It involves four key phases
Phase 1 Learning the ropes
Phase 2-Finding a job
Phase 3 Succeeding in your data science career
Phase 4 Enjoying the benefits of your grit and passion
Allow me to elaborate on each phase below
Phase 1 Learning the ropes
Know whether you are cut out for being a data scientist and then begin on your journey following the three steps laid out below-
First Step- Self assess whether you have the following skills, which in my opinion are must have for you to achieve success in Data Science life
Besides, due to Democratization of ML/AI, Google, IBM and other such companies have made it easier for all of us to have access to and grow our knowledge on Big data, ML/AI tools and techniques. Some of the Free tools which you should try to take out for a spin are -
o Google machine learning stack - tensorflow
o Apache Spark
o IBM Watson
o Microsoft Azure
Just do google and you will find links for the above stacks. Let’s get started with free learnings.
Third Step- Once you have sampled some free courses and you decide to join a data science course here is guide on how to choose the right program for yourself-
Self-Paced vs Instructor led- Prefer instructor led as this will give you more opportunities to clarify your doubts. Per a statistic, 80–90% students don’t complete their self-paced/videos based course.
Online vs Class-room- Some people prefer classrooms for face to face learning and interaction with fellow students. However, online courses are equally effective if you are self-motivated.
Quality of Instructors- There are two kind of trainers in the market. 1- Who have done some courses themselves and now doing the training 2- Industry practitioners. These are the people who have worked for significant years in the industry. You should always prefer to learn from industry practitioners with significant work experience. I should warn you- unfortunately there are some fake personalities in this industry so please do check trainer’s linkedin profile yourself and see how many people have endorsed the instructor or faculty for the data science skills. If you see no or very little endorsements, it's a red flag.
Placement Assistance- Check whether the institute is providing placement assistance or not. Also ask for statistics on how many people have been successfully placed by them. Ultimately this is one of the main reason why are contemplating taking a course after all.
Practice Case studies and Assignments- Choose the course which is giving you several real world industry datasets and problems to work on. Prefer the ones which have Learning Management System (LMS) on top of that for supplementary learning.
Ongoing Help- You will not master the topics in 1 or 2 months it will take several months for you to build comfort on these topics. An institute which is providing long term help with your learning needs and answering your queries in the future is preferred.
Certification- All else being equal, a certification from reputed institute will be better.
Quizzes and Assignments- It is critical to get your understanding evaluated on a periodic basis via quizzes and assignments. A good institute will give tons of quizzes and assignments and will provide the grading and feedback.
Price- How can we forget this? Evaluate whether the course is value for money or not. Compare the contents and number of hours. An institute that gives more contents and more contact hour for each dime you are paying is better.
Phase 2 Finding a job
This could be tricky but here are few pointers to help you
Here are my Top 10 Pointers to ensure durable success
The future of Big data and Data Analytics is really bright. Per IBM, 90% of the data that we have in the world today has been generated in last 2 years!! Everyday we are generating 2.5 Quintilian Bytes ( 2,500,000 Terabytes) of data. This data comes in from all over the place such as social media, sensors, transactions, pictures, videos and so on. The growth of this data is expected to be even faster in coming decades.
Bottom line is that Big Data is here to stay and will require a lot of data scientist and machines to churn through this data to draw actionable insights and intelligence.
What it means for You?
If you are job seeker with ML/AI Big Data skills, at least the next 10-15 years will be a booming period for you, where tech giants such as Google, Facebook, Microsoft, IBM, other companies across all verticals, and startups across the world will be equally interested in hiring a talent like yourself.
If you are an entrepreneur with a good ML/AI Big Data related idea, there will be plenty of opportunities for you to raise money to fuel your business growth. Per a Kalaari statistic, $6B USD has been raised by AI startups since 2014!
Hope this helps you.
Phase 1 Learning the ropes
Phase 2-Finding a job
Phase 3 Succeeding in your data science career
Phase 4 Enjoying the benefits of your grit and passion
Allow me to elaborate on each phase below
Phase 1 Learning the ropes
Know whether you are cut out for being a data scientist and then begin on your journey following the three steps laid out below-
First Step- Self assess whether you have the following skills, which in my opinion are must have for you to achieve success in Data Science life
- Love for numbers and quantitative stuff
- Grit to keep on learning
- Some programming experience (preferred)
- Structured thinking approach
- Passion for solving problems
- Willingness to learn statistical concepts
Besides, due to Democratization of ML/AI, Google, IBM and other such companies have made it easier for all of us to have access to and grow our knowledge on Big data, ML/AI tools and techniques. Some of the Free tools which you should try to take out for a spin are -
o Google machine learning stack - tensorflow
o Apache Spark
o IBM Watson
o Microsoft Azure
Just do google and you will find links for the above stacks. Let’s get started with free learnings.
