WGU D596 the Data Analytics Journey Task 1 | Latest 2025 Update with complete
solutions.
Brennen Goode
The Data Analytics Journey — D596
Task 1
The Data Analytics Life Cycle and Real-World Application
A. The Seven Phases of the Data Analytics Life Cycle
The data analytics life cycle includes seven important steps that help organizations go
from identifying a problem to finding a data-driven solution.
1. Business Understanding
This step focuses on identifying the real business problem. You need to
understand the organization’s goals and what success looks like for the business
you're working for.
Example: A hospital wants to reduce patient discharge wait times.
How to gain expertise: Practice active listening, learn about the business, and
ask thoughtful questions. You can also gain insight by reading industry reports,
attending stakeholder meetings, or working closely with business leaders.
2. Data Acquisition
This is where data is collected from various sources like internal databases,
APIs, or external files.
Example: Pulling patient time logs from hospital systems.
How to gain expertise: Learn SQL and Python for data retrieval. Familiarity with
, APIs, web scraping, and cloud data platforms like AWS or Azure also helps.
3. Data Cleaning
Data often comes with errors or missing values. Cleaning ensures your data is
accurate and usable.
Example: Filling in or removing missing patient discharge times.
How to gain expertise: Use Python libraries like Pandas. You should also study
data profiling techniques and tools like OpenRefine.
4. Data Exploration
This step involves analyzing the data visually and statistically to discover
patterns and trends.
Example: Visualizing delays in specific hospital departments.
How to gain expertise: Learn tools like Tableau or Seaborn. Also, understand
how to use summary statistics and correlation analysis to spot significant trends.
5. Predictive Modeling
You build models to predict future outcomes using machine learning.
Example: Predicting which patients will likely stay longer.
How to gain expertise: Practice with Scikit-learn or R. Completing online courses
on supervised learning, logistic regression, and neural networks will deepen your
knowledge.
solutions.
Brennen Goode
The Data Analytics Journey — D596
Task 1
The Data Analytics Life Cycle and Real-World Application
A. The Seven Phases of the Data Analytics Life Cycle
The data analytics life cycle includes seven important steps that help organizations go
from identifying a problem to finding a data-driven solution.
1. Business Understanding
This step focuses on identifying the real business problem. You need to
understand the organization’s goals and what success looks like for the business
you're working for.
Example: A hospital wants to reduce patient discharge wait times.
How to gain expertise: Practice active listening, learn about the business, and
ask thoughtful questions. You can also gain insight by reading industry reports,
attending stakeholder meetings, or working closely with business leaders.
2. Data Acquisition
This is where data is collected from various sources like internal databases,
APIs, or external files.
Example: Pulling patient time logs from hospital systems.
How to gain expertise: Learn SQL and Python for data retrieval. Familiarity with
, APIs, web scraping, and cloud data platforms like AWS or Azure also helps.
3. Data Cleaning
Data often comes with errors or missing values. Cleaning ensures your data is
accurate and usable.
Example: Filling in or removing missing patient discharge times.
How to gain expertise: Use Python libraries like Pandas. You should also study
data profiling techniques and tools like OpenRefine.
4. Data Exploration
This step involves analyzing the data visually and statistically to discover
patterns and trends.
Example: Visualizing delays in specific hospital departments.
How to gain expertise: Learn tools like Tableau or Seaborn. Also, understand
how to use summary statistics and correlation analysis to spot significant trends.
5. Predictive Modeling
You build models to predict future outcomes using machine learning.
Example: Predicting which patients will likely stay longer.
How to gain expertise: Practice with Scikit-learn or R. Completing online courses
on supervised learning, logistic regression, and neural networks will deepen your
knowledge.
