Data Analytics Course 2 (Week 2)

Attribute

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

Pivot Chart

A chart created from the fields in a pivot 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

Data

A collection of facts

Some challenges when working with big data sets

A lot of organizations deal with data overload and way too much unimportant or irrelevant information. Important data can be hidden deep down with all of the non-important data, which makes it harder to find and use. This can lead to slower and more inefficient decision-making time frames. The data you need isn't always easily accessible. Current technology tools and solutions still struggle to provide measurable and reportable data. This can lead to unfair algorithmic bias. There are gaps in many big data business solutions.

Report

A static collection of data periodically given to stakeholders

Dashboards

A tool that monitors live, incoming data

Analytical Dashboard

Analytical: consists of the datasets and the mathematics used in these sets Analytic dashboards contain a vast amount of data used by data analysts. These dashboards contain the details involved in the usage, analysis, and predictions made by data scientists. Certainly the most technical category, analytic dashboards are usually created and maintained by data science teams and rarely shared with upper management as they can be very difficult to understand. The analytic dashboard below focuses on metrics for a company's financial performance.

Key skills for triumphant results

As a data analyst, your own skills and knowledge will be the most important part of any analysis project. It is important for you to keep a data-driven mindset, ask lots of questions, experiment with many different possibilities, and use both logic and creativity along the way. Note that there is a difference between making a decision with incomplete data and making a decision with a small amount of data. You learned that making a decision with incomplete data is dangerous. But sometimes accurate data from a small test can help you make a good decision.

Businesses and other organizations use data to make better decisions all the time. They do this in two ways,

Data driven or data inspired decision making

EX of Quantitative questions for reviews

How many negative reviews are there? What's the average rating? How many of these reviews use the same key words?

Metrics can also be combined into formulas that you can plug your numerical data into.

Revenue by individual person = Metric Data = all of the information behind the metric above

Small Data

Small, specific data points typically involving a short period of time, which are useful for making day-to-day decisions These kinds of data tend to be made up of datasets concerned with specific metrics over a short, well defined period of time. EX: how much water you drink in a day Small data can be useful for making day to day decisions but doesn't have a huge impact on business operations Might use spreadsheets to organize/analyze small datasets

Data Analyst

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

Strategic Dashboard

Strategic: focuses on long term goals and strategies at the highest level of metrics A wide range of businesses use strategic dashboards when evaluating and aligning their strategic goals. These dashboards provide information over the longest time frame—from a single financial quarter to years. They typically contain information that is useful for enterprise-wide decision-making. Below is an example of a strategic dashboard which focuses on key performance indicators (KPIs) over a year.

Quantitative data tools

Structured interviews Surveys Polls

Problem Type

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

Data driven decision making

Using facts to guide business strategy

The three (or four) V words for big data

Volume, variety, and Velocity Volume: The amount of data Variety: The different kinds of data Velocity: How fast the data can be processed Some analysts also consider Veracity Veracity: The quality and reliability of data

The goal of all data analysts is to use data to draw accurate conclusions and make good recommendations. It all starts with having complete, correct, and relevant data But keep in mind, it is possible to have solid data and still make the wrong choices. It is up to data analysts to interpret the data accurately.

When data is interpreted incorrectly, it can lead to huge losses EX (New Coke): in 1985, Coca Cola introduced new Coke and remove the original based on a 200,000 person study. Through the study, people said they prefer New Coke. New coke was a massive flop. The data was incomplete as it didn't ask the question of New Coke replacing the original. This made people mad even if data said people preferred New Coke. The data was incomplete and therefore caused Coca Cola money as a result When data is used strategically, businesses can transform and grow their revenue. EX (Crate and Barrel): At Crate and Barrel, online sales jumped more than 40% during stay-at-home orders to combat the global pandemic. Currently, online sales make up more than 65% of their overall business. They are using data insights to accelerate their digital transformation and bring the best of online and offline experiences together for customers. BigQuery enables Crate and Barrel to "draw on ten times [as many] information sources (compared to a few years ago) which are then analyzed and transformed into actionable insights that can be used to influence the customer's next interaction. And this, in turn, drives revenue."

Query Language

A computer programming language used to communicate with a database

Structured Query Language (SQL)

A computer programming language used to communicate with a database

Pivot Table

A data summarization tool used to sort, reorganize, group, count, total, or average data

Spreadsheet

A digital worksheet

data science

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

Return on Investment

A formula that uses the metrics of investment and profit to evaluate the success of an investment

Metric Goal

A measurable goal set by a company and evaluated using metrics

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

Algorithm

A process or set of rules followed for a specific task

Fairness

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

Relevant Question

A question that has significance to the problem to be solved

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 question

A question that specifies a timeframe to be studied

Leading Question

A question that steers people toward a certain response

Measurable Question

A question whose answers can be quantified and assessed

Action-oriented question

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

Metric

A single, quantifiable type of data that is used for measurement

Quantitative data

A specific and objective measure, such as a number, quantity, or range Specific and objective measures of numerical facts The what? How many? How often? In other words, things you can measure. EX: How many commuters take the train every week Financial Analysts mostly work in this space

Dashboards are part of a business journey

Dashboards help stakeholders navigate the path of the project inside the data. If you add clear markers and highlight important points on your dashboard, users will understand where your data story is headed. Then, you can work together to make sure the business gets where it needs to go.

