Big data has become a major component in the tech world today thanks to the actionable insights and results businesses can glean. However, the creation of such large datasets also requires understanding and having the proper tools on hand to parse through them to uncover the right information. To better comprehend big data, the fields of data science and analytics have gone from largely being relegated to academia, to instead becoming integral elements of Business Intelligence and big data analytics tools.
However, it can be confusing to differentiate between data analytics and data science. Despite the two being interconnected, they provide different results and pursue different approaches. If you need to study data your business is producing, it’s vital to grasp what they bring to the table, and how each is unique. To help you optimize your big data analytics, we break down both categories, examine their differences, and reveal the value they deliver.
What Is Data Science?
Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. The field primarily fixates on unearthing answers to the things we don’t know we don’t know. Data science experts use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in an effort to establish solutions to problems that haven’t been thought of yet.
Data scientists’ main goal is to ask questions and locate potential avenues of study, with less concern for specific answers and more emphasis placed on finding the right question to ask. Experts accomplish this by predicting potential trends, exploring disparate and disconnected data sources, and finding better ways to analyze information.
What Is Data Analytics?
Data analytics focuses on processing and performing statistical analysis of existing datasets. Analysts concentrate on creating methods to capture, process, and organize data to uncover actionable insights for current problems, and establishing the best way to present this data. More simply, the field of data and analytics is directed toward solving problems for questions we know we don’t know the answers to. More importantly, it’s based on producing results that can lead to immediate improvements.
Data analytics also encompasses a few different branches of broader statistics and analysis which help combine diverse sources of data and locate connections while simplifying the results.
What Is the Difference?
While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope. Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries.
Another significant difference between the two fields is a question of exploration. Data science isn’t concerned with answering specific queries, instead parsing through massive datasets in sometimes unstructured ways to expose insights. Data analysis works better when it is focused, having questions in mind that need answers based on existing data. Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.
•Skills Required to Become a Data Scientist
Anyone interested in building a strong career in this domain should gain critical skills in three departments: analytics, programming, and domain knowledge. Going one level deeper, the following skills will help you carve out a niche as a data scientist:
•Strong knowledge of Python, SAS, R, Scala
•Hands-on experience in SQL database coding
•Ability to work with unstructured data from various sources like video and social media
•Understand multiple analytical functions
•Knowledge of machine learning
•Skills Required to Become a Data Analyst
A data analyst should be able to take a specific question or topic, discuss what the data looks like, and represent that data to relevant stakeholders in the company. If you’re looking to step into the role of a data analyst, you must gain these four key skills:
•Knowledge of mathematical statistics
•Fluent understanding of R and Python
•Data wrangling
•Understand PIG/ HIVE
•Working in 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, business analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.
•Characteristics of Data Analysts
Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers. Some data analysts choose to pursue an advanced degree, such as a master’s in analytics, in order to advance their careers.
Working professionals that are considering changing careers could benefit if they have experience in mathematical or statistical fields. Adding the pursuit of an advanced degree in the data industry will greatly impact their job opportunities and make for a smooth transition into a data analysis position.
•Skills and Tools
Top data analyst skills include data mining/data warehouse, data modeling, 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.
•Working in 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.
•Characteristics of Data Analysts
Drew Conway, data science expert and founder of Alluvium, describes a data scientist as someone who has mathematical and statistical knowledge, hacking skills, and substantive expertise. As such, many data scientists hold degrees such as a master’s in data science.
•Skills and Tools
These include machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming
•Roles and Responsibilities
Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.
