Problem Types

Question 1

What are the common types of problems you’ll solve as a Data Analyst?

Answer 1

1. Making predictions
2. Categorizing things
3. Spotting something unusual
4. Identifying themes
5. Discovering connections
6. Finding patterns

Question 2

Describe Making Predictions

Answer 2

Using data to make an informed decision about how things may be in the future

Example
A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions. Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can’t guarantee future results, but they can help predict the best placement of advertising to reach the target audience.

Question 3

Describe Categorizing things

Answer 3

Assigning information to different groups or clusters based on common features

Example
An example of a problem requiring analysts to categorize things is a company’s goal to improve customer satisfaction. Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores.

Question 4

Desribe Spotting Something Unusual

Answer 4

Identifying data that is different from the norm

Example
A company that sells smart watches that help people monitor their health would be interested in designing their software to spot something unusual. Analysts who have analyzed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn’t trend normally.

Question 5

Describe Identifying Themes

Answer 5

This takes grouping categorized information into broader concepts

Example
User experience (UX) designers might rely on analysts to analyze user interaction data. Similar to problems that require analysts to categorize things, usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. Themes are most often used to help researchers explore certain aspects of data. In a user study, user beliefs, practices, and needs are examples of themes.

Question 6

Describe Discovering Connections

Answer 6

Finding similar challenges faced by different entities and combining data and insights to address them

Example
A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.

Question 7

Describe Finding Patterns

Answer 7

Using historical data to understand what happened in the past and is therefore likely to happen again

Example
Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data. For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window.

Question 8

What is the difference between categorizing things and identifying themes?

Answer 8

Categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.