Foundations-Data, Data, Everywhere Notes Readings PDF

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During each course of the program, you will complete lots of hands-on assignments and projects based on both day-to-day life and the practical activities of a data analyst....

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Week 1 Introducing Data Analytics During each course of the program, you will complete lots of hands-on assignments and projects based on both day-to-day life and the practical activities of a data analyst. Along the way, you will learn how to ask the right questions and understand objectives. You will also learn how to effectively clean and organize large amounts of data to make it ready for high-quality analysis. On top of that, you will get hands-on experience using all kinds of tools and techniques that will help you recognize patterns and uncover relationships between data points. And to help you communicate the results of your analysis, you will learn how to design visuals and dashboards. There is even an opportunity to create a case study, which you can highlight in your resume to show what you have learned to potential employers Foundations: Data, Data, Everywhere Introducing data analytics: Data helps us make decisions, in everyday life and in business. In this first part of the course, you will learn how data analysts use tools of their trade to inform those decisions. You will also get to know more about this course and the overall program expectations. Thinking analytically: Data analysts balance many different roles in their work. In this part of the course, you will learn about some of these roles and the key skills that are required. You will also explore analytical thinking and how it relates to data-driven decision making. Exploring the wonderful world of data: Data has its own life cycle, and data analysts use an analysis process that cuts across and leverages this life cycle. In this part of the course, you will learn about the data life cycle and data analysis process. They are both relevant to your work in this program and on the job as a future data analyst. You will be introduced to applications that help guide data through the data analysis process. Setting up a data toolbox: Spreadsheets, query languages, and data visualization tools are all a big part of a data analyst’s job. In this part of the course, you will learn the basic concepts to use them for data analysis. You will understand how they work through examples provided. Discovering data career possibilities: All kinds of businesses value the work that data analysts do. In this part of the course, you will examine different types of businesses and the jobs and tasks that analysts do for them. You will also learn how a Google Data Analytics Certificate will help you meet many of the requirements for a position with these organizations. Completing the Course Challenge: At the end of this course, you will be able to put everything you have learned into perspective with the Course Challenge. The Course Challenge will ask you questions about the main concepts you have learned and then give you an opportunity to apply those concepts in two scenarios.

Data is a collection of facts that can be used to draw conclusions, make predictions, and assist in decision-making. 

Real-life roles and responsibilities of a junior data analyst

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How businesses transform data into actionable insights

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Spreadsheet basics

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Database and query basics

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Data visualization basics

Skill sets you will build: 

Using data in everyday life

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Thinking analytically

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Applying tools from the data analytics toolkit

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Showing trends and patterns with data visualizations

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Ensuring your data analysis is fair

Ask What you will learn: How data analysts solve problems with data The use of analytics for making data-driven decisions Spreadsheet formulas and functions Dashboard basics, including an introduction to Tableau Data reporting basics Skill sets you will build: Asking SMART and effective questions Structuring how you think Summarizing data Putting things into context Managing team and stakeholder expectations Problem-solving and conflict-resolution

Prepare What you will learn: How data is generated Features of different data types, fields, and values Database structures The function of metadata in data analytics Structured Query Language (SQL) functions Skill sets you will build:

Ensuring ethical data analysis practices Addressing issues of bias and credibility Accessing databases and importing data Writing simple queries Organizing and protecting data Connecting with the data community (optional)

Process What you will learn: Data integrity and the importance of clean data The tools and processes used by data analysts to clean data Data-cleaning verification and reports Statistics, hypothesis testing, and margin of error Resume building and interpretation of job postings (optional) Skill sets you will build: Connecting business objectives to data analysis Identifying clean and dirty data Cleaning small datasets using spreadsheet tools Cleaning large datasets by writing SQL queries Documenting data-cleaning processes

Process What you will learn: Steps data analysts take to organize data How to combine data from multiple sources Spreadsheet calculations and pivot tables SQL calculations Temporary tables Data validation Skill sets you will build: Sorting data in spreadsheets and by writing SQL queries

Filtering data in spreadsheets and by writing SQL queries Converting data Formatting data Substantiating data analysis processes SHare What you will learn: Design thinking How data analysts use visualizations to communicate about data The benefits of Tableau for presenting data analysis findings Data-driven storytelling Dashboards and dashboard filters Strategies for creating an effective data presentation Skill sets you will build: Creating visualizations and dashboards in Tableau Addressing accessibility issues when communicating about data Understanding the purpose of different business communication tools Telling a data-driven story Presenting to others about data

Act What you will learn: Programming languages and environments R packages R functions, variables, data types, pipes, and vectors R data frames Bias and credibility in R R visualization tools R Markdown for documentation, creating structure, and emphasis Skill sets you will build: Coding in R

