Data Analytics-I
Chapter 1 Introduction of Data Analytics
Introduction of Data Analytics / Definition of Data Analytics
Data Analytics also known as Predictive Analytics, is all about automating insights into a dataset through usage
of queries and data aggregation procedures. It can represent various dependencies between input variables and
discover hidden patterns in the dataset under analysis.
Data analysis is defined as a process of cleaning, transforming, and modelling data to discover useful
information for business decision making.
Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Data Analytics is the science of examining raw data with the purpose of finding and drawing conclusions about
the information in the data using methods from statistics and machine learning.
Data Analytics goes beyond the concept of data mining by analysing semi-structured and unstructured data from
different sources and in different formats e.g. text mining.
,THE HISTORY OF DATA ANALYTICS
Data analytics is the process of manipulating data to extract useful trends and hidden patterns which can help us
derive valuable insights to make business predictions.
1890-Herman Hollerith invents the Hollerith Tabulating Machine which reduced crunching of census data from
10years to 3months!
1962 John Tukey writes a paper title “The Future Of Data Analysis”, where he brought into question the
relationship between statistics and analysis.
1970-Edgar F. Codd presents his framework for relational databases.
1989-Howard Dresner at Gartner proposes the term “Business Intelligence.”1990s-Data Mining is born
following the success of the concept of data warehouses introduced by William H. Inman.
1991-Tim Bernes Lee sets out the specifications for a worldwide, interconnected web of data accessible to
anyone across the world, now the internet.
2004-A whitepaper on MapReduce from Google inspires open source software projects like Apache Hadoop
and Apache Cassandra to deal with huge volumes of data through distributed computing.
2008-Jeff Hammerbacher and DJ Patil, then at Facebook and LinkedIn coin the term “data scientist” to describe
their work and it then becomes a buzzword.
September 2010 Hilary Mason and Chris Wiggins write in “A Taxonomy of Data Science”.
Big companies like Google and Facebook used big data analytics. In 2010, retailers, banks, manufacturers, and
healthcare companies began to understand the value of being big data analytics
2013-IBM shows statistics that 90% of the world’s data was created in the preceding 2 years!
, Types of Data Analytics
1. Predictive (forecasting)
2. Descriptive (business intelligence and data mining)
3. Prescriptive (optimization and simulation)
4. Diagnostic analytics
5. Cluster Analytics
6. Cognitive Analytics
Data Analytics and its Types
1) Descriptive Analytics
Descriptive analytics looks at data and analyze past event for insight as to how to approach future events. It
looks at past performance and understands the performance by mining historical data to understand the cause of
success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance
uses this type of analysis.
The descriptive model quantifies relationships in data in a way that is often used to classify customers or
prospects into groups. Unlike a predictive model that focuses on predicting the behavior of a single customer,
Descriptive analytics identifies many different relationships between customer and product.
Common examples of Descriptive analytics are company reports that provide historic reviews like:
Data Queries
Reports
Descriptive Statistics
Data dashboard
Chapter 1 Introduction of Data Analytics
Introduction of Data Analytics / Definition of Data Analytics
Data Analytics also known as Predictive Analytics, is all about automating insights into a dataset through usage
of queries and data aggregation procedures. It can represent various dependencies between input variables and
discover hidden patterns in the dataset under analysis.
Data analysis is defined as a process of cleaning, transforming, and modelling data to discover useful
information for business decision making.
Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Data Analytics is the science of examining raw data with the purpose of finding and drawing conclusions about
the information in the data using methods from statistics and machine learning.
Data Analytics goes beyond the concept of data mining by analysing semi-structured and unstructured data from
different sources and in different formats e.g. text mining.
,THE HISTORY OF DATA ANALYTICS
Data analytics is the process of manipulating data to extract useful trends and hidden patterns which can help us
derive valuable insights to make business predictions.
1890-Herman Hollerith invents the Hollerith Tabulating Machine which reduced crunching of census data from
10years to 3months!
1962 John Tukey writes a paper title “The Future Of Data Analysis”, where he brought into question the
relationship between statistics and analysis.
1970-Edgar F. Codd presents his framework for relational databases.
1989-Howard Dresner at Gartner proposes the term “Business Intelligence.”1990s-Data Mining is born
following the success of the concept of data warehouses introduced by William H. Inman.
1991-Tim Bernes Lee sets out the specifications for a worldwide, interconnected web of data accessible to
anyone across the world, now the internet.
2004-A whitepaper on MapReduce from Google inspires open source software projects like Apache Hadoop
and Apache Cassandra to deal with huge volumes of data through distributed computing.
2008-Jeff Hammerbacher and DJ Patil, then at Facebook and LinkedIn coin the term “data scientist” to describe
their work and it then becomes a buzzword.
September 2010 Hilary Mason and Chris Wiggins write in “A Taxonomy of Data Science”.
Big companies like Google and Facebook used big data analytics. In 2010, retailers, banks, manufacturers, and
healthcare companies began to understand the value of being big data analytics
2013-IBM shows statistics that 90% of the world’s data was created in the preceding 2 years!
, Types of Data Analytics
1. Predictive (forecasting)
2. Descriptive (business intelligence and data mining)
3. Prescriptive (optimization and simulation)
4. Diagnostic analytics
5. Cluster Analytics
6. Cognitive Analytics
Data Analytics and its Types
1) Descriptive Analytics
Descriptive analytics looks at data and analyze past event for insight as to how to approach future events. It
looks at past performance and understands the performance by mining historical data to understand the cause of
success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance
uses this type of analysis.
The descriptive model quantifies relationships in data in a way that is often used to classify customers or
prospects into groups. Unlike a predictive model that focuses on predicting the behavior of a single customer,
Descriptive analytics identifies many different relationships between customer and product.
Common examples of Descriptive analytics are company reports that provide historic reviews like:
Data Queries
Reports
Descriptive Statistics
Data dashboard
