Data Analytics Course 2 Week 1

Data Analysis Process

1. Ask In the ask step, we define the problem we're solving and make sure that we fully understand stakeholder expectations. 2. Prepare 3. Process 4. Analyze 5. Share 6. Act

6 Common Problems in Data Analysis

1. Making predictions 2. Categorizing things 3. Spotting something unusual 4. identifying themes 5. discovering connections 6. finding patterns

Structured Thinking involves:

1. Recognizing the current problem or situation 2. Organizing available information 3. Revealing gaps and opportunities 4. Identifying your options

Attribute

A characteristic or quality of data used to label a column in a table

database

A collection of data stored in a computer system

dataset

A collection of data that can be manipulated or analyzed as one unit

Common Problem: Spotting something unusual

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.

Common Problem: Making Predictions

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.

Query Language

A computer programming language used to communicate with a database

Spreadsheet

A digital worksheet

data science

A field of study that uses raw data to create new ways of modeling and understanding the unknown

GAP Analysis

A method for examining and evaluating the current state of a process in order to identify opportunities for improvement in the future

Cloud

A place to keep data online, rather than a computer hard drive

Function

A preset command that automatically performs a specified process or task using the data in a spreadsheet

Fairness

A quality of data analysis that does not create or reinforce bias

Specific Questions

A question that is simple, significant, and focused on a single topic or a few closely related ideas

Unfair question

A question that makes assumptions or is difficult to answer honestly

Time-bound questions (definition)

A question that specifies a timeframe to be studied

Leading Questions

A question that steers people toward a certain response

Common Problem: Categorizing things

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.

Data Analysis Process (Phase 5)

Everyone shares their results differently so be sure to summarize your results with clear and enticing visuals of your analysis using data viz tools like graphs or dashboards. This is your chance to show the stakeholders you have solved their problem and how you got there. Sharing will certainly help your team: Make better decisions Make more informed decisions Lead to stronger outcomes Successfully communicate your findings

You must also take into consideration fairness when crafting questions

Fairness ensure your questions don't create or reinforce bias Another example of unfair questions is one that makes assumptions Fairness also means crafting questions that make sense to everyone Questions need to be clear and straight forward Unfair questions can lead to unreliable feedback and missed opportunities to gain valuable insights

Making data-driven decisions:

In analytics, data drives decision making. In this part of the course, you'll explore data of all kinds and its impact on decision making. You'll also learn how to share your data through reports and dashboards.

Structured Thinking

In this process, you address a vague, complex problem by breaking it down into smaller steps, and then those steps lead you to a logical solution. Structured Thinking: is the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options.

Data Analysis Process (Phase 1)

It's impossible to solve a problem if you don't know what it is. These are some things to consider - Define the problem you're trying to solve - Make sure you fully understand the stakeholder's expectations - Focus on the actual problem and avoid any distractions - Collaborate with stakeholders and keep an open line of communication - Take a step back and see the whole situation in context

Things to avoid when asking questions

Leading questions: questions that only have a particular response EX: This product is too expensive, isn't it? This is a leading question because it suggests an answer as part of the question. Better question might be: "What price (or price range) would make you consider purchasing this product?" Close Ended questions: questions that ask for a one-word or brief response only EX: Were you satisfied with the customer trial? This is a closed-ended question because it doesn't encourage people to expand on their answer. A better question might be, "What did you learn about customer experience from the trial." Vague Questions: questions that aren't specific or don't provide context EX: Does the tool work for you? This question is too vague because there is no context Better question might be: "When it comes to data entry, is the new tool faster, slower, or about the same as the old tool? If faster, how much time is saved? If slower, how much time is lost?"

Common problems: finding patterns

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.

Data Analysis Process (Phase 6)

Now it's time to act on your data. You will take everything you have learned from your data analysis and put it to use. This could mean providing your stakeholders with recommendations based on your findings so they can make data-driven decisions.

Possible ways to correct BAD SMART questions above

On a scale of 1-10 (with 10 being the most important) how important is your car having four-wheel drive? What are the top five features you would like to see in a car package? What features, if included with four-wheel drive, would make you more inclined to buy the car? How much more would you pay for a car with four-wheel drive? Has four-wheel drive become more or less popular in the last three years?

Time-bound questions

Question specifies the time to be styudied EX what environmental factors changed in Durham, North Carolina between 1983 and 2004 that could cause Pine Barrens tree frogs to disappear from the Sandhills Regions? Limits the time to be studied between 1983-2004

Measurable Questions

Questions that can be quantified and assessed Lets us arrive at a concrete number EX BAD: why did a recent video go viral? GOOD: how many times was our video shared on social channels the first week it was posted?

Try no to ask "close ended questions.

Questions that do not give the other person a chance to respond/elaborate. Can be answered with a yes or a no

Action-oriented questions

Questions that encourage change Problem solving is all about seeing the current state and transforming it into the ideal future state Action oriented questions help us get there EX BAD: how can we get customers to recycle our product packaging? GOOD: what design features will make our packaging easier to recycle?

