The Inspiration for this career in Data
Whenever we learn a skill, our return on investment is value creation or job opportunities. In the end, its all about a career path. As a certified data scientist by EMC, I have walked this journey. And certainly feel that this experience should help other's to make a sound decision. I did my graduation in Statistics and Masters in Business Management, making a career shift into Analytics, made a positive impact on my career. Data science career path starts with researching, learning and applying skills in data analytics.
Its an industry expectation that a resource in any data science role should be job role ready. Key attributes which we seek are problem identification in any industrial or business process, constant learning approach and articulation of concepts. Technically doing a data science course is not the end of learning nor beginning of a great career. Your career path starts with may be self learning through video course. Alternatively learning path also could start with learning with a school at under graduate or graduate level. Of course knowledge is of prime importance, source is not the key always. Most of the data analyst have learned their base skills in data science using scientific articles published on internet. This is my personal experience gained during interviews of MBA graduates, data science engineers and experienced resources. It's highly recommended that you have a mentor or guide. This will help you to visualize the short and long term scope of your learning path. Market would give you advantage in terms of expected dividends in terms of job satisfaction and salary.
Job role in Data Science
At the end of your learning path you could fit in one of these Job role
- Research Assistant
- Data Analyst
- Data Engineer
- Project Manager
- ML Engineer
- Data Scientist
- Research Guide
- ML Architect
This list is from the perspective of skill set, experience and interest attributes. You would get a good idea about the roles, qualification and responsibilities if you check here. These are actual job opening's in niche data science roles.
A fresher who has learned data science may opt for a research role wherein he or she can pursue further learning. Organization like Microsoft, Google and IBM prefer employees who have done internship or research projects in academics. They have an edge in terms of patent filing. This obviously is an import aspect of knowledge based firms as Intellectual Property is valuable.
A fresher with good skills in SQL programming along with his regular data science skills may opt for a Data Analyst role. ETL tools like Talend, Informatica and so on along with SQL forms key firepower in their arsenal. Their primary role is to extract, transform and load the data. Of late, the term is re-interpreted as extract, load and transform because of the advent of data warehouse on cloud and big data. They may also use tools like Spotfire from TIBCO or Tableau for data visualizations.
A candidate in this role typically is responsible for data pipeline and processing. With more automated and optimized data funnels as primary building blocks of Software-as-a-Service model, their role is important. They make extensive use of scripting languages like python or low level languages like Java, C, C++ and so on.
Project manager in a data science are typically for delivery management of product development or service delivery. Using skills like agile, waterfall, CPM and PERT techniques it starts right from conception to final closure of project. An MBA from business school would be an added advantage in this role. In many companies this profile would be interfacing with client as well. Team management is part of their responsibility. They are responsible for process and timeline delivery.
Strong mathematical background with focus on algorithms would be the focus area. They usually have a Masters in Computer Science or any other degree in pure science or applied sciences. Substantial part of their time needs to be on hard core programming and logic building. A good understanding of machine learning algorithms is mandatory for this portfolio.
Thought leader having implemented data science in projects and proven solution implementation. Technically qualified and certified internationally in data science. A data scientist would have got exposure to all or some of the above roles in previous job roles. International certification organizations are EMC, DASCA, Microsoft, IBM and CAP to name a few.
Research guides are more or less data scientist persona but their experience is spread more on the academic side as compared to industry. Academic research leads to generation of new algorithms and concepts. But there are times that the inspiration would have initiated from industry. They usually have a PhD in Computer Science or pure science.
A progression from ML Engineer is the next stage into ML Architect. Strong in coding, algorithms, data bases, data structures, logic and implementation of product both in development, testing and production environment. They are responsible for designing the architectural flow, design and structure of the product. Overall process ownership is their responsibility.
The key ingredients which needs to be present in a data science resource
- A bachelor or Master degree in Technology or Computer Science
- Business domain, a Masters in Business Administration would be good
- A full time course in Machine learning, data science or business analytics. Alternatively do self learning or certification from professional training institute.
- A internship with a startup analytics firm would increase valuation of your resume
- During learning course, a portfolio of good projects from domain like healthcare, banking, insurance, supply chain adds weight to your resume
- Exposure to video, voice, image and text analytics projects
- Having worked in teams for capstone projects is positive
Before making your first move, do a self evaluation. These basically check your aptitude and technical skills. Failing in any of these doesn't mean we cannot have data science as our career. Passing them will boost your confidence. Attempting will certainly make us aware of the composition needed for this career. Take a free evaluation on this link for statistics. Test your technical coding flow assessment.
General tool kit in your data science journey would have some of these below. Link provided has open source free course.
Get certified from accredited organization rather than training institute
Its a good practice to get trained in a certificate data science program under a PG or Diploma grade in a University course. Alternatively professional institutes are also good to get trained. Do a proper review of the courseware subject to budget and expected outcomes. A details review of the good Universities and institutes is something I would be writing soon, but not in this blog. Let's focus on the fact that no matter where you learn data science. Getting certified from a third party accredited certification authority is good to have on your resume. Here are few options which I would recommend.
- Associate Level Data Science Certification from DELL EMC
- Associate Level Data Scientist - Advance Analytics from DELL EMC
- Specialist Level Data Scientist - Advanced Analytics from DELL EMC
- Professional Data Engineer from Google
- AWS Certified Data Analytics - Speciality from AWS
- TensorFlow Developer Certificate from TensorFlow
- IBM Data Science Professional Certificate from Coursera
- Chartered Data Scientist from The Association of Data Scientists
- Open Certified Data Scientist from Open CDS
- Microsoft Certified Azure Data Scientist Associate from Microsoft
Whats next ?
Add experience in your portfolio by doing some projects on challenging problems. These could be open source data from platforms like University of California, Irvine. For complex challenges use Kaggel. A virtual or on premise internship with a startup firm in product development is advisable. Make sure to work on a variety of technology and business combination problem for model development.
Get advise from a mentor. This could be your faculty or an experienced person in the industry. Connect with people on LinkedIn and converse. Ask seniors for guiding you as a mentor. This would help you to shape your portfolio mix to optimum. You can connect with me on LinkedIn. Your mentor can help you with specific industry interview skills. Occasionally request them to take a mock interview. They would have taken many screenings for candidates and would certainly guide in right direction.
Try experimenting on the cloud. Deploy your environment on a cloud like AWS, Google or Azure. Try experimenting but with caution. AWS provides one year free tier access on micro instances on certain services. Google has the GPC free compute for experimentation. Google has also come up with the Google COLAB, accessible by your Gmail account credentials.
Synchronised Resume and LinkedIn Profile
Build a profession resume which has clarity. Download a professional template and keep updating as you progress in your career path. The journey starts from the day you start learning. Make sure the information is synched across Job sites and Professional Networking sites like LinkedIn.
Be grounded, I strongly believe my knowledge about data science, machine learning and AI is less than 1%. The 99% drives me to learn more, and I am still learning. Be good to others without expecting. Help others and grow.
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