class: center, middle, inverse, title-slide .title[ # BAA1030 - Data Analytics and Story Telling ] .subtitle[ ## Lecture 1: Introduction ] .author[ ### Damien Dupré - Dublin City University ] --- # Module Contact Details .pull-left[ ## Damien Dupré, PhD - email: damien.dupre@dcu.ie - phone: 00353 (0)1 700 6360 - office: Q233 DCU Business School ] .pull-right[ <img src="https://pbs.twimg.com/profile_images/1221820283159490565/96a3XnSg_400x400.jpg" width="60%" style="display: block; margin: auto;" /> ] --- # About Me #### Developement of the DynEmo Facial Expression Database (Master) * Dynamic and spontaneous emotions * Assessed with self-reports and by observers #### Analysis of Emotional User Experience of Innovative Tech. (Industrial PhD) * Understand users' acceptance of technologies from their emotional response * Based on multivariate self-reports #### Evaluation of Emotions from Facial and Physiological Measures (Industrial PostDoc) * Applications to marketing, sports and automotive industries * Dynamic changes with trend extraction techniques (2 patents) #### Performance Prediction using Machine Learning (Academic PostDoc) * Application to sport analytics * Big Data treatment (> 1 million users with activities recorded in the past 5 years) --- # Module Content ## Knowledge - Data Organisation - Data Analytics Pipeline - Data Cleaning - Professional Visualisation Outputs - Academic Statistical Analyses - First Steps in Programming Languages ## Skills - EXCEL - PowerBI/Google Looker Studio - TABLEAU - R & RStudio or Python & Jupyter The lecture will alternate between **theory sessions** and **practice sessions**. --- # Module Content ## Targets - **Short Term**: Success in BAA1030 assignments and winning the EDHEC [Data Viz Challenge](https://www.tableau.com/academic/edhec-business-school-starts-data-viz-challenge-students-across-europe) - **Mid Term**: Success in all other DCU lectures (academic data analytics style and conventions) - **Long Term**: Professional data analytic expertise (CV optimised with essential data analytics skills) ## Professional Requirements <span><i class="fas fa-bomb faa-burst animated faa-slow " style=" color:blue;"></i></span> Data Analytics is the best, easiest and highly paid job you can obtain with your master, see by yourself: - [MSc Management Business – Deep Dive with Cathal Dunne, AIB, Data Quality Analyst](https://business.dcu.ie/msc-management-business-deep-dive-with-cathal-dunne-aib-data-quality-analyst/) - [Let's ask to ChatGPT](https://chat.openai.com/chat) - [Data Analyst Positions on Indeed.com](https://ie.indeed.com/jobs?q="data analyst"&l=Dublin%2C County Dublin) --- # Module Content <img src="https://c.tenor.com/DIWxMkR9kyUAAAAC/fortune-and-glory-indiana-jones.gif" width="100%" style="display: block; margin: auto;" /> --- # Module Organisation ### Lecture Every Week - Wednesday, between 11am and 1pm - Recordings on-demand and not shareable ### Practice at Home - Practice with the technologies and methods shown during the lectures - Essential for the assignments ### Optional but Strongly Adviced Online Trainings - Free and Short Courses Online (Kubicle, Udemy + other learning platforms) --- # Assessment Structure ### Assignment 1. Tableau Online Dashboard (40%) > Deadline 23/03/2025 > Design a professional looking data dashboard aiming to communicate a narrative (also called storytelling) ### Assignment 2. Quarto Report (50%) > Deadline 13/04/2025 (Only 3 weeks after Assignment 1) > Using R and RStudio, reproduce the results presented in the tableau dashboard with a Quarto report ### Kubicle Online Courses (10%) > Deadline 01/05/2025 --- # Assessment Structure ### <span><i class="fas fa-exclamation-triangle faa-flash animated faa-slow " style=" color:red;"></i></span> Warning - Each assignment's instruction will be uploaded on loop after the deadline of the previous assignment. - Please submit professional outputs, as I'm not expecting student's approximative work from you any more. - Please avoid any form of plagiarism, copy-pasting or group collaborations. I