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magistraleinformatica:dmi:start [03/10/2021 alle 23:57 (3 anni fa)]
Anna Monreale [First Semester]
magistraleinformatica:dmi:start [22/03/2024 alle 20:34 (6 giorni fa)] (versione attuale)
Anna Monreale [First Semester]
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-====== Data Mining (309AA) - 9 CFU A.Y. 2021/2022 ======+====== Data Mining (309AA) - 9 CFU A.Y. 2023/2024 ======
  
 **Instructor:** **Instructor:**
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     * [[anna.monreale@unipi.it]]        * [[anna.monreale@unipi.it]]   
 **Teaching Assistant:** **Teaching Assistant:**
-  * **Francesca Naretto** +  * * **Lorenzo Mannocci** 
-    * KDDLab, SNS, Pisa +    * University of Pisa 
-    * [[francesca.naretto@sns.it]]  +    * [[lorenzo.mannocci@phd.unipi.it]]  
  
 ====== News ====== ====== News ======
-  * [23.09.2021] ** Please, fill this document: [[https://docs.google.com/spreadsheets/d/1YzHs_JSYPWYqnmkM7ccQc1WZSzGP7UsgxdBF-h5LcEA/edit?usp=sharing|Student-Lists anf Project groups]]. On Teams you can find instructions for GroupID ** +  * [05.09.2023] ** The lectures will start on 27th September 2023**  
-  * [06.09.2021] The first lecture of this course will take place on Thursday, 16 Sept 2021. + 
-  * [08.09.2021]People that intend to attend the course online should use this link: https://teams.microsoft.com/l/team/19%3aWKvq4kg0XbKZ5pEeiZcarbBXPCYsTvTwMkKZs2PWiHA1%40thread.tacv2/conversations?groupId=aea1385b-6721-4d90-a169-c97f7d066eca&tenantId=c7456b31-a220-47f5-be52-473828670aa1    +
 ====== Learning Goals ====== ====== Learning Goals ======
      * Fundamental concepts of data knowledge and discovery.      * Fundamental concepts of data knowledge and discovery.
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      * Data preparation      * Data preparation
      * Clustering      * Clustering
-     * Classification & Regression+     * Classification
      * Pattern Mining and Association Rules      * Pattern Mining and Association Rules
      * Outlier Detection      * Outlier Detection
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 ^  Day of Week  ^  Hour  ^  Room  ^  ^  Day of Week  ^  Hour  ^  Room  ^ 
-|  Wednesday |  14:00 - 16:00  |  Room C  - Online  |  +|  Wednesday |  09:00 - 11:00  |  Room C1  |  
-|  Thursday  |  14:00 - 16:00  |  Room C  - Online  |  +|  Thursday  |  09:00 - 11:00  |  Room C1  |  
-|  Friday    |  09:00 - 11:00  |  Room A1 -  Online  +|  Friday    |  09:00 - 11:00  |  Room  
  
  
  
 **Office hours - Ricevimento:** **Office hours - Ricevimento:**
-Anna Monreale: Wednesday: 11:00-13:00 online using Teams (Appointment by email) +Anna Monreale: Tuesday: 11:00-13:00 by online using Teams or at the Department of Computer Science, room 374/E (Please ask an appointment by email). 
-Francesca NarettoMonday: 15:00-18:00 online using Teams (Appointment by email)+Lorenzo MannocciTDB
  
- +A [[https://teams.microsoft.com/l/team/19%3ajujTZ5yI6IyKkRl1YEGY0Iisg7RhlW1YTam_NO3-OOE1%40thread.tacv2/conversations?groupId=2ce9fd1a-3f23-47b0-92cd-8652f8be9ed6&tenantId=c7456b31-a220-47f5-be52-473828670aa1|Teams Channel]] will be used ONLY to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will **NOT** be live-streamed, but recordings of the lecture or of the previous years will be made available here for non-attending students.  
 ====== Learning Material -- Materiale didattico ====== ====== Learning Material -- Materiale didattico ======
  
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     * [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php]]     * [[http://www-users.cs.umn.edu/~kumar/dmbook/index.php]]
     * Chapters 4,6 and 8 are also available at the publisher's Web site.     * Chapters 4,6 and 8 are also available at the publisher's Web site.
-  * Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. **GUIDE TO INTELLIGENT DATA ANALYSIS.** Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7 
   * Laura Igual et al.** Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications**. 1st ed. 2017 Edition.   * Laura Igual et al.** Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications**. 1st ed. 2017 Edition.
   *  Jake VanderPlas. **[[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data.]]** 1st Edition.    *  Jake VanderPlas. **[[http://shop.oreilly.com/product/0636920034919.do| Python Data Science Handbook: Essential Tools for Working with Data.]]** 1st Edition. 
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 ===== Software===== ===== Software=====
  
