Data refers to raw facts that are yet to be processed.
With lots of raw facts that are yet to be found or even processed, the
data specialists came into existence; data scientists, data engineers,
and data analysts.
They all have their different functions and roles and are often mistaken
for one another.
Data are facts collected to get information or come to a particular
conclusion.
Data Science is defined as creating new ways of modeling and
understanding the unknown by using raw data. Data Analysis is the
collection, transformation, and organization of data to draw
conclusions, make predictions, and drive informed decision-making
while data analytics refers to the use of tools, and techniques to
analyze data and extract meaningful predictions.
While data scientists find new ways of understanding data using the
unknown and raw data, data analysts find answers to existing
questions by creating insights from data sources.
Data analysts are storytellers. As a data analyst, your data must be
able to tell a story, it must be able to make predictions and pass on
information to the people you are sharing your conclusions with. It
must be understandable and aesthetically pleasing. Data Analyst tools
include:
1. Spreadsheets such as Google Sheets, Microsoft Excel, etc.
2. Databases and Query Languages such as Python and R.
3. Visualization tools such as Tableau and Power BI
Type of Junior/Associate Data Analyst:
*Healthcare Analyst
*Marketing Analyst
*BI Analyst
*Financial Analyst
Data ecosystems are made up of various elements that interact with
one another to produce, manage, store, analyze, and share data.
Subject matter experts can look at the result of data analysis results
, and identify any inconsistencies.
Processes for data analysis- Ask, Prepare, Process, Analyze, Share,
and Act.
1. Ask questions and define the problem: Business
challenge/Objective/Questions.
2. Prepare data by collecting and storing the information: Data
generation, collection, storage, and data management.
3. Process data by cleaning and checking the information: Data
cleaning and data integrity.
4. Analyze data to find patterns, relationships, and trends: Data
exploration, visualization, and analysis.
5. Share data with your audience: Communicating and interpreting
results.
6. Act on the data and use the analysis results: Putting your insights to
work to solve the problem.
Data-driven decisions are decisions made based on the insights
from data sources directed towards solving the problem. Data-driven
decision-making happens right from the beginning to the end of your
data analysis and it is directed by analytical thinking. Analytical
thinking involves identifying and defining a problem and then solving it
by using data in an organized step-by-step manner.
ANALYTICAL SKILLS
1. Curiosity: is all about wanting to learn about something.
2. Understanding context: Understanding the condition in which
something exists or happens.
3. Having a technical mindset: A technical mindset is the ability to
break things down into smaller pieces and work with them in an
orderly and logical way.
4. Data design: how you organize information.
5. Data strategy: the management of the people, processes, and tools
used in data analysis.
KEY ASPECTS TO ANALYTICAL THINKING
With lots of raw facts that are yet to be found or even processed, the
data specialists came into existence; data scientists, data engineers,
and data analysts.
They all have their different functions and roles and are often mistaken
for one another.
Data are facts collected to get information or come to a particular
conclusion.
Data Science is defined as creating new ways of modeling and
understanding the unknown by using raw data. Data Analysis is the
collection, transformation, and organization of data to draw
conclusions, make predictions, and drive informed decision-making
while data analytics refers to the use of tools, and techniques to
analyze data and extract meaningful predictions.
While data scientists find new ways of understanding data using the
unknown and raw data, data analysts find answers to existing
questions by creating insights from data sources.
Data analysts are storytellers. As a data analyst, your data must be
able to tell a story, it must be able to make predictions and pass on
information to the people you are sharing your conclusions with. It
must be understandable and aesthetically pleasing. Data Analyst tools
include:
1. Spreadsheets such as Google Sheets, Microsoft Excel, etc.
2. Databases and Query Languages such as Python and R.
3. Visualization tools such as Tableau and Power BI
Type of Junior/Associate Data Analyst:
*Healthcare Analyst
*Marketing Analyst
*BI Analyst
*Financial Analyst
Data ecosystems are made up of various elements that interact with
one another to produce, manage, store, analyze, and share data.
Subject matter experts can look at the result of data analysis results
, and identify any inconsistencies.
Processes for data analysis- Ask, Prepare, Process, Analyze, Share,
and Act.
1. Ask questions and define the problem: Business
challenge/Objective/Questions.
2. Prepare data by collecting and storing the information: Data
generation, collection, storage, and data management.
3. Process data by cleaning and checking the information: Data
cleaning and data integrity.
4. Analyze data to find patterns, relationships, and trends: Data
exploration, visualization, and analysis.
5. Share data with your audience: Communicating and interpreting
results.
6. Act on the data and use the analysis results: Putting your insights to
work to solve the problem.
Data-driven decisions are decisions made based on the insights
from data sources directed towards solving the problem. Data-driven
decision-making happens right from the beginning to the end of your
data analysis and it is directed by analytical thinking. Analytical
thinking involves identifying and defining a problem and then solving it
by using data in an organized step-by-step manner.
ANALYTICAL SKILLS
1. Curiosity: is all about wanting to learn about something.
2. Understanding context: Understanding the condition in which
something exists or happens.
3. Having a technical mindset: A technical mindset is the ability to
break things down into smaller pieces and work with them in an
orderly and logical way.
4. Data design: how you organize information.
5. Data strategy: the management of the people, processes, and tools
used in data analysis.
KEY ASPECTS TO ANALYTICAL THINKING