Writing functions in R Accessing data in R Cleaning data in R Generating data visualizations in R

capstone

What you will learn: 

How a data analytics portfolio distinguishes you from other candidates

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Practical, real-world problem-solving

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Strategies for extracting insights from data

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Clear presentation of data findings

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Motivation and ability to take initiative

Skill sets you will build: 

Building a portfolio

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Increasing your employability

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Showcasing your data analytics knowledge, skill, and technical expertise

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Sharing your work during an interview

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Communicating your unique value proposition to a potential employer

Gap analysis is a method for examining and evaluating how a process works currently in order to get where you want to be in the future. Improving accessibility, increasing efficiency, and reducing carbon emissions are examples of improvements that gap analysis can help accomplish. The act phase is when insights are put into action. This involves a company or organization implementing a plan to solve the original business problem.

data is a collection of facts Your Notes This collection can include numbers, pictures, videos, words, measurements, observations, and more.  Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decisionmaking. Edit Delete  

Certificate

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1:34 - 1:45 Data evolves over time which means this analysis or analytics, as we call it, can give us new information throughout data's entire life cycle.

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Businesses need a way to control all that data so they can use it to improve processes, identify opportunities and trends, launch new products, serve customers, and make thoughtful decisions. F

Case Study: New data perspectives

As you have been learning, you can find data pretty much everywhere. Any time you observe and evaluate something in the world, you’re collecting and analyzing data. Your analysis helps you find easier ways of doing things, identify patterns to save you time, and discover surprising new perspectives that can completely change the way you experience things. Here is a real-life example of how one group of data analysts used the six steps of the data analysis process to improve their workplace and its business processes. Their story involves something called people analytics — also known as human resources analytics or workforce analytics. People analytics is the practice of collecting and analyzing data on the people who make up a company’s workforce in order to gain insights to improve how the company operates. Being a people analyst involves using data analysis to gain insights about employees and how they experience their work lives. The insights are used to define and create a more productive and empowering workplace. This can unlock employee potential, motivate people to perform at their best, and ensure a fair and inclusive company culture. The six steps of the data analysis process that you have been learning in this program are: ask, prepare, process, analyze, share, and act. These six steps apply to any data analysis. Continue reading to learn how a team of people analysts used these six steps to answer a business question. An organization was experiencing a high turnover rate among new hires. Many employees left the company before the end of their first year on the job. The analysts used the data analysis process to answer the following question: how can the organization improve the retention rate for new employees? Here is a break down what this team did, step by step.

First up, the analysts needed to define what the project would look like and what would qualify as a successful result. So, to determine these things, they asked effective questions and collaborated with leaders and managers who were interested in the outcome of their people analysis. These were the kinds of questions they asked:

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What do you think new employees need to learn to be successful in their first year on the job?

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Have you gathered data from new employees before? If so, may we have access to the historical data?

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Do you believe managers with higher retention rates offer new employees something extra or unique?

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What do you suspect is a leading cause of dissatisfaction among new employees?

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By what percentage would you like employee retention to increase in the next fiscal year?

It all started with solid preparation. The group built a timeline of three months and decided how they wanted to relay their progress to interested parties. Also during this step, the analysts identified what data they needed to achieve the successful result they identified in the previous step - in this case, the analysts chose to gather the data from an online survey of new employees. They developed specific questions to ask about employee satisfaction with different business processes, such as hiring and onboarding, and their overall compensation. Rules were established for who would have access to the data collected - in this case, anyone outside the group wouldn't have access to the raw data, but could view summarized or aggregated data. For example, an individual's compensation wouldn't be available, but salary ranges for groups of individuals would be viewable. They finalized what specific information would be gathered, and how best to present the data visually. The analysts brainstormed possible project- and datarelated issues and how to avoid them.

The group sent the survey out. Great analysts know how to respect both their data and the people who provide it. Since employees provided the data, it was important to make sure all employees gave their consent to participate. The data analysts also made sure employees understood how their data would be collected, stored, managed, and protected. In order to maintain confidentiality and protect and store the data effectively, access was restricted to a limited number of analysts. Collecting and using data ethically is one of the responsibilities of a

data analyst. Then the data was cleaned to make sure it was complete, correct, and relevant. Certain data was aggregated and summarized without revealing individual responses. The raw data was uploaded to an internal data warehouse for an additional layer of security.

