Welcome back. 

At this point, you've been introduced to the world of data analytics and 

what data analysts do. 

You've also learned how this course will prepare you for 

a successful career as an analyst. 

Coming up, you'll learn all the ways data can be used, and 

you'll discover why data analysts are in such high demand. 

I'm not exaggerating when I say every goal and success that my team and 

I have achieved couldn't have been done without data. 

Here at Google, all of our products are built on data and 

data-driven decision making. 

From concept to development to launch, 

we're using data to figure out the best way forward. And we're not alone. 

Countless other organizations also see the incredible value in data 

and, of course, the data analysts who help them make use of it. 

So we know data opens up a lot of opportunities. 

But to help you wrap your head around all the ways you can actually use data, 

let's go over a few examples from everyday life. 

You might not realize it, but people analyze data all the time. 

For instance, I'm a morning person. 

A long time ago, I realized that I'm happier and 

more productive if I get to bed early and wake up early. 

I came to this conclusion after noticing a pattern in my day-to-day experiences. 

When I got seven hours of sleep and woke up at 6:30, I was the most successful. 

So I thought about the relationship between this pattern and my daily life, 

and I predicted that early to bed early to rise would be the right choice for me. 

And I'm definitely my best self when I wake up bright and early. 

I bet you've identified patterns and relationships in your life, too. 

Maybe about your own sleep cycle or how you feel after eating certain foods, 

or what time of day you like to workout. 

All of these are great examples of real life patterns and relationships 

that you can use to make predictions about the right actions to take, and 

that is a huge part of data analysis right there. 

Now, let's put this process into a business setting. 

You may remember from an earlier video that there's a ton of data out there. 

And every minute of every hour of every day, more data is being created. 

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. 

For businesses to be on top of the competition, 

they need to be on top of their data. 

That's why these companies hire data analysts to control the waves of data they 

collect every day, makes sense of it, and then draw conclusions or make predictions. 

This is the process of turning data into insights, and 

it's how analysts help businesses put all their data to good use. 

This is actually a good way to think about analysis: turning data into insights. 

As a reminder, the more detailed definition you learned earlier is 

that data analysis is the collection, transformation, 

and organization of data in order to draw conclusions, 

make predictions, and drive informed decision-making. 

So after analysts have created insights from data, what happens? 

Well, a lot. 

Those insights are shared with others, decisions are made, and 

businesses take action. 

And here's where it can get really exciting. 

Data analytics can help organizations completely rethink something they do or 

point them in a totally new direction. 

For example, maybe data leads them to a new product or unique service, or 

maybe it helps them find a new way to deliver an incredible customer experience. 

It's these kinds of aha moments that can help businesses reach another level, 

and that makes data analysts vital to any business. 

Now that you know more of the amazing ways data is being used every day, 

you can see why data analysts are in such high demand. 

We'll continue exploring how analysts can transform data into insights that lead to 

action. 

And before you know it, you'll be ready to help any organization find new and 

exciting ways to transform their data.

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arlier you learned about how data analysts at one organization 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.

Review the six phases of data analysis

Before you write your entry in your learning log, reflect on the case study from earlier. The data analysts 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: 

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. 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. 

Access your learning log

To use the template for this course item, click the link below and select “Use Template.” 

Link to learning log template: Consider how data analysts approach tasks

OR

If you don’t have a Google account, you can download the template directly from the attachment below.

Learning Log Template_ Consider how data analysts approach tasks

DOCX File

Reflection

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.

Hi. I'm Cassie, and I lead 

Decision Intelligence for Google Cloud. 

Decision Intelligence is a combination of 

applied data science and 

the social and managerial sciences. 

It is all about harnessing the power and beauty of data. 

I help Google Cloud and its customers turn 

their data into impact and 

make their businesses and the world better. 

A data analyst is an explorer, 

a detective, and an artist all rolled into one. 

Analytics is the quest for inspiration. 

You don't know what's going to 

inspire you before you explore, 

before you take a look around. 

When you begin, you have no idea what 

you're going to find and 

whether you're even going to find anything. 

You have to bravely dive into the 

unknown and discover what lies in your data. 

There is a pervasive myth that 

someone who works in data 

should know the everything of data. 

I think that that's unhelpful because 

the universe of data has expanded. 

It's expanded so much 

that specialization becomes important. 

