# Google Analytics - 1 Foundations- Module 1 Introducing [[data analytics]] and [[Analytical thinking]], [[Data-driven decision-making]] and the most important ==[[data analysis process]]== ## Overview >[! quote] Data helps us make decisions in both everyday life and in business. In this part of the course, you’ll learn how data analysts use a variety of tools and skills to inform those decisions. You’ll also get to know more about this course and the overall program expectations. ## Module Learning Objectives >[! info] Module 1 Learning Objectives > - [x] Define key concepts involved in data analytics including data, data analysis, and data ecosystem > - [x] Discuss the use of data in everyday life decisions > - [x] Identify the key features of the learning environment and their uses > - [x] Describe principles and practices that will help to increase one's chances of success in this certificate > - [x] Explain the use of data in organizational decision-making > - [x] Describe the key concepts to be discussed in the program, including learning outcomes ## Glossary of terms in Module 1 Each term in the course will have it's own evergreen note. Below is a list of terms from this module [[Analytical Skillset]] [[Analytical thinking]] [[context]] [[data]] [[Data Analysis]] [[Data analyst]] [[data analytics]] [[Data Design]] [[Data-driven decision-making]] [[data ecosystem]] [[Data Science]] [[Data Strategy]] [[data visualizations]] [[dataset]] [[Gap analysis]] [[root cause]] [[technical mindset]] [[data visualizations|visualization]] --- ## Get Started ### Welcome Video >[! cue] def [[data]] >[! info]- Data > **Data** is a collection of facts. Datum is a single fact/information. >[!cue] def [[Data analysis]] >[! info]- Data Analysis >The collections, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. >[! cue ] Data is ubiquitous Data is everywhere. - we use data everyday, like reviewing product reviews before purchasing. or using a step count on a fitness tracker. Don't just _use data_ we also **_create_** data with nearly every single interaction. Data is therefore ==consumed and created== at greater and greater speed and volume everyday. Example: - Google 40k searches per second! 3.5B per day >[!cue] [[Data Analyst]] >[! info]- Data Analyst >Someone who collects, transforms, and organizes data in order to draw conclusions, make predictions, and drive informed decision-making. > >This is the person whose role is to **do** the [[Data Analysis|data analysis]]. #### Google's data analysis process/framework. >[! warning] IMPORTANT >This process is THE framework from which Google will teach analytics. It will come up again and again and again in this module. It is iterative meaning each step is the input to the next step. >[! cue] [[data analysis process]]: 6 steps APP-ASA >[!info]- The data analysis process >- Ask >- Prepare >- Process >- Analyze >- Share >- Act ### Certificate syllabus. The course and certificate syllabus outlined the content for the rest of the Foundations course. Also helped to set expectations on what is required in the course and what to do if deadlines are missed. ### Google Analytics Certificate Roadmap (interactive deck) - The certificate course sequence represented as sections of the [[data analysis process]] presented earlier bookended with the foundational course (this course) and the capstone at the end of the sequence. 1. Foundations 2. Ask 3. Prepare 4. Process 5. Analyze 6. Share 7. Act 8. Capstone Review each option uncovers what you will learn and skills that I will be building. I rated each topic and skill that I noted out of from 0 to 5. #### Self-Rating! >[! tip] self-rating system >0 = no experience / never heard of it. >1 = Know of but not used. / aware with no experience. >2 = Have used but not a lot / aware but not proficient. >3 = Have used at a proficient basic level >4 = Use a lot and can teach up to intermediate level >5 = I can teach others at any level basic to PhD #### Foundations Learning topics of note; - spreadsheet basics: rating = 4 - database and query basics: rating = 2 - data viz basics: rating = 3 Skills of note - Thinking analytically: rating = 5 - applying tools from a toolkit: rating = 3 - ensuring data analysis is fair: rating = 2 #### Ask learning topics of note; - spreadsheet formulas and functions: rating = 4 - dashboard basics, including an intro to Tableau: rating = 1 - Data reporting basics: rating =3 skills of note; - SMART and effective questions: rating = 0 - structuring how one thinks: self-rated at a 5 without truly knowing what this skill is. - summarizing data: rating = 3 - putting things into context. rating = 3 needs practice. which is when I thought I should contextualize and give self-ratings. ;) - managing team and stakeholder expectations. rating =2 needs a lot of practice - problem solving and conflict resolution. rating = 1 this might sound harsh but