It is one of the hottest debated topics and also the most searched Google query – how to make a career in data science or more importantly making mid-level career changes to data analytics. Undoubtedly, there is a ton of free advice on the world wide web about making an effective transition to this high-demand field, but it still may be difficult to pivot to the lucrative big data careers. Analytics India Magazine curates the best advice on how to make an effective mid-level career change.
Analytics India Magazine answers the most burning questions professionals planning mid-career pivots have on top of their mind
1. It’s possible to start a career without having a formal degree in data science
While having an accreditation is often listed as a job requirement, startups and even large corporations with complex, overlapping job roles often hire people based on their skill-set, experience and the willingness to learn. Data science is a multi-disciplinary field and requires people from varied backgrounds to work together. One has to find how they can be a good fit in the team and since this field is up and coming, knowing people can really help in landing that coveted job.
2. I am good at programming and am interested in pursuing a career in Big Data and Analytics
This is a commonly asked question and applies to a large swathe of IT professionals who have four-eight years of experience in and are adept programmers with no prior experience in data and analytics. Their concern stems from the non-analytics experience and wish for a more robust computer science background. In this scenario, one should consider whether they have the background skills such as experience in data warehousing, database design and distributed systems to make a foray in big data architecture. Additionally, having technical skills for example knowledge of Python, SQL and analytics software will be an additional advantage to get hired.
3. Making an organic data science career transition
- This is what most mid-level career professionals aspire for. Making an organic switch to the world of big data and analytics is a tough job but attainable. Most professionals and industry leaders have dished out their data science cheat sheets – for beginners and advanced professionals wherein learners can pick up essential skills about Python, R, machine learning , Hadoop and more. There is also a great selection of books, free training resources, webinars, salary and job surveys and career related interview questions to help learners prepare from ground up. Here’s a cheat sheet for Data Mining in R and Python.
- Another way to make an organic switch to this field is by adding more data-driven capabilities to your day-to-day job role.
- Your network can help you change careers at mid-level: Join the community and tune into the conversation. Join active forums and websites and forums and follow well-respected thought leaders in the industry to stay on top of new trends and ideas
4. Would learning SAS help me gain a foot in the door?
Yes, SAS is still in high demand and you can command a huge salary in analytics industry just by showing good proficiency in it. But, do not just stop here. Recruiters today are looking for strong understanding of statistical methods as well and when to apply them.
The only real difference between SAS and Python is that Python is open source while SAS has steep licensing, but it is still far ahead in competition as the best statistical software deployed by companies worldwide. So, here’s the difference – if you are a startup, you can’t afford SAS, but analysts in analytics world rely heavily on SAS.
Hence, having a SAS certification is a definite plus and can get you a job. But then, SAS just form a cog in wheel and SAS have the alternatives like Python and R.
5. How can I find a data science project within my current role?
It’s often easy to say to get some real-world practice to gain experience. Now, does that mean doing a side project at your day job or putting together a passion project that you are deeply interested in your spare time. You can start building your resume with hands on data science projects, that could be in Python, R or Matlab. Learning the data science pipeline can help one build their resume and also enable one to pick one major skillset (for example, Natural Language Processing, data visualization, machine learning, data wrangling, web scraping etc) and master it.
6. Should I look for a job in a startup that will eventually help me become a data scientist or start off with an internship in a big company?
Since data science is a broad field, you can’t become an expert overnight. Data science professionals that we spoke to usually go about the mid-level change in two ways – they either look for a job in a startup or average-sized small companies that give them intensive job training on the whole breadth of data science – optimization algorithms, machine learning or take up a short-term internship in an established firm. Over the years, there has been a spurt in a number of startups on a lookout for data savvy professionals. These startups can be a stepping stone towards roles such as data analyst, statistical programmer, reporting analyst and business analyst.
7. Would data science certifications or boot camps designed for working professionals in getting a data analyst position?
With the recent push towards data science, there are a slew of data science courses (purely short-term online, MOOCs, executive and hybrid programs) that give hands-on training, connect you with potential employers and give networking opportunities. In fact, we believe networking opportunities and having a bunch of interesting data science projects are crucial to get in front of the right decision-makers. Here’s a word of caution about “Data Science Certifications”, you can’t really be truly sure about its market value. on the other hand, it isn’t really necessary to get a MSc/PhD in related fields to gain a foothold in DS. There are companies that give data science training for three-seven weeks to prospective hires for a fee and even run programs with mentors.