It's very, very difficult for 

one person to know and be the everything of data. 

That's why we need these different roles. 

The advice that I give folks who are entering the space 

is to pick their specialization based on which flavor, 

which type of impact best suits their personality. 

Now, data science, the discipline of making data useful, 

is an umbrella term that encompasses three disciplines: 

machine learning, statistics, and analytics. 

These are separated by 

how many decisions you know 

you want to make before you begin with them. 

If you want to make a few important decisions under 

uncertainty, that is statistics. 

If you want to automate, in other words, make many, many, 

many decisions under uncertainty, 

that is machine learning and AI. 

But what if you don't know 

how many decisions you want to make before you begin? 

What if what you're looking for is inspiration? 

You want to encounter your unknown unknowns. 

You want to understand your world. 

That is analytics. 

When you're considering data science and 

you're choosing which area to specialize in, 

I recommend going with your personality. 

Which of the three excellences 

in data science feels like a better fit for you? 

The excellence of statistics is rigor. 

Statisticians are essentially philosophers, 

epistemologists. 

They are very, very careful about 

protecting decision-makers from coming 

to the wrong conclusion. 

If that care and rigor is what you are passionate about, 

I would recommend statistics. 

Performance is the excellence of 

the machine learning and AI engineer. 

You know that's the one for you if someone says to you, 

"I bet that you couldn't build 

an automation system that performs this task with 

99.99999 percent accuracy," and 

your response to that is, "Watch me." 

How about analytics? 

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? 

If that's you, then 

analytics is probably the best fit for you. 

A piece of advice that I have 

for analysts getting started on 

this journey is it can 

be pretty scary to explore the unknown. 

But I suggest letting go a little bit of 

any temptations towards perfectionism and instead, 

enjoying the fun, the thrill of exploration. 

Don't worry about right answers. 

See how quickly you can unwrap this gift 

and find out if there is anything fun in there. 

It's like your birthday, unwrapping a bunch of things. 

Some of them you like. Some of them you won't. 

But isn't it fun to know what's actually in there?

Hello again. You've already learned about being a data analyst 

and how this program will help prepare you for your future career. 

Now, it's time to 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. Ecosystems can be large, like the jungle in a tropical rainforest 

or the Australian outback. 

Or, tiny, like tadpoles in a puddle, or bacteria on your skin. 

And just like the kangaroos and koala bears in the Australian outback, 

data lives inside its own ecosystem too. 

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. 

People like you. 

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 the data ecosystem, and as a data analyst, it's 

your job to harness the power of that data ecosystem, find the right information, 

and provide the team with analysis that helps them make smart decisions. 

For example, you could tap into your retail store's database, 

which is an ecosystem filled with customer names, addresses, 

previous purchases, and customer reviews. 

As a data analyst, you could use this information to predict what these 

customers will buy in the future, 

and make sure the store has the products and stock when they're needed. 

As another example, 

let's think about a data ecosystem used by a human resources department. 

This ecosystem would include information like postings from job websites, 

stats on the current labor market, 

employment rates, and social media data on prospective employees. 

A data analyst could use this information to help their team recruit new workers 

and improve employee engagement and retention rates. 

But data ecosystems aren't just for stores and offices. They work on farms, too. 

Agricultural companies regularly use data ecosystems that 

include information including geological patterns in weather movements. 

Data analysts can use this data to help farmers predict crop yields. 

Some data analysts are even using data ecosystems to save real 

environmental ecosystems. 

At the Scripps Institution of Oceanography, coral reefs all over 

the world are monitored digitally, so they can see how organisms change over time, 

track their growth, and measure any increases or 

declines in individual colonies. 

The possibilities are endless. 

Okay, now let's talk about some common misconceptions you might come across. 

First is the difference between data scientists and data analysts. 

It's easy to confuse the two, but what they do is actually very different. 

Data science is defined as creating new ways of modeling and 

understanding the unknown by using raw data. 

Here's a good way to think about it. 

Data scientists create new questions using data, while analysts find 

answers to existing questions by creating insights from data sources. 

There are also many words and 

phrases you'll hear throughout this course, that are easy to get mixed up. 

For example, data analysis and data analytics sound the same, 

but they're actually very different things. Let's start with analysis. 

