Data Science & Data Analytics

Harvard Business Review has named ‘Data scientist’ as the “sexiest job of the21st century”.With advent of AI , a steep rise in demand for Data Science professionals is seen.Basic Degree and relevant certifications is good to quick launch your careers. Data Analytics‘ and ‘Data Science‘ are used  quiet interchangeably ,albeit incorrectly. Although, there is a noticeable difference in spite of many similarities.

Data Science refers to the process of extraction of useful insights from data. Data science as a strategic move fosters Data driven decision making by using multiple computer technologies,scientific processes, methods ,Statistics and Machine learning with Algorithms to automate predictive analysis to project future trends based on past patterns.They generate their own questions,the solutions to which can potentially help the business to scale, grow or survive their competition. These analytical observations and reports needs to be communicated  effectively to a non-technical audience.According to a recent survey the global data science platform market is expected to reach $128.21 billion by 2022,from $19.75 billion in 2016. 

BIG DATA  Any interaction on web includes exchange of data—e.g Online shopping,Music preferences,Prime time watches,Places we go,Instagram feed, Transactions we carry,facial recognition in our phones or simply Google Surfing.Data is enormous and huge.This colossal Data is BIG DATA. 90% of worlds data is created in just couple of years.This data becomes the base for data analytics.The scope and velocity of data is the toughest challenge. Too much of data available creates a havoc.Businesses are incapable of handling such big volumes of data, fail to efficiently transform this Big data into actionable insights.

Data Analytics A data analyst  conducts research, analyse the ‘trends’ and draw useful interpretation with Data given but on a Micro level unlike data scientist.Someone who merely curates meaningful insights and analyse data.They conduct analysis on data to find answers to a given set of questions .In simple way Data Analyst does “day-to-day” analysis while Data Scientist works on “what ifs“.  

Methodologies in Data Analytics

1. Exploratory data analysis (EDA), which aims to find patterns and relationships in data
2. Confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false

Life Cycle – Analytics

Analysis of any sort needs to be carried on some ‘data’ , which is again based on a real time Problem or a Question. 

1.  Specifies a ‘question’ or a problem.that defines what data is required and where to retrieve it from?
2. Data is collected from various sources ranging from internal organizational databases to web.Data can be Numerical/Categorical/Structured /Unstructured.
3. Data from step-2 needs to be processed.A Data Model might be needed as well.A Data Model might be needed as well.
4.  Any unnecessary,incomplete data, duplicacies or errors needs to be processed.An organised and uniform data is the output from this stage.
5. Meaningful interpretations and insights are drawn from data using different analytical techniques.Statistical Data Models as Correlation, Regression Analysis are used to identify the relations among data variables.The process might require additional Cleansing & Collection.                                                                                             6. Data analysts can choose any of data visualization techniques, such as tables,charts and Graphs to communicate  analysis reports to users.

Major Differences 

	Data Analytics	Data Science
Scope	Micro	Macro
Goal	Find answers to a given set of business problem or questions.	Discover new question/s to drive innovation. They find solutions to ‘What if’s’
Major Fields	Healthcare, Gaming, Travel, Industries with immediate data needs,   corporate analytics	Machine learning, AI, search engine engineering
Use of  Big-Data	Yes , Structured Dataset	Yes , Unstructured Dataset
SkillSet	Data Modelling,           Predictive Analytics, Advanced Statistics,         Coding Skills	BI Tools,                   Intermediate statistics, Coding Skills,Regular Expression (SQL)

Promising Data Science Careers

• Business Intelligence (BI) Developer
• Data Architect.
• Infrastructure Architect.
• Data Analyst.
• Enterprise Architect.
• Data Engineer.
• Applications Architect.
• Data Scientist.

Technical Skills Required to be a Data Scientist

One needs to have in depth knowledge of subjects like Mathematics / Statistics,Linear algebra, Optimization, Probability Theory, Computer Coding, Data structure, Algorithms to make through this domain. Any relevant certification on the following technical skillset could help you break through this domain.

• Coding in Python, R,Java,Perl,C or C++.The most commonly used coding language is Python.
• Hadoop Platform (Big Data).
• SQL Database/Coding.
• Apache Spark.
• Machine Learning and AI(Artificial Learning).
• Data Visualization.
• Unstructured data.

Data today is premium.We are living in a Data Centric Era.Data science is by far the most promising career an IT professional can opt for. Scarcity of niche talent in this domain makes it all the more demanding. In spite of all confusion around Data Analytics and Data Science , there are some common technical grounds they share.Today the business is data driven. It is the best time ever to master Data Science. Get started with the latest Data Science based certifications and its Training by Pinnacledu.com