility and availability of data science as a course were asked to the respondents, the first significant value is very close to 0.05, i.e. 0.057 which almost builds a relation with age in the category of (18-26) years of young students comprising of engineers, management and other PG students. In the second question on the availability of the course on internet as a platform, p value is 0.017 which lets us accept the null hypothesis. 

• H7: There is a significant relationship between Gender and respondents’ perception and knowledge regarding Data Science, Data Analyst and Data Engineer are synonymous terms.

Fig.7

Questions were asked to the respondents to know their perception level on the terms and check whether there exist significance with gender and perception and knowledge, calculated p value is 0.624 which is enough to reject our null hypothesis and confirm that there doesn’t exist any relation.

• H8: There is a significant relationship between Gender and respondents’ perception towards career growth through Data Science. 

Fig.8

Comparing the p values 0.625 with 0.05 it seen and quite understood that Gender has no significant relationship with perception towards career growth through Data Science. Both the genders seem to perceive on a equal tone.

• H9: Gender plays a significant role in indentifying the perception that data science allows job diversity in MSME’s through AI learning experience.(Q3&Q4)

Fig.9

Here the two questioned grouped to know what the respondents thought upon data science opening a path in MSME’s (Ministry of Micro, Small and Medium Enterprises) through AI (Artificial Intelligence) learning experience and whether the gender played any role in it. Both the p values 0.527 and 0.308 clearly show that is not the fact. Male and females gave positive responses on it.

• H10: There is a significant relationship between gender and professionals’ perception about early introduction of Data Science in curriculum and its certification weight. 

Fig.10

These two particular questions asked respondents about introduction of data science in preliminary level and the actual weight the certification matters in Indian concept. On finding out the significant values p= 0.238 and p= 0.726 it is sorted that gender plays no significant relation with the same. Both males and females perception was proportionate. 

• H11: There is no significant relationship between Age and respondents’ perception and knowledge regarding Data Science, Data Analyst and Data Engineer are synonymous terms

Fig.11

Obtaining the p value of 0.020 we can reject the alternate hypothesis. The age category (18-25) showed positive responds more than the second category and was quite thorough with the terms and difference and definitions on later questioning and lacked in depth definition.

• H12: There is a significant relationship between Age and respondents’ perception towards career growth through Data Science.

Fig.12

As per the above results got the p value is 0.026 we can accept our null hypothesis. Engineering as well as management students in the age category of (18-25) showed significant perception on data science towards career growth. Other results obtained through similar tests gave p value of 0.293, 0.586, 0.346 and 0.356 respectively. None were significant enough to establish a relationship with the independent variables.

Performing one way ANOVA tests on currently involved and specialization gave results like:

• H13: Significant relationship of awareness and perception with currently involved status

Fig.13

Only one value p= 0.055 is almost close to the significant value, where the question was awareness involving data science courses were only taught as masters and post masters courses with no scope for bachelors full time yet. Yet, it is not enough to build a significant relationship.

Fig.14

All the significant values are greater than the Null Hypothesis values so it can be rejected. Also, the F values are quite larger compared to the significant values. It means higher the F value lower would be the significance. Undergraduate , post graduate and doctoral students showed a higher perception value towards data science but not enough to build a significant relationship.

Significant relationship of awareness and perception with specialization status

Fig.15

From the above data it is seen that in terms of specialization there exists significance (0.022) when it comes to awareness of data science only being taught as post graduate. Respondents were classified as engineers, management students, Master of Science and research scholars, specialization wise from these, engineers especially CSE and ECE and management students showed more awareness.

Fig.16

Here, no such significance could be build that would establish a relation as no significant value is close to 0.05. We can accept the fact that there exists no significance with any specialization particularly for the above mentioned. 

From the study a number of inferences have been noted. This research paper aimed at studying student’s awareness and perception towards data science which results in as a sudden trend or certain factors like “employability”, “Emergence of AI” or linking “Innovation to Entrepreneurship” through various start-ups in future.

Demographic factors included gender age, current involvement and specialization from a sample population of 80 were calibrated. The most important outcome of this research is students have awareness of the terminologies but lacked in depth knowledge of the field. This test main aim was to distinctly assess each group of student in the colleges and find out which demographic factor was particularly significant and responsible for the perception and awareness. It was necessary to know the thinking of youth who comprises of the grass root level, how much aware they were and how much they perceive before enrolling themselves in Massive Open Online Courses and shift towards data science with concrete idea what to do with the learning of subject. The extent of the paper could be implemented on students of IIT’s and IIM’s and other states in India. One of the most important things about MOOC’s is the dropout rate from the courses. This paper can be further open for such discussions.

The authors declare that they have no conflict of interest

No funding sources

The study was approved by the NIT Silchar, Assam, India.

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