The usual mistakes made by Data Scientist aspirants

Author : Analytics Educator

Data Scientist has been a buzz word in India, and many job seekers are getting attracted towards this field. However, it’s been observed that most of the candidates make certain inadvertent mistakes and are facing lots of hurdles to get a job in data science.

Usually people have been led to believe a lot of rosy picture about Data Science – there’s been a lots of job opportunities, salary level is significantly higher than most of the other jobs, lots of onsite opportunities are there. Let me tell you that they are actually 100% correct. However, the bitter truth remains that a very small percentage of people, who are aspiring to get into Data Science, are actually not landing up with a job. The situation is more or less same throughout the country, but acute in Kolkata, West Bengal.

Let’s try to summarize the problems:

  1. Lack of effort into it: The most common path people are following to be a data scientist is by enrolling themselves into a training institute. Post their enrollments, they start taking the course quite lightly and do not put the required effort. Hence, they neither gather enough knowledge, nor the confidence to clear the interviews. Everybody needs to understand that at the interview it is expected that the candidate would be a professional. A professional is not one who would know everything, but one should have absolute expertise in whatever technology/language/algorithms they have studied.
  2. Lack of proper guidance: Another common problem is dearth of proper training institute. Though there are many institutes, but very few proper training centers are there where trainers have profound knowledge. Most of the institutes are run by trainers who do not possess any real work experience. Hence, the students also receive very superficial training, which actually impacts their interviews adversely.
  3. False placement promise: The first thing we check before choosing a training institute is the placement. Unfortunately, in the field of Data Science, we really have a handful of institutes who could arrange campus interview. However, all the institutes would be having flashy website claiming placement. The students fail to understand that most of these institutes are making false promises, and make their website look, as though as soon as you pay their fees you will get the job.
  4. Study unimportant subjects: It also happens that the students are studying too many unnecessary subjects simultaneously, without understanding their importance. Hence, they put more emphasis on unimportant subjects and neglect the more important ones. As the part of standard curriculum, people will teach them VBA, SQL, MS Access, Hadoop etc. VBA, and MS Access are completely outdated, and it is not at all used in data science projects. SQL is a data extraction language, but not capable of doing any analysis. Hadoop is used for storage and processing data, but unable to deploy any machine learning algorithms alone.
  5. Practice Interview sessions: This is one aspect, which neither the centers cover nor the candidates practice. Many a times due to lack of practice they are unable to gauge their weakness and performs poorly during an interview

End Note:

If you are aspiring to be data scientist then focus on your learning the most. You should pick up either R or Python or both (if you can spend at least 2 hours on study per day), and the machine learning algorithms – at least 3-4 of them. Learn it from an actual data scientist, practice mock interviews and then start applying for the jobs.