-  * Python - Anaconda (3.7 version!!!): Anaconda is the leading open data science platform powered by Python. [[https://www.anaconda.com/distribution/| Download page]] (the following libraries are already included)+  * Python - Anaconda (at least 3.7 version!!!): Anaconda is the leading open data science platform powered by Python. [[https://www.anaconda.com/distribution/| Download page]] (the following libraries are already included)
   * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | Documentation page]]   * Scikit-learn: python library with tools for data mining and data analysis [[http://scikit-learn.org/stable/ | Documentation page]]
   * Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. [[http://pandas.pydata.org/ | Documentation page]]   * Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. [[http://pandas.pydata.org/ | Documentation page]]
  
    
-====== Class Calendar (2021/2022) ======+====== Class Calendar (2023/2024) ======
  
 ===== First Semester  ===== ===== First Semester  =====
  
 ^ ^ Day ^ Topic ^ Learning material ^ References ^ Video Lectures ^ ^ ^ Day ^ Topic ^ Learning material ^ References ^ Video Lectures ^
-  15.09  14:15‑16:00 Lecture deleted  | | | | +|1.  |  27.09  | Overview. Introduction to KDD   | {{ :magistraleinformatica:dmi:1-overview-2023.pdf |}} {{ :magistraleinformatica:dmi:1-intro-dm.pdf |}}|Chap. 1 Kumar Book |[[https://unipiit.sharepoint.com/:v:/s/a__td_59044/EYjxO1YANqtMnr8upJa3X4oBp3wEsdjef8iSXN7LL7jcxQ?e=Jd80j9|Introduction DM - Video1]] [[ https://unipiit.sharepoint.com/:v:/s/a__td_59044/Eesf2mgGU1hMjMH4qH_xJewBKtee3TWrullu269byR2bnA?e=JJ4AUx|Introduction DM - Video2]]| 
-|1.|  16.09  14:15‑16:00 | Overview. Introduction to KDD  | {{ :magistraleinformatica:dmi:2021-1-overview.pdf |}}{{ :magistraleinformatica:dmi:1-intro-dm.pdf |}} | Chap. 1 Kumar Book| [[https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Meeting%20in%20_General_-20210916_140839-Meeting%20Recording.mp4?web=1|Video 1]]  [[ https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Meeting%20in%20_General_-20210916_151538-Meeting%20Recording.mp4?web=1|Video 2]] | +|2.  |  28.09  | Data Understanding | {{ :magistraleinformatica:dmi:2-data_understanding.pdf |}}|Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 |  
-|2.|  17.09   09:00-10:45 | Data Understanding | {{ :magistraleinformatica:dmi:2-data_understanding.pdf | Slides DU}} |Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 | [[ https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Data%20Mining%20Lecture-20210917_071017-Meeting%20Recording.mp4|Video 1]]  [[ https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Data%20Mining%20Lecture-20210917_101809-Meeting%20Recording.mp4|Video 2]] | +|3.  |  29.09  | Data Understanding & Data Preparation  {{ :magistraleinformatica:dmi:3-data_preparation.pdf |}} |Chap.2 Kumar Book and additioanl resource of Kumar Book:[[https://www-users.cs.umn.edu/~kumar001/dmbook/data_exploration_1st_edition.pdf|Exploring Data]] If you have the first ed. of KUMAR this is the Chap 3 | | 
-|3.|  22.09  14:15-16:00 | Data Understanding + Data Preparation        | {{ :magistraleinformatica:dmi:3-data_preparation.pdf |}} | Chap. Kumar Book | [[https://unipiit.sharepoint.com/sites/a__td_50479/Shared%20Documents/General/Recordings/Data%20Mining%20Lecture-20210922_120312-Meeting%20Recording.mp4|Video]] | +|4.  |  04.10  | Data Preparation & Data Similarities |  {{ :magistraleinformatica:dmi:4-data_similarity.pdf |}} | Data Similarity is in Chap. 2  | [[https://unipiit.sharepoint.com/:v:/s/a__td_59044/EWaYURxnzPdIiLiqjkS4LM8B8sme_xmm0LwtK9EptuP0Jg?e=dsZojO|DP+Similarities]] The last minutes of the lecture were not recorded because of the connection| 