I am open to learning. #### Prepare Learning topics of note; - features of different data types, fields, values; rating - weak 3 - database structures rating 2 - function of metadata in data analytics. rating 4 but with reservations - SQL functions; self-rating = 3 Skills of note; - ethical data analysis practices! rating = 3 WANT MORE OF THIS - addressing issues of bias and credibility. rating = 2 - accessing databases and importing data rating = 2 (wish this was higher, however if I knew _everything_ prior then I wouldn't need this course. ) - writing simple queries; rating = 2 with similar reservations - Organizing and protecting data; self rating = 3 - Connecting with the data community.... hence this blog/notes. rating = 4 #### Process Learning topics of note - importance of cleaning data. self-rating = 3 - data cleaning verification rating = 1. no practical experience with these. I think - statistics, hypothesis testing, and margin of error... self rating = 1 for sure a weakness in my background. That made materials in data science so hard to get through. Skills of note - connecting business objectives to analytics projects slef rating = 1 - identifying clean and dirty data self rating = 3 - cleaning small datasets using spreadsheet tools. self rating = 4 (survival techniques) - cleaning large datasets by writing SQL queries. self rating = 1 - Documenting data-cleaning processes self rating = 2 #### Analyze Learning topics of note - data analyst process to organize data. self-rating = 1 (i have not used a formal process before) - combining data from more than one source. self rating = 3... weak 3. can do in python but will take time to engineer a solution without relying on a bot. - Spreadsheet calculations and pivot tables. self-rating = 1 (I can't define what a pivot table is and how it does or at least not clearly. ) - SQL calculations (like aggregate functions?) self-rating= 2 - Temporary tables. self rating = 1 - Data validation. self rating = 0 Skills of note - sorting data in spreadsheets using SQL ... self-rating = 0 TIL that this is possible. - Filtering data in spreadsheets using SQL ... self-rating = 0 same as above - converting data. self-rating = 2 (like string to number class) can do in python. - Formatting data. self rating = 2 same as above. - Substantiating data analysis processes self rating = 0 - seeking feedback from others self rating = 3 I can work on this skill #### Share learning topics of note - Design thinking. Self-rating = 1 - benefits of tableau, self-rating = 0 - data-driven story telling, self-rating = 0 - dashboards and dashboard filters, self-rating = 0 - Creating an effective data presentation, self rating = 1 Skills of note - Using Tableau to create visualizations and dashboards self rating = 1 - Addressing accessibility concerns in presenations self-rating = 2 - Understanding the purpose of different business comm tools. self-rating = 3 - telling a data-driven story self-rating = 1 - presenting to others about data, self-rating = 4 (comforatble from the bootcamp but could use work) - Answering questions about data, self-rating = 2 (I get uncomfortable with this) #### Act Learning topics of note - R, R packages, R functions etc, R - Everything. Self rating = 1 - R markdown slef-rating Markdown component = 4 Skills of note - coding in R, self-rating = 1 - reporting on data analysis to stakeholders. self-rating = 2 #### Capstone Learning topics of note - the value of the "portfolio" self-rating = 3... I have one but did not maintain it for more than two years. I let skills atrophy. - practical problem-solving. self-rating = 3 - Strategies for extracting insights from data self-rating = 2 - Clear presentation of data findings, self-rating = 2 - Motivation and ability to take initiative, self-rating = 4 Skills of note - sharing your work during an interview self-rating 2. - Communicating your unique value proposition to a potential employer. self-rating=3 ### Video Introduction to the course (4:59) >[!cue] [[data analysis process]] Again re-iterating the [[data analysis process]] APP-ASA Ask, Prep, Process, Analyze, Share, Act ### Reading: Helpful resources and tips #### Requirement for the certificate is to 1. Pass all graded assignments in all eight courses of the cert program. 2. Pay a fee. [See the help article on Fees](https://www.coursera.support/s/article/209818963-Payments-on-Coursera?language=en_US) #### Evaluation of current analytics skills. I rated myself earlier and am interested in building a solid foundation. Though, an evaluation can be accessed [here](https://www.google.com/url?sa=j&url=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffoundations-data%2Fsupplement%2FeLxYk%2Fevaluate-your-current-data-analytics-skills&uct=1694010451&usg=YdNP7B0ts9woYPYl1upCB_Ti06k.