# Google Data Analytics Professional Certificate ## Course Description and Portal >[! cue] [Course Portal](https://www.coursera.org/professional-certificates/google-data-analytics#courses) ### Key Outcomes from course - Gain understanding on practices and processes used by jr. associate analysts in their day-to-day. - Understand how to clean data and organize it for analysis - Learn key analytical skills (data cleaning, analysis and visualization) and tools (spreadsheets, SQL, R, Tableau) >[!cue] Most excited for this topic - ==Learn how to visualize and present data findings in the form of dashboards, presentations, and commonly used visualization platforms.== ### Skills [[Data Analysis]] | [[creating case studies]] | [[data visualizations]] | [[data cleaning]] | [[developing a portfolio]] | [[Data Collection]] | [[Spreadsheet]] | [[Metadata]] | [[SQL]] | [[Data Ethics]] | [[Data Aggregation]] | [[Data Calculations]] | [[R Markdown]] | [[R Programming]] | [[Rstudio]] | [[Tableau]] | [[Data Integrity]] | [[Sample Size Determination]] | [[Decision-Making]] | [[Problem Solving]] | [[Questioning]] ## 1. Foundations: Data, Data, Everywhere. >[! info] This course began on 23 October, 2024 and was completed on 28 October, 2024 >All connected notes are living documents. >>This means that [[learning in public]] is a messy process and imperfect. I am not trying to be wikipedia. ;) > >Please [contact me](https://www.nickvanbergen.com/#contact) should there be any incorrect statements or fuzzy items. >[!cue] [Course 1 Link](https://www.coursera.org/learn/foundations-data/home/module/1) ### 1.0 Glossary of terms from this course - [[Analytical Skillset]] | [[Analytical thinking]] | [[Attribute]] - [[Business Task]] - [[context]] - [[data]] | [[Data Analysis]] | [[Data analyst]] | [[data analytics]] | [[Data Design]] | [[Data-driven decision-making]] | [[data ecosystem]] | [[Data Science]] | [[Data Strategy]] | [[data visualizations|data visualization]] | [[Database]] | [[dataset]] - [[Fairness]] | [[Formula]] | [[Function]] - [[Gap analysis]] - [[Oversampling]] | [[Observation]] - [[Query]] | [[Query language]] - [[root cause]] - [[Self-Reporting]] | [[stakeholder]] | [[SQL]] | [[Spreadsheet]] - [[technical mindset]] ### 1.1 [[Google Analytics - 1 Foundations - Module 1]] >[! 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. >[! info] Module 1 Learning Objectives > - [ ] Define key concepts involved in data analytics including [[data]], [[Data Analysis]], and [[data ecosystem]] > - [ ] Discuss the use of data in everyday life decisions > - [ ] Identify the key features of the learning environment and their uses > - [ ] Describe principles and practices that will help to increase one's chances of success in this certificate > - [ ] Explain the use of data in organizational decision-making > - [ ] Describe the key concepts to be discussed in the program, including learning outcomes >[! Abstract] Module 1 Key Take aways >1. [[data analysis process]] is very important. >2. [[Data-driven decision-making]] means keeping data at the heart of every decision. >3. [[Analytical Skillset]] and [[Analytical thinking]] both require flexibility ### 1.2 [[Google Analytics - 1 Foundations - Module 2]] >[! quote] In this part of the course, you'll learn about the data life cycle and data analysis process. They are both relevant to your work in this program and on the job. You’ll also be introduced to applications that help guide data through the data analysis process. >[! info] Module 2 Learning Objectives > - [ ] Identify key software applications critical to the data analyst and their work; includes spreadsheets, databases, query languages, and visualization tools. > - [ ] Identify relationships between the [[data analysis process]] and the courses in the [[Nexus - Google Analytics Certificate Course|Google Analytics Certificate]] > - [ ] Explain the [[data analysis process]] making specific references to each phase. > - [ ] Discuss the use of data in everyday life decisions > - [ ] Discuss the role of the spreadsheet, query languages, and data visualization tools in [[data analytics]] > - [ ] Discuss the phases of the [[data life cycle]] > >[! Abstract] Module 2 Key Take aways >1. [[data life cycle]]; Plan, Capture, Manage, Analyze, Archive, Destroy >2. [[data analysis process]]; Ask, Prepare, Process, Analyze, Share, Act. >3. [[data life cycle]] <> [[data analysis process]] >4. Data Tools: [[Spreadsheet]] vs [[Database]] ### 1.3 [[Google Analytics - 1 Foundations - Module 3]] >[! quote] Spreadsheets, query languages, and data visualization tools are all a big part of a data analyst’s job. In this part of the course, you’ll learn the basic concepts to use them for data analysis. You’ll also understand how they work through interesting examples. >[! info] Module 3 