5 Skills Data Analysts Need | Google Data Analytics Certificate

this video is part of the google data analytics certificate providing you with job-ready skills to start or advance your career in data analytics get access to practice exercises quizzes discussion forums job search help and more on casera and you can earn your official certificate visit grow.google datacert to enroll in the full learning experience today [Music] earlier i told you that you already have analytical skills you just might not know it yet when learning new things sometimes people overlook their own skills but it's important you take the time to acknowledge them especially since these skills are going to help you as a data analyst in fact you're probably more prepared than you think don't believe me well let me prove it let's start by defining what i'm talking about here analytical skills are qualities and characteristics associated with solving problems using facts there are a lot of aspects to analytical skills but we'll focus on five essential points their curiosity understanding context having technical mindset data design and data strategy now you may be thinking i don't have these kinds of skills or i only have a couple of them but stay with me and i bet you'll change your mind let's start with curiosity curiosity is all about wanting to learn something curious people usually seek out new challenges and experiences this leads to knowledge the very fact that you're here with me right now demonstrates that you have curiosity all right that was an easy one now think about understanding context context is the condition in which something exists or happens this can be a structure or an environment a simple way of understanding context is by counting to five one two three four five all of those numbers exist in the context of one through five but what if a friend of yours said to you one two four five three well the three would be out of context simple right but it can be a little tricky there's a good chance that you might not even notice the three being out of context if you aren't paying close attention that's why listening and trying to understand the full picture is critical in your own life you put things into context all the time for example let's think about your grocery list if you group together items like flour sugar and yeast that's you adding context to your groceries this saves you time when you're at the baking out at the grocery store let's look at another example have you ever shuffled a deck of cards and noticed a joker if you're playing a game that doesn't include jokers identifying that card means you understand it's out of context remove it and you're much more likely to play a successful game alright so now we know you have both curiosity and the ability to understand context let's move on to the third skill a technical mindset a technical mindset involves the ability to break things down into smaller steps or pieces and work with them in an orderly and logical way for instance 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 when you take something that seems like a single task like paying your bills and break it into smaller steps with an orderly process that's using a technical mindset now let's explore the fourth part of an analytical skill set data design data design is how you organize information as a data analyst design typically has to do with an actual database but again the same skills can easily be applied to everyday life for 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 the contact in a quick and simple way the last but definitely not least the fifth and final element of analytical skills is data strategy data strategy is the management of the people processes and tools used in data analysis let's break that down you manage people by making sure they know how to use the right data to find solutions to the problem you're working on for processes it's about making sure the path to that solution is clear and accessible and for tools you make sure the right technology is being used for the job now you may be doubting my ability to give you an example from real life that demonstrates data strategy but check this out imagine mowing a lawn step one would be reading the owner's manual for the mower that's making sure the people involved well you in this example know how to use the data available the manual would instruct you to put on protective eyewear and closed toed shoes then it's on to step two making the process the path clear and accessible this would involve you walking around the lawn picking up large sticks or rocks that might get in your way finally for step three you check the lawnmower your tool to make sure it has enough gas and oil and is in working condition so the lawn can be moaned safely so there you have it now you know the five essential skills of a data analyst curiosity understanding context having a technical mindset data design and data strategy i told you that you are already an analytical thinker now you can start actively practicing these skills as you move through the rest of this course [Music] now that you know the five essential skills of a data analyst you're ready to learn more about what it means to think analytically people don't often think about thinking thinking is second nature to us it just happens automatically but there are actually many different ways to think some people think creatively some think critically and some people think in abstract ways let's talk about analytical thinking analytical thinking involves identifying and defining a problem and then solving it by using data in an organized step-by-step manner so as data analysts how do we think analytically well to answer that question we will now talk about a second set of five the five key aspects to analytical thinking they are visualization strategy problem orientation correlation and finally big picture and detail-oriented thinking let's start with visualization in data analytics visualization is the graphical representation of information some examples include graphs maps or other design elements visualization is important because visuals can help data analysts understand and explain information more effectively think about it like this if you are trying to explain the grand cane to someone using words would be much more challenging than showing them a picture a visualization of the grand canyon would help you make your point much quicker now let's talk about the second part of analytical thinking being 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 next up on the analytical thinking checklist being problem oriented data analysts use a problem-oriented approach 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 will move forward with different strategies and processes but the number one goal would always be solving the problem of keeping inventory on the shelves data analysts also ask a lot of questions this helps improve communication and saves time while working on a solution an example of that would be surveying customers about their experiences using a product and building insights from those questions to improve that product this leads us to the fourth quality of analytical thinking being able to identify a correlation between two or more pieces of data a correlation is like a relationship you can find all kinds of correlations in data maybe it's the relationship between the length of your hair and the amount of shampoo you need or maybe you notice a correlation between a rainier season leading to a high number of umbrellas being sold but as you start identifying correlations and data there's one thing you always want to keep in mind 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 we'll learn more about that later and now