Course 2: Week 1 - From issue to action

Question 1

Structured thinking

Answer 1

• 6 steps that break the data analysis process into smaller, manageable parts. Namely:
• Ask, prepare, process, analyse, share and act.
• This process involves:
  1. Recognising the problem
  2. Organising the availing information
  3. Revealing gaps and opportunities
  4. Identifying your options

Question 2

Problem Types

Answer 2

• Making predictions - using data to make informed decision about how things may be in the future
• Categorising things - assigning information groups with shared features
• Spotting something unusual - identifying data different form the norm
• Identifying themes - grouping information into broader concepts
• Discovering connections - find similar challenges faced by different entities, and then combine data and insights to address them
• Finding Patterns - using historical data to understand what happened in the past and is therefore likely to happen again

Question 3

Making Predictions

Answer 3

• A company that wants to know the best advertising method to bring in new customers
• 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 4

Categorising things

Answer 4

• A company’s goal is 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 5

Spotting something unusual

Answer 5

• A company sells smart watches that help people monitor their health is be interested in designing their software to spot something unusual.
• Analysts who have analysed 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 6

Identifying themes

Answer 6

• User experience (UX) designers rely on analysts to analyse 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.
• By now you might be wondering if there is a difference between categorizing things and identifying themes.
  The best way to think about it is: categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.

Question 7

Discovering connections

Answer 7

-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 analysing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.

Question 8

SMART Methodology

Answer 8

• Method to ask question which produce best answers.
• Specific - question are simple, significant, and focused on a single topic or a few closely related ideas.
• Measurable - Can answer be measured/quantified and assessed. i.e. rating system.
• Action orientated - Will answers to questions provide that helps you devise action plan. Questions encourage change.
• Relevant - Is question about the particular problem you are trying to solve
• Time Bound - Are answers to question relevant to specific time being studied?

Question 9

Importance of data

Answer 9

• The goal of all data analysts is to use data to draw accurate conclusions and make good recommendations.
• But this start with having complete, correct, and relevant data. And this usually starts with asking the right questions.
• It is up to data analysts to interpret the data accurately. When data is interpreted incorrectly, it can lead to huge losses.
• Therefore just having ‘data’ is not enough. The data must first be relevant, and correct. And then be interpreted correctly for it to produce positive change.