Learning objectives >- [ ] Describe [[Spreadsheet]], [[Query language|query language]], and [[data visualizations]] tools, giving specific examples. >- [ ] Demonstrate an understanding of the uses, basic features, and functions of a [[Spreadsheet]] >- [ ] Explain the basic concepts involved in the use of [[SQL]] including specific examples of [[Query|queries]] >- [ ] Identify the basic concepts involved in [[data visualizations]], giving specific examples. >[! Abstract] Module 3 Key Take aways >1. Strive for visualizations to be interactive and compelling to be effective >2. Think about how to present the data and incorporate that into a plan during the analysis phase. Visualization is a process. >3. Selecting the right tool often requires knowledge of the volume of data for a project as well as the type of visualization. Planning is key ### 1.4 [[Google Analytics - 1 Foundations - Module 4]] >[! quote] In this part of the course, you’ll examine different types of businesses and the jobs and tasks that analysts do for them. You’ll also learn how a Google Data Analytics Certificate will help you meet many of the requirements for an analyst position with these organizations. >[! info] Module 4 Learning Objectives >- [ ] Describe the role of a data analyst with specific reference to job roles >- [ ] Discuss how the [[Nexus - Google Analytics Certificate Course|Google Analytics Certificate]] can help a condidate meet the requirements of a given job >- [ ] Explain how a business task may be appropriate for data analyst, with reference to fairness and the value of the data analyst. >- [ ] Identify companies that would potentially hire data analysts >- [ ] Determine whether the use of data constitutes fair or unfair practices >- [ ] Understand the different ways organizations use data >- [ ] Explain the concept of [[Data-driven decision-making]] including specific examples. >[! Abstract] Module 4 Key Take aways >1. Fairness and completeness are incredibly important. >2. [[Business Task]] is a new concept for me. Remember the distinction between, Problem, Question, and Issue: see [[Google Analytics - 1 Foundations - Module 4#Question vs Problem!|Question vs Problem section]] >3. Asking for help from Team should be a priority over going lone wolf. Don't make mistakes. <hr> ## 2. Ask Questions to Make Data-Driven Decisions >[! info] This course began on 29 October, 2024 and was completed on 08 November, 2024 >All connected notes are living documents. >>This means that [[learning in public]] is a messy process and imperfect. I am not trying to be wikipedia. ;) > >Please [contact me](https://www.nickvanbergen.com/#contact) should there be any incorrect statements or fuzzy items. There are 4 modules in this course. ### 2.0 Glossary of terms from Course 2 - [[SMART methodology - Action-oriented|action-oriented question]] | [[Algorithm]] | [[AVERAGE]] - [[Big data]] | [[Borders]] - [[Cell reference]] | [[cloud]] | [[COUNT]] - [[Dashboard]] | [[data analysis process]] | [[Data-inspired decision-making]] | [[data life cycle]] - [[Equation]] - [[Fill handle]] | [[Filtering]] - [[Attribute|header]] - [[Leading question]] - [[Math expression]] | [[Math function]] | [[MAX]] | [[SMART methodology - Measurable|measurable question]] | [[Metric]] | [[Metric goal]] | [[MIN]] - [[Open data]] | [[Operator]] | [[Order of operations]] - [[Pivot chart]] | [[Pivot table]] | [[Problem domain]] | [[Problem types]] - [[Qualitative data]] | [[Quantitative data]] - [[Range]] | [[Reframing]] | [[SMART methodology - Relevant|relevant question]] | [[Report]] | [[Return on investment (ROI)]] | [[Revenue]] - [[Scope of work (SOW)]] | [[Small data]] | [[SMART methodology]] | [[Sorting]] | [[SMART methodology - Specific|specific question]] | [[Structured thinking]] | [[SUM]] - [[SMART methodology - Time-bound|time-bound question]] | [[Turnover rate]] - [[Unfair question]] ### 2.1 [[Google Analytics - 2 Ask - Module 1 - Ask Effective Questions]] >[! quote] Data analysts are constantly asking questions in order to find solutions and identify business potential. In this part of the course, you’ll learn about effective questioning techniques that will help guide your analysis. >[! info] Module 1 Learning Objectives >- [ ] Explain the characteristics of effective questions with reference to the [[SMART methodology]] >- [ ] Discuss the common types of problems addresed by the data analyst >- [ ] Explain how each step of the problem-solving roadmap contributes to common analysis scenarios >- [ ] Explain the [[data analysis process]], making specific references to each phase; [[data analysis process - Ask]], [[data analysis process - Prepare]], [[data analysis process - Process]], [[data analysis process - Analyze]], [[data analysis process - Share]], [[data analysis process - Act]] >- [ ] Describe the key ideas associated with [[Structured thinking]] including the [[Problem domain]], [[Scope of work (SOW)]], and [[context]] >[! abstract] Module 1 key takeaways >1. There are 6 common problem types: [[Problem types - finding patterns|Finding Patterns]], [[Problem types - making predictions|Making Predictions]], [[Problem types - discovering connections|Discovering Connections]], [[Problem types - identifying themes|Identifying Themes]], [[Problem types - spotting something unusual|Spotting something unusual]], [[Problem types - categorizing|Categorizing]] >2. Watchout for the subtelty on [[Problem types - making predictions]] vs [[Problem types - finding patterns]] and [[Problem types - categorizing]] vs [[Problem types - identifying themes]] >3. Be [[SMART methodology|SMART]] and ask effective questions be ensuring that questions are [[SMART methodology - Specific|specific]], [[SMART methodology - Measurable|measurable]], [[SMART methodology - Action-oriented|action-oriented]], [[SMART methodology - Relevant|relevant]], and [[SMART methodology - Time-bound|time-bound]] >4. Always consider [[Fairness]] when thinking of questions and avoid [[Leading question]] or [[Closed-ended question]] ### 2.2 [[Google Analytics - 2 Ask - Module 2 - Make data-driven decisions]] >[! quote] In analytics, data drives decision-making, and this is your opportunity to explore data of all kinds and its impact on all sorts of business decisions. You’ll also learn how to effectively share your data through reports and dashboards. >[! info] Module 2 Learning Objectives >- [ ] Discuss the use of data in the decision-making process >- [ ] Compare and contrast [[Data-driven decision-making]] and [[Data-inspired decision-making]] >- [ ] Explain the difference between [[Quantitative data]] and [[Qualitative data]] including reference to their use and specific examples. >- [ ] Discuss the importance and benefits of [[Dashboard]] and [[Report]] to the data analyst with reference to [[Tableau]] and [[Spreadsheet|spreadsheets]] >- [ ] Differentiate between [[data]] and [[Metric|metrics]], giving specific examples >- [ ] Demonstrate an understanding of what is involved in using a mathematical approach to analyze a problem. >[! abstract] Module 2 Key take aways. >1. [[Big data]] not always great, but consider [[4 - V's of big data]]. >2. [[Metric]] are important, begin to consider how something can be measured. >3. [[Quantitative data]] and [[Qualitative data]] are both important. >4. A [[Pivot table]] is a summarization tool that can sort, count, average, group and total [[Small data]] ### 2.3 [[Google Analytics - 2 Ask - Module 3 - Spreadsheet Magic]] >[! quote] Spreadsheets are a key data analytics tool. Here you’ll learn both why and how data analysts use spreadsheets in their work. You’ll also investigate how structured thinking helps analysts understand problems and come up with solutions. >[! info] Module 3 Learning Objectives >- [ ] Discuss the data analyst's use of [[Spreadsheet|spreadsheets]] with reference to roles and responsibilities >- [ ] Demonstrate the use of [[Spreadsheet]] to complete basic tasks of the data analyst including entering and organizing data >- [ ] Demonstrate an understanding of the use of [[Formula|formula]] in [[Spreadsheet|spreadsheets]] including a definition and examples >- [ ] Compare [[Formula]] and [[Function]] with reference to similarities and differences >- [ ] Describe the key ideas associated with [[Structured thinking]] including [[Problem domain]], [[Scope of work (SOW)]], and [[context]] >[! abstract] Module 3 Key Takeaways >- [[Spreadsheet]] and [[Function#Spreadsheet functions|Spreadsheet functions]] make analysis tasks easy and help organize the data on a spreadsheet >- [[Structured thinking]] , [[SMART methodology]], [[context]], and [[Scope of work (SOW)|SOW]] are all important tools to use in ASK phase of the analytical process. >- Always identify the [[Problem domain]] to serve as foundation work in understanding and asking key questions. ### 2.4 [[Google Analytics - 2 Ask - Module 4 - Always Remember the Stakeholder]] >[! quote] Successful data analysts balance the needs and expectations of their team and the stakeholders they support. In this part of the course, you’ll learn strategies for managing stakeholder expectations while establishing clear communication with your team. >[! info] Module 4 Learning Objectives >- [ ] Discuss communication best practices for the data analyst including reference to office communication, conflict resolution, facilitating meetings, and status reports >- [ ] Discuss the importance of focus on [[stakeholder]] expectations >- [ ] Identify common limitations with data, with specific reference to speed versus accuracy and responding to time-sensitive requests >[! abstract] Module 