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Business AnalyticsCredits3Faculty NameSandipan KarmakarProgram EMBA 2020-21Academic Year and Term 2020-21, Term-IVCourse DescriptionIn the modern era of exorbitantly soaring quantity of raw data as well as processed information, challenges faced by decision makers including management professionals primarily consist of visualizing, analyzing and opting for the best or nearly best decisions which may help the organization in accomplishing many challenges. Business Analytics is the scientific process of transforming data into insights for making better decisions used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making. This course is aimed at providing the exposure and hands on experience to the attendees on Business Analytics using Data Mining and Machine Learning principles. Along with this the attendees will be exposed to Python to aid in computing utilizing the Data Mining and Machine Learning algorithms. Course Leaning OutcomesAt the end of the course, you should Be able to define a Business problem as an Analytics problem.Be able to choose from various Analytics techniques in a given situation.Be able to understand, interpret and recommend actions based on Analytics output.Be able use Python for statistical analysis, Descriptive and Predictive analyticsSession PlanSessionTopics/ActivitiesReading1Introduction to Business Analytics – Basic Tasks of AnalyticsParadigms of AnalyticsSome Examples of Tasks of AnalyticsT 2-3 Descriptive AnalyticsWorking with DatasetsHandling Missing ValuesExploratory Data Analysis & VisualizationT4-5Introduction to Python & built-in Libraries – Basic Algebraic ComputationsStatistical AnalysesWriting Functions Built-in Libraries – NumPy, SciPy, Matplotlib, Pandas, Scikit-Learn T6-8Linear RegressionSimple Linear RegressionModel DiagnositicsMultiple Linear RegressionVariable EncodingMulti-collinearityResidual AnalysisT & Notes 9-11Classification ProblemsBinary Logistic RegressionGain Chart Lift ChartClassification Tree T & Notes11-15Advanced AnalyticsGradient Descent AlgorithmsAdvanced Regression ModelsAdvanced ML AlgorithmsK-NN AlgorithmEnsemble MethodsRandom ForestBoostingT & Notes16-17Clustering Hierarchical & k-means ClusteringMeasuring Clustering GoodnessT & Notes18Application 1: Building Recommender System19Application 2: Building Text Mining System20Course Discussion, Doubt Clearing and Project DiscussionEvaluation2 Quizzes of 15% + 15% weight. (Best Two out of Three) – Online Mid Term of 20% weight – Online Individual projects of 20% weight – Online End-term of 30% weight – Online No Makeup Exams. Marks for missed components will be equated to the minimum of the other attended components (in percentage terms). Reading PYTHON DATA ANALYTICS by FABIO NELLI; Second edition (2018), Apress, ISBN-13: 978-1-4842-3912-4 (T)Reference TextsINTRODUCTION TO MACHINE LEARNING WITH PYTHON by ANDREAS C. MUELLER and SARAH GUIDO, O’Reilly (R)Academic IntegrityMalpractice in any form will be dealt with as per manual of policies ................
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