-|4.|  23.09  14:15-16:00 | Data Preparation + Data Similarities.|{{ :magistraleinformatica:dmi:4-data_similarity.pdf |}}       Data Similarity is in Chap. 2  | +|5.  |  05.10  | Python-LAB: Data Understanding | {{ :magistraleinformatica:dmi:dataunderstanding.zip | DU notebooks and data}} |  | [[https://unipiit.sharepoint.com/:v:/s/a__td_59044/EYWSZBIG7X1MoFOev5Th_cIBprLLN-AwSBMamgGzNju0Sw?e=jzdPx8|Python Lab on DU]]| 
-|5.|  24.09  09:00-10:45 | Introduction to Clustering. Center-based clustering: kmeans| {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}}  {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}}     Clustering is in Chap. 7  | +  06.10  | Suppressed |  |  | | 
-|6.|  29.09  14:15-16:00 | Hierarchical clustering       | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}} | Chap. 7 Kumar Book |  | +|6.  |  11.10  | Introduction to Clustering. Centroid-based ClusteringK-means algorithm. | {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}} {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}} | Chap. Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EV-fDd75MIxGmazA79kFHCYBI78yYwqy7AFE5h9MN2rRqg?e=YVgdjS|Video 1: Introduction to Clustering + K-means - Part 1]] - Video of previous years
-|7.|  30.09  14:15-16:00 | Density based clustering. Clustering validityLabDU | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}}  {{ :undefined:dataund.zip Notebook DU tips}} {{ :magistraleinformatica:dmi:adult_du.zip Another Notebook on DU}}  | Chap. Kumar Book  +|7 |  12.10  | Centroid-based ClusteringK-means variants. | {{ :magistraleinformatica:dmi:6.2-basic_cluster_analysis-kmeans-variants.pdf |}} | Chap. 7 Kumar Book {{ :magistraleinformatica:dmi:clusteringmixturemodels.pdf |}} {{ :magistraleinformatica:dmi:xmeans.pdf |}}| [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ETySd1UWIzxCoAKilzaXO_MBW8oXZZCjf5FEhyywGIdJBg?e=Xq2jdo|Video 2: Introduction to Clustering + K-means - Part 2]]]  [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQTbbvqF2kJOgEsFQ1WF48cBjWf2wgTCbOjxcQzn9MyVzw?e=KQ7gEZ|Video 1: Center-based clustering - Bisecting K-means, Xmeans, EM ]];Videos of previous years| 
-|8.|  01.10  09:00-10:45 | Python Lab - Clustering {{ :magistraleinformatica:dmi:tips_clustering.ipynb_complete.zip | Notebook CLustering Tips}}   |   | +|  |  13.10  | Suspension of teaching |  |  | Recording in Teams Channel | 
-|9.|  06.10  14:15-16:00        |  | |  | +|8.|  18.10  | Hierarchical and density based CLustering | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}} {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} |  Chap. 7 Kumar Book | Recording in Teams Channel  | 
-|10.| 07.10  14:15-16:00 |  | | | +|9.|  19.10  | Clustering Validity & Python LabClusterig K-means | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} |  Chap. 7 Kumar Book| Recording in Teams Channel  | 
-|   08.10  09:00-10:45 Lecture canceled |       | +|10.|  20.10 Python Lab: Clusterig Density based and hierarchical + Introduction to Classification |{{ :magistraleinformatica:dmi:clustering.zip | Notebook on Clustering}} {{ :magistraleinformatica:dmi:9.chap3_basic_classification-2023.pdf |}} | Chap.3 Kumar Book |Recording in Teams Channel | 
- +|11.|  25.10 | Decision Trees & Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book | Recording in Teams Channel     
 +|12.|  26.10 | Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book |     
 +|13.|  27.10 | Rule-based Classifiers |{{ :magistraleinformatica:dmi:10-rule-based-classifiers.pdf |}} | Chap.4 Kumar Book |  Recording in Teams Channel  |   
 +|14.|  02.11 | Rule-based Classifiers + Instance based Classifiers| {{ :magistraleinformatica:dmi:10-knn.pdf |}}| Chap.4 Kumar Book | Recording in Teams Channel     