&opi=73833047&source=chat) #### Best practices to complete this program. - set aside time. - work at own pace. no rushing, hacking or shortcuts. truly experience the learning process. - be curious - Follow along ... - on-screen coding demos have a ==step-by-step guide==. the guide is a reading. the guide can have additional information and details. - Take notes. .... LOL ok! - Review exemplars. - exemplars are completed assignments. - use them to compare your work to. - possibly use if stuck, or to be inspired in a different approach. ### Career Support > [!cue] OUTCOMES! - ==Build a Career Identity== - Struggling with this one. I don't want to be a generalist anymore. - [[_Career Identity]] means the unique value you bring to the workforce. - [Coursera Career Identity video](https://www.youtube.com/watch?v=_xbT4qMrot4) 10 minutes - **Strengths**: What skills, knowledge, and talents set me apart? - Assessments can help to explore your strengths. - Interview colleagues and friends. - **Motivations**: What fuels and motivates me most? - **Values**: What values guide me? - identity statements are living statements. >[! tip]- Career Identity Statement Template 8:04 >I am a `<role(s)>` with `<# of years>` of experience doing `<accomplishment>`. My greatest strength is `<strength>`, and I have a talent for `<strength>`. I am passionate about `<motivation>`, and I value `<value>`. > - Join a study group. - groups held M-TH - [Register here](https://lu.ma/GoogleCareerCertificatesStudyGroup?utm_source=course) - Connect with others in the community. - Update your Coursera profile. ### Module, course and certificate glossaries. Glossary for the module are at the end of each module Glossary for the course are at the end of each course Certificate glossary includes all terminology used throughout the entire certificate course sequence. >[!note] I want to build out a linked glossary in this knowledge base for my own reference. These terms will grow over time and should be the basis of the evergreen notes. ### Reading: Evaluate your current data analytics skills. This reading urges people who already have experience in analytics to take the more advanced courses. - Google Advanced Data Analytics Certificate - Google Business Intelligence Certificate I didn't know either of those existed and given my self-ratings on a prior exercise, I will continue with the basic course. I would like to still note these evaluation questions because I should be able to answer them in the future. #### Data Analyst Process Questionnaire >[!quote]- Process questionnaire from course material verbatim >1. "I have a thorough understanding of data-driven decision-making and how it helps organizations guide their business strategy based on facts." >2. "I am able to ask questions and make hypotheses about business problems and use them to guide me through" >3. "I know the steps to verify data credibility and perform data validation." >4. "I understand data modeling and know how organizations use it as a tool to understand their data." >5. "I can select and design visualizations that help me effectively communicate analysis insights to stakeholders." #### Data Analyst Technical Skill Questionnaire >[! quote]- Data Analyst Technical Skill Questionnaire Verbatim >1. "I’m able to join data from multiple sources to use for data analysis." >2. "I can sort data in both a spreadsheet and a database." >3. "I’m able to clean data by ensuring it contains no duplicate or incorrect entries and is in the correct format." >4. "I know how to create data visualizations using a spreadsheet, Tableau, and R." >5. "I can write a SQL command that would select several columns from a table." >6. "I understand packages in R and can select and install the packages I need to complete specific tasks." So I cannot answer yes to all of the question. I can't even _sorta_ answer them. So, I am proceeding as a humble n00b. The information on the advanced stuff sounds interesting in that it involves Python and data science topics. ### Reading: Sharing in Discussion Forums Connecting with classmates is essential in any online class. Part "hi my name is" and part networking participation in the discussions is key for the google course. Also update your coursera profile. Upvote posts where it is helpful to do so. No Bloat though. Report abuse == there is a code of conduct here so general decorum is a must. You can even follow threads and get updates as the discussion grows with new posts. ### Discussion Prompt: In this activity I answered some ice breaker type questions. After entering responses you are encouraged to interact with fellow students. There are a lot of them, but I took some time to read some of the responses. - students are from all over the world. Is this certificate conducted in other languages? That is AMAZING. Super impressed by other's command of English and i am feeling like I need to broaden and learn some more languages and visit a few more places. ### Interactive Deck: Commit to