T5E’s Placement survey 2022-23 was conducted during February 2023 to study the statistics of the Placement season of 2022-23. The survey witnessed a total of 256 responses across the entire graduating class (UG+PG). The respondents spanned across various degrees (albeit disproportional) and departments who appeared for the placement season. Close to 75% of the respondents were UG’s (B.Tech + DD), and the rest were PG’s (MSc+M.Tech+MBA+PhD+MS)
Note: The correlations found in these articles are from the inputs of the sample size of the number of respondents, although it may or may not reflect the entire placement statistics.
The survey focused on several aspects, such as Preparation, Opinion, Academics, and Career, relevant to the placement statistics of respondents. The following charts showcase the general demographics of the survey across students of different degrees and branches.
Note: Do glance through the above charts to understand the demographics of the respondents of this survey. These charts are key to explanations regarding the results we got for several questions, which depend heavily on one’s department/degree. Female students are underrepresented as compared to male students and there exist higher numbers of respondents among departments like ME, EE, and CE.
In this article, we explore the preparation aspect of this placement season.
Communication skills are often tested during interviews and can sometimes become a crucial factor in evaluating a candidate. It is observed that most students rated their communication skills as a 4 out of 5. However, there has been a decline in the average rating as compared to the last year’s placement season, possibly due to the infrequent offline peer interaction as a result of online classes.
When did you start your placement preparations?
It is not surprising that many students find it difficult to schedule their placement preparations, as around 66% of the respondents started their preparation just before the placements. However, a few respondents were able to begin their preparations a year before the start of the season. Interestingly, there seems to be an improvement in planning for placement preparations as the percentage of students starting just before the placement season has decreased from 75%. Online Sources are the most common way of preparing among students, followed by Insti Courses and Books.
Role of PoRs and Internships for placement preparations
On average, the importance of internships in placement preparations was rated highly at 2.9 out of 5. Practical exposure and real-time work experience have been found to enhance critical thinking, boost productivity, and help respondents acclimatize to the domains they are preparing for.
Regarding the relationship between Positions of Responsibility (PoRs) and placements, which is often a topic of debate, respondents had mixed opinions. The role of PoRs was rated 1.7 out of 5 on average in this survey.
The survey covered five major domains under preparation: Coding, Analytics, Consulting, Product Management, and Finance. The core sector was excluded, considering the significant differences in job roles and the corresponding preparation needed to secure them amongst branches.
Over 50% of the respondents indicated their inclination towards preparing for coding and analytics profiles, highlighting their popularity among students. Typically, coding skills are evaluated in the preliminary round and are essential for being shortlisted for further rounds. On average, respondents rated their coding skills at 3.4 out of 5.
There is a robust correlation between respondents’ coding skills and the CTC offered to them. Both the mean and median CTC increased in tandem with the rating, signifying that strong coding skills may serve as a stepping stone towards a higher CTC. Coding platforms are important in learning to apply one’s coding knowledge through competitive coding and helps one with familiarizing themselves with coding tests on the same platforms. Leetcode and Hackerrank were the most common options by the respondents, which is consistent with the previous year’s results.
Regarding programming languages, Python and C++ are the two most widely-known languages, with 92% of respondents familiar with Python and 58% familiar with C++. Interestingly, nearly 95% of respondents who knew C++ were also familiar with Python. Given that Python is an easily coded language with diverse applications, it was at the top of many respondents’ list of technical skills. This trend is similar to that of the previous year, with no significant changes observed.
This section encompasses domains such as Data Science, Business Analysis, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Data Analysis.
Machine Learning skills are frequently necessary to pursue a career in AI/ML-related fields, including predictive modeling in data science. On average, respondents rated their ML skills as 3.4 out of 5 and their data interpretation skills as an average of 4.1.
There is a noticeable correlation between ML skills and the CTC offered, with a surprising initial dip in CTC from level 1 to 2 of ML skills, followed by an increase thereafter. Although there is a decline in the mean CTC after level 4, the median CTC continues to rise. It’s worth noting that mean values can be influenced by extreme values, which may result in skewness. Nonetheless, possessing ML skills can be advantageous for a career in analytics.
About 90% of the survey respondents claimed to have knowledge of machine learning, while over 70% stated that they were familiar with data structures, algorithms, and data analytics. Deep learning and SQL were reported to be known by more than 60% of the respondents. When it comes to tools, approximately 90% of the respondents indicated their familiarity with MS Excel/Google Sheets, while only 22% were familiar with Power BI. The popularity of Excel/Google Sheets was greater than any other tool.
Approximately 60% of the respondents stated that they had not taken part in any machine learning (ML) or data science competitions, while 30% had participated in at least one competition related to data science and ML.
Interestingly, a trend was observed between participation in machine learning (ML) and data science competitions and CTC among respondents. The median CTC of participants showed a correlation between their involvement in competitions, as evident in both mean and median values.
Consulting is the third most selected profile and often has shades of analytics and finance, which increases the probability of students who prepared for profiles like analytics, finance, business management, etc being placed.
Considering that preparation for this profile involves extensive case prep sessions among students, the survey asked respondents about their case group size. Around 15 percent of the respondents work themselves, more than half have a team of three to tackle the case groups.
There’s an interesting correlation between quantitative skills and the CTC offered, a constant surge in mean CTC with respect to increment of the effectiveness of the skill indicates it’s importance for the preparation of this profile.
Finance is considered one of the most important profiles in the non-core sector. Some students possess certifications in CFA (Chartered Finance Analyst), NCFM (NSE Academy Certification in Financial Markets), FRM (Financial Risk Manager), etc.
A couple of respondents took FRM, CFA, and NCFM, one each from CFI, and EPAT.
The useful material was attached in the Article, PPOs and Extra-Curriculars.
A stable profile with decent placement offers and product development covers domains like product management, product design, etc.
Over 80 of the respondents were well familiar with Canva. Around 40 percent are proficient in AutoCAD, used in core-related coursework, and Canva is often used in PoR work; these are software preferred by students prior to preparation. More than half of the respondents are familiar with Figma, a tool that might help in making case submissions.
Pitch decks/Case decks played a significant role in the payrolls; there’s a positive correlation between the number of pitch decks and CTC Offered. The median CTC for the respondents who had made 1-2 decks was 15.75 LPA, 3-4 decks had 18 LPA, and 4+ decks showcased a median CTC of 19.5 LPA.