 +|15.|  03.11 |Naive Bayesian Classifier. SVM. Ensemble Classifiers| {{ :magistraleinformatica:dmi:11_2023-naive_bayes.pdf |}} {{ :magistraleinformatica:dmi:14_svm_2023.pdf |}} {{ :magistraleinformatica:dmi:13_ensemble_2023.pdf |}}| Chap.Kumar Book | Recording in Teams Channel   |   
 +|16.|  08.11 | Python Lab: Classification|  {{ :magistraleinformatica:dmi:classification.zip |}} | | Recording in Teams Channel     
 +|17.|  09.11 | NN Classifiers| {{ :magistraleinformatica:dmi:15_neural_networks_2023.pdf |}} | Chap.4 Kumar Book | Recording in Teams Channel     
 +|18.|  10.11 | Python Lab: NN & Imbalanced Classification | {{ :magistraleinformatica:dmi:imbalanced_classification.zip |}} |  | Recording in Teams Channel   |   
 +|19.|  15.11 | Association Rule Mining: Apriori | {{ :magistraleinformatica:dmi:17_association_analysis.pdf |}} | Chap.5 Kumar Book |  Recording in Teams Channel  |   
 +|20.|  16.11 | Association Rule MiningEvalaution and FP-Growth  | {{ :magistraleinformatica:dmi:17_2023-fp-growth.pdf |}} | Chap.5 Kumar Book  Recording in Teams Channel  | 
 +|21. 17.11 | Sequential Pattern Mining | {{ :magistraleinformatica:dmi:18_sequential_patterns_2023.pdf |}} | Chap.6 Kumar Book |  Recording in Teams Channel  | 
 +|22.|  22.11 | Sequential Pattern Mining: timing constraint. Time Series Analysis: Similarities, Distances and Transformations| {{ :magistraleinformatica:dmi:22_time_series_similarity_2023.pdf |}} | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf |Overview on Time Series]]  |  Recording in Teams Channel 
 +|23.|  23.11 |  Time Series AnalysisShapelet & Motif| {{ :magistraleinformatica:dmi:23_time_series_motif-shapelets2023.pdf |}} | {{ :magistraleinformatica:dmi:shaplet.pdf |}} | Recording in Teams Channel   | 
 +|24.|  24.11  Time Series Analysis: Shapelet & Motif; introduction to ethics and privacysame slides of the previous lecture and {{ :magistraleinformatica:dmi:19_ethics_privacy_2023_intro.pdf |}}  | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}} [[https://www.cs.ucr.edu/~eamonn/MatrixProfile.html|Papers and resourse on motif]] |  Recording in Teams Channel  
 +|25.|  29.11 | Python Lab: ARM, SPM, Time series transformations  | {{ :magistraleinformatica:dmi:ar_spm.zip |}} {{ :magistraleinformatica:dmi:timeseries.zip |}} |  | Recording in Teams Channel 
 +|26.|  30.11 | Python Lab: Time series analysis  | notebooks in the zip file of the previous lecture| | Recording in Teams Channel   | 
 +|27. 01.12 | Privacy in AI and Big Data Analytics  | {{ :magistraleinformatica:dmi:19_ethics_privacy2023.pdf |}} This set of slides include alse the introduction of the lecture 24.11.2023 |{{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} {{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} {{ :magistraleinformatica:dmi:prudence.pdf |}} {{ :magistraleinformatica:dmi:chapter-ppdm.pdf |}}| Recording in Teams Channel   | 
 +|28.|  06.12 | Explainable AI | {{ :magistraleinformatica:dmi:20_explainability_2023.pdf |}}|{{ :magistraleinformatica:dmi:lore-tabular.pdf |}} {{ :magistraleinformatica:dmi:xai-survey.pdf |}} {{ :magistraleinformatica:dmi:imagexai.pdf |}} {{ :magistraleinformatica:dmi:timeseriesxai.pdf |}}| Recording in Teams Channel   | 
 +|29.|  07.12 | Explainable AI | {{ :magistraleinformatica:dmi:21_anomaly_detection_2023.pdf |}} {{ :magistraleinformatica:dmi:anomaly_detection.zip |}}| | Recording in Teams Channel   | 
 +|30.|  13.12 | Anomaly Detection | {{ :magistraleinformatica:dmi:21_anomaly_detection_2023.pdf |}} | | Recording in Teams Channel   | 
 +|31-32.|  14.12 9-11| Lab Python in AD + Lab Python in XAI| {{ :magistraleinformatica:dmi:anomaly_detection.zip |}}| | Recording in Teams Channel   | 
 +|33.|  15.12 9-11| Lab Python in XAI + Paper Presentation| | |    | 
 +|34.|  18.12 09-11| Paper Presentation| | |    | 
 +|35.|  20.12 09-11| Paper Presentation| | |    | 
 +|36.|  21.12 09-11| Paper Presentation| | |    |
  
