Activity Guide and Evaluation Rubric - Unit - 1 - Step 2 - Big Data Analytics and Machine Learning
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School
Universidad Abierta y a Distancia de México**We aren't endorsed by this school
Course
UNIDAD 1
Subject
Information Systems
Date
Dec 19, 2024
Pages
8
Uploaded by KidAntelopePerson2042
1Universidad Nacional Abierta y a Distancia Vicerrectoría Académica y de Investigación Course: Big Data Integration Code: 203008077 Activity Guide and Evaluation Rubric –Step 2 Big Data Analytics and Machine Learning 1.Activity Description Type of activity: IndividualEvaluation moment: Intermediate Unit 1Highest score of the activity: 175 points The activity starts on: Wednesday, September 4, 2024The activity ends on: Sunday, October 13, 2024With this activity, you are expected to achieve the following learning outcomes: Distinguishes what problems are and what are not big data through the reformulation from the data science for a correct choice of model predictions based on data. The activity consists of: Dear Student at the end of step 2, you should have the following products. •Activity 1. Conceptual Map •Activity 2. Description of Data domain •Activity 3. Description of Data training, validation, and test •Activity 4. The distinction of Machine Learning for computer processing •Activity 5. Pass and obtain accreditation Big Data 101, for the IBM certification. •Activity 6. Socialization in the Forum For the development of the activity, it is recommended: ➢Read the activity guide and rubric evaluation carefully.
2➢Review each of the steps proposed for the solution of the 6 activities, it is recommended not to skip steps and read them in order. ➢Review the general guidelines for the preparation of the learning evidence to be delivered. ➢Make significant contributions in the forum for the development of the work. Detailed description of the activities to be carried out Activity 1. Conceptual Map For the development of this exercise, it is necessary to review the references in the Learning Environment (Unit 1 - Historical interpretation and review of Big Data-Contents and bibliographic references) After reviewing the suggested references, the student must make a conceptual map with the following concepts: • Business Analytics. • Data and Statistical Methods. For the construction of the conceptual map, tools such as Cmaptools, GoCongr, PowerPoint, among others, can be used; then, this conceptual map has to be shared in the discussion forum. Activity 2. Description of Data domain Using an illustrative scheme, you should portray a Venn Diagram of the 5 Vs attributes of Big Data, including the following points for statistics domain: • Description of Data processing. • Data analysis.
3• Data visualization. Activity 3. Description of Data training, validation, and test Taking into account the bibliographic references and others sources, the student must create a presentation of 3 slides including the explanation of the Data training, validation and test. Activity 4. The distinction of Machine Learning for computer processing Based on the references, you have to make a comparison chart where you include and explain the importance of Machine Learning for computer processing (Unsupervised, Supervised and Reinforcement Learning). Activity 5. Pass and obtain accreditation Big Data 101, for the IBM certification. For the development of this exercise, it is necessary to review the references in the Learning Environment (Unit 1 - Historical interpretation and review of Big Data-Contents and bibliographic references). After reviewing the suggested references, the student will go on the Cognitive Class platform as a continuation of your academic progression. The task involves your enrollment in a course that builds upon the concepts and lessons covered in our prior learning guide activities. By successfully completing this course, you will not only exhibit your comprehensive understanding of the material but also obtain the esteemed IBM certification, symbolizing your mastery in this domain. Activity 6. Socialization in the Forum
4You must share the development of Activity 5 in the forum and provide feedback on the Exercise that some classmates shared too. For the development of the activity consider that: In the Initial Information Environment, you must: •Review the presentation of the course, accept the rules and conditions for the development of the course and review the course agenda. In the Learning Environment, you must: •Revise the bibliographic reference of the Unit 1 - Historical interpretation and review of Big Data. •Share in the discussion forum the development of Activity 5. •Feedback about Activity 5 shared by a classmate in the discussion forum. In the Evaluation Environment, you must: •Upload the PDF document with the parameters requested in the step 2 Evidences of individual work:The individual evidence to be submitted is: The final document delivered must have a cover, the development of the 6 activities and bibliographical references Evidences of collaborative work: No collaborative evidence is required in this activity. 2.General Guidelines for the Development of Evidences to Submit For Individualevidences, consider the following: •Make a general survey of the course and of each of the environments before approaching the development of the activities. •Identify the resources and referents of the unit to which the activity corresponds.
