How does Data Science differ from business analytics?
Data Science and Business Analytics are often used interchangeably but are different domains. For a layman, they would be the same. However, for a professional, using these terms in the right manner is very important as both data science, and business analytics can broadly impact the business directly. According to a government-sponsored database, ONET OnLine, a business analyst earns an average of $88,550 per year while a data scientist earns $122,840 per year.
Difference between Data Science and Business Analytics:
|Data Science||Business Analytics|
|It is the science that uses data algorithms, statistics, and technology. Data science aims to provide actionable insights on an array of both structured and unstructured data||It is the study of mostly structured business data that aims at providing solutions to specific business problems and hurdles|
|It uses both structured and unstructured data||It uses mostly structured data|
|Data science is the combination of traditional analytics practices and computer science knowledge (including coding)||Business analytics is inclined towards statistics more (it does not involve much coding)|
|Technology, Finance, E-commerce, Academic are some industries where data science applications are used||Finance, Technology, Marketing, Retail are some industries where business analytics applications are used|
|Artificial Intelligence and Machine Learning are the future applications of data science||Cognitive Analytics and Tax analytics are the future applications of business analytics/td>|
|Data Science is used to forecast insights more and not much for making business decisions||Business Analytics results are considered as crucial decision-makers|
|It involves the study of trends and patterns||It specifically works on business problems|
Overlapping of Data Science and Business Analytics:
As discussed, data science and business analytics are unique fields with differences in between. The most significant difference is the scope of problems addressed by both technologies. If we put it simply, data science is the science of data that uses algorithms, statistics, and technology. Data science provides insights on massive structured and unstructured data and it focuses on solving a broader perspective, for example, consumer behaviour.
On the contrary, business analytics is a statistical study of structured business data. The focus of business analytics is to provide solutions to specific business roadblocks and problems.
These two terms are often overlapped or used interchangeably. A data science problem could be wrongly addressed to be solved with the help of business analytics. The repercussions of using business analytics in this context could be unhelpful because the techniques used in data science are different than business analytics. Using the wrong tools to analyse and assess a data set can lead to unsatisfactory and undesirable results.
Data science is not limited to only statistical or algorithmic aspects, instead, it is an umbrella term for programming, statistics, data analytics, and everything related to mining large data sets. The end product of data science is business analytics, which can be further categorised into statistical analysis and business intelligence.
Data Science: An Overview
Data science is the study of data that uses data algorithms, statistics, and technology. To find solutions and predict outcomes for the respective problem, data science is used.
According to payscale.com, in India, the median pay scale of data scientist jobs in INR 700000. As an entry-level professional, you can earn up to INR 550000, while the experienced professionals can earn up to INR 1800000.
To draw insights from numbers, text, images, videos, and audio, a data scientist applies machine learning algorithms. Hugo Bowne-Anderson wrote in the Harvard Business Review, “Data scientists lay a solid data foundation to perform robust analytics. Then they use online experiments, among other methods, to achieve sustainable growth.”
Data scientists build personalised data products and machine learning pipelines to better understand the business and customers and make better decisions accordingly. Data science is about machine learning, data products, deep learning, decision making, testing, and infrastructure.
Skills required to be a Data Scientist:
To be a data scientist, the following core skills are required:
It would help if you have a keen sense of detecting glitches, patterns, statistical tests, and likelihood estimators.
Computer science and programming:
Day-to-day, as a data scientist, you will encounter substantial data sets. You will be required to write various programming languages such as Python, R, and SQL to find answers to the problems.
It would be best to be acquainted with statistical models and machine learning algorithms as a data scientist. These models and algorithms will automatically enable computers to learn from data.
Multivariable calculus and linear algebra:
For building a machine learning model, significant mathematical knowledge will be needed.
Data visualisation and storytelling:
After finding insights from the data, you will be required to communicate your findings. To communicate and describe actionable insights to technical and non-technical audiences, data scientists use data visualisation tools.
Business Analytics: An Overview
By analytics to provide data-driven recommendations, business analytics bridges the gap between information technology and business. A deep understanding of business, data, statistics, and computer science is required to be a successful business analyst.
According to payscale.com, entry-level professionals can earn around INR 350000, professionals in their mid-career can expect around INR 850000, and highly experienced professionals can bag up to INR 1200000.
What does a business analyst do?
A business analyst acts as a facilitator, communicator, and mediator. The aim is to seek the best way to improve and increase business efficiency through strategy, analytic solutions, technology, and more.
Skills required to be a Business Analyst:
The following skills are required to be a business analyst:
Business is managed with a massive amount of data. As a business analyst, you must be able to clean and make the most use of the data interpreted.
Data Visualisation and storytelling:
Data visualisation is defined as the graphical representation of data and information. Infographics like charts, graphs, and maps are used to communicate trends, outliers, and patterns in data.
Analytical reasoning ability:
Analytical reasoning ability consists of reasoning, critical thinking, communication, research, and data analysis. To apply descriptive, predictive, and prescriptive analytics to solve the problems and hurdles in a business, analytical reasoning ability is required.
Mathematical and statistical skills:
For modelling, inference, estimation, and forecasting, mathematical and statistical skills are required. You must have the ability to collect, organise, and interpret numerical data.
Written and communication skills:
It becomes easy to communicate with the team and recommend improvements and ideas regarding business growth with better communication skills.
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