Data Analytics and Big Data

Soraya SEDKAOUI, Researcher at TRIS Laboratory (University of Montpellier, France); HDR & Lecturer in Khemis Miliana University (Algeria); Data analyst at SRY Consulting, Montpellier, France).

• Acknowledgments xiPreface xiiiIntroduction xviiGlossary xxiPart 1: Towards an Understanding of Big Data: Are You Ready? 1Chapter 1: From Data to Big Data: You Must Walk Before You Can Run 31.1. Introduction 31.2. No analytics without data 41.2.1. Databases 51.2.2. Raw data 51.2.3. Text 61.2.4. Images, audios and videos 61.2.5. The Internet of Things 61.3. From bytes to yottabytes: the data revolution 71.4. Big data: definition 101.5. The 3Vs model 121.6. Why now and what does it bring? 151.7. Conclusions 19Chapter 2: Big Data: A Revolution that Changes the Game  212.1. Introduction 212.2. Beyond the 3Vs 222.3. From understanding data to knowledge 242.4. Improving decision-making 272.5. Things to take into account 312.5.1. Data complexity 312.5.2. Data quality: look out! Not all data are the right data 322.5.3. What else?…Data security 332.6. Big data and businesses 342.6.1. Opportunities 342.6.2. Challenges 362.7. Conclusions 40Part 2: Big Data Analytics: A Compilation of Advanced Analytics Techniques that Covers a Wide Range of Data 41Chapter 3: Building an Understanding of Big Data Analytics 433.1. Introduction 433.2. Before breaking down the process What is data analytics? 443.3. Before and after big data analytics 473.4. Traditional versus advanced analytics:What is the difference? 493.5. Advanced analytics: new paradigm 523.6. New statistical and computational paradigm within the big data context 543.7. Conclusions 58Chapter 4: Why Data Analytics and When Can We Use It? 594.1. Introduction 594.2. Understanding the changes in context 604.3. When real time makes the difference 634.4. What should data analytics address? 644.5. Analytics culture within companies 684.6. Big data analytics application: examples 714.7. Conclusions 75Chapter 5: Data Analytics Process: There’s Great Work Behind the Scenes 775.1. Introduction 775.2. More data, more questions for better answers 785.2.1. We can never say it enough: “there is no good wind for those who don’t know where they are going” 785.2.2. Understanding the basics: identify what we already know and what we have yet to find out 795.2.3. Defining the tasks to be accomplished 805.2.4. Which technology to adopt? 805.2.5. Understanding data analytics is good but knowing how to use it is better! (What skills do you need?) 815.2.6. What does the data project cost and how will it pay off in time? 825.2.7. What will it mean to you once you find out? 825.3. Next steps: do you have an idea about a “secret sauce”? 835.3.1. First phase: find the data (data collection) 845.3.2. Second phase: construct the data (data preparation) 855.3.3. Third phase: go to exploration and modeling (data analysis) 855.3.4. Fourth phase: evaluate and interpret the results (evaluation and interpretation) 865.3.5. Fifth phase: transform data into actionable knowledge (deploy the model) 875.4. Disciplines that support the big data analytics process 885.4.1. Statistics 885.4.2. Machine learning 885.4.3. Data mining 895.4.4. Text mining 905.4.5. Database management systems 905.4.6. Data streams management systems 915.5. Wait, it’s not so simple: what to avoid when building a model? 915.5.1. Minimize the model error 945.5.2. Maximize the likelihood of the model 955.5.3. What about surveys? 955.6. Conclusions 99Part 3: Data Analytics and Machine Learning: the Relevance of Algorithms 101Chapter 6. Machine Learning: a Method of Data Analysis that Automates Analytical Model Building . 1036.1. Introduction 1036.2. From simple descriptive analysis to predictive and prescriptive analyses: what are the different steps? 1046.3. Artificial intelligence: algorithms and techniques 1066.4. ML: what is it? 1096.5. Why is it important? 1136.6. How does ML work? 1166.6.1. Definition of the business need (problem statement) and its formalization 1176.6.2. Collection and preparation of the useful data that will be used to meet this need 1176.6.3. Test the performance of the obtained model 1186.6.4. Optimization and production start 1186.7. Data scientist: the new alchemist 1206.8. Conclusion 122Chapter 7: Supervised versus Unsupervised Algorithms: a Guided Tour 1237.1. Introduction 1237.2. Supervised and unsupervised learning 1247.2.1. Supervised learning: predict, predict and predict! 1247.2.2. Unsupervised learning: go to profiles search! 1277.3. Regression versus classification 1297.3.1. Regression 1307.3.2. Classification 1337.4. Clustering gathers data 1417.4.1. What good could it serve? 1417.4.2. Principle of clustering algorithms 1447.4.3. Partitioning your data by using the K-means algorithm 1487.5. Conclusion 151Chapter 8. Applications and Examples 1538.1. Introduction 1538.2. Which algorithm to use? 1538.2.1. Supervised or unsupervised algorithm: in which case do we use each one? 1548.2.2. What about other ML algorithms? 1578.3. The duo big data/ML: examples of use 1618.3.1. Netflix: show me what you are looking at and I’ll personalize what you like 1628.3.2. Amazon: when AI comes into your everyday life 1658.3.3. And more: proof that data are a source of creativity 1688.4. Conclusions 171Bibliography 173Index 181