Problem Solving with Data Analytics | Google Data Analytics Certificate

this vid 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] if you completed course one we met briefly at the beginning but for those of you who are just joining us my name is ximena and i'm a google finance data analyst i think it's really wonderful that you're here with me learning about the fascinating field of data analytics learning and education have always been very important to me when i was young my mom always said i can't leave you an inheritance but i can give you an education that opens doors that always pushed me to keep learning and that education gave me the confidence to apply for my job at google now i get to do really meaningful work every day just recently i worked as an analyst on a team called verily life sciences we were helping to get life-saving medical supplies to those who need it most to do this we forecasted what health care professionals would need on hand and then share that information with networks the information that my team provided helped make data-driven decisions that actually saved lives i'm thrilled to be your instructor for this course we're going to talk about the difference between effective and ineffective questions and learn how to ask great questions that lead to insights that can help you solve business problems you will discover that effective questions help you to make the most of all the data analysis faces you may remember that these faces include ask prepare process analyze share and act in the ask step we define the problem we're solving and make sure that we fully understand stakeholder expectations this will help keep you focused on the actual problem which leads to more successful outcomes so we'll begin this course by talking about problem solving and some of the common types of business problems that data analysts help solve and because this course focuses on the ask phase you'll learn how to craft effective questions to help you collect the right data to solve those problems next we'll talk about the many different types of data you'll learn how and when each is the most useful you'll also get a chance to explore spreadsheets further and discover how they can help make your data analysis even more effective and then we'll start learning about structure thinking structure thinking is the process of recognizing the current problem or situation organizing available information revealing gaps and opportunities and identifying the options in this process you address a very complex problem by breaking it down into smaller steps and then those steps lead you to a logical solution we'll work together to be sure you fully understand how to use structure thinking and data analysis finally we'll learn some proven strategies for communicating with others effectively i can't wait to share more about my passion for data analytics with you so let's get started in this video i'm going to share an interesting data analytics case study it will illustrate how problem solving relates to each phase of the data analysis process and shed some light on how these faces work in the real world it's about a small business that used data to solve a unique problem it was facing the business is called anywhere gaming repair it's a service provider that comes to you to fix your broken video game systems or accessories the owner wanted to expand his business by hiring more repair specialists to serve more customers he knew advertising as a proven way to get more customers but he wasn't sure where to start there are all kinds of different advertising strategies including print billboard tv commercials public transportation podcast and radio one of the key things to think about when choosing an advertising method is your target audience in other words the specific people you're trying to reach for example if a medical equipment manufacturer wanted to reach doctors placing an ad in a health magazine would be a smart choice or if a catering company wanted to find new cooks it might advertise using a poster at a bus stop near a cooking school both of these are great ways to get your ad seen by your target audience the second thing to think about is your budget and how much the different advertising methods will cost for instance a tv ad is likely to be more expensive than a radio ad a large billboard will probably cost more than a small poster on the back of a city bus the business owner asked the data analyst maria to make a recommendation she started with the first step in the data analysis process ask maria began by defining the problem that needed to be solved to do this she first had to zoom out and look at the whole situation in context that way she could be sure that she was focusing on the real problem and not just his symptoms this leads us to another important part of the problem solving process collaborating with stakeholders and understanding their needs for anywhere gaming repair stakeholders included the owner the vice president of communications and the director of marketing and finance working together marie and the stakeholders agreed on the problem not knowing their target audience's preferred type of advertising next up was the prepare phase where maria collected data for the upcoming analysis process but first she needed to better understand the company's target audience people with video game systems after that maria collected data on the different advertising methods this way she would be able to determine which was the most popular one with the company's target audience then she moved on to the process step here maria cleaned the data to eliminate any errors or inaccuracies that could get in the way of the result as we've learned when you clean data you transform it into a more useful format create more complete information and remove outliers then it was time to analyze in this step maria wanted to find out two things first who's most likely to own a video gaming system and second where are these people most likely to see an advertisement maria first discovered that people between the ages of 18 and 34 are most likely to make video game related purchases so she could confirm that anywhere gaming repairs target audience was people 18 to 34 years old this was who they should be trying to reach with this in mind maria then learned that both tv commercials and podcasts are very popular with people in the target audience because maria knew anywhere gaming repair had a limited budget and understanding the high cost of tv commercials her recommendation was to advertise and podcast because they are more cost effective now that she had her analysis it was time for maria to share her recommendation so the company could make a data-driven decision she summarized her results using clear and compelling visuals