BUSINESS ANALYTICS
Unit 1: Introduction: What is business analytics? Historical Overview of data analysis, Data Scientist vs. Data Engineer vs.
Business Analyst, Career in Business Analytics, What is data science, Why Data Science, Applications for data science,
Data Scientists Roles and Responsibility
Unit 2: Data: Data Collection, Data Management, Big Data Management, Organization/sources of data, Importance of
data quality, Dealing with missing or incomplete data, Data Visualization, Data Classification Data Science Project Life
Cycle: Business Requirement, Data Acquisition, Data Preparation, Hypothesis and Modeling, Evaluation and
Interpretation, Deployment, Operations, Optimization.
Unit 3: Introduction to Data Mining, The origins of Data Mining, Data Mining Tasks, OLAP and Multidimensional data
analysis, Basic concept of Association Analysis and Cluster Analysis.
Unit 4: Introduction to Machine Learning: History and Evolution, AI Evolution, Statistics Vs Data Mining Vs, Data Vs, Data
Science, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Frameworks for building Machine
Learning Systems.
Unit 5: Application of Analytics Business Analysis: Retail Analytics, Marketing Analytics, Financial Analytics, Healthcare
Analytics, Supply Chain Analytics.
____________________________________________________________________________
Unit 1: Introduction To BUSINESS ANALYTICS
Business analytics - Business analytics refers to the statistical methods and computing technologies
for processing, mining and visualizing data to uncover patterns, relationships and insights that enable
better business decision-making. Business analytics involves companies that use data created by their
operations or publicly available data to solve business problems, monitor their business fundamentals,
identify new growth opportunities, and better serve their customers. Business analytics uses data
exploration, data visualization, integrated dashboards, and more to provide users with access to
actionable data and business insights.
Benefits of business analytics - Modern organizations need to be able to make quick decisions to
compete in a rapidly changing world, where new competitors spring up frequently and customers’
habits are always changing. Organizations that prioritize business analytics have several advantages
over competitors who do not.
Faster and better-informed decisions: Having a flexible and expansive view of all the data an
organization possesses can eliminate uncertainty, prompt an organization to take action faster, and
improve business processes. If an organization’s data suggests that sales of a particular product line
are declining precipitously, it might decide to discontinue that line. If climate risk impacts the
harvesting of a raw material another organization depends on, it might need to source a new material
from somewhere else. It’s especially helpful when considering pricing strategies.
How a company prices its goods or services is based on thousands of data points, many of which do
not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to
access real-time data to make smarter short- and long-term pricing data is critical. For organizations
that want to incorporate dynamic pricing, business analytics enables them to use thousands of data
points to react to external events and trends to identify the most profitable price point as frequently
as necessary.
Single-window view of information: Increased collaboration between departments and line-of-
business users means that everyone has the same data and is talking from the same playbook. Having
that single pane of glass shows more unseen patterns, enabling different departments to understand
,the company’s holistic approach and increase an organization’s ability to respond to changes in the
marketplace.
Enhanced customer service: By knowing what customers want, when and how they want it,
organizations encourage happier customers and build greater loyalty. In addition to an
improved customer experience, by being able to make smarter decisions on resource allocation or
manufacturing, organizations are likely able to offer those goods or services at a more affordable
price.
____________________________________________________________________________
Who is a Business Analyst?
A Business Analyst is a professional who uses data analysis and critical thinking skills to help
organizations improve their processes, products, and services.
Business analysts evaluate past and current business data with the primary goal of improving decision-
making processes within organizations. They work with stakeholders to identify areas for
improvement, and then gather and analyze data to develop solutions that address those areas.
Responsibilities:
➢Some of the responsibilities of a business analyst include:
➢Conducting research and analysis to identify areas for improvement ➢Collecting and analyzing data
to develop solutions
➢Collaborating with stakeholders to define project objectives and scope
➢Developing and implementing business processes and systems
➢Monitoring and evaluating the effectiveness of implemented solutions
➢Communicating project progress and results to stakeholders
Skills and Requirements:
➢Business analysts typically have strong analytical, critical thinking, and problem-solving skills.