Qualitative data tools

Focus groups Social media text analysis In-person interviews

Data Design

How information is organized

Big Data

Large, complex datasets typically involving long periods of time, which enable data analysts to address far-reaching business problems Big data on the other hand has larger, less specific datasets covering a longer period of time. They usually have to be broken down to be analyzed. Big data is useful for looking at large- scale questions and problems, and they help companies make big decisions When you're working with data on this larger scale, you might switch to SQL.

Mathematical Thinking

Mathematical thinking is a powerful skill you can use to help you solve problems and see new solutions. looking at a problem and logically breaking it down step-by-step, so you can see the relationship of patterns in your data, and use that to analyze your problem. This kind of thinking can also help you figure out the best tools for analysis because it lets us see the different aspects of a problem and choose the best logical approach. Using mathematical thinking, you can break this problem down into a step-by-step process to help you find patterns in the data. By considering all of the individual parts of this problem logically, mathematical thinking helped us see new perspectives that led us to a solution.

Different industries use all kinds of different metrics. But there's one thing they all have in common: they're all trying to meet a specific goal by measuring data.

Metric Goal: A measurable goal set by a company and evaluated using a metrics There are a lot of possible goals EX Organization wants to meet a number of sales per month Or maybe a number of repeat customers per month By using metrics to focus on individual aspects of your data, you can start to see the story your data is telling. Metric goals and formulas are great ways to measure and understand data but there are many ways to do this as well that we will explore.

Operational Dashboard

Operational: short-term performance tracking and intermediate goals Operational dashboards are, arguably, the most common type of dashboard. Because these dashboards contain information on a time scale of days, weeks, or months, they can provide performance insight almost in real-time. This allows businesses to track and maintain their immediate operational processes in light of their strategic goals. The operational dashboard below focuses on customer service.

Stakeholders

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

Pros and Cons of Dashboards

Pros - Dynamic, automatic, and interactive. They give your team more access to information being recorded, can interact with data by playing w/filters, and because they are dynamic they have long term value - More stakeholder access. Can be more efficient than having to continually pull reports to provide to them - Low maintenance - Visually appealing Cons - Labor-intensive to design - Can be less efficient than reports if they are not used very often. - Can be confusing - If base table breaks, need a lot of maintenance to get it back up and running - Potentially uncleaned data. If you aren't used to looking at data through a dashboard, you might get lost in it.

Pros and Cons of Reports

Pros - Great for giving snapshots of high-level historical data for an organization - Can be designed and sent out periodically (often on a weekly or monthly basis) as an organized and easy to reference information - Quick to design and easy to use as long as you continually maintain them. - Since reports use static data, they reflect data that's already been cleaned and sorted Cons - Reports need regular maintenance - Not very visually appealing - Since reports aren't automatic and dynamic, reports don't show live, evolving data (Static) - For live reflection of incoming data, dashboards are the way to go

Analytical skills

Qualities and characteristics associated with using facts to solve problems

Creating a dashboard (One example of step by step process)

Step 1: Identify the stakeholders who need to see the data and how they will use it You will need to ask effective questions during this step Step 2: Design the dashboard (what should be displayed) Use these tips to help make your dashboard design clear, easy to follow, and simple: Use a clear header to label the information Add short text descriptions to each visualization Show the most important information at the top Step 3: Create mock-ups if desired This is optional, but a lot of data analysts like to sketch out their dashboards before creating them. Step 4: Select visualizations you will use on the dashboard. You have a lot of options here and it all depends on what data story you are telling. If you need to show a change of values over time, line charts or bar graphs might be the best choice. If your goal is to show how each part contributes to the whole amount being reported, a pie or donut chart is probably a better choice. Step 5: Create filters as needed Filters show certain data while hiding the rest of the data in a dashboard. This can be a big help to identify patterns while keeping the original data intact.

Technical Mindset

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

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

Context

The condition in which something exists or happens

Data visualization

The graphical representation of data

data strategy

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

Data inspired decision making

The process of exploring different data sources to find out what they have in common

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 Tasks

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

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

Revenue

The total amount of income generated by the sale of goods or services

Here are some benefits that come with big data:

When large amounts of data can be stored and analyzed, it can help companies identify more efficient ways of doing business and save a lot of time and money. Big data helps organizations spot the trends of customer buying patterns and satisfaction levels, which can help them create new products and solutions that will make customers happy. By analyzing big data, businesses get a much better understanding of current market conditions, which can help them stay ahead of the competition. As in our earlier social media example, big data helps companies keep track of their online presence—especially feedback, both good and bad, from customers. This gives them the information they need to improve and protect their brand.

EX of Qualitative questions for reviews

Why are customers unsatisfied? How can we improve their experience?

data ecosystem

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

Qualitative data

A subjective and explanatory measure of a quality or characteristic Things that can't be measured by numerical data EX: Hair color This data is great for answering WHY questions EX: Why people might like a certain celebrity more than others With qualitative data, we can see numbers visualized as charts or graphs Qualitative data can then give us a more high level understanding of why the numbers are the way they are. This helps add context to a problem.

Smart Technology

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