With admissions around the corner, those students trying to opt for degrees in data usually have to tussle between terms like “data science”, “big data” and “data mining”. Here, we will focus on important distinctions between data analytics and data science as it is one of the most sought after topic which relates directly to students career.
Data Analytics vs. Data Science
While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modelling and production using prototypes, algorithms, predictive models, and custom analysis.
Data Analytics
The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.
Data analysts have a range of fields and titles, including (but not limited to) database analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst.
Note: The best data analysts must have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.
Data analysts can have a background in physics, mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers.
Skills and Tools: Top data analyst skills include data mining/data warehouse, data modelling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis.
Roles and Responsibilities: Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.
Data Science
Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks. Job description of data scientists is required to know all the way from data technologies to scripting languages to statistical programming.
Skills and Tools: These include machine learning, software development, Hadoop, Java, SQL, SPSS, Data wrangler, data analysis, MATLAB, python, and object-oriented programming
Roles and Responsibilities: Data scientists are typically tasked with designing data modelling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.
Choosing Between a Data Analytics and Data Science Career
Once the student has understood the differences between data analytics and data science, he/she can start evaluating which path is the right fit for them. To determine which path is best aligned one must consider three key factors: Educational and professional background, Interest and Career trajectory.
While data analysts and data scientists are similar in many ways, their differences are rooted in their professional and educational backgrounds. As mentioned above, data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. To align their education with these tasks, analysts typically pursue an undergraduate degree in a science, technology, engineering, or math (STEM) major, and sometimes even an advanced degree. They also seek out experience in math, science, programming, databases, modelling, and predictive analytics.
Data scientists, on the other hand, are more focused on designing and constructing new processes for data modelling and production. In addition, because they use a variety of techniques to comb through data—including data mining and machine learning—an advanced degree, such as a master’s or PhD
Data scientists are much more technical and mathematical [than data analysts] and therefore it requires them to have a background in computer science as well.
Data analysts love numbers, statistics, and programming and they work almost exclusively in databases to uncover data points from complex and often disparate sources.
Data scientists are required to have a blend of math, statistics, and computer science, as well as an interest and knowledge of the business world. If this description better aligns with your background and experience, perhaps a role as a data scientist is the right pick.
Either way, understanding which career matches your personal interests will help you get a better idea of the kind of work that you’ll enjoy and likely excel at.
Different levels of experience are required for data scientists and data analysts, resulting in different levels of compensation for these roles.
Data scientists who have typically advanced skill set and good experience are considered more senior than data analysts. Usually, they are better compensated for their work. Data scientists received highest salary boost for IT professional in last few years. The career trajectory for professionals in data science is positive as well, with many opportunities for advancement to senior roles such as data architect or data engineer.
Qualification
Master’s or Ph.D. in statistics, mathematics, or computer science
Experience using statistical computer languages such as R, Python, SQL, etc.
Experience in statistical and data mining techniques, including generalized linear model/regression, random forest, boosting, trees, text mining, social network analysis
Experience working with and creating data architectures
Knowledge of machine learning techniques such as clustering, decision tree learning, and artificial neural networks
Knowledge of advanced statistical techniques and concepts, including regression, properties of distributions, and statistical tests
5-7 years of experience manipulating data sets and building statistical models
Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
Experience analyzing data from third-party providers, including Google Analytics, Site Catalyst, Coremetrics, AdWords, Crimson Hexagon, Facebook Insights, etc.
Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
Which Is Right for You?
Data analysts and data scientists have job titles that are deceptively similar given the many differences in role responsibilities, educational requirements, and career trajectory.
The fact that different companies have different ways of defining roles is a significant reason for this confusion. In practice, titles don’t always reflect one’s actual job activities and responsibilities accurately. For instance, some startups use the title “data scientist” to attract talent for their analyst roles.
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