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D204: exam 2023 business understanding /#95 Complete Questions & Answers.
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D204: exam 2023 business understanding /#95Complete Questions & Answers.
Business Understanding/ Discovery phase - -An analyst defines the major questions of
interest that need to be answered, determines the needs of the stakeholders, and
assesses the resource constraints of the project. Define project outcomes.
-Data Acquisition - -Collecting data phase. Data is collected and stored, for easy
retrieval from a database, perhaps a component of a data warehouse, by using a
language like SQL. Web scraping and surveys to acquire data.
-Data Cleaning - -Also known as data cleansing, data wrangling, data munging, and
feature engineering. Analyst will use SQL, Python, R, or Excel to perform data
modifications and transformations
-Data Exploration - -Analyst begins to understand the basic nature of data, the
relationships within it (btw data variables), the structure of the dataset, the presence of
outliers, and the distribution of data values. This phase uses data visualization tools and
numerical summaries such as measures of central tendency and variability.
-Predictive Modeling - -Allows the analyst to move beyond describing the data to
creating models that enable predictions of outcomes of interest. Python and R are used
in automating the training and use of models.
-Data Mining - -Looks for patterns in large sets of data. Tools are Python and R. Also
called Machine learning. A specialized segment of data mining techniques that
continually update to improve modeling over time.
-Data Exploration - -An initial step to uncover initial patterns and using both manual
and automated methods.
-Data Mining - -An in- depth step to discover patters using automated methods like
machine learning.
-Data Reporting - -Analyst tells the story of the data and uses graphs or interactive
dashboards to inform others of the findings from the analyses. Tools such as Tableau is
used to spot trends and patterns. Goal is to give actionable insight to stakeholders.
-Descriptive Analytics - -What happened? - Observation/Describe event. It is the
interpretation of historical data to better explain market developments.
-Diagnostic Analytics - -Why did it happen? - Explains the reason for the event. It
enables the extraction of value from data by posing the right questions and conducting
in-depth investigations into the problems.
-Predictive Analytics - -What will happen? - Correlation. Predicts what will happen in
the future. It uses data, statistical algorithms, and machine learning techniques to
, determine the JS of potential outcomes. The aim is to have the best assessment of what
will happen in the future, rather than simply understanding what has happened.
-Linear Regression - -Used to predict the value of variable based on the value of
another variable. The variable to be predicted is called dependent variable and the
variable used to predict the target variable is independent variable.
-Prescriptive Analytics - -How can we make it happen? - Keywords:
Change/Action/Solution/Causality/Manipulation/Decision Making. It helps
organizations make decisions.
-Predictive and Prescriptive analytics - -What are two forward-looking tools used by
business leaders? ___________ analytics uses collected data to come up with future
outcomes, and ____________ analytics takes that data and make decisions that cause
future outcomes.
-Structured - -Which data type is numbered and labeled stored in an organized
framework with columns and rows, e.g. Sql, databases, Excel, etc.
-Semi-Structured - -Which data type is loosely organized in categories using tags. e.g.
Emails, CSV, XML, JSON doc., etc.
-Unstructured - -Which data type is text heavy, information not organized in clearly
defined framework. e.g. text, videos, audios, etc.
-Quantitative - -Which data type is known as numerical, parametric, or interval data.
-Qualitative - -Which data type is known as nominal or ordinal. Describes the basic
features of the data in a study.
-Relational Database - -What is a collection of data items with predefined relationships
between them e.g. collection of tables.
-Data Sources - -In house, open Data, web server, data lake, data warehouse, self-
generated are types of what?
-Python - -Which data analytics application/process is open-source general-purpose
programming language. Python provides a more general approach and has several
libraries that are useful to data science. Used by engineers and programmers.
-R - -Which data analytics application/process is an open-source programming
languages with new libraries or tools added continuously.
-Tableau - -Which data analytics application/process is a visual analytics engine that
makes it easier to create interactive visual analytics in the form of dashboards.
-Application Programming Interface (API) - -Which data analytics application/process
is a software intermediary that allows two applications to talk to each other. In other
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