From healthcare to the tech industry to online shopping to streaming services, the arena of big data has evolved in many different areas of life.
Data science and data analytics are two buzzing fields of computing and technology.
The field of data science and data analytics is growing at an astronomical rate. Companies all over the globe are looking for professionals who can reap maximum benefits from the vast amounts of data they have.
Both these fields have entered the mainstream. Though, you need to have adequate knowledge of both these concepts, similarities and differences they hold.
Ready to strengthen your grip on these concepts?
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Data Science – An Overview
Data science is touted as a multidisciplinary arena focusing mainly on getting actionable insights from large and structured data sets. This field mainly fixates on unleashing the answers to the things we want to know.
The main agenda of a data scientist is to use several data techniques to get the answers. These techniques generally incorporate predictive analysis, computer science and machine learning to pass through the bulk data sets with the effort to establish imperative solutions.
In a nutshell, the aim of data science is to research and parse raw and unstructured data.
Data Analytics – An Overview
Data analytics is a technique where an analyst mainly concentrates on creating methods for capturing, processing and organising data to uncover actionable insights related to current problems.
More simply, this field is directed towards finding solutions to the problems whose answers we are looking for. A data analyst job involves identifying solutions to several problems.
Difference between Data Science and Data Analytics
Data Science and data analytics are touted as two sides of the same coin. Each of them basically deals with large amounts of data with a unique approach. Data science is regarded as the umbrella that encompasses the field of data analytics.
Data Science includes the concepts of machine learning, computer science, statistics, artificial intelligence, data mining, ML algorithms and data inference. On the other hand, the field of data analytics is concerned with concepts like mathematics, statistics and statistical analysis.
Though, both the terms have certain differences that every aspirant should know. Below is a compilation of some of the major differences between both integral big data concepts.
Basis of Differences
The primary focus of data science is to find the correlation between the two large datasets. It seeks to unfold or discover novel and unique questions to drive business innovations. Data science can involve some programming-heavy tasks.
However, data analytics is defined as the branch of data science which focuses mainly on getting the specific answer to all the questions that data science may bring forth. It helps in fostering data-driven innovations. Moreover, data analytics is also possible in a no-code manner.
In data science, Python is considered the widely used language alongside the usage of other languages like C++ and C#.
Though, the knowledge of popular languages like Python and R is not essential for the field of data analytics but may be used.
Use of Machine Learning
In the field of data science, machine learning is a small component, as its main purpose is to be used in predictive analytics (a part of data analytics). Machine learning is a computationally heavy task and is done to accurately predict a specific outcome, or to tell us how to achieve a specific outcome.
The scope of data science is macro. It includes several subfields like machine learning, computer science, artificial intelligence, data mining, and data visualisation.
On the other hand, the scope of data analytics is micro. This field is only limited to analytical methods and techniques regarding statistical tools.
In order to become a data scientist, you need to gain critical skills in departments like analytics, domain knowledge and programming language knowledge. You need to have the following:
- Strong knowledge of languages like R, SAS, Python
- SQL database coding experience
- You should have the ability to work with unstructured data
- Machine learning knowledge
On the other hand, a data analyst should be capable of asking questions regarding relevant data looks. If you are looking to step inside a data analyst role, you need to gain skills like:
- Mathematical statistics knowledge
- Understanding of Python and R
- Understanding PIG
- Data Wrangling
Software and Tools to Use
In the case of data scientists, you can use tools like Hadoop, TensorFlow, Spark, and MySQL.
However, when it comes to the field of data analysis, you can make use of several tools like Excel Business Intelligence or SAS
Roles and Responsibilities
The role of a data scientist is vast and versatile. Data scientists mainly perform the tasks of designing and modelling data or creating algorithms and other predictive models for extracting information from reliable sources that are needed to solve all the complex problems.
Data analysts are mainly responsible for designing, structuring and managing data and its systems in an aligned way. Moreover, you can also use statistical tools for the interpretation of data sets or preparing reports for effectively communicating trends. So that, you can get relevant findings.
The field of data science is growing at an exponential rate. This industry offers some high-paying jobs and lucrative opportunities in the field of Information technology and tech industry.
However, data analysts also get the opportunity of lucrative jobs. Though, they are paid less in comparison to the data scientists. You can choose from the wide options of jobs and kickstart your career in the right direction.
Data science and data analytics are today’s buzzwords. Both fields offer some of the best job opportunities while unfolding a new arena for aspirants to kick-start their career. Learn in-depth about both fields with this blog.
You can also enrol in data science courses and data analytics courses to give a powerful push to your career.