In the modern data-savvy world, Big Data is becoming more and more important for tech businesses with each passing day. Data analytics and data science are two of the most important tools for converting Big Data into actionable insights and decisive facts.
For anyone obsessed with data and a fascination to go through large chunks of the same to produce insights and help companies grow, a career in data analytics or data science is the go-to option.
Although data science and data analytics are interconnected, both of them follow a different approach and therefore, produce different results. Hence, it is important to know the differences between the two before opting for one.
Before starting off with the differences between data science and data analytics, let’s first have a detailed introduction about the two competitors first.
Data Science
The multidisciplinary field of data science aims to seek actionable insights from giant sets of structured and unstructured data. Various techniques belonging to computer science, machine learning, predictive analytics, and statistics are used for doing so.
Typically, data scientists need to explore disconnected data sources, identifying better ways of analyzing information, and predicting potential trends.
The primary role of a data scientist is to analyze the data and list down questions that the business or the employer didn’t even know existed.
Data scientists are also responsible for recognizing the potential boulevards for studying giant stores of data and information that can help the employer in some way. If you simply can’t wait to get started with data science, here’s how to become a data scientist.
What does a Data Scientist Do?
Typically, a data scientist is responsible for collecting and analyzing data, churn out the same for actionable insights, and then sharing these obtained insights with the company or the employer.
As per the Columbia University’s Introduction to Data Science class, a data scientist is the one who spends significant time in collecting, cleansing, and organizing data.
In addition to knowing how to collect and cleanse data, a data scientist must also be versed in building algorithms, designing experiments, finding patterns, and sharing the same with team members and business professionals in an easy-to-understand format.
Data Analytics
Data analytics aims for performing statistical analysis and processing of existing datasets. It focuses on formulating methods for capturing, processing, and organizing data in the aim of identifying actionable insights for existing problems.
There are several distinct branches or specializations of data analytics that help in combining an array of data sources and locating data connections while simplifying results. Amazingly, anyone interested can become a data analyst with no experience.
What does a Data Analyst Do?
Typically, a data analyst is supposed to go through data, and prepare reports and visualizations to explain the kind of potential insights the data under examination can offer.
A data analyst in some scenarios might also be required to fill-in business owners about understanding specific queries with the help of data charts and visualizations.
The job role of a data analyst can also be understood as the first step in proceeding towards the role of a data scientist. In a sense, you can also consider data analysts as junior or assistant data scientists.
Data Analytics vs Data Science: The Comparison
Although we often use the two terms i.e. data analytics and data science interchangeably, there are several prominent differences between the two.While Data Science is a specialization that combines a number of disciplines into one, data analysis is a process dedicated to cleansing, inspecting, modeling, and transforming data with the aim of discovering vital information, making deductions, and helping with decision-making.
3 important differences between data analytics and data science are:
Exploration
An important difference between data analytics and data science lies in terms of exploration. While data analytics is meant for finding actionable data, data science aims for finding out the right questions that a business must be asking.
The field of data science focuses on establishing potential trends based on the extant set of data. Also, it focuses on comprehending better ways of analyzing and modeling the available data.
Unlike data analytics, data science isn’t about answering specific queries. Instead, it focuses on parsing through giant-sized datasets for finding out meaningful insights. Data analytics works ideally when there are already questions for which answers are important to find.
Data science generates insights that focus on questions that need to be asked. Then data analytics helps discover answers to these questions at hand.
Primary Fields
AI, corporate analysis, machine learning, and search engine engineering are the leading fields of data science. On the other hand, gaming, industries requiring immediate big data assistance, healthcare, and travel firms are the major primary fields for data analytics.
Scope
The term data science is an umbrella term for a collection of fields that are used for mining bigger datasets. Data analytics, on the other hand, is a specialized version of data science. It is meant for realizing actionable insights meant to be readily employed for existing queries to get instantly observable results.
The Synergy among Data Analytics and Data Science: The Bigger Picture
Aside from having functions that are vastly connected, data analytics and data science can be understood as a dual system or a complementary pair or simply the sides of the same coin.
Data science lays essential foundations while parsing big data-sets for creating important initial observations, future trends, and potential insights. Such information is immensely useful for data modeling, enhancing AI algorithms, and improving machine learning.
Once data science is able to note down important questions that weren’t supposed to be there, data analytics helps in finding answers to those questions in addition to finding actionable insights with real, practical applications.
Let us understand the cooperation between data analytics and data science with an example of the popular video streaming service Netflix.
An enormous amount of unstructured data is generated by Netflix in the form of audio, video, text, and other files. Using a traditional approach for processing this data is not a practical option. Instead, Big Data tools are used for processing the same. Now enters the Data Science.
A data scientist at Netflix will be responsible for:
- Enhancing content quality by combining user feedback with viewing behavior-related intrinsic factors to form powerful content quality prediction and checking models. Additional factors contributing to such models include machine learning models and techniques pertaining to NLP (Natural Language Processing) and text mining.
- Making the streaming experience better by using real-time algorithms that run as soon as the playback starts and determine the bitrate, best server to download the content, etc.
- Optimizing content caching by locating the content that is closer to the targeted Netflix users. This is done by observing the behavior of Netflix users and then making relevant decisions regarding content caching.
Understanding the impact of QoE (Quality of Experience) on user behavior by using the data to understand and/or predict user behavior. Although there are several QoE metrics, the bitrate and the rebuffer rate are the two important ones in this case.
