Big data is something that, in the words of renowned behavioural economist Dan Ariely, "everyone talks about, nobody actually knows how to do, everyone thinks everyone else is doing, so everyone pretends they are doing."
This idea is relevant to a lot of data terms. Even the professionals struggle to define terms like "data science," "data analysis," "big data," and "data mining," despite the fact that they are frequently used. Here, we concentrate on one of the distinctions that is most crucial to your career: the frequently hazy line between data analytics and data science.
Data Science vs. Data Analytics
While both data scientists and analysts use data, the key distinction between the two is in how they use it.
To assist firms make more strategic decisions, data analysts analyse enormous data sets to find trends, build charts, and produce visual presentations.
On the other hand, data scientists use prototypes, algorithms, predictive models, and unique analyses to create and build new methods for data modelling and production.
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Working in data analytics
Although the duties of data analysts may differ among businesses and industries, they always use data to make informed decisions and address issues. They use a variety of methods to evaluate well-defined sets of data in order to provide practical business solutions, such as explaining why sales declined in a certain quarter or how internal attrition influences revenue.
Database analyst, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst are just a few of the many specialties and titles available to data analysts. The best data analysts can explain quantitative results to non-technical coworkers or clients while yet possessing technical proficiency.
Data analysts' characteristics
Data analysts can come from a mathematical or statistical experience, or they can complement a non-quantitative background by gaining the skills required to make judgments using data. To further their professions, some data analysts decide to earn a graduate degree, like a master's in analytics.
If a working professional has experience in a statistical or quantitative discipline, they may be more suited for a career change. It will be much easier for them to shift into a data analysis profession if they also pursue an advanced degree in the data sector.
Tools and Skills
Data mining/data warehouse, data modelling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis are some of the top data analyst abilities.
Roles and responsibilities
Data analysts are frequently in charge of creating and managing data systems and databases, analysing large data sets using statistical methods, and creating reports that clearly describe trends, patterns, and forecasts based on pertinent results.
Working in data science
On the other hand, data scientists develop statistical models, write algorithms, and ask questions to estimate the unknown. Heavy coding is the primary distinction between a data analyst and a data scientist. Data scientists are able to simultaneously organise any data sets using a variety of tools and create their own automated frameworks and systems.
Data analysts' characteristics
A data scientist is someone who possesses mathematical and statistical knowledge, hacking skills, and substantive experience, according to Drew Conway, a data science expert and the creator of Alluvium. As a result, a lot of data scientists have academic credentials like a master's in data science.
Tools and Skills
These include Python, Hadoop, Java, data mining/data warehousing, object-oriented programming, machine learning, and software development.
Roles and Responsibilities
To extract the data required by an organisation to solve complicated challenges, data scientists are often entrusted with establishing data modelling procedures as well as developing algorithms and predictive models.
Choosing between a career in data science and analytics
Once you are clear on the distinctions between data analytics and data science and are able to pinpoint what each career includes, you can begin determining which career path is the best fit for you. You should take into account three important variables to decide which path is most compatible with your personal and professional objectives.
1. Take into account your personal experiences.
Martin Schedlbauer, professor and director of the information, data science, and data analytics programmes within Northeastern University's Khoury College of Computer Sciences, including the Master of Science in Computer Science and Master of Science in Data Science, notes that while data analysts and data scientists are similar in many ways, their differences are rooted in their professional and educational backgrounds.
In order to help businesses make better strategic decisions, data analysts analyse huge data sets to find trends, build charts, and produce visual presentations. Analysts generally seek undergraduate STEM degrees, and perhaps even graduate degrees in analytics or a similar discipline, to ensure that their education is in line with these objectives. Additionally, they look for candidates with background in science, math, programming, databases, modelling, and predictive analytics.
2. Consider your interests.
Are statistics and numbers your thing, or do you also have a thing for business and computer science?
Numbers, statistics, and programming are their three favourite things. As the guardians of their organization's data, they spend practically all of their time searching databases for data points from intricate and frequently dispersed sources. According to Schedlbauer, data analysts should also have a thorough understanding of the sector they specialise in. If this describes you, a career in data analytics might be the perfect fit for your interests.
A combination of arithmetic, statistics, and computer science are needed for data scientists, in addition to an interest in and working understanding of the business sector. If this job description more closely fits your background and experience, becoming a data scientist might be the best choice.
You'll have a better notion of the type of work that you'll love and probably thrive at if you know which career suits your particular interests. Take your time and give this aspect of the equation some thought because doing so can help to ensure that you are happy in your career for many years to come.
3. Take into account the pay and professional path you want.
Data scientists and data analysts are paid differently for their tasks since they need various levels of experience.
Earning potential for data analysts is from $83,750 to $142,500. However, because these professionals mostly work with databases, they can raise their wages by learning new programming languages like R and Python. Data analysts with more than ten years of experience, however, frequently take advantage of their earning potential and move on to other positions, according to PayScale. Following the completion of an advanced degree, two popular job changes, according to Blake Angove, director of technology services at IT staffing agency LaSalle Network, include moving into a developer or data scientist position.
Data scientists are regarded as being more senior than data analysts because they frequently possess advanced skills, a doctorate degree, and more experience. As a result, they frequently receive more pay for their services. RHT estimates that the average yearly income for data scientists is between $105,750 and $180,250.
What data profession is best for you?
Given the considerable variances in function responsibilities, educational requirements, and career paths, data analysts and data scientists have job titles that are misleadingly identical. Data Analytics and Business Intelligence course (DA/BI course) is one of the best best data analytics programs offered by Syntax Technologies in the market. The program is designed to train people with little to no programming background to become data professionals that combine analytical skills and programming skills - using data manipulation, data visualization, data cleansing and much more to make sense of real-world data sets and create data dashboards/visualizations to share your findings.