Introduction to Data Analytics
This is an introductory course on data analytics that is designed to prepare you for a career as a junior
data analyst. Businesses today recognize the untapped value in data and data analytics as a crucial
factor for business competitiveness. To drive their data and analytics initiatives, companies are hiring
and upskilling people, expanding their teams, and creating centers of excellence to set up a
multipronged data and analytics practice in their organizations. This has created a significant supply and
demand mismatch in skilled data analysts, making it a highly sought-after and well-paid profession.
Who is this course for?
This course is for fresh graduates from any stream, working professionals considering a mid-career
transition, data-driven decision-makers, or anyone in analytics-enabled roles. The course introduces you
to the core concepts, processes, and tools you need to gain entry into data analytics or even to
strengthen your current role as a data-driven decision-maker.
The Data Ecosystem
A modern data ecosystem includes a whole network of interconnected, independent, and continually
evolving entities. It includes data that has to be integrated from disparate sources, different types of
analysis and skills to generate insights, active stakeholders to collaborate and act on insights generated,
and tools, applications, and infrastructure to store, process, and disseminate data as required.
Data Sources
Data is available in a variety of structured and unstructured datasets residing in text, images, videos,
click streams, user conversations, social media platforms, IoT devices, real-time events that stream data,
legacy databases, and data sourced from professional data providers and agencies. The sources have
never before been so diverse and dynamic.
The Data Analysis Process
Working with so many different sources of data, the first step is to pull a copy of the data from the
original sources into a data repository. At this stage, you're acquiring the data you need, working with
data formats, sources, and interfaces through which this data can be pulled in. Reliability, security, and
integrity of the data being acquired are some of the challenges you work through at this stage.
Once the raw data is in a common place, it needs to get organized, cleaned up, and optimized for access
by end-users. The data will also need to conform to compliances and standards enforced in the
organization. The key challenges at this stage could involve data management and working with data
repositories that provide high availability, flexibility, accessibility, and security.
Finally, we have our business stakeholders, applications programmers, analysts, and data science use
cases, all pulling this data from the enterprise data repository. The key challenges at this stage could
, include the interfaces, APIs, and applications that can get this data to the end-users in line with their
specific needs.
Emerging Technologies
Cloud computing, machine learning, and big data are some of the new and emerging technologies that
are shaping today's data ecosystem and its possibilities. Thanks to cloud technologies, every enterprise
today has access to limitless storage, high-performance computing, open-source technologies, machine
learning technologies, and the latest tools and libraries. Data scientists are creating predictive models by
training machine learning algorithms on past data. Also, big data today, we're dealing with data sets that
are so massive and so varied that traditional tools and analysis methods are no longer adequate, paving
the way for new tools and techniques and also new knowledge and insights.
The Role of Data Engineers, Analysts, Scientists, Business Analysts, and Business Intelligence Analysts
Data engineering converts raw data into usable data; data analytics uses this data to generate insights,
and data scientists use data analytics and data engineering to build predictive models. Business analysts
leverage the work of data analysts and data scientists to look at possible implications for their business
and the actions they need to take or recommend. BI analysts do the same, except their focus is on the
market forces and external influences that shape their business.
Data Analysis: Understanding the Process and Types
Business analysts and business intelligence analysts use data from the past to predict the future. Data
professionals start their career in one of the data roles and transition to another role within the data
ecosystem by supplementing their skills. Data analysis is the process of gathering, cleaning, analyzing,
and mining data, interpreting results, and reporting the findings. It helps businesses understand their
past performance and informs their decision making for future actions.
Types of Data Analysis
Descriptive Analytics: Helps answer questions about what happened over a given period of time by
summarizing past data and presenting the findings to stakeholders.
Diagnostic Analytics: Helps answer the question why did it happen. It takes the insights from descriptive
analytics to dig deeper to find the cause of the outcome.
Predictive Analytics: Helps answer the question what will happen next. Historical data and trends are
used to predict future outcomes.