the final piece of the analytical thinking puzzle big picture thinking this means being able to see the big picture as well as the details a jigsaw puzzle is a great way to think about this big picture thinking is like looking at a complete puzzle you can enjoy the whole picture without getting stuck on every tiny piece that went into making it if you only focus on individual pieces you wouldn't be able to see past that which is why big picture thinking is so important it helps you zoom out and see possibilities and opportunities this leads to exciting new ideas or innovations on the flip side detail-oriented thinking is all about figuring out all of the aspects that will help you execute a plan in other words the pieces that make up your puzzle there are all kinds of problems in the business world that can benefit from employees who have both a big picture and a detail-oriented way of thinking most of us are naturally better at one or the other but you can always develop the skills to fit both pieces together and now that you know the five aspects of analytical thinking visualization strategy problem orientation correlation and big picture and detail-oriented thinking you can put them to work for you when you're working with data and as you continue through this course you'll learn how [Music] let's recap what we've learned about analytical thinking so far the five key aspects are visualization strategy problem orientation correlation and using big picture and detail-oriented thinking and we've seen how you already use them in your everyday life we also talked about how different people naturally use certain types of thinking but that you can absolutely grow and develop the skills that might not come as easily to you this means you can become a versatile thinker which is a very important part of data analysis you might naturally be an analytical thinker but you can learn to think creatively and critically and be great at all three the more ways you can think the easier it is to think outside the box and come up with fresh ideas but why is it important to think in different ways well because in data analysis solutions are almost never right in front of you you need to think critically to find out the right questions to ask but you also need to think creatively to get new and unexpected answers let's talk about some of the questions data analysts ask when they're on the hunt for a solution here's one that will come up a lot what is the root cause of a problem a root cause is the reason why a problem occurs if we can identify and get rid of a root cause we can prevent that problem from happening again a simple way to wrap your head around root causes is with the process called the five whys in the five wise you ask why five times to reveal the root cause the fifth and final answer should give you some useful and sometimes surprising insights here's an example of the five why's in action let's say you wanted to make a blueberry pie but couldn't find any blueberries you'd be trying to solve a problem by asking why can't i make a blueberry pie the answer would be there are no blueberries at the store there's why number one so you then ask why were there no blueberries at the store and discover that the blueberry bushes don't have enough fruit this season that's why number two next you'd ask why was there not enough fruit this would lead to the fact that birds were eating all the berries why number three asked and answered now we get to why number four ask why a fourth time and the answer would be that although the birds normally prefer mulberries and don't eat blueberries the mulberry bushes didn't produce fruit this season so the birds are eating blueberries instead and finally we get to why number five which should reveal the root cause a late frost damaged the mulberry bushes so they didn't produce any fruit so you can't make a blueberry pie because of a late frost months ago see how the five wives can reveal some very surprising root causes this is a great trick to know and it can be a very helpful process in data analysis another question commonly asked by data analysts is where are the gaps in our process for this many people will use something called gap analysis gap analysis lets you examine and evaluate how a process works currently in order to get where you want to be in the future businesses conduct gap analysis to do all kinds of things such as improve a product or become more efficient the general approach to gap analysis is understanding where you are now compared to where you want to be then you can identify the gaps that exist between a current and future state and determine how to bridge them a third question that data analysts ask a lot is what did we not consider before this is a great way to think about what information or procedure might be missing from a process so you can identify ways to make better decisions and strategies moving forward these are just a few examples of the kinds of questions data analysts use at their jobs every day as you begin your career i'm sure you'll think of a whole lot more the way data analysts think and ask questions plays a big part in how businesses make decisions that's why analytical thinking and understanding how to ask the right questions can have such a huge impact on the overall success of a business later we'll talk more about how data-driven decisions can lead to successful outcomes [Music] let's look at the concept of data-driven decision-making and why it's more likely to lead to successful outcomes you might remember that data-driven decision-making involves using facts to guide business strategy data analysts can tap into the power of data to do all kinds of amazing things with data they can gain valuable insights verify their theories or assumptions better understand opportunities and challenges support an objective help make a plan and much more in business data-driven decision-making can improve the results in a lot of different ways for example say a dairy farmer wants to start making and selling ice cream they could guess what flavors customers would like but there's a better way to get the information the farmer could survey people and ask them what flavors they prefer this gives the farmer the data they need to pick ice cream flavors people will enjoy here's another example let's say the president of an organization is curious about what perks employees value most she asked the human resources director who says people value casual dress code it's a gut feeling but the hr director backs it up with the fact that he sees a lot of people wearing jeans and t-shirts but what if this company were to use a more structured employee feedback process such as a survey it might reveal that employees actually enjoy free public transportation cards the most the human resources director just didn't realize that because he drives to work these are just some of the benefits of data-driven decision making it gives you greater confidence about your choice and your abilities to address business challenges it helps you become more proactive when an opportunity presents itself and it saves you time and effort when working towards a goal now let's learn more about how these five skills help you tap into all the potential of data-driven decision-making first think about curiosity and context the more you learn about the power of data the more curious you're likely to become you'll start to see patterns in relationships in everyday life whether you're reading the news watching a movie or going to an appointment across town the analysts take their thinking a step further by using context to make predictions research answers and eventually draw conclusions about what they've discovered this natural process is a great first