Possible now to switch to BI/hadoop?

[Sushil]

Hi,

I have a mainframe experience of 5 years, is it possible now to switch to BI/Hadoop? Is there anyone who has done this and can share the experience. Or guide in general.

[Anuj Dhawan]

If your company choose to provide you with the training, it'd be easy. If your company has some projects in which they are looking for candidates in those projects, it might also help again.

Start with basics of Hadoop, then learn architecture of Hadoop. There are open source tools as well for Hadoop, start with those.

Good Luck!

[Sushil]

If your company choose to provide you with the training, it'd be easy. If your company has some projects in which they are looking for candidates in those projects, it might also help again.

Start with basics of Hadoop, then learn architecture of Hadoop. There are open source tools as well for Hadoop, start with those.

Thanks. Are there some possibility of these on mainframes?

[nicc]

Are there some possibility of these on mainframes?

Did you search or are you just hoping someone else will search for you?

[Anuj Dhawan]

If you look on google, you'll find a number of resources. A dedicated effort will get you what you are looking for, for sure.

[enrico-sorichetti]

the first rule for somebody wishing to make a change is ...
DO THE WORK YOURSELF, DO NOT WAIT FOR THE OTHER TO DO IT FOR YOU

nowadays the net provides many sources of information, use them

and as far as switching <job> it is always possible to do it,
it will need dedication, and a lots of effort, but nothing prevents You from trying
investigate the pros and cons , research the opportunities, train, ...

the main points are the pro and the cons, nobody apart Yourself can give the proper weight to each one

[Neeraj N]

https://www.ibmbigdatahub.com/blog/hado ... -mainframe

[Mukesh Mistry]

Can we not have dedicated Forums here which talk about such technologies on mainframes? That will be great.

[Sushil]

Can we not have dedicated Forums here which talk about such technologies on mainframes? That will be great.

Looks like it is not an emerging area in mainframes?

[atulraj123]

Yes, it is certainly possible to switch to a career in data science, even if you don't have a background in the field. Data science is a highly dynamic and interdisciplinary field that welcomes individuals from diverse backgrounds. Here's a step-by-step guide on how to make the transition:

1. Learn the Basics:

Programming Skills: Start by learning a programming language commonly used in data science, such as Python or R. These languages are essential for data manipulation, analysis, and modeling.

Statistics and Mathematics: Gain a foundational understanding of statistics and mathematics, including concepts like probability, linear algebra, and calculus.

Data Analysis Tools: Familiarize yourself with data analysis tools like pandas (Python) or data frames (R) for data manipulation and exploration.

2. Take Online Courses or Enroll in a Data Science Program:

Consider taking online courses, such as those offered on platforms like Coursera, edX, or Udacity. Look for comprehensive data science programs that cover a wide range of topics.

Some universities and institutes offer formal data science programs, including certificates, diplomas, or degrees. Research these options to see if they align with your goals.

3. Work on Projects:

Apply your knowledge by working on data science projects. Start with small, personal projects to build your portfolio. Kaggle is a great platform for finding datasets and participating in data science competitions.

As you gain confidence, collaborate on open-source projects or contribute to data science communities.

4. Specialize:

Data science has various subfields, including machine learning, natural language processing, computer vision, and more. Consider specializing in an area that aligns with your interests and career goals.
5. Network and Connect:

Attend data science meetups, conferences, and webinars to connect with professionals in the field. Networking can provide valuable insights and job opportunities.
6. Build a Portfolio:

Create a portfolio that showcases your projects, skills, and expertise. A strong portfolio is essential when applying for data science roles.
7. Apply for Jobs:

Start applying for entry-level data science positions, such as data analyst or junior data scientist roles. Tailor your resume and cover letter to highlight your skills and projects.
8. Prepare for Interviews:

Be prepared for technical interviews, which may include coding challenges, statistical questions, and discussions about your projects. Practice your problem-solving skills.
9. Continue Learning:

Data science is a rapidly evolving field. Stay up-to-date with the latest techniques, tools, and trends by reading research papers, taking advanced courses, and attending workshops.
10. Be Persistent:

Switching careers can be challenging, but persistence is key. Keep applying, learning, and improving your skills. It may take time, but with dedication, you can successfully transition into data science.
Remember that your existing skills and background can be assets in data science, as they may provide you with unique insights and perspectives in your new career. The key is to start learning, apply your knowledge, and build a strong foundation in data science to make a successful transition.