Third Step- Once you have sampled some free courses and you decide to join a data science course here is guide on how to choose the right program for yourself-
Self-Paced vs Instructor led- Prefer instructor led as this will give you more opportunities to clarify your doubts. Per a statistic, 80–90% students don’t complete their self-paced/videos based course.
Online vs Class-room- Some people prefer classrooms for face to face learning and interaction with fellow students. However, online courses are equally effective if you are self-motivated.
Quality of Instructors- There are two kind of trainers in the market. 1- Who have done some courses themselves and now doing the training 2- Industry practitioners. These are the people who have worked for significant years in the industry. You should always prefer to learn from industry practitioners with significant work experience. I should warn you- unfortunately there are some fake personalities in this industry so please do check trainer’s linkedin profile yourself and see how many people have endorsed the instructor or faculty for the data science skills. If you see no or very little endorsements, it's a red flag.
Placement Assistance- Check whether the institute is providing placement assistance or not. Also ask for statistics on how many people have been successfully placed by them. Ultimately this is one of the main reason why are contemplating taking a course after all.
Practice Case studies and Assignments- Choose the course which is giving you several real world industry datasets and problems to work on. Prefer the ones which have Learning Management System (LMS) on top of that for supplementary learning.
Ongoing Help- You will not master the topics in 1 or 2 months it will take several months for you to build comfort on these topics. An institute which is providing long term help with your learning needs and answering your queries in the future is preferred.
Certification- All else being equal, a certification from reputed institute will be better.
Quizzes and Assignments- It is critical to get your understanding evaluated on a periodic basis via quizzes and assignments. A good institute will give tons of quizzes and assignments and will provide the grading and feedback.
Price- How can we forget this? Evaluate whether the course is value for money or not. Compare the contents and number of hours. An institute that gives more contents and more contact hour for each dime you are paying is better.
 |
| How to get a job as a Data Scientist Tutorial |
Phase 2 Finding a job
This could be tricky but here are few pointers to help you
- If you are a fresher (0–2 years experience), it will be easier.
- If you are doing something similar in your current role it will be much easier for you to demonstrate your suitability to a potential employer. If you are involved in something totally different then it will be bit harder
- You need to know the concepts and practical application. Ideally you should have some projects where you have already applied the skills.
- If you have participated in some competition ( such as Kaggle) and done well, do highlight these activities in your resume prominently. There are companies which will offer your jobs if you are able to do well in these competitions
- Several employers now-a-days have Hackathon and open challenges for any one to participate. If you do well, you get a job for yourself!
- One thing that always works is to go through a reference in the company
- Complete FREE courses offered by Coursera etc. and mention that on your resume.
- Create visibility for yourself by participating in blogs and forums
- Don’t insist on finding a full time role from the get go. Be open to join as an intern or work for minimum benefits to just build the experience. Later on you can capitalize it.
- All else being equal, smaller companies may be more willing to take you in, compared to MNC etc.
- Last but not the least, never give up! If you really want it, you will get it.
Here are my Top 10 Pointers to ensure durable success
- Learn as much as possible. Spend 4 to 5 hours every week on the learning and development and knowing the latest in the industry
- Challenge status quo. Never assume that whatever is being done is following the most effective approach
- Believe that you are equal to everybody else in the hierarchy. Don’t be afraid to speak your mind
- Focus on Innovation and coming out with the earth shattering ideas rather than doing the business as usual.
- Focus on developing great communication skills and soft skill as this is one of the biggest gap I have seen in the analytics professionals
- Don't become a one trick pony. try to get exposure in different industries and different functional areas.
- Participate in competitions and events such as Kaggle, to know where you stand vis a vis your peer group.
- Try to write white papers and blogs on your subject matter expertise.
- Develop domain expertise as without that analytics is not effective.
- Finally, always maintain a clear visibility of your strength and opportunities and any blind spots. Actively seek feedback from your peer group and your superiors.
The future of Big data and Data Analytics is really bright. Per IBM, 90% of the data that we have in the world today has been generated in last 2 years!! Everyday we are generating 2.5 Quintilian Bytes ( 2,500,000 Terabytes) of data. This data comes in from all over the place such as social media, sensors, transactions, pictures, videos and so on. The growth of this data is expected to be even faster in coming decades.
Bottom line is that Big Data is here to stay and will require a lot of data scientist and machines to churn through this data to draw actionable insights and intelligence.
What it means for You?
If you are job seeker with ML/AI Big Data skills, at least the next 10-15 years will be a booming period for you, where tech giants such as Google, Facebook, Microsoft, IBM, other companies across all verticals, and startups across the world will be equally interested in hiring a talent like yourself.
If you are an entrepreneur with a good ML/AI Big Data related idea, there will be plenty of opportunities for you to raise money to fuel your business growth. Per a Kalaari statistic, $6B USD has been raised by AI startups since 2014!
Hope this helps you.
Post a Comment