step in becoming more data driven having a technical mindset comes next everyone has instincts or as in the case of our human resources director example gut feelings data analysts are no different they have gut feelings too but they've trained themselves to build on those feelings and use a more technical approach to explore them they do this by always seeking out the facts putting them to work through analysis and using the insights they gain to make informed decisions next we come to data design which has a strong connection to data-driven decision-making to put it simply designing your data so that is organized in a logical way makes it easy for data analysts to access understand and make the most of available information and it's important to keep in mind that data design doesn't just apply to databases this kind of thinking can work with all sorts of real life situations too the basic idea is this if you make decisions that are informed by data you are more likely to make more informed and effective decisions the final ability is data strategy which incorporates the people processes and tools used to solve a problem this is a big one to remember because data strategy gives you a high level view of the path you'll need to take to achieve your goals also data driven decision making isn't a one person job it's much more likely to be successful if everyone is on board and on the same page so it's important to make sure specific procedures are in place and that your technology being used is aligned with your data-driven strategy now you know how these five essential analytical skills work towards making better data-driven decisions so far many of the examples you've heard are hypothetical that means they could be true in theory but aren't specific real-world cases next we'll look at some real examples i can't wait to share how data analysts put data to work for amazing results [Music] i'm going to share some case studies that highlight the incredible work data analysts do each of these scenarios shows off the power of data-driven decision making in unexpected ways the first story is about google as i mentioned a little while back here at google our mission is to organize the world's information and make it universally accessible and useful all of our products from idea to development to launch are built on data and data-driven decision-making there are tons of examples here at google of people using facts to create business strategy but one of the most famous ones has to do with google's human resources so here's how it went the hr department wanted to know if there was value in having managers were their contributions worthwhile or should everyone just be an individual contributor to answer that question google's people analytics team look at past performance reviews and employee surveys the data they found was plotted on a graph because as you've learned visuals are extremely helpful when trying to understand a problem or concept the graph revealed that googlers had positive feelings about their managers but the data was pretty general and the team wanted to learn more so they dug deeper and split the data into quartiles a quartile divides data points into four equal parts or quarters here's where the really cool stuff started happening the data analysts discovered that there was a big difference between the very top and the very bottom quartiles as it turned out the teams with the best managers were significantly happier more productive and more likely to want to keep working at google this confirmed that managers were valued and make a big difference therefore the idea of having only individual contributors was not implemented but there was still more work to do just knowing that great managers create great results doesn't lead to actionable insights you have to identify what exactly makes a great manager so the team took two additional steps to collect more data first they launched an awards program where employees can nominate their favorite managers for every submission you had to provide examples or data about what makes that manager great the second step involved interviewing managers who are graft on the top and bottom quartiles this helped the analytics team see the differences between successful and less successful management behaviors the best behaviors were identified as were the most common reasons for a manager needing improvement the final step was sharing these insights and putting a procedure in place for evaluating managers with these qualities in mind this data-driven decision continues to create an exceptional company culture for my colleagues and me thanks data another interesting example comes from the nonprofit sector non-profits are organizations dedicated to advancing a social cause or advocating for a particular effort such as food security education or the arts in this case data analysts research how journalists can make a more meaningful impact for the non-profits they would write about because journalists write for newspapers magazines and other news outlets they can help non-profits reach readers like you and me who then take action to help non-profits reach their goals for instance say you read about the problem of climate change in an online magazine if the article is effective you learn more about the cause and might even be compelled to make greener choices in your day-to-day life volunteer for a non-profit or make a donation that's an example of the journalists work bringing about awareness understanding and engagement so back to the story the data analysts used the tracker to monitor story topics clicks web traffic comments shares and more then they evaluated the information to make recommendations for how the journalists could do their jobs even better in the end they came up with some great ideas for how non-profits and journalists can motivate people everywhere to work together and make the world a better place there's really no end to what you can do as a data analyst as you progress through this program you'll discover even more possibilities great job following along with the topics in these past few videos you learned all about analytical skills and the five key characteristics of data analysts you probably even learned that you're a pro at most of these already next you discovered what it means to think analytically and the specific skills data analysts develop to help them do it you explore tools and processes that enable data analysts to pinpoint a problem and ask the right questions in order to solve it finally some real world stories helped illustrate why data-driven decision making is usually more successful than other methods you're building a wonderful foundation for your career as a data analyst with every video your skills will continue to expand and your understanding of key data analytics concepts will only get stronger soon you'll have a chance to test out everything you've learned congratulations on finishing this video from the google data analytics certificate access the full experience including job search help and start to earn the official certificate by clicking the icon or the link in the description watch the next video 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Keywords: Grow with Google, Career Change, Tech jobs, Google Career Certificate, Google Career Certificates, Job skills, Coursera, Certification, Google, professional certificates, professional certificate program, Data analyst, Data analytics, Data analysis, Data analytics for beginners, What is data analytics, Sql, Data, R Programming, Spreadsheets, database, root cause, data-driven decision making, data analysis in excel, excel data analysis, skills

Id: CRVgnpda6Y8

Length: 26min 44sec (1604 seconds)

Published: Thu Mar 04 2021