You've already learned that data analysis is the collection, transformation, 

and organization of data in order to draw conclusions, 

make predictions, and drive informed decision-making. 

Data analytics in the simplest terms is the science of data. 

It's a very broad concept that encompasses everything from the job of managing and 

using data to the tools and methods that data workers use each and every day. 

So when you think about data, data analysis and 

the data ecosystem, it's important to understand that all of these 

things fit under the data analytics umbrella. 

All right, now that you know a little more about the data ecosystem and 

the differences between data analysis and data analytics, 

you're ready to explore how data is used to make effective decisions. 

You'll get to see data-driven decision-making, in action.

So far, you've discovered that there are many different ways data can be 

used. In our everyday lives, 

we use data when we wear a fitness tracker or 

read product reviews to make a purchase decision. 

And in business, we use data to learn more about our customers, 

improve processes, and help employees do their jobs more effectively. 

But this is just the tip of the iceberg. 

One of the most powerful ways you can put data to work is with data-driven decision-making. 

Data-driven decision-making is defined as using facts to guide business strategy. 

Organizations in many different industries are empowered to make better, 

data-driven decisions by data analysts all the time. 

The first step in data-driven decision-making is figuring out the business need. 

Usually, this is a problem that needs to be solved. 

For example, a problem could be a new company needing to establish better 

brand recognition, so it can compete with bigger, more well-known competitors. 

Or maybe an organization wants to improve a product and needs to figure out how to 

source parts from a more sustainable or ethically responsible supplier. 

Or, it could be a business trying to solve the problem of unhappy employees, 

low levels of engagement, satisfaction and retention. 

Whatever the problem is, once it's defined, a data analyst finds data, 

analyzes it and uses it to uncover trends, patterns and relationships. 

Sometimes the data-driven strategy will build on what's worked in the past. 

Other times, it can guide a business to branch out in a whole new direction. 

Let's look at a real-world example. 

Think about a music or movie streaming service. 

How do these companies know what people want to watch or listen to, 

and how do they provide it? 

Well using data-driven decision-making, 

they gather information about what their customers are currently listening to, 

analyze it, then use the insights they've gained to make suggestions for 

things people will most likely enjoy in the future. 

This keeps customers happy and 

coming back for more, which in turn means more revenue for the company. 

Another example of data-driven decision-making can be seen in the rise of 

e-commerce. 

It wasn't long ago that most purchases were made in a physical store, 

but the data showed people's preferences were changing. 

So a lot of companies created entirely new business models that remove 

the physical store, and let people shop right from their computers or 

mobile phones with products delivered right to their doorstep. 

In fact, data-driven decision-making can be so powerful, 

it can make entire business methods obsolete. 

For example, data helped companies completely move away from 

corded phones and replace them with mobile phones. 

By ensuring that data is built into every business strategy, 

data analysts play a critical role in their companies' success, but 

it's important to note that no matter how valuable data-driven decision-making is, 

data alone will never be as powerful as data combined with human experience, 

observation, and sometimes even intuition. 

To get the most out of data-driven decision-making, it's important to include 

insights from people who are familiar with the business problem. 

These people are called subject matter experts, and they have the ability to look 

at the results of data analysis and identify any inconsistencies, 

make sense of gray areas, and eventually validate choices being made. 

Organizations that work this way put data at the heart of every business strategy, 

but also benefit from the insights of their people. 

It's a win-win. 

As a data analyst, you play a key role in empowering these organizations to make 

data-driven decisions, which is why it's so important for 

you to understand how data plays a part in the decision-making process.

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Detectives and data analysts have a lot in common. Both depend on facts and clues to make decisions. Both collect and look at the evidence. Both talk to people who know part of the story. And both might even follow some footprints to see where they lead. Whether you’re a detective or a data analyst, your job is all about following steps to collect and understand facts.

Analysts use data-driven decision-making and follow a step-by-step process. You have learned that there are six steps to this process:

1. Ask questions and define the problem.
2. Prepare data by collecting and storing the information.
3. Process data by cleaning and checking the information.
4. Analyze data to find patterns, relationships, and trends.
5. Share data with your audience.
6. Act on the data and use the analysis results.

But there are other factors that influence the decision-making process. You may have read mysteries where the detective used their gut instinct, and followed a hunch that helped them solve the case. Gut instinct is an intuitive understanding of something with little or no explanation. This isn’t always something conscious; we often pick up on signals without even realizing. You just have a “feeling” it’s right.