➢They also need to be effective communicators, as they often work with stakeholders from different
departments and levels of the organization.
➢Additionally, they should have a good understanding of business processes and be able to work with
data and technology, including software tools for data analysis and project management.
Business Analytics Applications
⦁Management of customer Relationships
⦁Financial and marketing activities
⦁Manufacturing & Operations
⦁Infrastructure Mgmnt
⦁Supply chain management
,⦁Human resource planning
⦁Pricing decisions
____________________________________________________________________________
Difference between Data Scientist, Data Engineer, Data Analyst
In the world of big data and analytics, there are three key roles that are essential to any data-driven
organization: data scientist, data engineer, and data analyst. While the job titles may sound similar,
there are significant differences between the roles. In this article, we will explore the differences
between data scientist, data engineer, and data analyst, and how each of these roles contributes to
the overall success of a data-driven organization.
Generally, we hear different designations about CS Engineers like Data Scientist, Data Analyst and Data
Engineer. Let us discuss the differences between the above three roles.
Data Analyst –
The main focus of this person’s job would be on optimization of scenarios, say how an employee can
improve the company’s product growth. Data Cleaning and organizing of raw data, analyzing
and visualization of data to interpret the analysis and to present the technical analysis of data. Skills
needed for Data Analyst are R, Python, SQL, SAS, SAS Miner. A data analyst is responsible for
collecting, organizing, and analyzing data to identify patterns and insights that can be used to make
data-driven decisions. Data analysts work with structured data, such as spreadsheets and databases,
and are responsible for creating reports and dashboards that communicate key insights to
stakeholders.
Key Responsibilities of a Data Analyst:
Collecting and cleaning structured data sets
Creating reports and dashboards to communicate key insights to stakeholders
Identifying patterns and trends in data to drive business decisions
Collaborating with data scientists and data engineers to ensure data quality and consistency
Staying up-to-date with the latest data analysis tools and techniques
Data Scientist – The predominant focus will be on the futuristic display of data. They provide both
supervised and unsupervised learning of data, say classification and regression of data, Neural
networks. The continuous regression analysis would be using machine learning techniques. Skills
needed for Data Scientist are R, Python, SQL, SAS, Pig, Apache Spark, Hadoop, Java, Perl. A data
scientist is responsible for collecting, analyzing, and interpreting complex data sets using statistical and
machine learning techniques. The data scientist works with a wide variety of data, including
structured, unstructured, and semi-structured data, and is responsible for finding patterns, trends,
and insights that can be used to drive business decisions.
Key Responsibilities of a Data Scientist:
Collecting and cleaning large data sets
Building predictive models using statistical and machine learning techniques
Communicating insights and recommendations to stakeholders
Developing data visualizations to communicate complex data in a simple manner
Collaborating with data engineers to ensure data is accurate and consistent
Staying up-to-date with the latest data science techniques and technologies
Data Engineer –
, Data Engineers concentrate more on optimization techniques and building of data in a proper
manner. The main aim of a data engineer is continuously improving the data consumption. Mainly a
data engineer works at the back end. Optimized machine learning algorithms were used for
maintaining data and to make data to be available in most accurate manner. Skills needed for Data
Engineer are Pig, Hive, Hadoop, MapReduce techniques. A data engineer is responsible for designing
and implementing the infrastructure and tools needed to collect, store, and process large amounts of
data. Data engineers work with a wide variety of data storage technologies, such as Hadoop, NoSQL,
and SQL databases, and are responsible for ensuring the data is accurate, consistent, and available for
analysis.
Key Responsibilities of a Data Engineer:
Designing and implementing data pipelines to collect and process large amounts of data
Managing and optimizing data storage technologies such as Hadoop, NoSQL, and SQL databases
Building and maintaining data warehouses and data lakes
Ensuring data quality and consistency across multiple sources
Working with data scientists to ensure the accuracy and consistency of the data used for analysis
Staying up-to-date with the latest data storage technologies and best practices
Unit 1: Introduction: What is business analytics? Historical Overview of data analysis, Data Scientist vs. Data Engineer vs.
