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10 Things To Focus On While Transitioning To Analytics Domain

10 Things To Focus On While Transitioning To Analytics Domain

Srishti Deoras
  • Below, we have curated a checklist to help candidates looking to transition to the data science field.

Data science and analytics remain one of the most sought-after careers for some time now. To that end, the domain has been witnessing a heavy influx of professionals from across the spectrum. Here, we have curated a checklist of skills, suggestions etc to help a candidate transition to the data analytics field. The insights are extracted from our previous interactions with leading companies hiring data scientists.

Familiarity with data and numbers: The first and foremost requirement for a data scientist is to be comfortable with data and numbers. Data scientists have to deal with huge volumes of data daily, and an appetite for numbers will make things a lot easier.

Focusing on fundamentals of analytics: It’s essential to get the analytics fundamentals (such as algorithms, models, programming languages, SQL) etc) down cold to carve a successful career in the domain. A rounded view of different data science domains such as data engineering, business analytics etc. will help you choose the specialisation and the skillsets to focus on. 

Being multi-dimensional: It behoves data scientists to be multi-dimensional. Meaning, a candidate should be able to perform tasks such as data modelling to creating algorithms to data visualisation. A one-dimensional way of thinking and focusing on only one aspect of data science might slow down your growth trajectory. 

Problem-solving skills: The candidate should have an innate curiosity and problem-solving skills to excel in the data science field. Problem-solving is a crucial step to deconstruct business problems and build relevant solutions and algorithms to tackle them. Those looking to transition into data science should study industry use cases to hone their analytical thinking skills.

Solving business problems: Candidates looking to pivot into the data science domain should be capable of solving critical business problems using data science tools. Companies look for candidates who can solve business problems and develop business strategies. Having a good grip on the client’s business is critical for overall success.

Build deep specialisation: Data opens up a galaxy of possibilities. But to make a name for yourself, it’s crucial to pick up a specialisation and build on it. Learning programming languages, database management system, data frameworks, libraries, BI tools can equip you for the long haul.

Don’t mistake tools for techniques: Experts advise that one should not keep chasing tools alone as they will not remain the same, given the field’s fast-paced nature. Instead, focusing on fundamentals and the basic techniques will help in the long run. 

See Also

Don’t just focus on coding and programming language: When a candidate is looking to transition into data science, the most common advice that comes your way is to learn programming languages such as Python, R etc. While coding and learning programming languages have merits, it is not the only requirement to transition into a data science role. Many people think learning Python is equivalent to learning analytics, which is not true. 

Focus on communication and presentation skills: While this may be a requisite for almost all fields, soft skills in data science come with a huge payoff. It especially becomes important as one progresses in the career. The job of data scientists involves communicating with the stakeholders to relay business insights. Good communication and storytelling capabilities are key to going that extra mile in your career.

Keep on upskilling: Lastly, data science is all about tools and technologies that keep evolving at warp speed. Therefore, to make a successful career in data science, it is of utmost importance to keep upskilling and be clued in on the latest developments. Being a continuous learner and having a finger on the market’s pulse can boost your data science career by leaps and bounds. 

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