completing the program. This was interesting in asking for a written commitment. I have noticed though, that I tend to not finish online courses. Is it because I am old and need a classroom? Is it because I am neurodivergent and need all distractions removed? Is it a discipline thing? Anyway completing the commitment allowed me to affirm myself and my motivations. :) ### Reading: Program surveys. - survey participation is optional and not a graded component of the curriculum. - I will probably complete them though because why not? I may in the future need to conduct surveys so why not get some karmic credit in the account? There are four surveys - the entry survey. - the course survey - the exit survey (course completion survey) Details are not important for the purpose of this notebook. ### Interactive Deck: Entry Survey Notes not needed <hr> ## Transform Data into Insights ### Video: Data analytics in everyday life (4:07) Identifying patterns and the relationships that form them; that is data analysis. In the business setting. Business need a way to control all that new data to - improve on existing processes. - identify new opportunities - Launch new products Why they hire data analysts make sense of the data draw conclusions or make predictions. Turning data into insights. >[! cue] Def: [[Data Analysis]] (2:56) >[! info]- Data Analysis >The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. Analytics can help shape business success by having the insights lead to action ### Reading: New data perspectives >[!cue] APP-ASA: Ask, prep, process, analyze, share, act Another dose of the [[data analysis process]] framework. This time in a case-study from a team of analysts involved in _people analytics_ [[People Analytics]] is a type of analysis conducted by HR on the people that make up a company. The intent is to understand how to improve how the company operates. #### Ask >[! cue] Def: [[data analysis process - Ask|Ask]] The key here is ask **effective** questions and to identify key [[stakeholder]] who are interested in the outcome. This process informs the goal of this stage of the process: _what the project actually looks like and what would success look like._ In the case study the team asked questions of the stakeholders like: - What is the most important thing new hires need to know... - by what percentage would you like employee retention to increase next year... - what can explain why some managers have higher retention rates - do you have any relevant data? By looking at the questions asked it seems that a key metric they are going to be looking at is retention rate. some of the questions seem to also suggest that they have a hypothesis in mind that dissatisfaction is causal or at least correlated with lower retention. #### Prepare >[! cue] Def: [[data analysis process - Prepare|Prepare]] This phase requires the analysts to plan and communicate several key subprocesses. - What the agreed timeline is for the project and ensure stakeholder alignment. - Identify data needs and requirements to access that data. - what data is needed - where is the data - get access to the data by asking/getting permission to access. - document data management process for the project. - document and mitigate any privacy concerns (name, gender, income... anything that is protected) - The analysts decide before **what is going to be measured** , how the measurements will be done, and how will the results be communicated. In the case study, the analysts created a survey for new employees. Developing specific questions to include. They also finalized what will be measured, how the findings will be presented, and what possible pitfalls might occur and how to avoid them. #### Process >[!cue] Def: [[data analysis process - Process|Process]] is more than just cleaning. To this point, I thought that the process step had something to do with data cleaning. According to the reading, the process step is the literal procedure that the data analysts will follow in the execution of the analysis. This involves [[Data Ethics]], [[data cleaning]] This INCLUDES data processing/cleaning so the data is fit for the project at hand. Also includes using data ethically have transparent processes that define how data is ==collected, stored, managed, and protected== This step involved analysts putting in place procedure/process that - limited access to data - data cleaning / sanitization standards to protect certain data #### Analyze >[!cue] Def: [[data analysis process - Analyze|Analyze]] This is literally the shortest section in the case study. The analysts .... _analyzed_ their processed data and found a few key findings. At this stage critical documentation was captured about the results. Again, this is a matter of data ethics to have a well documented analysis despite the results against any existing hypothesis. This form of objectivity makes the results trustworthy