of the analysis this helped her stakeholders understand the solution to the original problem finally anywhere gaming repair took action they worked with a local podcast production agency to create a 30-second ad about their services the ad ran on podcasts for a month and it worked they saw an increase in customers after just the first week by the end of week 4 they had 85 new customers there you go effective problem solving using data analysis phases in action now you've seen how the six phases of data analysis can be applied to problem solving and how you can use that to solve real-world problems [Music] before i shared how data analysis helped a company figure out where to advertise its services an important part of this process was strong problem solving skills as a data analyst you'll find that problems are at the center of what you do every single day but that's a good thing think of problems as opportunities to put your skills to work and find creative and insightful solutions problems can be large or small simple or complex no problem is like another and they all require a slightly different approach but the first step is always the same understanding what kind of problem you're trying to solve and that's what we're going to talk about now data analysts work with six basic problem types making predictions categorizing things spotting something unusual identifying themes discovering connections and finding patterns think back to a real world example from the previous video in that example anywhere gaming repair wanted to figure out how to bring in new customers so the problem was how to determine the best advertising method for anywhere gaming repairs target audience to help solve this problem the company used data to envision what would happen if it advertised in different places now nobody can see the future but the data helped them make an informed decision about how things would likely work out so their problem type was making predictions now let's think about the second problem type categorizing things here's an example of a problem that involves categorization let's say a business wants to improve its customer satisfaction levels data analysts could review recorded calls to the company's customer service department and evaluate the satisfaction level of each caller they could identify certain keywords or phrases that come up during the phone calls and then assign them to categories such as politeness satisfaction dissatisfaction empathy and more categorizing these keywords gives us data that lets the company identify top performing customer service representatives and those who might need more training this leads to happier customers and higher customer service scores okay now let's talk about a problem that involves spotting something unusual some of you may have a smart watch my favorite app is for health tracking these apps can help people stay healthy by collecting data such as their heart rate sleep patterns exercise routine and much more there are many stories out there about health apps actually saving people's lives one is about a woman who was young athletic and had no previous medical problems one night she heard a beep on her smart watch a notification said her heart rate had spiked now in this example think of the watch as a data analyst the watch was collecting and analyzing health data so when her resting heartbeat rate was suddenly 120 beats per minute the watch spotted something unusual because according to its data the rate was normally around 70. thanks to the data her smart watch gave her the woman went to the hospital and discovered she had a condition which could have led to life-threatening complications if she hadn't gotten medical help now let's move on to the next type of problem identifying themes we see a lot of examples of this in the user experience field user experience designers study and work to improve the interactions people have with products they use every day like apps websites and even coffee makers let's say a user experience designer wants to see what customers think about the coffee maker his company manufactures this business collects anonymous survey data from users which can be used to answer this question but first to make sense of it all he will need to find themes that represent the most valuable data especially information he can use to make the user experience even better so the problem the user experience designer's company faces is how to improve the user experience for its coffee makers the process here is kind of like finding categories for the keywords and phrases in customer service conversations but identifying themes goes even further by grouping each insight into a broader theme then the designer can pinpoint the themes that are most common in this case he learned users often couldn't tell if the coffee maker was on or off he ended up optimizing the design with improved placement and lighting for the on off button leading to product improvement and happy users now we come to the problem of discovering connections this example is from the transportation industry and uses something called third-party logistics third-party logistics partners help businesses ship products when they don't have their own trucks planes or ships a common problem these partners face is figuring out how to reduce wait time wait time happens when a truck driver from a third party logistics provider arrives to pick up shipment but it's not ready so she has to wait that costs both company time and money and it stops the trucks from getting back on the road to make more deliveries so how can they solve this well by sharing data the partner companies can view each other's timelines and see what's causing shipments to run late then they can figure out how to avoid those problems in the future so a problem for one business doesn't cause a negative impact for the other for example if shipments are running late because one company only delivers monday wednesdays and fridays and the other company only delivers tuesdays and thursdays then the companies can adjust to do deliveries on the same day to reduce waiting time for customers all right we've come to our final problem type finding patterns oil and gas companies are constantly working to keep their machines running properly so the problem is how to stop machines from breaking down one way data analysts can do this is by looking at patterns in the company's historical data for example they could investigate how and when a particular machine broke down in the past and then generate insights into what led to the breakage in this case the company saw a pattern indicating that machines began breaking down at faster rates when maintenance wasn't kept up in 15-day cycles they can then keep track of current conditions and intervene if any of