According to AIM Research, the demand for data analytics roles has increased by 23% in 2022. Evidently, the explosion of the analytic job market has triggered a talent overflow to the domain. Now, even professionals from non-technical backgrounds are considering a switch, thanks to the glamour and attractive salaries that come with the data science territory.
Northeastern University, Boston, has listed the average salaries of a data science professional in recent research:
- Big Data Engineer: USD 127,250–219,500
- Database Manager: USD 108,000–183,000
- Database Developer: USD 98,250–167,750
- Database Administrator: USD 77,000–159,250
- Data Analyst/Report Writer: USD 81,750–138,000
- Data Architect: USD 111,500–187,750
- Data Modeler: USD 79,000–164,500
- Data Scientist: USD 102,750–175,000
- Data Warehouse Analyst: USD 77,750–$160,000
- Business Intelligence Analyst: USD 85,750–178,000
Without proper guidance, transitioning from a non-analytical job profile to the world of data analytics can be bewildering. That said, the switch is a lot easier for professionals from IT, finance, UX, marketing and sales, HR backgrounds. Below, we have put together a list to help professionals from non-technical backgrounds segue into data science roles.
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Step 1: Get certified
Full-time degrees are no longer a requirement to get a data analytics job. However, one does need a structural approach to learn the relevant skills, and project-based courses are your best bet. The benefits of taking such certification courses are:
- Mastering fundamentals
- Relevant certificates
- Career counselling
Based on the type of certification, a course can last from 2 to 8 months. The best data analytics certifications to start off include:
- Cloudera Data Platform (CDP) Generalist Certification
- Dell EMC Proven Professional Data Science Associate
- AWS Big Data Specialty Certification
- Associate Certified Analytics Professional (aCAP)
- SAS Certified Data Scientist
- IBM Data Science Professional Certificate
Big enterprises use data analytics certifications as a filter to shortlist the most qualified candidates.
Step 2: Create a portfolio
Data analytics is a hands-on field, and real project experiences can help you stand out from the crowd. There are many ways to build your data analytics portfolio– choosing a data science course with a capstone project is one of them. Winning Kaggle or MachineHack competitions will boost your cred. Additionally, posting on code repositories like GitHub and working on open-source projects in the data domain can also help you get the prospective employer’s attention.
Step 3: Upgrade your skill set
One of the most essential skills to handle analyticals tasks is the ability to read, write and analyse data with code. Being fluent in SQL, R, Python, AWS, Azure, Java, C#, ETL etc can put your resume at the top of the heap.
Step – 4: Network
Networking is key to landing the right job. Building portfolios in GitHub, Kaggle etc is also one way to get the word out. Reaching out to data science professionals on platforms like LinkedIn and Discord, being part of the vibrant open-source data science communities, attending workshops etc, can also give you visibility. More often than not, proper guidance can be the difference between success and failure in the data science domain. Keeping track of the data analytics mavens can bring you up to speed on the latest developments and help gain insights into the data analytics ecosystem.
Some of the noteworthy data professionals influencing the world of data are:
Ronald Van Loon – Financial markets trainer
Merv Adrian – Vice president at Gartner
Marcus Borba – Global thought leader and influencer in AI, ML
Kirk Borne – Chief science officer at DataPrime Inc.
Carla Gentry – Chief data scientist at Analytical Solutions