and credible. #### Share >[!cue] Def: [[data analysis process - Share|Share]] Results were shared in the way agreed upon in the preparation step. In the case study the results were shared carefully and only with managers who were asked to deliver the results to their teams. This way of sharing the insights from the analysis allowed managers to put the results in appropriate context that would be beyond what the analyst would know/do. #### Act >[!cue] Def: [[data analysis process - Act|Act]] This stage is to implement changes/recommendations. In the case study, the analysts backed up their initial findings by implementing an anual survey. Between year one and two they learned that their recommendations were successful in the metric they were trying to improve. Yay. ### Reading: How data analysts approach tasks If you guessed: _is it the 6-step process that has been repeated over and over and over?_ > My apologies. It is late at night here and I am a bit punchy. >[! cue] [[data analysis process]] IS FOUNDATIONAL. APP-ASA: Ask, Prep, Process, Analyze, Share, Act! The purpose of each of the steps is to gain insights from data being analyzed. The process helps analysts break down to a series of smaller tasks. #### [[data analysis process - Ask|Ask]] >[!cue] Def: [[data analysis process - Ask|Ask]] >[! info] Ask >Understand the problem to be solved or the question to be answered. To do so, an analyst must ask effective questions of the stakeholders. #### [[data analysis process - Prepare|Prepare]] >[!cue] Def: [[data analysis process - Prepare|Prepare]] >[! info] Prepare >Find and collect the data needed to answer the questions. Knowing >- data sources >- gather data >- validate / verify accuracy >- usability: it the data you are getting useful in answering your questions? #### [[data analysis process - Process|Process]] >[!cue] Def: [[data analysis process - Process|Process]] >[! info] Process > The funzone! > [[data cleaning]] and organization occur here. > Key is to ensure the data is ready to be analyzed. Deal with missing values, find and deal with inconsistencies, migrating formats...etc. I know I keep changing the idea here... it is because I am streaming thoughts to my note and interrogating myself when information changes... hence this is not wikipedia but more of an experiment in [[learning in public]] . This means things are messy, misspelled etc. #### [[data analysis process - Analyze|Analyze]] >[!cue] Def: [[data analysis process - Analyze|Analyze]] >[! info] Analyze >Doing the necessary analysis to get answers to the questions. Even if the answer is that we fail to answer the question. #### [[data analysis process - Share|Share]] >[!cue] Def: [[data analysis process - Share|Share]] >[! info] Share > Where you present your findings to decision makers. > Sharing can be done with reports, presentations, or just visualizations. > > Tools: google sheets / spreadsheet, [[Tableau]], [[R Programming]] #### [[data analysis process - Act|Act]] >[!cue] [[data analysis process - Act|Act]] >[! info] Act >Putting the data insights int **act**ion. The ==process is iterative==, with each phase results being the input to the next phase. ### Video: Cassie: Dimensions >[!cue] Perspectives on Data Roles w/a Googler Decision intelligence for google cloud help google cloud customers turn their data into insights. Myth someone that works in data should know the everything of data. Which type of impact one would like to make should dictate what specialization they enter into. >[!quote] [[Data Science]], the discipline of making data useful, is an umbrella term that encompasses three disciplines. >1. Machine Learning >2. Statistics >3. Analytics If you want to make important decisions under uncertainty, then statistics. - excellence: Rigor Make many decisions under uncertainty then ML/AI - excellence: Performance Looking for the unknown unknowns, that is analytics. - excellence: speed. How quickly can you work your data. are you excited of ambiguity. See how quickly you can unwrap this gift and see if there is anything fun. Don't worry about if there is anything in there you like... Cool video. However, >[! question] How has GenAI disrupted the "excellence" for analytics? GenAI tools have been shown to take ambiguous data and run analysis on it and even provide insights. <hr> ## Understand the Data Ecosystem ### Reading: Origins of the [[data analysis process]] - data analysis is from statistics. - archeo-statistics? Goes back to ancient Egypt. - earliest examples of spreadsheets ==WHAT?!