will easily spot rephrased sentences, similar figure design and code sharing. --- # Kubicle Courses #### Excel Essentials - [Data Manipulation and Formatting](https://app.kubicle.com/courses/data-manipulation-and-formatting) (150 Minutes) - [Formulas and Functions](https://app.kubicle.com/courses/formulas-and-functions) (210 Minutes) #### Data Analysis - [Lookups and Database Functions](https://app.kubicle.com/courses/lookups-and-database-functions) (150 Minutes) - [Pivot Tables](https://app.kubicle.com/courses/pivot-tables) (150 Minutes) #### Data Presentation Fundamentals - [Communicating Data Effectively](https://app.kubicle.com/courses/communicating-data-effectively) (90 Minutes) - [Telling Stories with Data](https://app.kubicle.com/courses/telling-stories-with-data) (60 Minutes) - [Presenting Your Data](https://app.kubicle.com/courses/presenting-your-data) (90 Minutes) #### Visualization Fundamentals - [Visual Data Thinking](https://app.kubicle.com/courses/visual-data-thinking) (60 Minutes) - [Applying Visual Data Skills](https://app.kubicle.com/courses/applying-visual-data-skills) (60 Minutes) - [Visualization in Practice](https://app.kubicle.com/courses/visualization-in-practice) (30 Minutes) --- class: inverse, mline, center, middle # Data Analytics and Big Data --- # What are Big Data? ### Definition The term Big Data corresponds to a table containing observations (i.e. database or dataset) that is **too long, too large or too complex to be handled by conventional tools** ### Microsoft Excel's Limits (v16.77 - Office 365): - Total number of rows: **1,048,576 rows** - Total number columns: **16,384 columns** .pull-left[ <img src="https://qph.fs.quoracdn.net/main-qimg-e4072574da0c7a785bc4b138b694189f" width="60%" style="display: block; margin: auto;" /> ] .pull-right[ *Have you ever tried to scroll down to the end of Excel? Because I did!* ] --- # Data vs. Information Without data, an organization could not successfully complete most business activities. However, organisations need to convert these data into meaningful information - Data consists of raw facts - Information is often confused with the term data ### Example: Sales Manager - Knowing number of sales for each representative (fact – data) - Knowing total monthly sales (transformed – information) <div class="nomnoml html-widget html-fill-item" id="htmlwidget-30e4409849e39179307f" style="width:720px;height:216px;"></div> <script type="application/json" data-for="htmlwidget-30e4409849e39179307f">{"x":{"code":"\n#fill: #FEFEFF\n#lineWidth: 1\n#zoom: 4\n#direction: right\n\n#stroke: black\n#direction: right\n#align: center\n[Data]->[Transformation Process]\n[Transformation Process]->[Information]","svg":false,"png":null},"evals":[],"jsHooks":[]}</script> --- # Value of Information - **Goals**: Helps decision makers achieve organisational goals - **Performance**: Valuable information helps people and organisations perform - **Accuracy**: Inaccurate/Incomplete information leads to Poor Decisions and can result in High Cost for the organisation -- ### Data Analytics - The science of using data to build models that lead to better decisions that in turn add value to individuals, companies and institutions - The analysis of data, typically large sets of data, by the use of mathematics, statistics, and computer software <div class="figure" style="text-align: center"> <div id="htmlwidget-2e17466358a46d139971" style="width:800px;height:150px;" class="grViz html-widget"></div> <script type="application/json" data-for="htmlwidget-2e17466358a46d139971">{"x":{"diagram":"\n digraph {\n \"Business performance results\" -> \"What happened?\" [dir = both]\n \"Business performance results\" -> \"Why did it happen?\" [dir = both]\n \"Business performance results\" -> \"What do we want to happen?\" [dir = both]\n }","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script> <p class="caption">Data analytics provides an integrated view of business performance.