 +  
 ====== Exams ====== ====== Exams ======
-**Mid-term Project **+**Project **
  
 A project consists in data analyses based on the use of data mining tools.  A project consists in data analyses based on the use of data mining tools. 
-The project has to be performed by a team of 2/3 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 20 pages of text including figures. The students must deliver both: paper (single column) and  well commented Python Notebooks.+The project has to be performed by a team of 3 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 25 pages of text including figures. The students must deliver both: paper (single column) and  well commented Python Notebooks.
  
 +  * First part of the project consists in the **assignments** described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub.pdf | Project Description}}
 +  - **Dataset: {{ :magistraleinformatica:dmi:gun-data.zip | Dataset Files}}** 
 +  - **Deadline**: the fist part has to be delivered within <del> November 19th, 2023</del> November 26th, 2023. Send an email to: anna.monreale@unipi.it, lorenzo.mannocci@phd.unipi.it
    
 +  * Second part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub-updated.pdf |Updated Project Description}}
 +     - **Deadline**: Jan 8, 2024 
 +
 +  * Third part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub-complete.pdf |Updated Project Description}}
 +   - **Deadline**:   Jan 8, 2024 
 +
 +
 +**Students who did not deliver the above project within **Jan 8, 2024** need to ask by email a new project to the teachers. The project that will be assigned will require about 20 days of work and after the delivery it will be discussed during the oral exam. **
 +
 ** Paper Presentation (OPTIONAL)** ** Paper Presentation (OPTIONAL)**
  
-Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions. They only need to present the project (see next point).+Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions on the entire program. They only need to present the project (see next point) and answer open question only on the topics which will not be covered by the project. The paper presentation can be done by the group or by a single person.
  
 **Oral Exam** **Oral Exam**
-  * **Project presentation** (with slides) – 10 minutes: mandatory for all the students +  * **Project presentation** (with slides) – 10-15 minutes: mandatory for all the students with question fo understanding the details of any part of the project. 
-  * ** Open questions ** on the entire program: optional only for students opting for paper presentation. +  * ** Open questions on the entire program **for students who will not opt for paper presentation 
-  +  * ** Open questions on the topics which will not be covered by the project ** only for students opting  for paper presentation. 
- +  * Group presentations of the project are preferred. If this is impossible please contact me for finding a solution.
  
 +**How to book for the exam colloquium? **
 + 
 +In https://esami.unipi.it/ you can find the dates for the exam: one for January and one for February. Each student must do the registration on one of the 2 dates. These are not the dates of the colloquium or project delivery but we will use the list of registered students for organizing the exam dates. After that deadline we will share with you a calendar for the oral exam.
  
-===== Reading About the "Data Scientist" Job =====+
  
-** ... a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the "sexiest" around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them. ** 
  
-//Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.// 
  
-  * Data, data everywhere. The Economist, Feb. 2010 {{:dm:economist--010.pdf|download}} +====== Previous years ===== 
-  * Data scientist: The hot new gig in tech, CNN & Fortune, Sept. 2011 [[http://tech.fortune.cnn.com/2011/09/06/data-scientist-the-hot-new-gig-in-tech/|link]] +[[DM-INF 2022-2023]]
-  * Welcome to the yotta world. The Economist, Sept. 2011 {{:dm:economist-2012-dm.pdf|download}} +
-  * Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, Sept 2012 [[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1|link]] +
-  * Il futuro è già scritto in Big Data. Il SOle 24 Ore, Sept 2012 [[http://www.ilsole24ore.com/art/tecnologie/2012-09-21/futuro-scritto-data-155044.shtml?uuid=AbOQCOhG|link]] +
-  * Special issue of Crossroads - The ACM Magazine for Students - on Big Data Analytics {{:dm:crossroadsxrds2012fall-dl.pdf|download}} +
-  * Peter Sondergaard, Gartner, Says Big Data Creates Big Jobs. Oct 22, 2012: [[https://www.youtube.com/watch?v=mXLy3nkXQVM|YouTube video]]+
  
-  * Towards Effective Decision-Making Through Data Visualization: Six World-Class Enterprises Show The Way. White paper at FusionCharts.com. [[http://www.fusioncharts.com/whitepapers/downloads/Towards-Effective-Decision-Making-Through-Data-Visualization-Six-World-Class-Enterprises-Show-The-Way.pdf|download]]+[[DM-INF 2021-2022]]
  
-====== Previous years ===== 
 [[DM-INF 2020-2021]] [[DM-INF 2020-2021]]
  
magistraleinformatica/dmi/start.1633305456.txt.gz · Ultima modifica: 03/10/2021 alle 23:57 (3 anni fa) da Anna Monreale