== #til - [Connect Report post: _The first spreadsheets existed 4,600 years ago_](https://connectreport.com/blog/the-first-spreadsheets/) >[! cue] data analysis process:moving from data to decision >[! tip] [[data analysis process]] >six steps >1. [[data analysis process - Ask|Ask]] business challenge, objective, or question >2. [[data analysis process - Prepare|Prepare]]: [[data generation]], [[Data Collection]], [[data storage]], and [[data management]] >3. [[data analysis process - Process|Process]]: [[data cleaning]] [[Data Integrity]] >4. [[data analysis process - Analyze|Analyze]]: [[exploratory data analysis]], [[data visualizations]], [[analysis]] >5. [[data analysis process - Share|Share]]: communicating results >6. [[data analysis process - Act|Act]]: putting the insights to work. >[! cue] [[EMC]]'s process is cyclical Example variation; EMC 1. Discovery 2. Pre-processing data 3. Model Planning 4. Model Building 5. Communicate Results 6. Operationalize. Some differences however core principles are the same. >[!cue] [[SAS]] process iterative SAS variant. This variant has an extra step (or it connects the end of the process with the beginning) 1. Ask 2. Prepare 3. Explore 4. Model 5. Implement 6. Act 7. Evaluate >[! cue] Project-based variant >[Understanding the data analytics project life cycle](http://pingax.com/Data%20Analyst/understanding-data-analytics-project-life-cycle/) The project-based variation omits the _Act_ step. 1. Identify the problem 2. Designing data requirements 3. pre-processing data 4. performing data analysis 5. visualizing data >[!cue] Big data analytics process >[Big data adoption and planning considerations](https://www.informit.com/articles/article.aspx?p=2473128&seqNum=11&ranMID=24808) This is a _bigger_ list of 9 items 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 ### Video: What is the [[data ecosystem]] (4:00) Ecosystems in general a group of elements that interact with one another. >[!cue] Def [[data ecosystem]] >[! info] [[data ecosystem]] >The various elements that interact with on another in order to produce, manage, store, organize, analyze, and share data. - includes hardware and software tools and the people that use them. >[! cue] Def [[cloud]] >[! info] The [[cloud]] >A place to keep data online, remotely, rather than a local computer hard drive. As an analyst job to harness the power of the ecosystem. Data ecosystems are not just for office settings, also used in agriculture. #### Misconceptions. >[!cue] [[Data Science]] <> [[Data Analysis]] The most common misconception is that Data science t the same as Data Analyst >[! cue] Def: [[Data Science]] >[! info] [[Data Science]] >Creating new ways of modeling and understanding the unknown by using raw data. >- Data scientists generate new questions using data. >- Data analysts answers to existing questions creating insights from existing data. >[! cue] Def [[Data Analysis]] (3:55) <> [[data analytics]] (4:04) >[! info] [[Data Analysis]] >The collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. >[! info] [[data analytics]] The science of data ### Video: How data informs better choices (4:00) >[!cue] Def [[Data-driven decision-making]] (0:32) >[! info] [[Data-driven decision-making]] >Using Facts to guide business strategy The key question is finding _What is the business need?_ - usually is a problem that needs to be solved. Once established, an analyst or team can dive into existing data to begin the analytics process and get really really ridiculously specific about their research question then get after the data and analyze it Data alone is not enough. >[! cue] Def [[Subject Matter Experts]] >[! info] Subject matter experts (SME) >Ability to review the outcome of analysis, validate choices, make sense of any gray areas, add needed context to problems, etc. To get the most out of data driven decision making, important to include insights from people familiar with the business problem. These are SME's Utilizing SME, and Data analysis empowers the organization to make effective and powerful changes. ### Reading: Data and gut instinct Going with gut instinct without data to back it up can lead to mistakes or an undesired outcome. Being data-driven is key. sometimes an analyst does not have the time to know all the data and thus needs to make a call on the reliance of business experience or gut instinct. >[! cue] Useful question: > How do I define success for this project? >[! cue] Questions to help guide the analyst These questions can help guide the analyst to find the perfect balance of data-driven and business acumen. > - 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? ### Practice Assignment: Test knowledge of [[data ecosystem]] Learning objectives: - [x] Define key concepts involved in data analytics including [[data]], [[Data Analysis]], and [[data ecosystem]]. - [x] Discuss the use of [[data]] in everyday life decisions - [x] Explain the concept of data-driven decision-making including specific examples. #### 1. Multiple choice: Which discipline does the scenario best fall ? The intent of this question is to evaluate your understanding of the concepts [[data analytics]], [[Data Science]], [[Data Analysis]], and [[Data wrangling]]. You are trying to select the