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In the world of big data, you will often encounter two disciplines: data science and data analytics. They both require different (but overlapping in certain areas) competencies and skill sets.
Nonetheless, both fields of study are highly lucrative and offer good opportunities to those interested in going the extra mile.
If you're looking to make some important career decisions, but you're unsure of which option to choose, read on about the top points of differentiation.
The fields of data analytics and data science are enriching and specialized. This means that if you're entering any of the two professions, you need to be well-prepared to tackle the educational challenges that might come your way.
A basic bachelor's degree is required to start a career as a data analyst. To embark on this career path, you need to opt for an undergraduate program that will give you a working understanding of SQL and query development for RDBMS and data structure schema operations.
You will also need knowledge of statistical programming using R or Python. Additionally, knowledge of machine learning (ML), artificial intelligence (AI), custom algorithm development, data management around information collection and storage are extra advantages.
In short, you need an undergraduate degree in IT, computer science, maths, or statistics to kick-start your career in data analytics.
An aspiring data scientist's safest bet is to seek a bachelor's and master's degree in Computer Science, Information Technology, mathematics, or statistics. If you wish to change your career trajectory and adopt a career as a data scientist, you need a minor degree in any one of these streams.
The core knowledge required for data science roles should prepare you for provisioning, collecting, organizing, processing, and modeling business data.
Additionally, you can gain expertise in data visualization, API-based data collection, and preparation. A degree in applied mathematics and statistics will further help you with exploratory data analysis, allowing you to follow and establish patterns, devise test models for bespoke challenges, and much more.
Lastly, an additional skill set in ML and AI is handy when establishing models for AI-based predictions. Thus, an undergraduate degree in data science, computer science, or computer engineering should set you off on a data scientist's career path.
As a data analyst, your job roles and responsibilities will vary as you start your journey in these fields. Depending on your level of expertise, you might notice certain changes which will help you tackle difficult situations in your job role.
In data analytics, you will primarily be analyzing, visualizing, and mining business-specific data.
On the whole, data analytics roles will need you to handle responsibilities like:
Data science includes driving insights and drawing inferences from contextual data within the business.
Some additional responsibilities might include:
Since both roles are specialized, they require specific skill sets before you can excel in either of the fields. To make the most out of either profession, you need to advance your skill sets and make the most out of what you can.
Analytics requires advanced knowledge of intermediate statistics with problem-solving skills.
Additionally, it's best if you can upskill yourself in the following:
Despite being an IT-oriented role, becoming a data analyst doesn't require you to hail from an engineering background.
Instead, it's worthwhile to learn statistics, database management, and data modeling, along with predictive analytics, to master the tricks of the trade.
In data science, you need to be savvy in mathematics, advanced statistics, predictive modeling, Machine Learning, and programming in the following fields:
One or more of these tools are essential to master data analytics and data science roles. To be the best at what you do, we advise you to learn as many of these as possible.
Irrespective of which field you choose, the idea is to land a good, well-paying job. Depending on which role you choose, the job roles will also change accordingly.
Here are some popular career choices to look forward to in data analytics and data science.
All in all, data scientists have a more advanced skill set. As a result, the average data scientist earns more than the average data analyst. But you can always start your career as a data analyst and then lean towards data science later on.
Besides data analytics and data science, a few other domains are available if you're interested in data-centric roles. For starters, you can look at data architecture and data engineering positions. There are plenty of courses available in the market, which can help you hone your skills in these fields.
ABOUT THE AUTHOR
Gaurav Siyal (55 Articles Published)
Gaurav Siyal has two years of writing experience, writing for a series of digital marketing firms and software lifecycle documents.
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