Need Help on Career Guidance

sap
data_science

#1

Hi, I need help on how to build a career as data scientist from other industry.

I have an experience of 5.6 years in SAP BW now I want to become a data scientist.

Please let me know the process?


#2

Hi Hariteja,

I was also worked for same SAP BI/BW for 4 years in Infosys and presently working as DS…but u need to think many ways about learning in DS ,

  1. Why u wanna to change u r career from one platform to another platform
  2. presently market is good for DS but every requirement is expecting many things from candidate
    3.Python,R with AI (Artificial Intelligence) ,Deep Learning , ANN(Artificial Neural Networks) is asking many companies
  3. MNC will expect minimum of 3 years experience with extra skill set
    5.You need to prove u r self in interviews with high mathematics background along with DS skill set.

Yesterday i have checked few requirements like below
Python,
R with AI (Artificial Intelligence) ,Deep Learning , ANN(Artificial Neural Networks)
Scala
Big Data
Spark
SQL
Basic Java Knowledge
.
.
.
etc

I am saying all this about to think and plan accordingly …but not to scare u .
Check the stackoverflow communities and also job portals like what they are asking for experienced and any fresher requirements ?

please reach me here for any more information.


#3

Hi heriteja,
if you want switch your career from SAP to DS then you need to prepare from beginning
here i mention some tips to prepare for DS kindly follow it:

1. Choose the right role

There are a lot of varied roles in data science industry. A data visualization expert, a machine learning expert, a data scientist, data engineer etc are a few of the many roles that you could go into. Depending on your background and your work experience, getting into one role would be easier than another role. For example, if you a software developer, it would not be difficult for you to shift into data engineering. So, until and unless you are clear about what you want to become, you will stay confused about the path to take and skills to hone.

What to do, if you are not clear about the differences or you are not sure what should you become? I few things which I would suggest are:

  • Talk to people in industry to figure out what each of the roles entail
  • Take mentorship from people – request them for a small amount of time and ask relevant questions. I’m sure no one would refuse to help a person in need!
  • Figure out what you want and what you are good at and choose the role that suits your field of study.

2. Take up a Course and Complete it

Now that you have decided on a role, the next logical thing for you is to put in dedicated effort to understand the role. This means not just going through the requirements of the role. The demand for data scientists is big so thousands of courses and studies are out there to hold your hand, you can learn whatever you want to. Finding material to learn from isn’t a hard call but learning it may become if you don’t put efforts.

What you can do is take up a MOOC which is freely available, or join an accreditation program which should take you through all the twists and turns the role entails. The choice of free vs paid is not the issue, the main objective should be whether the course clears your basics and brings you to a suitable level, from which you can push on further.

When you take up a course, go through it actively. Follow the coursework, assignments and all the discussions happening around the course. For example, if you want to be a machine learning engineer, you can take up Machine learning by Andrew Ng. Now you have to diligently follow all the course material provided in the course. This also means the assignments in the course, which are as important as going through the videos. Only doing a course end to end will give you a clearer picture of the field.

Some good MOOCs to look for include:

  1. Analytics Edge on edX
  2. Machine Learning from Andrew Ng

3. Choose a Tool / Language and stick to it

As I mentioned before, it is important for you to get an end-to-end experience of whichever topic you pursue. A difficult question which one faces in getting hands-on is which language/tool should you choose?

This would probably be the most asked question by beginners. The most straight-forward answer would be to choose any of the mainstream tool/languages there is and start your data science journey. After all, tools are just means for implementation; but understanding the concept is more important.
4. Join a peer group

Now that you know that which role you want to opt for and are getting prepared for it, the next important thing for you to do would be to join a peer group. Why is this important? This is because a peer group keeps you motivated. Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier.

The most preferable way to be in a peer group is to have a group of people you can physically interact with. Otherwise you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and interacting with the batch mates.

Even if you don’t have this kind of peer group, you can still have a meaningful technical discussion over the internet. There are online forums which give you this kind of environment. I will list a few of them

  1. [Analytics Vidhya]
  2. [StackExchange]
  3. [Reddit]

5. Focus on practical applications and not just theory

While undergoing courses and training, you should focus on the practical applications of things you are learning. This would help you not only understand the concept but also give you a deeper sense on how it would be applied in reality.

A few tips you should do when following a course:

  • Make sure you do all the exercises and assignments to understand the applications.
  • Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, understand the assumptions, what it does and how to interpret the results. You can always develop a deeper understanding at a later stage.
  • Take a look at the solutions by people who have worked in the field. They would be able to pinpoint you with the right approach faster.

6. Follow the right resources

To never stop learning, you have to engulf each and every source of knowledge you can find. The most useful source of this information is blogs run by most influential Data Scientists. These Data Scientists are really active and update the followers on their findings and frequently post about the recent advancement in this field.

7. Work on your Communication skills

People don’t usually associate communication skills with rejection in data science roles. They expect that if they are technically profound, they will ace the interview. This is actually a myth. Ever been rejected within an interview, where the interviewer said thank you after listening to your introduction?

8. Network, but don’t waste too much time on it!

Initially, your entire focus should be on learning. Doing too many things at initial stage will eventually bring you up to a point where you’ll give up.

Gradually, once you have got a hang of the field, you can go on to attend industry events and conferences, popular meetups in your area, participate in hackathons in your area – even if you know only a little. You never know who, when and where will help you out!

Please read all information and kindly do the same

All the very best for your career.
thank you!!!