correct discipline of these choices given the scenario in the question stem. #### 2. Fill in the blank. Terminology test This question is testing knowledge about [[data ecosystem]], what it is and what it is not given the definition in the question stem. #### 3. Multiple selection: concept test This "select all that apply" question is testing knowledge of [[Subject Matter Experts]] and their role in [[Data-driven decision-making]]. #### 4. Example This question is testing understanding of basic concept of [[Data-driven decision-making]] <hr> ## Embrace Your Data Analyst Skills ### Video: Key data analyst skills (6:00) >[!cue] DEF [[Analytical Skillset]] (:30) >[!info] Analytical Skillset >Qualities and characteristics associated with solving problems using facts. >5 Dimensions >1. Curiosity >2. Understanding [[context]] >3. Having a [[technical mindset]] >4. [[Data Design]] >5. [[Data Strategy]] ### Reading: using data analytics skills in a business scenario Using a case study to apply the [[Analytical Skillset]] The reading specifies the objectives for conducting the EDA for the client. The reading also specifies a dataset with 8 fields. #### Curiosity What kinds of questions would I ask based on the data and how it relates to the objectives of the EDA? - By being curious can come up with questions that can be answered. Curiosity drives analysts to discover just how much info they can get out of data in expected or unexpected ways. #### Understanding Context - by contextualizing begin to understand why the data does what it does. - some factors that help contextualize - time of year - budget - genre of film - seasonality #### Technical Mindset approach problems, datasets, life in a systematic and logical manner. the skill is to break things down into processes and scope. #### Data Design The data design skill extends your technical mindset. This involves how you structure the data. For example sorting spreadsheet of box office results by revenue to better see if any relationships exist between revenue and another field like genre or production budget. Having good skill at organizing a dataset will make discovering insights easier. #### Data Strategy management of people processes or tools... so in the case study the analyst would need to decide on which tool to utilize and how it fits in the requirements that the stakeholders provided. Example given was if stakeholders wanted some simple dashboard, that could be built in Excel. On the other hand if they want a dashboard that updates every time new data is available, then they want a more robust tool like Tableau. >[! attention] Important >>[! quote] The data strategy you select should be based on the dataset and the deliverables. ### Interactive Deck: Practice data analyst skills This was a flash card simulator with the five dimensions of [[Analytical Skillset]] ### Practice Quiz >[! important] This quiz is graded. Only four questions on this quiz. - must know the 5 dimensions of the [[Analytical Skillset]] <hr> ## Analytical Thinking for Effective Outcomes ### Video: _All about thinking analytically_ (5:00) >[!cue] Thinking about thinking. (0:29) This video introduces how Google thinks about _thinking_. There are 5 aspects to thinking analytically >[! info] [[Analytical thinking]] >The process of identifying and defining a problem, then solving it by using data in an organized step-by-step manner. > >There are 5 aspects to thinking ... >1. with [[data visualizations]] >2. Strategy / strategizing >3. about solving the problem / Problem Orientation >4. how things can relate to each other / Correlation >5. in Big-picture and also in detail-oriented perspectives ### Video: _Explore core analytical skills_ (4:00) Thinking in different and creative ways is a skill that can be practiced. It is critical to practice throughout the practice of analytics. The easier it is to think differently, the easier it may be to arrive at crucial insights. Some example questions and solutions for your toolbox. >[!cue] Root Cause Analysis >[! question] What is the [[root cause]] of a problem? >A method of conducting a root cause analysis is asking _why?_ five times. >> problem is that X. >> > why is X? , answer 1 >> > > Why is answer 1?, answer 2 >> > > > why is answer 2?, answer 3 >> > > > >why is answer 3?, answer 4 >> > > > >>why is answer 4?, possible root cause answer. >In this method, each answer/or insight is input to the next layer. when you are at the fifth layer typically that is is the cause ... maybe.. not so sure. >[!cue] [[Gap analysis]] >[! question] What are the gaps in our process? >Gap analysis is conducted by looking at the current state and comparing it to a desired future state and identify the gaps that are needed to be overcome in order to achieve the future state. >[! cue] Procedural analysis >[! question] What did we not consider before? >What information or procedure might