IBM Data Analyst Complete Course | Data Analyst
Tutorial For Beginners
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.
Congratulations on choosing to be on this journey, and good luck!
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.
Prescriptive Analytics: Helps answer the question what
should be done about it. By analyzing past decisions and
events, the likelihood of different outcomes is estimated
on the basis of which a course of action is decided.
Tutorial For Beginners
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.
Congratulations on choosing to be on this journey, and good luck!
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.
Prescriptive Analytics: Helps answer the question what
should be done about it. By analyzing past decisions and
events, the likelihood of different outcomes is estimated
on the basis of which a course of action is decided.
