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They will learn to produce original works through the use of computer programs and will be trained in the art of programming.\nMaster\u2019s Programme (3 years)\nThe Masters programme will be taught over three years and is divided into six academic year. The first three years will be taught within the department, and the last three years will be taught from University of Agder, Grimstad.\nThe Masters programme will cover the following content:\nData\nStructure, format and interpretation of digital data. The students will be given an introduction into the field of Data and information science, learning about data and information infrastructure, it\u2019s collection, storage, processing, inter-operability and various applications.\nData science\nThesis project\nThe three main parts of the thesis project are:\na) Exploratory Data Analysis: Students will conduct a five-step data analysis with respect to a large dataset, with the goal of solving the following:\nWhat is the typical variability in the dataset?\nWhat is the range of the data?\nWhich variables can be excluded from the analysis due to issues with the data?\nWhich variables are likely to be important in the analysis?\nb) Exploratory Data Visualisation: Students will conduct a visual analysis of the data in order to identify trends, patterns and connections within the data. The goal of this analysis is to generate visual hypotheses of the data.\nc) Building a predictive model: Students will design a predictive model for a large scale problem in the field of Data analytics that is relevant to the specific group of data. The final product must be presented in a public version that can be used by other schools of computer science or to be communicated to a larger audience outside of the academic environment.\nEssential Knowledge and Competencies\nIn order to succeed in the Masters programme you will need to meet the standards set in the course. These standards are very high and therefore it is not enough to just know a little about the given course; you will have to demonstrate understanding of the subject<\/p>\n\n\n\n <\/p>\n\n\n\n\n <\/p>\n\n\n\n Q:\nredshift – compare month\/year date with date formatted to date\nI have a db with a table that has a column formatted like this:\ndf2.january_2011\ndf2.february_2011\n…\nI am trying to create a view that shows only records from the last year, but the following query does not compare the date data to the date formatted as date.\nselect * from df2 where YEAR(df2.january_2011) = YEAR(CAST(df2.date_format as date))\nI have tested the following queries, and it seems the compare is not the case\nselect * from df2 where YEAR(df2.january_2011) = YEAR(CAST(df2.date_format as date)) and YEAR(df2.date_format) = YEAR(CAST(df2.january_2011 as date))\nselect * from df2 where YEAR(df2.january_2011) = YEAR(CAST(df2.date_format as date)) and YEAR(CAST(df2.date_format as date)) = YEAR(CAST(df2.january_2011 as date))\nIf I run the following, I get the desired results:\nselect * from df2 where year(date_format) = year(to_date(df2.january_2011, ‘dd-mon-yy’))\nCan someone please tell me where I am going wrong with my query?\nI am using Redshift\nA:\nYou need to use the %Y function instead of year(), and then use the modulus operator to indicate that you want the “last year”:\nselect *\nfrom df2\nwhere YEAR(january_2011) = YEAR(TO_DATE(date_format, ‘dd-mon-yy’)) MOD 12;\nNote that your 1st and 3rd queries have a bug. Rather than\nYEAR(df2.january_2011) = YEAR(CAST(df2.date_format as date))\nit should be\nYEAR(df2.january_2011) = YEAR(CAST(df2.date_format as date))\nand \nYEAR(CAST(df2.january_2011 as date)) = YEAR(CAST(df2.january_2011 as date))<\/p>\n\n\n\n One desktop with at least 8 GB RAM\nOne monitor capable of 1920×1080 (Full HD)\nSource:\nWhy should you play Stellar Pathways?\n– Stellar Pathways is a game about space exploration – We wanted to explore new systems, explore new star systems, meet new and old friends, make our own new life in the universe.\n– It’s a dynamic game. 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