Business Analyst, Career in Business Analytics, What is data science, Why Data Science, Applications for data science,
Data Scientists Roles and Responsibility
Unit 2: Data: Data Collection, Data Management, Big Data Management, Organization/sources of data, Importance of
data quality, Dealing with missing or incomplete data, Data Visualization, Data Classification Data Science Project Life
Cycle: Business Requirement, Data Acquisition, Data Preparation, Hypothesis and Modeling, Evaluation and
Interpretation, Deployment, Operations, Optimization.
Unit 3: Introduction to Data Mining, The origins of Data Mining, Data Mining Tasks, OLAP and Multidimensional data
analysis, Basic concept of Association Analysis and Cluster Analysis.
Unit 4: Introduction to Machine Learning: History and Evolution, AI Evolution, Statistics Vs Data Mining Vs, Data Vs, Data
Science, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Frameworks for building Machine
Learning Systems.
Unit 5: Application of Analytics Business Analysis: Retail Analytics, Marketing Analytics, Financial Analytics, Healthcare
Analytics, Supply Chain Analytics.
____________________________________________________________________________
Unit 1: Introduction To BUSINESS ANALYTICS
Business analytics - Business analytics refers to the statistical methods and computing technologies
for processing, mining and visualizing data to uncover patterns, relationships and insights that enable
better business decision-making. Business analytics involves companies that use data created by their
operations or publicly available data to solve business problems, monitor their business fundamentals,
identify new growth opportunities, and better serve their customers. Business analytics uses data
exploration, data visualization, integrated dashboards, and more to provide users with access to
actionable data and business insights.
Benefits of business analytics - Modern organizations need to be able to make quick decisions to
compete in a rapidly changing world, where new competitors spring up frequently and customers’
habits are always changing. Organizations that prioritize business analytics have several advantages
over competitors who do not.
Faster and better-informed decisions: Having a flexible and expansive view of all the data an
organization possesses can eliminate uncertainty, prompt an organization to take action faster, and
improve business processes. If an organization’s data suggests that sales of a particular product line
are declining precipitously, it might decide to discontinue that line. If climate risk impacts the
harvesting of a raw material another organization depends on, it might need to source a new material
from somewhere else. It’s especially helpful when considering pricing strategies.
How a company prices its goods or services is based on thousands of data points, many of which do
not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to
access real-time data to make smarter short- and long-term pricing data is critical. For organizations
that want to incorporate dynamic pricing, business analytics enables them to use thousands of data
points to react to external events and trends to identify the most profitable price point as frequently
as necessary.
Single-window view of information: Increased collaboration between departments and line-of-
business users means that everyone has the same data and is talking from the same playbook. Having
that single pane of glass shows more unseen patterns, enabling different departments to understand
,the company’s holistic approach and increase an organization’s ability to respond to changes in the
marketplace.
Enhanced customer service: By knowing what customers want, when and how they want it,
organizations encourage happier customers and build greater loyalty. In addition to an
improved customer experience, by being able to make smarter decisions on resource allocation or
manufacturing, organizations are likely able to offer those goods or services at a more affordable
price.
____________________________________________________________________________
Who is a Business Analyst?
A Business Analyst is a professional who uses data analysis and critical thinking skills to help
organizations improve their processes, products, and services.
Business analysts evaluate past and current business data with the primary goal of improving decision-
making processes within organizations. They work with stakeholders to identify areas for
improvement, and then gather and analyze data to develop solutions that address those areas.
Responsibilities:
➢Some of the responsibilities of a business analyst include:
➢Conducting research and analysis to identify areas for improvement ➢Collecting and analyzing data
to develop solutions
➢Collaborating with stakeholders to define project objectives and scope
➢Developing and implementing business processes and systems
➢Monitoring and evaluating the effectiveness of implemented solutions
➢Communicating project progress and results to stakeholders
Skills and Requirements:
➢Business analysts typically have strong analytical, critical thinking, and problem-solving skills.