This is an introductory course on data analytics that is designed to prepare you for a career as a junior
data analyst. Businesses today recognize the untapped value in data and data analytics as a crucial
factor for business competitiveness. To drive their data and analytics initiatives, companies are hiring
and upskilling people, expanding their teams, and creating centers of excellence to set up a
multipronged data and analytics practice in their organizations. This has created a significant supply and
demand mismatch in skilled data analysts, making it a highly sought-after and well-paid profession.
Who is this course for?
This course is for fresh graduates from any stream, working professionals considering a mid-career
transition, data-driven decision-makers, or anyone in analytics-enabled roles. The course introduces you
to the core concepts, processes, and tools you need to gain entry into data analytics or even to
strengthen your current role as a data-driven decision-maker.
The Data Ecosystem
A modern data ecosystem includes a whole network of interconnected, independent, and continually
evolving entities. It includes data that has to be integrated from disparate sources, different types of
analysis and skills to generate insights, active stakeholders to collaborate and act on insights generated,
and tools, applications, and infrastructure to store, process, and disseminate data as required.
Data Sources
Data is available in a variety of structured and unstructured datasets residing in text, images, videos,
click streams, user conversations, social media platforms, IoT devices, real-time events that stream data,
legacy databases, and data sourced from professional data providers and agencies. The sources have
never before been so diverse and dynamic.
The Data Analysis Process
Working with so many different sources of data, the first step is to pull a copy of the data from the
original sources into a data repository. At this stage, you're acquiring the data you need, working with
data formats, sources, and interfaces through which this data can be pulled in. Reliability, security, and
integrity of the data being acquired are some of the challenges you work through at this stage.
Once the raw data is in a common place, it needs to get organized, cleaned up, and optimized for access
by end-users. The data will also need to conform to compliances and standards enforced in the
organization. The key challenges at this stage could involve data management and working with data
repositories that provide high availability, flexibility, accessibility, and security.
Finally, we have our business stakeholders, applications programmers, analysts, and data science use
cases, all pulling this data from the enterprise data repository. The key challenges at this stage could
, include the interfaces, APIs, and applications that can get this data to the end-users in line with their
specific needs.
Emerging Technologies
Cloud computing, machine learning, and big data are some of the new and emerging technologies that
are shaping today's data ecosystem and its possibilities. Thanks to cloud technologies, every enterprise
today has access to limitless storage, high-performance computing, open-source technologies, machine
learning technologies, and the latest tools and libraries. Data scientists are creating predictive models by
training machine learning algorithms on past data. Also, big data today, we're dealing with data sets that
are so massive and so varied that traditional tools and analysis methods are no longer adequate, paving
the way for new tools and techniques and also new knowledge and insights.
The Role of Data Engineers, Analysts, Scientists, Business Analysts, and Business Intelligence Analysts
Data engineering converts raw data into usable data; data analytics uses this data to generate insights,
and data scientists use data analytics and data engineering to build predictive models. Business analysts
leverage the work of data analysts and data scientists to look at possible implications for their business
and the actions they need to take or recommend. BI analysts do the same, except their focus is on the
market forces and external influences that shape their business.
Data Analysis: Understanding the Process and Types
Business analysts and business intelligence analysts use data from the past to predict the future. Data
professionals start their career in one of the data roles and transition to another role within the data
ecosystem by supplementing their skills. Data analysis is the process of gathering, cleaning, analyzing,
and mining data, interpreting results, and reporting the findings. It helps businesses understand their
past performance and informs their decision making for future actions.
Types of Data Analysis
Descriptive Analytics: Helps answer questions about what happened over a given period of time by
summarizing past data and presenting the findings to stakeholders.
Diagnostic Analytics: Helps answer the question why did it happen. It takes the insights from descriptive
analytics to dig deeper to find the cause of the outcome.
Predictive Analytics: Helps answer the question what will happen next. Historical data and trends are
used to predict future outcomes.