4 Key take aways. >- Stakeholders and team members hold important roles. >- know the difference between [[Qualitative data]] and [[Quantitative data]] >- Spreadsheets are quite useful, esp. on [[Small data]] >- ASK QUESTION ALL THE TIME >- USE [[SMART methodology|SMART]] to ask effective questions. <hr> ## 3. Prepare Data for Exploration >[! info] This course began on 08 November, 2024 and was completed on 11 November, 2024 >All connected notes are living documents. >>This means that [[learning in public]] is a messy process and imperfect. I am not trying to be wikipedia. ;) > >Please [contact me](https://www.nickvanbergen.com/#contact) should there be any incorrect statements or fuzzy items. ### 3.0 Glossary of terms from Course 3 - [[Access control]] | [[Administrative metadata]] | [[Agenda]] | [[Analytical thinking]] | [[Audio file]] - [[Bad data source]] | [[Bias]] | [[Boolean data]] - [[Confirmation bias]] | [[Consent]] | [[Continuous data]] | [[Cookie]] | [[CSV (comma separated values) file]] | [[Currency]] - [[Data anonymization]] | [[Data bias]] | [[Data element]] | [[Data Ethics]] | [[Data governance]] | [[Data interoperability]] | [[Data model]] | [[Data privacy]] | [[Data security]] | [[Data type]] | [[data visualizations|data visualization]] | [[Descriptive metadata]] | [[Digital photo]] | [[Discrete data]] - [[Ethics]] | [[Observer bias|Experimenter bias]] | [[External data]] - [[Fairness]] | [[Field]] | [[First-party data]] | [[Foreign key]] | [[FROM]] - [[General Data Protection Regulation of the European Union (GDPR)]] | [[Geolocation]] | [[Good data source]] - [[Internal data]] | [[Interpretation bias]] - [[Long data]] - [[Mentor]] | [[Metadata]] | [[Metadata repository]] - [[Naming conventions]] | [[Career Networking]] | [[Nominal data]] | [[Normalized database]] | [[Notebook]] - [[Observer bias]] | [[Open data]] | [[Openness]] | [[Ordinal data]] | [[Ownership]] - [[Pixel]] | [[Population]] | [[Primary key]] - [[Record]] | [[Redundancy]] | [[Relational database]] - [[Sample]] | [[Sampling bias]] | [[Schema]] | [[Second-party data]] | [[SELECT]] | [[Social media]] | [[String data type]] | [[Structural metadata]] | [[Structured data]] - [[Text data type]] | [[Third-party data]] | [[Transaction transparency]] - [[Unbiased sampling]] | [[United States Census Bureau]] | [[Unstructured data]] - [[Video file]] - [[WHERE]] | [[Wide data]] | [[World Health Organization]] ### 3.1 [[Google Analytics - 3 Prepare - Module 1 - Data types and structures]] >[! quote] A massive amount of data is generated every single day. In this part of the course, you will discover how this data is generated and how analysts decide which data to use for analysis. You’ll also learn about structured and unstructured data, data types, and data formats as you start thinking about how to prepare your data for analysis. >[! info] Learning objectives for Course 3, module 1 >- [ ] Explain how Kaggle can benefit a data analyst >- [ ] Explain how data is generated as part of our daily activities with reference to the types of data generated. >- [ ] Explain factors that should be considered when making decisions about [[Data Collection]] >- [ ] Explain the difference between [[Structured data]] and [[Unstructured data]] >- [ ] Discuss the differences between [[data]] and [[Data type]] >- [ ] Explain the relationship between [[Data type]], [[Field]], and values >- [ ] Discuss [[Wide data]] and [[Long data]] formats with references to organization and purpose >[! abstract] Key takeaways from Course 3, module 1 >- data comes in a couple formats [[Wide data]] and [[Long data]] there are pros and cons to each. >- [[First-party data]], [[Second-party data]], and [[Third-party data]] are all important to know. >- Most new data being generated is [[Unstructured data]] >- Will likely work with [[Structured data]] during junior level job. ### 3.2 [[Google Analytics - 3 Prepare - Module 2 - Data responsibility]] >[! quote] Before you work with data, you must confirm that it is unbiased and credible. After all, if you start your analysis with unreliable data, you won’t be able to trust your results. In this part of the course, you will learn to identify bias in data and to ensure your data is credible. You’ll also explore open data and the importance of data ethics and data privacy. >[! info] Learning objectives for Course 3, module 2 >- [ ] Explain what is involved in reviewing data to identify [[Bias]] >- [ ] Discuss the difference between biased and unbiased data >- [ ] Identify defferent types of bias including [[Confirmation bias]], [[Interpretation bias]], and [[Observer bias]] >- [ ] Discuss characteristics of credible sources of data including reference to untidy data. >- [ ] Explain the concept of [[Open data]] with reference to the ongoing debate in data analytics >- [ ] Define [[Data Ethics]] and [[Data privacy]] >- [ ] Explain the relationship between [[Data Ethics]] and [[Data privacy]] >- [ ] Demonstrate an understanding of the benefits of [[Data anonymization]] >- [ ] Demonstrate an awareness of the accessibility issues associated with [[Open data|open data]]. >[! abstract] Key takeaways from Course 3, module 2 >- [[Data Ethics]] is an active process, not a passive one. Never rely on assumptions. >- Bias can occur consciously or subconsciously and can take on many forms of bias. >- There is a trade-off from [[Openness]] of data and [[Data privacy|data privacy]]. ### 3.3 [[Google Analytics - 3 Prepare - Module 3 - Database essentials]] >[! quote] When you analyze large datasets, you’ll access much of the data from a database. In this part of the course, you will learn about databases, including how to access them and extract, filter, and sort the data they contain. You’ll also explore metadata to discover its many facets and how analysts use it to better understand their data. >[! info] Learning objectives for Course 3, module 3 >- [ ] Describe databases with references to their functions and components >- [ ] Explain [[Metadata]] as it relates to [[Database|databases]] >- [ ] Discuss the importance of metadata and how it relates to the work of a data analyst >- [ ] Recall the issues and steps involved in access data from multiple sources >- [ ] Explain the use of filters and sorting functionality in spreadsheets >- [ ] Demonstrate how to use spreadsheet functionality to import and inspect a given set of data >- [ ] Demonstrate how to use SQL functions to extract data from a given database. >[! abstract] Key takeaways from Course 3, module 3 ### 3.4 [[Google Analytics - 3 Prepare - Module 4 - Organize and protect data]] >[! quote] Good organizational skills are a big part of most types of work, especially data analytics. In this part of the course, you will learn best practices for organizing data and keeping it secure. You’ll also understand how analysts use file naming conventions to help them keep their work organized. >[! info] Learning objectives for Course 3, module 4 >- [ ] Explain steps that can be taken to secure data. >- [ ] Discuss the use of file-naming conventions used by data analysts >- [ ] Describe best practices for organizing data >[! abstract] Key takeaways from Course 3, module 4 ### 3.5 [[Google Analytics - 3 Prepare - Module 5 - Engage in the data community]] >[! quote] Having a strong online presence can be a big help for job seekers of all kinds. In this part of the course, you will explore how to manage your online presence. You’ll also discover the benefits of networking with other data analytics professionals. >[! info] Learning objectives for Course 3, module 5 >- [ ] Apply best practices to develop a network >- [ ] Explain the importance of networking with other data analysts including reference to mentorship and communication >- [ ] Apply best practices to manage a professional online presence >- [ ] Describe approaches to build an online presence as a data analyst >[! abstract] Key takeaways from Course 3, module 5 <hr> ## 4. Process Data from Dirty to Clean >[! info] This course began on 11 November, 2024 and was completed on 17 November, 2024 >All connected notes are living documents. >>This means that [[learning in public]] is a messy process and imperfect. I am not trying to be wikipedia. ;) > >Please [contact me](https://www.nickvanbergen.com/#contact) should there be any incorrect statements or fuzzy items. ### 4.0 Glossary of terms from Course 4 - [[AB testing]] | [[Accuracy]] - [[CASE]] | [[CAST]] | [[Changelog]] | [[Clean data]] | [[COALESCE]] | [[Compatibility]] | [[Completeness]] | [[CONCAT]] | [[CONCATENATE]] | [[Conditional formatting]] | [[Confidence interval]] | [[Confidence level]] | [[Consistency]] | [[COUNTA]] | [[COUNTIF]] | [[Cross-field validation]] - [[Data constraints]] | [[Data engineer]] | [[Data Ethics]] | [[Data governance]] | [[Data Integrity]] | [[Data manipulation]] | [[Data mapping]] | [[Data merging]] | [[Data range]] | [[Data replication]] | [[Data transfer]] | [[Data validation]] | [[Data warehousing specialist]] | [[DATEDIF]] | [[Delimiter]] | [[Dirty data]] | [[DISTINCT]] | [[Duplicate data]] - [[Estimated response rate]] - [[Field length]] | [[Find and replace]] | [[Float]] - [[Hypothesis testing]] - [[Incomplete data]] | [[Inconsistent data]] | [[Incorrect or inaccurate data]] - [[LEFT]] | [[LEN]] | [[Length]] - [[Mandatory]] | [[Margin of error]] | [[Merger]] | [[MID]] - [[Null]] - [[Outdated data]] - [[Random sampling]] | [[Regular expression]] | [[Remove duplicates]] | [[RIGHT]] - [[Soft skills]] | [[Split]] | [[Statistical power]] | [[Statistical significance]] | [[SUBSTR]] | [[Substring]] | [[Syntax]] - [[Text string]] | [[Transferable