Then, the analysts did what they do best: analyze! From the completed surveys, the data analysts would discover that a new employee’s experience with certain processes was a key indicator of overall job satisfaction. The analysts found that employees who experienced a long and complicated hiring process were most likely to leave the company. Employees who experienced an efficient and transparent evaluation and feedback process were most likely to remain with the company. The group knew it was important to document exactly what they found in the analysis, no matter what the results. To do otherwise would diminish trust in the survey process and reduce their ability to collect truthful data from employees in the future.

Just as they made sure the data was carefully protected, the analysts were also careful sharing the report. Only the managers who met or exceeded the minimum number of direct reports with submitted responses to the survey were eligible to receive the report. The group first presented the results to eligible managers to make sure they had the full picture. Then, they asked them to deliver the results to their teams. This gave the managers an opportunity to communicate the results with the right context. As a result, they could have productive team conversations about next steps to improve employee engagement.

The last stage of the process for the team of analysts was to work with leaders within their company and decide how best to implement changes and take actions based on the findings. The analysts recommended standardizing the hiring and evaluation process for all new hires based on the most efficient and transparent practices. A year later, the same survey was distributed to employees. Analysts anticipated that a comparison between the two sets of results would indicate that the action plan worked. Turns out, the changes improved the retention rate for new employees and the actions taken by leaders were successful!

Is people analytics right for you? One of the many things that makes data analytics so exciting is that the problems are always different, the solutions need creativity, and the impact on others can be great — even lifechanging or life-saving. As a data analyst, you can be part of these efforts. Maybe you’re even inspired to learn more about the field of people analytics. If so, consider learning more about this field and adding that research to your data analytics journal. You never know: One day soon, you could be helping a company create an amazing work environment for you and your colleagues!

Additional Resource To learn more about some recent applications of data analytics in the business world, check out the article “4 Examples of Business Analytics in Action” from Harvard Business School. The article reveals how corporations use data insights to optimize their decision-making process. Please note that the first example in the article contains a minor error in the second paragraph, but the example is still a valid one. https://online.hbs.edu/blog/post/business-analytics-examples Learning Log: Consider how data analysts approach tasks Earlier you learned about how data analysts at Google used data to improve employee retention. Now, you’ll complete an entry in your learning log to track your thinking and reflections about those data analysts' process and how they approached this problem. By the time you complete this activity, you will have a stronger understanding of how the six phases of the data analysis process can be used to break down tasks and tackle big questions. This will help you apply these steps to future analysis tasks and start tackling big questions yourself. Before you write your entry in your learning log, reflect on the case study from earlier. The data analysts at Google wanted to use data to improve employee retention. In order to do that, they had to break this larger project into manageable tasks. The analysts organized those tasks and activities around the six phases of the data analysis process:

Ask Prepare Process Analyze Share Act The analysts asked questions to define both the issue to be solved and what would equal a successful result.

Next, they prepared by building a timeline and collecting data with employee surveys that were designed to be inclusive.

They processed the data by cleaning it to make sure it was complete, correct, relevant, and free of errors and outliers.

They analyzed the clean employee survey data. Then the analysts shared their findings and recommendations with team leaders. Afterward, leadership acted on the results and focused on improving key areas.

In your learning log template, write 2-3 sentences (40-60 words) reflecting on what you’ve learned from the case study by answering each of the questions below:

Did the details of the case study help to change the way you think about data analysis? Why or why not? Did you find anything surprising about the way the data analysts approached their task? What else would you like to learn about data analysis? When you’ve finished your entry in the learning log template, make sure to save the document so your response is somewhere accessible. This will help you continue applying data analysis to your everyday life. You will also be able to track your progress and growth as a data analyst.

Decision Intelligence is a combination of applied data science and the social and managerial sciences. The excellence of an analyst is speed. How quickly can you surf through vast amounts of data to explore it and discover the gems, the beautiful potential insights that are worth knowing about and bringing to your decision-makers? Are you excited by the ambiguity of exploration? Are you excited by the idea of working on a lot of different things, looking at a lot of different data sources, and thinking through vast amounts of information, while promising not to snooze past the important potential insights? Are you okay being told, "Here is a whole lot of data. No one has looked at it before. Go find something interesting"? Do you thrive on creative, open-ended projects? What is the data ecosystem? explore the data ecosystem, find out where data analytics fits into that system, and go over some common misconceptions you might run into in the field of data analytics. To put it simply, an ecosystem is a group of elements that interact with one another. Data ecosystems are made up of various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data. These elements include hardware and software tools, and the people who use them. Data can also be found in something called the cloud. The cloud is a place to keep data online, rather than on a computer hard drive. So instead of storing data somewhere inside your organization's network, that data is accessed over the internet. So the cloud is just a term we use to describe the virtual location. The cloud plays a big part in t...