➢They also need to be effective communicators, as they often work with stakeholders from different
departments and levels of the organization.
➢Additionally, they should have a good understanding of business processes and be able to work with
data and technology, including software tools for data analysis and project management.
Business Analytics Applications
⦁Management of customer Relationships
⦁Financial and marketing activities
⦁Manufacturing & Operations
⦁Infrastructure Mgmnt
⦁Supply chain management
,⦁Human resource planning
⦁Pricing decisions
____________________________________________________________________________
Difference between Data Scientist, Data Engineer, Data Analyst
In the world of big data and analytics, there are three key roles that are essential to any data-driven
organization: data scientist, data engineer, and data analyst. While the job titles may sound similar,
there are significant differences between the roles. In this article, we will explore the differences
between data scientist, data engineer, and data analyst, and how each of these roles contributes to
the overall success of a data-driven organization.
Generally, we hear different designations about CS Engineers like Data Scientist, Data Analyst and Data
Engineer. Let us discuss the differences between the above three roles.
Data Analyst –
The main focus of this person’s job would be on optimization of scenarios, say how an employee can
improve the company’s product growth. Data Cleaning and organizing of raw data, analyzing
and visualization of data to interpret the analysis and to present the technical analysis of data. Skills
needed for Data Analyst are R, Python, SQL, SAS, SAS Miner. A data analyst is responsible for
collecting, organizing, and analyzing data to identify patterns and insights that can be used to make
data-driven decisions. Data analysts work with structured data, such as spreadsheets and databases,
and are responsible for creating reports and dashboards that communicate key insights to
stakeholders.
Key Responsibilities of a Data Analyst:
Collecting and cleaning structured data sets
Creating reports and dashboards to communicate key insights to stakeholders
Identifying patterns and trends in data to drive business decisions
Collaborating with data scientists and data engineers to ensure data quality and consistency
Staying up-to-date with the latest data analysis tools and techniques
Data Scientist – The predominant focus will be on the futuristic display of data. They provide both
supervised and unsupervised learning of data, say classification and regression of data, Neural
networks. The continuous regression analysis would be using machine learning techniques. Skills
needed for Data Scientist are R, Python, SQL, SAS, Pig, Apache Spark, Hadoop, Java, Perl. A data
scientist is responsible for collecting, analyzing, and interpreting complex data sets using statistical and
machine learning techniques. The data scientist works with a wide variety of data, including
structured, unstructured, and semi-structured data, and is responsible for finding patterns, trends,
and insights that can be used to drive business decisions.
Key Responsibilities of a Data Scientist:
Collecting and cleaning large data sets
Building predictive models using statistical and machine learning techniques
Communicating insights and recommendations to stakeholders
Developing data visualizations to communicate complex data in a simple manner
Collaborating with data engineers to ensure data is accurate and consistent
Staying up-to-date with the latest data science techniques and technologies
Data Engineer –
, Data Engineers concentrate more on optimization techniques and building of data in a proper
manner. The main aim of a data engineer is continuously improving the data consumption. Mainly a
data engineer works at the back end. Optimized machine learning algorithms were used for
maintaining data and to make data to be available in most accurate manner. Skills needed for Data
Engineer are Pig, Hive, Hadoop, MapReduce techniques. A data engineer is responsible for designing
and implementing the infrastructure and tools needed to collect, store, and process large amounts of
data. Data engineers work with a wide variety of data storage technologies, such as Hadoop, NoSQL,
and SQL databases, and are responsible for ensuring the data is accurate, consistent, and available for
analysis.
Key Responsibilities of a Data Engineer:
Designing and implementing data pipelines to collect and process large amounts of data
Managing and optimizing data storage technologies such as Hadoop, NoSQL, and SQL databases
Building and maintaining data warehouses and data lakes
Ensuring data quality and consistency across multiple sources
Working with data scientists to ensure the accuracy and consistency of the data used for analysis
Staying up-to-date with the latest data storage technologies and best practices
