D596 The Data Analytics Journey — TASK 2 PASSED NEW UPDATE 2026 Western Governors University

D596 The Data Analytics Journey — TASK 2 PASSED NEW UPDATE 2026 Western Governors University

The Data Analytics Journey — D596 Task 2

A. Self-Reflection on CliftonStrengths My top five strengths from the CliftonStrengths assessment were Empathy, Harmony, Consistency, Developer, and Relator. After reading the descriptions, I felt they gave a pretty accurate representation of how I see the world and interact with people.

Empathy and Harmony stood out the most. I’m usually aware of how others are feeling and try to understand their perspective, even when I don’t agree with them. I don’t think Harmony means I shy away from conflict altogether, it’s more about staying grounded and open to different views without feeling defensive or needing to be right. I like when people can express different ideas without it turning into a fight. But always keeping your opinions and beliefs to yourself for the sake of avoiding friction goes against human nature. The reflection illustrates me as a person who wouldn't believe this, which caught my attention. Consistency shows how much I value fairness. I don’t like when people get special treatment or when rules only apply to some. I believe everyone deserves the same level of respect and opportunity. Developer made a lot of sense too.

I get real satisfaction from helping people grow, especially when I can offer encouragement or guidance that helps them succeed. Relator also fits because I value deep, meaningful relationships over surface-level ones. I care about trust, loyalty, and real conversation.

B. Roles or Careers in Data Analytics There are a lot of different directions someone can go in the field of data analytics, but three major roles are: ● Data Analyst ● Data Scientist ● Machine Learning Engineer A data analyst focuses on understanding what questions an organization is trying to answer. They collect data, clean it, identify patterns, and create visuals or reports that communicate the findings clearly to others.

A data scientist works with more complex data and builds models using advanced statistical methods and machine learning. They try out different algorithms and work with large data sets to uncover trends or make predictions.

A machine learning engineer builds the systems that run the models created by data scientists. Their job is to make sure these models work smoothly in real-time environments. They focus on writing clean, efficient code and developing reliable software systems that can handle large amounts of data.

1. Differences Between the Roles Each role has a different focus. Data analysts are more business related and spend more time understanding problems, interpreting data, and communicating results. They don’t usually work with advanced models. Data scientists work more with math and algorithms, building predictive models and doing deeper analysis. Machine learning engineers are more on the engineering side of things. They focus on building the actual tools and systems that can run these models reliably and efficiently.

2. Academic Skills and Needs for Each Role A data analyst needs to understand statistics and have strong communication skills.

They should be comfortable using tools like Excel, SQL, Tableau, or Power BI. It also helps to have some background in business or industry knowledge to ask the right questions and make sure their work is useful.

A data scientist needs to have a stronger foundation in math, especially statistics, probability, calculus, and linear algebra. They also need programming skills in Python or R and should understand algorithms, data structures, and model validation. Courses in machine learning and data mining are especially helpful for this role.

A machine learning engineer needs strong programming and software development skills. They should know how to build efficient systems, understand deployment tools, and work with cloud platforms. It also helps to understand machine learning, but their focus is more on turning models into working software.

3. How Each Role Supports the Data Analytics Life Cycle The data analytics life cycle includes phases like business understanding, data acquisition, cleaning, exploration, modeling, mining, and reporting.

Data analysts play a big role at the beginning and end of this cycle. They work with stakeholders to understand the business questions and then help collect and clean the data. They also focus on reporting and visualization and presenting the results in a way people can use.

Data scientists are more involved in the middle of the process. They do deep data exploration, build predictive models, and mine data for patterns. They also help make sure the data is high-quality and meaningful.

Machine learning engineers work throughout the process but especially at the end.

They build the tools that automate and scale models, and they make sure everything runs efficiently in a real world setting. They also help create systems to gather and clean data automatically.

C. Career Goal Based on Strengths and Academic Track My goal is to become a data analyst, specifically a behavioral data analyst, where I can use data to support fair and people centered decisions. This career path stands out to me because it deals with technical work and interpersonal understanding. I’m really interested in applying my background in psychology to data analysis, especially when it comes to studying behavior and decision-making. Behavioral data can reveal so much about how people interact with products or environments and I want to help organizations use that data in a responsible and thoughtful way.

This role fits closely with my CliftonStrengths. Empathy helps me stay connected to the people behind the numbers, not just the numbers themselves. Harmony and Relator support my ability to collaborate with others and build strong working relationships.

Consistency reinforces my commitment to fairness and equal treatment, something that’s important when interpreting data that can impact real lives. Developer reflects my motivation to help others grow, whether that means supporting a team or making data more accessible and useful. Becoming a behavioral data analyst feels like the right way to blend my interest in human behavior with technical skills Ill further build with this program.