"Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, therefore everyone pretends they are doing it," said Dan Ariely, a well-known behavioural economics expert.
This principle applies to a wide range of data terminologies. Even professionals struggle to define words like "data science," "data analysis," "big data," and "data mining," which are commonly used. Here, we'll look at one of the more essential contrasts in your career: the often-confusing discrepancies between data analytics and data science.
Data Science vs. Data Analytics
While both data analysts and data scientists deal with data, the primary distinction is what they do with it.
Data analysts evaluate big data sets for trends, generate charts, and create visual presentations to assist corporations in making better strategic decisions
Data scientists, on the other hand, use prototypes, algorithms, predictive models, and specialised analyses to create and build new processes for data modelling and production.
Working as a Data Analyst
Data analysts' responsibilities vary by industry and company, but in general, data analysts use data to derive relevant insights and solve problems. They use an arsenal of diverse technologies to evaluate well-defined sets of data in order to address tangible business needs, such as why sales plummeted in a particular quarter, why a marketing campaign performed better in certain locations, how internal attrition affects revenue, and so on.
Database analysts, business analysts, market research analysts, sales analysts, finance analysts, marketing analysts, advertising analysts, customer success analysts, operations analysts, pricing analysts, and international strategy analysts are all examples of data analysts. The best data analysts are technical experts who can convey quantitative conclusions to non-technical colleagues or clients.
Data Analyst Characteristics
Data analysts can have a background in mathematics and statistics, or they can enhance a non-quantitative background by learning the skills required to make numbers-based decisions. To further their professions, some data analysts choose to seek advanced degrees, such as a master's degree in analytics.
Working professionals considering a career change may benefit from prior training in mathematical or statistical domains. Pursuing an advanced degree in the data industry will significantly increase their career options and allow for a smooth transition into a data analysis position.
Tools and skills
Data mining/data warehouse, data modelling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis are among the top data analyst abilities.
Responsibilities and Roles
Data analysts are frequently in charge of creating and maintaining data systems and databases, interpreting data sets with statistical techniques, and preparing reports that effectively explain trends, patterns, and predictions based on pertinent results.
Working as a Data Scientist
Data scientists, on the other hand, make educated guesses about the unknown by asking probing questions, designing algorithms, and developing statistical models. The primary distinction between a data analyst and a data scientist is extensive coding. Data scientists can use several tools to arrange unknown sets of data at the same time, as well as construct their own automation systems and frameworks.
Data Analyst Characteristics
A data scientist, according to Drew Conway, data science specialist and founder of Alluvium, is someone who possesses mathematical and statistical understanding, hacking abilities, and substantive experience. As a result, many data scientists have advanced degrees, such as a master's degree in data science.
Tools and skills
Machine learning, software development, Hadoop, Java, data mining/data warehousing, data analysis, Python, and object-oriented programming are some examples.
Responsibilities and Roles
Data scientists are often responsible with constructing algorithms and predictive models, as well as designing data modelling methods, to extract the information required by an organisation to address complicated challenges.
Choosing a Data Analytics or Data Science Career
You may begin considering which path is the best fit for you once you grasp the differences between data analytics and data science and can determine what each career includes. Three major aspects should be considered when determining which path is best aligned with your personal and professional goals.
1. Consider your personal history.
While data analysts and data scientists are similar in many ways, their differences stem from their professional and educational backgrounds, according to Martin Schedlbauer, professor and director of Northeastern University's Khoury College of Computer Sciences' information, data science, and data analytics programmes, which include the Master of Science in Computer Science and Master of Science in Data Science.
Data analysts, as previously stated, evaluate massive data sets to discover trends, construct charts, and create visual presentations to assist businesses in making better strategic decisions. Analysts often pursue an undergraduate degree in a science, technology, engineering, or math (STEM) major, as well as an advanced degree in analytics or a similar field, to align their education with these jobs. They are also looking for people with backgrounds in math, physics, programming, databases, modelling, and predictive analytics.
Data scientists, on the other hand, are more concerned with designing and building new data modelling and production processes. According to Schedlbauer, because they use a range of approaches to comb through data, such as data mining and machine learning, an advanced degree such as a master's in data science is required for professional progress.
"Data scientists are...much more technical and mathematical [than data analysts]," he says, adding that this necessitates a "stronger background in computer science."
When deciding on a job route, it is critical to examine the educational requirements. If you've already decided to invest in your career with an advanced degree, you'll most likely have the educational and experience to pursue either path. If you're still contemplating whether or not to return to school, you may be more motivated to pursue a data analytics post, as employers are more likely to consider people without a master's degree for these positions.
Whatever path you take, considering your current and desired level of education and experience should help you limit down your alternatives.
If you decide to seek a graduate degree to jumpstart your career, make sure to choose a programme that will assist you in reaching your objectives. Northeastern, for example, emphasises experiential learning, helping students to build the skills and hands-on experience required to flourish in the profession.
2. Consider your interests.
Are numbers and statistics your thing, or do you have interests in computer science and business?
Data analysts are passionate about numbers, statistics, and programming. They operate nearly entirely in databases as the gatekeepers for their organization's data, uncovering data points from complicated and frequently divergent sources. Schedlbauer believes that data analysts should have a thorough awareness of the industry in which they operate. If this describes you, a data analytics career may be the ideal professional fit for you.
Data scientists must have a background in math, statistics, and computer science, as well as an interest in and knowledge of business. If this description more closely matches your background and experience, a position as a data scientist may be the best fit for you.
In any case, knowing which occupation matches your personal interests can give you a better notion of the type of work you'll love and likely excel at. Take the time to consider this aspect of the equation, since matching your work to your hobbies can go a long way toward keeping you content in your career for years to come.
3. Consider your target income and professional path.
Data scientists and data analysts demand varying levels of experience, which results in varying amounts of income.
Data analysts can earn between $83,750 and $142,500 per year. These experts' wages can be increased by learning other computer abilities, such as R and Python, because they work mostly with databases.
PayScale reports that data analysts with more than 10 years of experience frequently maximise their earning potential and move on to other positions. According to Blake Angove, director of technology services at IT recruiting agency LaSalle Network, two popular career options after obtaining an advanced degree are transferring into a developer role or data scientist position.
According to Schedlbauer, data scientists are considered more senior than data analysts because they often have a doctoral degree, advanced abilities, and are often more experienced. As a result, they are frequently better compensated for their efforts. According to RHT, data scientists earn between $105,750 and $180,250 per year on average.
Professionals in data science have a promising career path, with many prospects for development to senior positions such as data architect or data engineer.
Which data job path is best for you?
Given the considerable variances in function responsibilities, educational requirements, and career trajectory, data analysts and data scientists have job titles that are deceptively similar. 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.