Try not to ask "leading questions":

Questions that lead you to answer in certain way

Relevant Questions

Questions that matter, are important, and have significance to the problem you're trying to solve EX BAD: why does it matter that Pine Barrens tree frogs started disappearing? This question won't help us come up with a solution GOOD: what environmental factors changed in Durham, North Carolina between 1983 and 2004 that could cause Pine Barrens tree frogs to disappear from the Sandhills Regions? This question will help us come up w/a solution

Visualization

Refer to data visualization

Specific Questions

Simple, significant, and focused on a single topic or a few closely related ideas Helps you collect info that's relevant to what we're investigating If a question is too general, try to narrow it down by focusing on one element EX BAD: are kids getting enough physical activities these days? GOOD: what percentage of kids achieve the recommended 60 minutes of physical activity at least five days a week?

Data Analyst

Someone who collects, transforms, and organizes data in order to draw conclusions, make predictions, and drive informed decision-making

Effective questions follow the SMART methodology

Specific, Measurable, Action Oriented, Relevant, and Time-bound

Mastering spreadsheet basics:

Spreadsheets are an important data analytics tool. In this part of the course, you'll learn both why and how data analysts use spreadsheets in their work. You'll also explore how structured thinking can help analysts better understand problems and come up with solutions.

Always remembering the stakeholder:

Successful data analysts learn to balance needs and expectations. In this part of the course, you'll learn strategies for managing the expectations of stakeholders while establishing clear communication with your team to achieve your objectives.

Observation

The attributes that describe a piece of data contained in a row of a table

Data Analysis

The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making

data visualization

The graphical representation of data

data strategy

The management of the people, processes, and tools used in data analysis

Analytical Thinking

The process of identifying and defining a problem, then solving it by using data in an organized, step-by-step manner

structured thinking

The process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying options

Business Task

The question or problem that data analysis resolves for a business

Root Cause

The reason why a problem occurs

Data Analytics

The science of data

data life cycle

The sequence of stages that data experiences, which include plan, capture, manage, analyze, archive, and destroy

Data Analysis Process (definition)

The six phases of ask, prepare, process, analyze, share, and act whose purpose is to gain insights that drive informed decision-making

Data ecosystem

The various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data

Common Problem: identifying themes

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. 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.

Questions to ask during the ASK phase

What are my stakeholders saying their problems are? Now that I've identified the issues, how can I help the stakeholders resolve their questions?

Questions to ask during the PROCESS phase

What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve? How can I clean my data so the information I have is more consistent?

Questions to ask during the PREPARE phase

What do I need to figure out how to solve this problem? What research do I need to do?

Examples of BAD SMART questions

What features do people look for when buying a new car? Specific: Does the question focus on a particular car feature? Measurable: Does the question include a feature rating system? Action-Oriented: Does the question influence creation of different or new feature packages? Relevant: Does the question identify which features make or break a potential car purchase? Time-bound: Does the question validate data on the most popular features from the last three years?

Questions to ask during the ANALYZE phase

What story is my data telling me? How will my data help me solve this problem? Who needs my company's product or service? What type of person is most likely to use it?

Important questions to ask when commencing a project

When is the project due? Are there any specific challenges to keep in mind? Who are the major stakeholders for this project, and what do they expect this project to do for them? Who am I presenting the results to?

Data Analysis Process (Phase 4)

You will want to think analytically about your data. At this stage, you might sort and format your data to make it easier to: Perform calculations Combine data from multiple sources Create tables with your results

Questions to ask during the ACT phase

How can I use the feedback I received during the share phase (step 5) to actually meet the stakeholder's needs and expectations?

Data Design

How information is organized

Problem Types

The various problems that data analysts encounter, including categorizing things, discovering connections, finding patterns, identifying themes, making predictions, and spotting something unusual

Asking effective questions:

To do the job of a data analyst, you need to ask questions and problem-solve. In this part of the course, you'll check out some common analysis problems and how analysts solve them. You'll also learn about effective questioning techniques that can help guide your analysis.

Data-driven decision making

Using facts to guide business strategy

Data

A collection of facts

Relevant question

A question that has significance to the problem to be solved

Three core roles in Data

- Data Analyst (SQL, spreadsheet, databases, create dashboards) - Data Engineer (Build data pipelines) - Data scientists (Machine learning models/statistical inferences) you don't have to know at the outset which path you want to go down.

Measurable Question

A question whose answers can be quantified and assessed

Action oriented questions

A question whose answers lead to change

Query

A request for data or information from a database

Formula

A set of instructions used to perform a calculation using the data in a spreadsheet

Common problems: discovering connections

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.

SMART methodology

A tool for determining a question's effectiveness based on whether it is specific, measurable, action-oriented, relevant, and time-bound

Data Analysis Process (Phase 3)

Clean data is the best data and you will need to clean up your data to get rid of any possible errors, inaccuracies, or inconsistencies. This might mean: Using spreadsheet functions to find incorrectly entered data Using SQL functions to check for extra spaces Removing repeated entries Checking as much as possible for bias in the data

Questions to ask during the SHARE phase

How can I make what I present to the stakeholders engaging and easy to understand? What would help me understand this if I were the listener?

Stakeholders

People who invest time and resources into a project and are interested in its outcome

Analytical Skills

Qualities and characteristics associated with using facts to solve problems

Technical mindset

The ability to break things down into smaller steps or pieces and work with them in an orderly and logical way

Context

The condition in which something exists or happens

Data Analysis Process (Phase 2)

You will decide what data you need to collect in order to answer your questions and how to organize it so that it is useful. You might use your business task to decide: What metrics to measure Locate data in your database Create security measures to protect that data