5•Before submitting the requested product, students should check that it meets all the requirements mentioned in this activity guide. •Participate in the discussion forum applying the rules of Virtual Netiquette, always showing respect for the ideas of your peers and the faculty. •Do not commit fraud, plagiarism or acts that threaten the normal academic development of activities Please keep in mind that all individual or collaborative written products must comply with the spelling rules and presentation conditions defined in this activity guide. Regarding the use of references, consider that the product of this activity must comply with APAstyle. In any case, make sure you comply with the rules and avoid academic plagiarism. You can review your written products using the Turnitin tool found in the virtual campus. Under the Academic Code of Conduct, the actions that infringe the academic order, among others, are the following: paragraph e) Plagiarism is to present as your own work all or part of a written report, task or document of invention carried out by another person. It also implies the use of citations or lack of references, or it includes citations where there is no match between these and the reference and paragraph f) To reproduce, or copy for profit, educational resources or results of research products, which have rights reserved for the University. (Acuerdo 029 - 13 de diciembre de 2013, artículo 99) The academic penalties students will face are: a) In case of academic fraud demonstrated in the academic work or evaluation, the score obtained will be zero (0.0) without any disciplinary measures being derived.
6b) In case of proven plagiarism in academic work of any nature, the score obtained will be zero (0.0), without any disciplinary measures being derived. 3.Evaluation Rubric Template Type of activity: Individual Evaluation moment: Intermediate Unit 1 The highest score in this activity is 175 points First evaluation criterion: The student distinguishes the main concepts of Business Analytics and Data and Statistical Methods. This criterion represents 35 points of the total of 175 points of the activity. High level: The student presented clearly the topic chosen in a conceptual map made with a digital tool. If your work is at this level, you can get between 20 points and 35 points Average level: The student presented in an acceptable way the topic synthesized in a conceptual map, however, the digital tool management is regular. If your work is at this level, you can get between 10 points and 19 points Low level: The student doesn’t synthesize the topics in a conceptual map. Either does not carry out the activity in the English language If your work is at this level, you can get between 0 points and 9 points Second evaluation criterion: Description of Data domain High level: The student clearly presented the chosen topic in the presentation prepared with a digital tool If your work is at this level, you can get between 20 points and 35 points
7This criterion represents 35 points of the total of 175 points of the activity. Average level: The student presented a discussion on the description of the data domain, however, some ideas lack clear arguments. If your work is at this level, you can get between 10 points and 19 points Low level: The student does not present description of the data domain. Either does not carry out the activity in the English language If your work is at this level, you can get between 0 points and 9 points Third evaluation criterion: Description of Data training, validation and test This criterion represents 35 points of the total of 175 points of the activity. High level: The student presented the Description of Data training, validation and test. If your work is at this level, you can get between 20 points and 35 points Average level: The student presented the description of the training, validation and testing of data, however, some comparisons lack clear arguments. If your work is at this level, you can get between 10 points and 19 points Low level: The student does not present the description of the training validation and data test. Either does not carry out the activity in the English languageIf your work is at this level, you can get between 0 points and 9 points Fourth evaluation criterion: The distinction of Machine Learning for computer processing.This criterion represents 35 points of the total of 175 points of the activity. High level: The student makes the distinction of Machine Learning for computer processing If your work is at this level, you can get between 20 points and 35 points Average level: The student makes the distinction of machine learning for computer processing, however, some ideas mentioned lack clarity.If your work is at this level, you can get between 10 points and 19 points
8Low level: The student does not present the distinction of machine learning for computer processing. Either does not carry out the activity in the English language If your work is at this level, you can get between 0 points and 9 points Fifth evaluation criterion: Pass and obtain accreditation Big Data 101, for the IBM certification This criterion represents 35 points of the total of 175 points of the activity. High level: The student enrolls in the course, completes it, also attaches the certificate and badges as evidence of their learning process.If your work is at this level, you can get between 20 points and 35 points Average level: The student enrolls in the course and passes it, but does not attach the certificate and the badges, or does not obtain these certifications, as evidence of their learning process. If your work is at this level, you can get between 10 points and 19 points Low level: The student does not enroll in the course or does not complete it. If your work is at this level, you can get between 0 points and 9 points