Why gut instinct can be a problem

At the heart of data-driven decision making is data. Therefore, it's essential that data analysts focus on the data to ensure they make informed decisions. If you ignore data by preferring to make decisions based on your own experience, your decisions may be biased. But even worse, decisions based on gut instinct without any data to back them up can cause mistakes.

Consider an example of a restaurant entrepreneur, partnering with a well known chef to develop a new restaurant in a bustling part of the city’s central shopping district. The well known chef has several restaurants across the city. Banking on their reputation, the restaurant entrepreneur and chef followed gut instinct and created another uniquely themed restaurant. However, fundraising efforts fell short to fund the opening of the restaurant after months of planning and preparation. The property will go back on the market to be sold at a loss. Had the entrepreneur done more research, they would've found data showing prospective customers in this new restaurant location were very different from the chef's other restaurants.

The more you understand the data related to a project, the easier it will be to figure out what is required. These efforts will also help you identify errors and gaps in your data so you can communicate your findings more effectively. Sometimes past experience helps you make a connection that no one else would notice. For example, a detective might be able to crack open a case because they remember an old case just like the one they’re solving today. It's not just gut instinct.

Data + business knowledge = mystery solved

Blending data with business knowledge, plus maybe a touch of gut instinct, will be a common part of your process as a junior data analyst. The key is figuring out the exact mix for each particular project. A lot of times, it will depend on the goals of your analysis. That is why analysts often ask, “How do I define success for this project?”

In addition, try asking yourself these questions about a project to help find the perfect balance:

• What kind of results are needed?
• Who will be informed?
• Am I answering the question being asked?
• How quickly does a decision need to be made?

For instance, if you are working on a rush project, you might need to rely on your own knowledge and experience more than usual. There just isn’t enough time to thoroughly analyze all of the available data. But if you get a project that involves plenty of time and resources, then the best strategy is to be more data-driven. It’s up to you, the data analyst, to make the best possible choice. You will probably blend data and knowledge a million different ways over the course of your data analytics career. And the more you practice, the better you will get at finding that perfect blend.

When you decided to join this program, you proved that you are a curious person. So let’s tap into your curiosity and talk about the origins of data analysis. We don’t fully know when or why the first person decided to record data about people and things. But we do know it was useful because the idea is still around today!  

We also know that data analysis is rooted in statistics, which has a pretty long history itself. Archaeologists mark the start of statistics in ancient Egypt with the building of the pyramids. The ancient Egyptians were masters of organizing data. They documented their calculations and theories on papyri (paper-like materials), which are now viewed as the earliest examples of spreadsheets and checklists. Today’s data analysts owe a lot to those brilliant scribes, who helped create a more technical and efficient process.

It is time to enter the data analysis life cycle—the process of going from data to decision. Data goes through several phases as it gets created, consumed, tested, processed, and reused. With a life cycle model, all key team members can drive success by planning work both up front and at the end of the data analysis process. While the data analysis life cycle is well known among experts, there isn't a single defined structure of those phases. There might not be one single architecture that’s uniformly followed by every data analysis expert, but there are some shared fundamentals in every data analysis process. This reading provides an overview of several, starting with the process that forms the foundation of the Google Data Analytics Certificate.

The process presented as part of the Google Data Analytics Certificate is one that will be valuable to you as you keep moving forward in your career:

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

Understanding this process—and all of the iterations that helped make it popular—will be a big part of guiding your own analysis and your work in this program. Let’s go over a few other variations of the data analysis life cycle.

EMC's data analysis life cycle

EMC Corporation's data analytics life cycle is cyclical with six steps:

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

EMC Corporation is now Dell EMC. This model, created by David Dietrich, reflects the cyclical nature of real-world projects. The phases aren’t static milestones; each step connects and leads to the next, and eventually repeats. Key questions help analysts test whether they have accomplished enough to move forward and ensure that teams have spent enough time on each of the phases and don’t start modeling before the data is ready. It is a little different from the data analysis life cycle this program is based on, but it has some core ideas in common: the first phase is interested in discovering and asking questions; data has to be prepared before it can be analyzed and used; and then findings should be shared and acted on.

For more information, refer to this e-book, Data Science & Big Data Analytics.