skills]] | [[TRIM]] | [[Typecasting]] - [[Unique]] - [[Validity]] | [[Verification]] | [[VLOOKUP]] ### 4.1 [[Google Analytics - 4 Process Module 1 - The importance of integrity]] >[! quote] Data integrity is critical to successful analysis. In this part of the course, you’ll explore methods and steps that analysts take to check their data for integrity. This includes knowing what to do when you don’t have enough data. You’ll also learn about random samples and understand how to avoid sampling bias. All of these methods will also help you ensure your analysis is successful. >[! info] Learning objectives for course 4, module 1 >- [ ] Describe statistical measures associated with data integrity including statistical power, hypothesis testing, and margin of error >- [ ] Describe strategies that can be used to address insufficient data >- [ ] Discuss the importance of sample size with reference to sample bias and random samples >- [ ] Describe the relationship between data and related business objectives >- [ ] Define data integrity with reference to types and risks >- [ ] Discuss the importance of pre-cleaning activities >[! abstract] Module 1 key take aways >There are multiple related terms in this section. Critical to understand what and why of [[Statistical power]], [[Statistical significance]], [[Margin of error]], [[Confidence level]] and sample size. ### 4.2 [[Google Analytics - 4 Process Module 2 - Clean data for more accurate insights]] >[! quote] Every data analyst wants to analyze clean data. In this part of the course, you’ll learn the difference between clean and dirty data. Then, you’ll practice cleaning data in spreadsheets and other tools. >[! info] Learning objectives for course 4, module 2 >- [ ] Differentiate between clean and dirty data >- [ ] Explain the characteristics of dirty data >- [ ] Describe data cleaning techniques with reference to identifying errors, redundancy, compatibility and continuous monitoring >- [ ] Identify common pitfalls when cleaning data >- [ ] Demonstrate an understanding of the use of spreadsheets to clean data >[! abstract] Key Takeaways from Course 4 module 2 >Data cleaning is important before any analysis is to be done. >Valuable and timesaving functions in spreadsheets to aid in swift cleaning workflows. ### 4.3 [[Google Analytics - 4 Process Module 3 - Data cleaning with SQL]] >[! quote] Knowing a variety of ways to clean data can make a data analyst’s job much easier. In this part of the course, you’ll use SQL to clean data from databases. In particular, you’ll explore how SQL queries and functions can be used to clean and transform your data before an analysis. >[! info] Learning objectives for course 4, module 3 >- [ ] Describe how SQL can be used to clean large datasets >- [ ] Compare spreadsheet data-cleaning functions to those associated with SQL in databases >- [ ] Develop basic SQL queries for use with databases >- [ ] Apply basic SQL functions for use in cleaning string variables in a database >- [ ] Apply basic SQL functions for transforming data variables >[! abstract] Key takeaways from Course 4, Module 3 >[[COALESCE]], [[CAST]] are my favorite new SQL functions that I know. ### 4.4 [[Google Analytics - 4 Process Module 4 - Verify and report on cleaning results]] >[! quote] When you clean data, you make changes to the original dataset. It’s important to verify the changes you make are accurate and to let your teammates know about the changes. In this part of the course, you’ll learn to verify that data is clean and report your data cleaning results. With verified clean data, you’re ready to begin analyzing! >[! info] Learning objectives for course 4, module 4 >- [ ] Describe the process involved in verifying the results of cleaning data >- [ ] Describe what is involved in manually cleaning data >- [ ] Discuss the elements and importance of data-cleaning reports >- [ ] Describe the benefits of documenting data cleaning process >[! abstract] Key take aways for module 4 >- SQL is very powerful for data cleaning. >- CHANGELOGS, need to use them and publish them too. ### 4.5 [[Google Analytics - 4 Process Module 5 - Add data to your resume]] >[! warning] the notes for module 5 or being kept private for now. All career outcomes related work is really for the benefit of those who are enrolled in the course. I also feel that my responses to many of these prompts are a bit too raw, even for learning in public experiments. >[! quote] Creating an effective resume will help you in your data analytics career. In this part of the course, you’ll learn all about the job application process. Your focus will be on building a resume that highlights your strengths and relevant experience. >[! info] Learning objectives for course 4, module 5 >- [ ] Identify key elements of a data analyst resume >- [ ] Demonstrate an understanding how previous experience may be added to a resume >- [ ] Discuss how a data analyst job description may be aligned to a particular area of interest >[! abstract] Module 5 key takeaways >>The notes for this module are not pulished because the advice given here should be reserved for those who paid for the certificate attendence. >Keep exploring. Follow interests. Utilize BPs when writing resume. ### 4.6 Google Analytics - 4 Process Module 6 - Course wrap up >[! quote] Review the course glossary and prepare for the next course in the Google Data Analytics Certificate program. <hr> --- ## 5. Analyze Data to Answer Questions >[! info] This course began on 18 November, 2024 and was completed on 25 November, 2024 >All connected notes are living documents. >>This means that [[learning in public]] is a messy process and imperfect. I am not trying to be wikipedia. ;) > >Please [contact me](https://www.nickvanbergen.com/#contact) should there be any incorrect statements or fuzzy items. ### 5.0 Glossary of Terms for Course 5 - [[Cell reference|Absolute reference]] | [[Aggregation]] | [[Aliasing]] | [[Array]] - [[Calculated field]] | [[COUNT DISTINCT]] - [[Data Aggregation]] | [[Data security]] | [[Data validation process]] - [[GROUP BY]] - [[INNER JOIN]] - [[JOIN]] - [[LEFT JOIN]] | [[LIMIT]] - [[MATCH]] | [[Modulo]] - [[ORDER BY]] | [[OUTER JOIN]] - [[Profit margin]] - [[RIGHT JOIN]] | [[ROUND]] - [[Subquery]] | [[Summary table]] | [[SUMPRODUCT]] - [[Temporary table]] - [[Underscores]] - [[VALUE]] ### 5.1 [[Google Analytics - 5 Analyze - Module 1 - Organize data for more effective analysis|Organize data for more effective analysis]] >[! quote] Organizing data makes the data easier to use in your analysis. In this part of the course, you’ll learn the importance of organizing your data through sorting and filtering. You’ll explore these processes in both spreadsheets and SQL as you continue to prepare your data. >[! note] Learning Objectives for Module 1 >- [ ] Describe what is involved in the data analysis process with reference to goals and key tasks >- [ ] Discuss the importance of organizing data before analysis with references to sorts and filters >- [ ] Describe sorting as it relates to data in a spreadsheet or database with reference to functionality and benefits >- [ ] Recall the steps involved in sorting and filtering data through the use of SQL queries > ### 5.2 [[Google Analytics - 5 Analyze - Module 2 - Format and Adjust Data|Format and Adjust Data]] >[! quote] As you move closer to analyzing your data, you will want to have the data formatted and ready to go. In this part of the course, you will learn all about converting and formatting data, including how to use SQL queries to combine data. You will also discover the value of feedback and support from your colleagues and how it can lead to new insights that you can apply to your work. ### 5.3 [[Google Analytics - 5 Analyze - Module 3 - Aggregate Data for Analysis|Aggregate Data for Analysis]] >[! quote] During an analysis, you might need to combine data to gain insights and complete business objectives. In this part of the course, you will explore the functions, procedures, and syntax to combine, or aggregate data. You will learn how to combine data within multiple cells in spreadsheets, and within multiple database tables using SQL queries. ### 5.4 [[Google Analytics - 5 Analyze - Module 4 - Perform data calculations|Perform Data Calculations]] >[! quote] Calculations are one of the more common tasks that data analysts perform during an analysis. In this part of the course, you will explore formulas, functions, and pivot tables in spreadsheets and SQL queries. All of these are used in data calculations. You will also learn about the benefits of using SQL to manage temporary database tables. <hr> ## 6. Share Data Through The Art of Visualization >[! warning]- This course is in process as of 26 November, 2024 >All connected notes are living documents. >>This means that [[learning in public]] is a messy process and imperfect. I am not trying to be wikipedia. ;) > >Please [contact me](https://www.nickvanbergen.com/#contact) should there be any incorrect statements or fuzzy items. ### 6.0 Glossary of Terms for Course 6 - [[Alternative text]] | [[Annotation]] | [[Area chart]] | [[AVERAGEIF]] - [[Balance]] | [[Bar graph]] | [[Box plot]] | [[Bubble chart]] | [[Bullet graph]] - [[Calculus]] | [[Causation]] | [[Channel]] | [[Chart]] | [[Circle view]] | [[Cluster]] | [[Column chart]] | [[Combo chart]] | [[CONVERT]] | [[Correlation]] | [[CREATE TABLE]] - [[Data composition]] | [[Data storytelling]] | [[Decision tree]] | [[Density map]] | [[Design thinking]] | [[Distribution graph]] | [[Diverging color palette]] | [[Donut chart]] | [[DROP TABLE]] | [[Dynamic