these issues happen again you've now learned the six basic problem type data analysts typically face as a future data analyst this is going to be valuable knowledge for your career [Music] now that we've talked about six basic problem types it's time to start solving them to do that data analysts start by asking the right questions in this video we're going to learn how to ask effective questions that lead to key insights you can use to solve all kinds of problems as a data analyst i ask questions constantly it's a huge part of the job if someone requests that i work on a project i ask questions to make sure we're on the same page about the plan and the goals and when i do get a result i question it is the data showing me something superficially is there a conflict somewhere that needs to be resolved the more questions you ask the more you'll learn about your data and the more powerful your insights will be at the end of the day some questions are more effective than others let's say you're having lunch with a friend and they say these are the best sandwiches ever aren't they well that question doesn't really give you the opportunity to share your own opinion especially if you happen to disagree and didn't enjoy the sandwich very much this is called a leading question because it's leading you to answer in a certain way or maybe you're working on a project and you decide to interview a family member say you ask your uncle did you enjoy growing up in malaysia he may reply yes but you haven't learned much about his experiences there your question was closed ended that means it can be answered with a yes or no these kinds of questions rarely lead to valuable insights now what if someone asks you do you prefer chocolate or vanilla well what are they specifically talking about ice cream pudding coffee flavoring or something else what if you like chocolate ice cream but vanilla in your coffee what if you don't like either flavor that's the problem with this question it's too vague and lacks context knowing the difference between effective and ineffective questions is essential for your future career as a data analyst after all the data analyst process starts with the ask phase so it's important that we ask the right questions effective questions follow the smart methodology that means they're specific measurable action-oriented relevant and time-bound let's break that down specific questions are simple significant and focused on a single topic or a few closely related ideas this helps us collect information that's relevant to what we're investigating if a question is too general try to narrow it down by focusing on just one element for example instead of asking a closed-ended question like are kids getting enough physical activities these days ask what percentage of kids achieve the recommended 60 minutes of physical activity at least five days a week that question is much more specific and can give you more useful information now let's talk about measurable questions measurable questions can be quantified and assessed an example of an unmeasurable question would be why did a recent video go viral instead you could ask how many times was our video shared on social channels the first week it was posted that question is measurable because it lets us count the shares and arrive at a concrete number okay now we've come to action-oriented questions action-oriented questions encourage change you might remember that problem-solving is about seeing the current state and figuring out how to transform it into the ideal future state well action-oriented questions help you get there so rather than asking how can we get customers to recycle our product packaging you could ask what design features will make our packaging easier to recycle this brings you answers you can act on all right let's move on to relevant questions relevant questions matter are important and have significance to the problem you're trying to solve let's say you're working on a problem related to a threatened species of frog and you asked why does it matter that pine barrens tree frog started disappearing this is an irrelevant question because the answer won't help us find a way to prevent these frogs from going extinct a more relevant question would be what environmental factors changed in durham north carolina between 1983 and 2004 that could cause pine bearing street frogs to disappear from the sand hills region this question would give us answers we can use to help solve our problem that's also a great example for our final point time bound questions time-bound questions specify the time to be studied the time period we want to study is 1983-2004 this limits the range of possibilities and enables the data analysts to focus on relevant data okay now that you have a general understanding of smart questions there's something else that's very important to keep in mind when crafting questions fairness we've touched on fairness before but as a quick reminder fairness means ensuring that your questions don't create or reinforce bias to talk about this let's go back to our sandwich example there we had an unfair question because it was phrased to lead you toward a certain answer this made it difficult to answer honestly if you disagreed about the sandwich quality another common example of an unfair question is one that makes assumptions for instance let's say a satisfaction survey is given to people who visit a science museum if the survey asks what do you love most about our exhibits this assumes that the customer loves the exhibits which may or may not be true fairness also means crafting questions that make sense to everyone it's important for questions to be clear and have a straightforward wording that anyone can easily understand unfair questions also can make your job as a data analyst more difficult they lead to unreliable feedback and missed opportunities to gain some truly valuable insights you've learned a lot about how to craft effective questions like how to use the smart framework while creating your questions and how to ensure that your questions are fair and objective moving forward you'll explore different types of data and learn how each is used to guide business decisions you'll also learn more about visualizations and how metrics or measures can help create success it's going to be great 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 in the course by clicking here and subscribe to our channel for more from upcoming google career certificates

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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, metrics, data visualization best practices, data driven marketing, data analyst day in the life, data analyst tutorial

Id: wSct_cbqzpM

Length: 23min 27sec (1407 seconds)

Published: Wed Mar 31 2021