be missing from a process. ### Reading: _Use the five whys for root cause analysis_ The reading presented two case studies showing how [[root cause]] analysis was done. a grocery store - Problem: increase in complaints on grocery deliveries. - Why? - Answer: Common themes in customer correspondence, " arrived damaged" - Why? - Answer: Not packed properly - Why? - Answer: Packers were not adequately trained on packing. - Why? - Answer: 35% are new to the company and have not completed training. - Why? - Answer: The training was not provided because it had not ... >[! question] What determines a sufficient level of explanatory depth for root cause analysis? ### Video: _Data drives successful outcomes_ The video discusses some examples of how being data-driven is beneficial. Then the presenter revisted the [[Analytical Skillset]] as it relates to [[Data-driven decision-making]] The presenter mentions that all of the examples so far are theoretical and meant to explain certain concepts. Going forward we will see a mix of both real-world and theoretical ideas. ### Video: _Witness Data Magic_ >[! cue] def Quartile >[! info] [[Quartile]] >A quartile divides data points into 4 equal parts. ### Discussion Prompt No notes. ### Practice Quiz This was graded. Needed 75% or higher to move to last segment in the module. Result is 100% <hr> ## Module Challenge ### Video: _What to expect moving forward_ The presenter gives some encouragement. As you can see from the file vault, there are a lot of associated notes. "That's a lot of data" ;) ### Glossary Module 1 Each term in the course will have it's own evergreen note. Below is a list of terms from this module [[Analytical Skillset]] [[Analytical thinking]] [[context]] [[data]] [[Data Analysis]] [[Data analyst]] [[data analytics]] [[Data Design]] [[Data-driven decision-making]] [[data ecosystem]] [[Data Science]] [[Data Strategy]] [[data visualizations]] [[dataset]] [[Gap analysis]] [[root cause]] [[technical mindset]] [[data visualizations|visualization]] ### Reading: _Assessment taking strategies_ This reading helps course participants prepare for assessments during the course. The reading offers helpful milestones and short checklists to assist in a few specific situations. #### Before taking an assessment. - review notes, materials. - The assessments are "open book" but challenge yourself to master the material. - Find a calming photo. - This was interesting because I have never been given the advice to find and use a calming totem before. #### During - review the entire test before filling in answers. - answer easy questions first. - multiple choice focus on eliminating the wrong ones first. - read each question twice. - I should repeat - ==**READ EACH QUESTION TWICE**== - Slow down - Trust your knowledge - Rest, hydrate, focus on the nice picture #### Feeling anxious? - Spell your name backwards, do an easy math problem - focus on calm steady breathing. - visualize success. #### Before submitting Check your work #### After submitting. - retaking is always an option if the score is not satisfactory. ### Assessment: Module 1 from here forward I will be noting some concepts that are critical to my path through the course. I will ==not be copying questions and answers here==. Why? Because of copyright, and it is a violation of the honor code. I know answers exist elsewhere, but I will rely on my own notes and methods to progress through. I will only be noting ideas/concepts that stuck out to me. Again the purpose of this project is to learn in public, not to become wikipedia of XYZ subjects. I really enjoy information and learning so I hope this is useful to someone somewhere sometime. For others in the course, Good Luck :) #### Result 97.5%, to pass is 80% I hoped to have a 100% result. I got tripped up on a question regarding [[Data-driven decision-making]]. I over thought the answer and altered my gut answer to include an incorrect option. Key takeaway: on assessments if uncertain, remain with your first or "gut-informed" answer. I had the response correct until I selected an option to be included that made my response incorrect. What is more interesting though is that the reason that I eliminated the option initially was also not correct. You see, I had misunderstood or misinterpreted why an option was incorrect. So to me, on reflecting on it, even if I had "gone with my gut" I still would have been short of understanding. Overall, I am not upset by the 2.5% miss on the test. I learned a lot in this module and am grateful for the fact that this course (and the others) exist to help so many people. Interesting! <hr> >[!summary-top] Module 1 Key takeaways >- [[data analysis process]] is very important. >- [[Analytical thinking]] requires more flexibility with how one approaches problems than I previously thought. >- In a business context, [[Data-driven decision-making]] is a core activity.