1.3.3.The data analysis toolbox - sj50179/Google-Data-Analytics-Professional-Certificate GitHub Wiki

Exploring data analyst tools

the most common ones you'll see analyst use are :

• Spreadsheets

  • The usefulness of your data depends on how well it's structured. When you put your data into a spreadsheet, you can see patterns, group information and easily find the information you need.
  • Formula : a set of instructions that performs a specific calculation using the data in a spreadsheet.
  • Function : a preset command that automatically performs a specific process or task using the data in a spreadsheet.

• Query languages for databases

  • Query language : a computer programming language that allows you to retrieve and manipulate data from a database - structured query language(SQL)

• Visualization tools

  • Data visualization : the graphical representation of information. This makes it easier for stakeholders to draw conclusions, make decisions, and come up with strategies. Some popular visualization tools are Tableau and Looker.

Q. Fill in the blank: A query language is a computer programming language that enables data analysts to retrieve and manipulate data from a _____.

• A. database

Key data analyst tools

As you are learning, the most common programs and solutions used by data analysts include spreadsheets, query languages, and visualization tools. In this reading, you will learn more about each one. You will cover when to use them, and why they are so important in data analytics.

Spreadsheets

Data analysts rely on spreadsheets to collect and organize data. Two popular spreadsheet applications you will probably use a lot in your future role as a data analyst are Microsoft Excel and Google Sheets.

Digital worksheets structure data in a meaningful way by letting you

• Collect, store, organize, and sort information
• Identify patterns and piece the data together in a way that works for each specific data project
• Create excellent data visualizations, like graphs and charts.

Databases and query languages

A database is a collection of structured data stored in a computer system. Some popular Structured Query Language (SQL) programs include MySQL, Microsoft SQL Server, and BigQuery.

Query languages

• Allow analysts to isolate specific information from a database(s)
• Make it easier for you to learn and understand the requests made to databases
• Allow analysts to select, create, add, or download data from a database for analysis

Visualization tools

Data analysts use a number of visualization tools, like graphs, maps, tables, charts, and more. Two popular visualization tools are Tableau and Looker.

These tools

• Turn complex numbers into a story that people can understand

• Help stakeholders come up with conclusions that lead to informed decisions and effective business strategies

• Have multiple features

  • Tableau's simple drag-and-drop feature lets users create interactive graphs in dashboards and worksheets

  • Looker communicates directly with a database, allowing you to connect your data right to the visual tool you choose

A career as a data analyst also involves using programming languages, like R and Python, which are used a lot for statistical analysis, visualization, and other data analysis.

As you will continue to learn, data analysts have a lot of tools to choose from. This is a first look at some of the possibilities, and you will explore all of these tools in-depth throughout this program.

Choosing the right tool for the job

As a data analyst, you will usually have to decide which program or solution is right for the particular project you are working on. In this reading, you will learn more about how to choose which tool you need and when.

Depending on which phase of the data analysis process you’re in, you will need to use different tools. For example, if you are focusing on creating complex and eye-catching visualizations, then the visualization tools we discussed earlier are the best choice. But if you are focusing on organizing, cleaning, and analyzing data, then you will probably be choosing between spreadsheets and databases using queries. Spreadsheets and databases both offer ways to store, manage, and use data. The basic content for both tools are sets of values. Yet, there are some key differences, too:

You don’t have to choose one or the other because each serves its own purpose. Generally, data analysts work with a combination of the two, as both tools are very useful in data analytics. For example, you can store data in a database, then export it to a spreadsheet for analysis. Or, if you are collecting information in a spreadsheet, and it becomes too much for that particular platform, you can import it into a database. And, later in this course, you will learn about programming languages like R that give you even greater control of your data, its analysis, and the visualizations you create.

As you continue learning about these important tools, you will gain the knowledge to choose the right tool for any data job.

Self-Reflection: Reviewing past concepts

So far we’ve learned about the data life cycle and the data analysis process. They cover the following steps:

1. Data life cycle:

• Plan
• Capture
• Manage
• Analyze
• Archive
• Destroy

2. Data analysis process:

• Ask
• Prepare
• Process
• Analyze
• Share
• Act

Reflection

Take a moment to consider what you’ve learned about these processes. In the text box below, write three to five sentences (60 - 100 words) explaining the relationship between the data life cycle and the data analysis process. How are they similar? How are they different?

While the data analysis process drives our projects and helps us reach our business goals, we have to understand the life cycle of the data we’ll be working with in order to use that process. We can’t analyze our data well if we don’t have a thorough understanding of our data. Similarly, we can collect all the data we want, but the data becomes worthless if we don’t have a good plan for analyzing the data. 데이터 분석 프로세스가 프로젝트를 추진하고 비즈니스 목표에 도달하는 데 도움을 주지만, 이 프로세스를 사용하려면 앞으로 사용할 데이터의 수명 주기를 이해해야 합니다. 데이터를 잘 이해하지 못하면 데이터를 잘 분석할 수 없습니다. 마찬가지로, 원하는 모든 데이터를 수집할 수 있지만, 데이터를 분석할 적절한 계획이 없으면 데이터는 무용지물이 됩니다.

Question 2

Next, in 3-5 sentences (60 - 100 words), explain the relationship between the ask phase of the data analysis process and the plan phase of the data life cycle. How are they similar? How are they different?

Both phases involve planning, and asking questions. They are different in that the ‘Ask’ phase in the data analysis process focuses on “big picture” strategic thinking about business goals, while the “Plan” phase focuses on “nuts and bolts” of the project, like what data you have access to, what data you need, and where you’re going to get it. 두 단계 모두 계획 수립과 질문입니다. 데이터 분석 프로세스의 '묻기' 단계는 비즈니스 목표에 대한 "큰 그림" 전략적인 사고에 초점을 맞추고, "계획" 단계는 어떤 데이터에 액세스할 수 있는지, 어떤 데이터가 필요한지, 어디서 얻을 것인지와 같은 프로젝트의 "너트 및 볼트"에 초점을 맞춘다는 점에서 다릅니다.

Test your knowledge on the data analysis toolbox

Question 1

Based on what you have learned in this course, spreadsheets are digital worksheets that enable data analysts to do which of the following tasks? Select all that apply.

• Store data

• Sort and filter data

• Choose a topic for data analysis

• Organize data in columns and rows

Correct. Spreadsheets enable data analysts to store, organize, sort, and filter data. This helps them see patterns, group information, and easily find the information they need.

Question 2

Fill in the blank: A set of instructions that performs a specific calculation using spreadsheet data is called _____.

• an operation

• a report

• a formula

• a program

Correct. A set of instructions that performs a specific calculation using spreadsheet data is called a formula.

Question 3

A database is a collection of data stored in a computer system.

• True

• False

Correct. A database is a collection of data stored in a computer system.

Question 4

In data analytics, SQL is an acronym meaning _____ query language.

• statistical

• syntax

• structured

• software

Correct. SQL stands for structured query language. It enables data analysts to communicate with a database.

Question 5

What is the term for the graphical representation of data?

• Data collection

• Data language

• Data visualization

• Data summary

Correct. Data visualization is the graphical representation of data.