</p> </div> --- # Styles of Data Analytics It uses a combination of historical information about past transactions or events and reference data about, for example, customers or products, to enable a wide variety of analyses and decision support techniques. - **Standard Reports:** Preformatted information for predefined backward-looking analysis. - **Academic Reports:** Application of research methods to business information using descriptive and inferential statistics. - **Dashboards:** Business performance metrics using specific variables presented in a tabular or graphical format. - **Alerts:** Communication to designated business people when a key business variable is outside a predefined performance standard or range. - **Predictive Analytics:** Application of historical business information to predict future the performance and potentially prescribe a favoured course of action. --- class: inverse, mline, center, middle # Data Analytics and Storytelling --- # Role of Data Storytelling > **Stories** are how we translate core, essential **content**<br>to different **forms**<br>for specific **audiences**. -- ### Purpose of stories: Visual communication plays an important role in a visual analytics process. No matter how advanced and sophisticated data visualisation techniques are used, if we failed to tell a compelling story by using the data visualisation designed, all the hard work and efforts will be wasted. -- ### Motivation: .pull-left[ **Exploratory analysis**: - exploring and understanding the data, conducting the analysis ] .pull-right[ **Explanatory analysis**: - explaining your findings from your analysis in a coherent narrative that leads to a call to action ] --- # Build your Story > When adding texts or visualisations, ask yourself: "Does this element support the point I want to make about the data?" -- ### Guiding Your Viewer Another way we can guide people through our visualization is by using **annotations**, which can be very helpful in guiding someone through our figure. However, think about only labelling the data that matters. -- ### Use your titles/captions! - Titles can guide people to the point of your figure - Primes people to know what to look for - "If there is a conclusion you want your audience to reach, state it in words" -- ### References - [Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) - [Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code](https://handsondataviz.org/) --- class: inverse, mline, left, middle <img class="circle" src="https://github.com/damien-dupre.png" width="250px"/> # Thanks for your attention and don't hesitate to ask if you have any question! [<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @damien_dupre](http://twitter.com/damien_dupre) [<svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> @damien-dupre](http://github.com/damien-dupre) [<svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M579.8 267.7c56.5-56.5 56.5-148 0-204.5c-50-50-128.8-56.5-186.3-15.4l-1.6 1.1c-14.4 10.3-17.7 30.3-7.4 44.6s30.3 17.7 44.6 7.4l1.6-1.1c32.1-22.9 76-19.3 103.8 8.6c31.5 31.5 31.5 82.5 0 114L422.3 334.8c-31.5 31.5-82.5 31.5-114 0c-27.9-27.9-31.5-71.8-8.6-103.8l1.1-1.6c10.3-14.4 6.9-34.4-7.4-44.6s-34.4-6.9-44.6 7.4l-1.1 1.6C206.5 251.2 213 330 263 380c56.5 56.5 148 56.5 204.5 0L579.8 267.7zM60.2 244.3c-56.5 56.5-56.5 148 0 204.5c50 50 128.8 56.5 186.3 15.4l1.6-1.1c14.4-10.3 17.7-30.3 7.4-44.6s-30.3-17.7-44.6-7.4l-1.6 1.1c-32.1 22.9-76 19.3-103.8-8.6C74 372 74 321 105.5 289.5L217.7 177.2c31.5-31.5 82.5-31.5 114 0c27.9 27.9 31.5 71.8 8.6 103.9l-1.1 1.6c-10.3 14.4-6.9 34.4 7.4 44.6s34.4 6.9 44.6-7.4l1.1-1.6C433.5 260.8 427 182 377 132c-56.5-56.5-148-56.5-204.5 0L60.2 244.3z"/></svg> damien-dupre.github.io](https://damien-dupre.github.io) [<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M16.1 260.2c-22.6 12.9-20.5 47.3 3.6 57.3L160 376V479.3c0 18.1 14.6 32.7 32.7 32.7c9.7 0 18.9-4.3 25.1-11.8l62-74.3 123.9 51.6c18.9 7.9 40.8-4.5 43.9-24.7l64-416c1.9-12.1-3.4-24.3-13.5-31.2s-23.3-7.5-34-1.4l-448 256zm52.1 25.5L409.7 90.6 190.1 336l1.2 1L68.2 285.7zM403.3 425.4L236.7 355.9 450.8 116.6 403.3 425.4z"/></svg> damien.dupre@dcu.ie](mailto:damien.dupre@dcu.ie)