visualizations]] - [[Emphasis]] | [[Engagement]] - [[Filled map]] | [[Framework]] - [[Gantt chart]] | [[Gauge chart]] - [[Having]] | [[Headline]] | [[Heat map]] | [[Highlight table]] | [[Histogram]] - [[Inner query]] - [[Label]] | [[Legend]] | [[Line graph]] | [[Live data]] - [[Map]] | [[Mark]] | [[MAXIFS]] | [[McCandless Method]] | [[MINIFS]] | [[Movement]] - [[Narrative]] - [[Outer query]] - [[Packed bubble chart]] | [[Pattern]] | [[Pie chart]] | [[Pre-attentive attributes]] | [[Proportion]] - [[R Programming|R]] | [[Ranking]] | [[Relativity]] | [[Repetition]] | [[Rhythm]] - [[Scatterplot]] | [[SELECT INTO]] | [[Sort range]] | [[Sort sheet]] | [[Spotlighting]] | [[Static data]] | [[Static visualization]] | [[Statistics]] | [[Story]] | [[Subtitle]] | [[Symbol map]] - [[Tableau]] - [[Unity]] - [[Variety]] | [[Visual form]] - [[WITH]] - [[X-axis]] - [[Y-axis]] ### 6.1 [[Google Analytics - 6 Share - Module 1 - Visualize Data|Visualize Data]] >[! quote] In this module, you’ll delve into the various types of data visualizations and explore what makes an effective visualization. You'll also learn about accessibility, design thinking, and other factors that will help you use data visualizations to effectively communicate data insights. >[! info] Learning Outcomes for Course X, Module Y >- [ ] Explain the key concepts involved in design thinking as they relate to data visualization >- [ ] Describe the use of data visualizations to talk about data and the results of data analysis >- [ ] Discuss accessibility issues associated with data visualization >- [ ] Explain the importance of data visualization to data analysts >- [ ] Describe the key concepts involved in data visualization >[! abstract] Key Takeaways from Course 6, Module 1 >- 1 >- 2 >- 3 ### 6.2 [[Google Analytics - 6 Share - Module 2 - Create data visualizations with Tableau|Create data visualizations with Tableau]] >[! quote] Tableau is a business intelligence and analytics platform that helps people visualize, understand, and make decisions with data. In this part of the course, you’ll become well-versed in Tableau’s dynamic capabilities and learn to inject creativity and clarity into your visualizations, ensuring that your findings are easy to understand. >[! info] Learning Outcomes for Course X, Module Y >- [ ] Identify Tableau as a data visualization tool and understand its uses >- [ ] Explain how data visualization can allow for creativity and clarity to appropriately present findings >- [ ] Select appropriate visuals for various presentation situations >- [ ] Identify different types of data visualizations and their uses >- [ ] Use multiple data sources to create a visualization >- [ ] Discuss accessibility issues associated with data visualization >[! abstract] Key Takeaways from Course 6, Module 2 >- 1 >- 2 >- 3 ### 6.3 [[Google Analytics - 6 Share - Module 3 - Craft data stories|Craft data stories]] >[! quote] Connecting your objective with your data through insights is essential to data storytelling. In this part of the course, you’ll get acquainted with the principles of data-driven storytelling and learn to craft compelling narratives using Tableau's dashboard and filtering capabilities, giving life to your data insights. >[! info] Learning Outcomes for Course X, Module Y >- [ ] Explain data-driven stories, including reference to their importance and their attributes >- [ ] Demonstrate an understanding of how to use Tableau to create dashboards and dashboard filters >- [ ] Explain how data stories can be used in different forms of on-the-job communication >[! abstract] Key Takeaways from Course 6, Module 3 >- 1 >- 2 >- 3 ### 6.4 [[Google Analytics - 6 Share - Module 4 - Develop presentations and slideshows|Develop presentations and slideshows]] >[! quote] In this part of the course, you’ll discover how to give an effective presentation about your data analysis. This final module teaches you to construct insightful presentations that resonate with your audience. You'll learn to anticipate and address potential questions and to articulate the limitations of your data, ensuring a robust and credible narrative for your stakeholders. >[! info] Learning Outcomes for Course X, Module Y >- [ ] Describe best practices for addressing the question-and-answer section of a presentation >- [ ] Consider the caveats and limitations associated with the data in a presentation >- [ ] Differentiate between strong and weak presentation content >- [ ] Describe how junior data analysts are expected to use their presentation skills >- [ ] Explain principles and practices associated with effective presentations >- [ ] Identify appropriate responses to presentation objections >[! abstract] Key Takeaways from Course 6, Module 4 >- 1 >- 2 >- 3 <hr> ## 7. Data Analysis with R Programming <hr> ## 8. Capstone <hr>