Data Analysis

big-picture thinking.

Puzzles

Understanding context

The analytical skill that has to do with how you group things into categories

A technical mindset

The analytical skill that involves breaking processes down into smaller steps and working with them in an orderly, logical way

Data design

The analytical skill that involves how you organize information

Data strategy

The analytical skill that involves managing the processes and tools used in data analysis

Select

The clause identifies the column you want to pull data from by name

From

The clause identifies the table where the column is located by name

Data Analysis

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

Technical Mindset Example

When paying your bills, you probably already break down the process into smaller steps. Maybe you start by sorting them by the date they're due. Next, you might add them up and compare that amount to the balance in your bank account. This would help you see if you can pay your bills now, or if you should wait until the next paycheck. Finally, you'd pay them.

SAS's iterative life cycle

1. Ask 2. Prepare 3. Explore 4. Model 5. Implement 6. Act 7. Evaluate

6 Phases

1. Ask: Business Challenge/Objective/Question 2. Prepare: Data generation, collection, storage, and data management 3. Process: Data cleaning/data integrity 4. Analyze: Data exploration, visualization, and analysis 5. Share: Communicating and interpreting results 6. Act: Putting your insights to work to solve the problem

Big data analytics life cycle

1. Business case evaluation 2. Data identification 3. Data acquisition and filtering 4. Data extraction 5. Data validation and cleaning 6. Data aggregation and representation 7. Data analysis 8. Data visualization 9. Utilization of analysis results

EMC's Data Analysis Life Cycle

1. Discovery 2. Pre-processing data 3. Model planning 4. Model building 5. Communicate results 6. Operationalize

Data life cycle based on research

1. Generation 2. Collection 3. Processing 4. Storage 5. Management 6. Analysis 7. Visualization 8. Interpretation

Project-based data analytics life cycle

1. Identifying the problem 2. Designing data requirements 3. Pre-processing data 4. Performing data analysis 5. Visualizing data

Query a request for data or information from a database.

A request for data or information from a database.

Analytical skills

Are qualities and characteristics associated with solving problems using facts

Phases of Data

Ask, prepare, process, analyze, share, and act

False

Data analysis is the various elements that interact with one another in order to provide, manage, store, organize, analyze, and share data.

Context

is the condition in which something exists or happens. This can be a structure or an environment. 1,2,3,4,5

Syntax

is the predetermined structure of a language that includes all required words, symbols, and punctuation, as well as their proper placement.

Data-driven decision-making

using facts to guide business strategy

The five key aspects to analytical thinking

visualization, strategy, problem-orientation, correlation, and finally, big-picture and detail-oriented thinking

Data-driven decision-making

An airline collects, observes, and analyzes its customers' online behaviors. Then, it uses the insights gained to choose what new products and services to offer. What business process does this describe?

Data

Collection of facts

Five Essential Skills

Curiosity, understanding context, having technical mindset, data design, and data strategy

False

In data analytics, a model is a group of elements that interact with one another.

Collection of data

Includes numbers, pictures, videos, words, measurements, observations and more.

technical mindset

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

Data Design

Is how you organize information.

Data Strategy

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

Correlation

Relationship (Correlation does not equal causation. In other words, just because two pieces of data are both trending in the same direction, that doesn't necessarily mean they are all related.)

Analytical thinking

involves identifying and defining a problem and then solving it by using data in an organized, step-by-step manner.

subject matter experts

The people very familiar with a business problem are called _____. They are an important part of data-driven decision-making.

Strategic

With so much data available, having a strategic mindset is key to staying focused and on track. Strategizing helps data analysts see what they want to achieve with the data and how they can get there. Strategy also helps improve the quality and usefulness of the data we collect. By strategizing, we know all our data is valuable and can help us accomplish our goals.

gut instinct

The term _____ is defined as an intuitive understanding of something with little or no explanation.

Data Design Example

Think about the way you organize the contacts in your phone. That's actually a type of data design. Maybe you list them by first name instead of last, or maybe you use email addresses instead of their names. What you're really doing is designing a clear, logical list that lets you call or text a contact in a quick and simple way.

The specifics

To execute a plan using detail-oriented thinking, what does a data analyst consider?

Subject-matter experts

To get the most out of data-driven decision-making, it's important to include insights from people very familiar with the business problem. Identify what these people are called.

data scientist

The primary goal of a data _____ is to create new questions using data.

Data Analysis Life Cycle

The process of going from data to decision.

Analytical skills

The qualities and characteristics associated with solving problems using facts

False

A furniture manufacturer wants to find a more environmentally friendly way to make its products. A data analyst helps solve this problem by gathering relevant data, analyzing it, and using it to draw conclusions. The analyst then shares their analysis with subject-matter experts from the manufacturing team, who validate the findings. Finally, a plan is put into action. This scenario describes data science.

Visualization

Graphical representation of information

Data Analysis Job

Someone who collects , transforms, and organizes data in order to help make informed decisions

Where

The clause narrows your query so that the database returns only the data with an exact value match or the data that matches a certain condition that you want to satisfy.

Data-driven decision-making

You have just finished analyzing data for a marketing project. Before moving forward, you share your results with members of the marketing team to see if they might have additional insights into the business problem. What practice does this support?

False

You read an interesting article about data analytics in a magazine and want to share some ideas from the article in the discussion forum. In your post, you include the author and a link to the original article. This would be an inappropriate use of the forum.

problem-oriented

in order to identify, describe, and solve problems. It's all about keeping the problem top of mind throughout the entire project. For example, say a data analyst is told about the problem of a warehouse constantly running out of supplies. They would move forward with different strategies and processes. But the number one goal would always be solving the problem of keeping inventory on the shelves.

Curiosity

is all about wanting to learn something. Curious people usually seek out new challenges and experiences. This leads to knowledge.

Gap analysis

lets you examine and evaluate how a process works currently in order to get where you want to be in the future.

data ecosystem

refers to the various elements that interact with one another to produce, manage, store, organize, analyze, and share data.