SAS's iterative life cycle

An iterative life cycle was created by a company called SAS, a leading data analytics solutions provider. It can be used to produce repeatable, reliable, and predictive results:

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

The SAS model emphasizes the cyclical nature of their model by visualizing it as an infinity symbol. Their life cycle has seven steps, many of which we have seen in the other models, like Ask, Prepare, Model, and Act. But this life cycle is also a little different; it includes a step after the act phase designed to help analysts evaluate their solutions and potentially return to the ask phase again. 

For more information, refer to Managing the Analytics Life Cycle for Decisions at Scale.

Project-based data analytics life cycle

A project-based data analytics life cycle has five simple steps:

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

This data analytics project life cycle was developed by Vignesh Prajapati. It doesn’t include the sixth phase, or what we have been referring to as the Act phase. However, it still covers a lot of the same steps as the life cycles we have already described. It begins with identifying the problem, preparing and processing data before analysis, and ends with data visualization.

For more information, refer to Understanding the data analytics project life cycle.

Big data analytics life cycle

Authors Thomas Erl, Wajid Khattak, and Paul Buhler proposed a big data analytics life cycle in their book, Big Data Fundamentals: Concepts, Drivers & Techniques. Their life cycle suggests phases divided into nine steps:

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

This life cycle appears to have three or four more steps than the previous life cycle models. But in reality, they have just broken down what we have been referring to as Prepare and Process into smaller steps. It emphasizes the individual tasks required for gathering, preparing, and cleaning data before the analysis phase.

For more information, refer to Big Data Adoption and Planning Considerations.

Key takeaway

From our journey to the pyramids and data in ancient Egypt to now, the way we analyze data has evolved (and continues to do so). The data analysis process is like real life architecture, there are different ways to do things but the same core ideas still appear in each model of the process. Whether you use the structure of this Google Data Analytics Certificate or one of the many other iterations you have learned about, we are here to help guide you as you continue on your data journey.

1.

Question 1

Which of the following statements best defines data?

1 point

Data is a collection of facts.

Data is the use of calculations and statistics.

Data is a business process.

Data is an assortment of questions.

2.

Question 2

Fill in the blank: In data analytics, the data ecosystem refers to the various elements that interact with one another to produce, manage, store, _____, analyze, and share data.

1 point

organize

merge

ingest

locate

3.

Question 3

Which of the following terms refers to the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making?

1 point

Data analysis

Data elements

Data insight

Data life cycle

4.

Question 4

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?

1 point

Performance measurement

Data-driven decision-making

Analytical thinking

Collaboration with stakeholders

Data: A collection of facts

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

Data analyst: Someone who collects, transforms, and organizes data in order to drive informed decision-making

Data analytics: The science of data

Data-driven decision-making: Using facts to guide business strategy

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

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

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

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1.

Question 1

What is the purpose of data analysis? Select all that apply.

1 point

To draw conclusions

To drive informed decision-making

To create models of data

To make predictions

2.

Question 2

Fill in the blank: A collection of elements that interact with one another to produce, manage, store, organize, analyze, and share data is known as a data ______ .

1 point

cloud

ecosystem

environment

model

3.

Question 3

Fill in the blank: The primary goal of a data _____ is to create new questions using data, instead of analyzing data to find answers to existing questions.

1 point

engineer

designer

analyst

scientist

4.

Question 4

Gut instinct is an intuitive understanding of something with little or no explanation.

1 point

True

False

5.

Question 5

In data-driven decision-making, a data analyst would share their results with subject matter experts and draw conclusions from their analysis. What else would a data analyst do in data-driven decision-making?

1 point

Identification of trends

Determining the stakeholders.

Survey customers about results, conclusions, and recommendations

Gather and analyze data

6.

Question 6

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

1 point

customers

competitors

subject-matter experts

stakeholders

7.

Question 7

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 process does this support?

1 point

Data management

Data-driven decision-making

Data science

Data analytics

8.

Question 8

When citing an article you found in a discussion forum, you should always do what?

1 point

Include your email address for people to send questions or comments.

Take credit for creating the article.

Give credit to the original author.

Check the article for typos or grammatical errors.

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I understand that submitting work that isn’t my own may result in permanent failure of this course or deactivation of my Coursera account.

Use the name on your government issued ID

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