INSTRUCTIONS Perform a literature review on a known topic in business analytics. It can be any topic on tools, methodologies or applications. Some examples include, but not limited to: 1. Use of predictive analysis in healthcare industry 2. Comparison of BI tools 3. Techniques of predictive analysis 4. Methods of representing multi-dimensional data in visualisations 5. Analytics techniques to improve logistics management 6. Security of data and privacy concerns in analytics Please note that this is an individual project. Discuss with your lecturer before week 7 to decide on a topic. The topic needs to be chosen before week 7. Based on your review you need to submit a report in IEEE format; see the word file in the moodle. Submit your report in a word or pdf format. Your report should be limited to 1200-1500 words.
NOTE : 1ST PART WAS PPT ATTACHED FOR YOUR REFERENCE.
MITS6002 Business Analytics Assignment 2 Research Study MITS6002 Assignment 2 Copyright © 2015-2019 VIT, All Rights Reserved. 2 Learning Outcomes The following learning outcomes have been covered in this assessment: LO3. Conduct research on a collection of business cases and perform statistical analysis as also interpret these outcomes to recommend appropriate business directions. LO4. Critically analyse a variety of business domains and adopt business analytics models appropriate to the domain that requires quantitative techniques for decision making. LO5. Recommend appropriate analytic tools and techniques to resolve complex business analytics problems in various industry sectors. Objective(s) This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student presentation skills and to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter. INSTRUCTIONS Perform a literature review on a known topic in business analytics. It can be any topic on tools, methodologies or applications. Some examples include, but not limited to: 1. Use of predictive analysis in healthcare industry 2. Comparison of BI tools 3. Techniques of predictive analysis 4. Methods of representing multi-dimensional data in visualisations 5. Analytics techniques to improve logistics management 6. Security of data and privacy concerns in analytics Please note that this is an individual project. Discuss with your lecturer before week 7 to decide on a topic. The topic needs to be chosen before week 7. Based on your review you need to submit a report in IEEE format; see the word file in the moodle. Submit your report in a word or pdf format. Your report should be limited to 1200-1500 words. MITS6002 Assignment 2 Copyright © 2015-2019 VIT, All Rights Reserved. 3 Submission Guidelines All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days). The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference web-sites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission. Your report should be a single word or pdf document containing your report. Data Analytics and Business Intelligence (Case Study of Video Gaming Industry) Data Analytics and Business Intelligence (Case Study of Video Gaming Industry) Student Name: AHMED PASHA Student I’d: 41927. Introduction and Descriptive Statistics The data analysis in this study is interesting in the investigation of trends in the video gaming industry with data collected from (Rush, 2016). The table on the side gives the summary statistics of the descriptive analysis of the numeric variables. 2 Visualizations Pie Charts are circular graphical representations of data magnitude attributes (Barbara & Susan, 2014). Chart below shows North America has the highest sales. Column charts are vertical bars representing frequencies (O'Neil & Schutt, 2013). Chart below shows DS and PS2 had highest frequencies. 3 Total Sales by Regions Pie Chart Total SalesNA_SalesEU_SalesJP_SalesOther_Sales2946.70000000023991850.2399999998693746.99999999996317656.9699999999109 Column Chart for Count of Platform Total3DSDCDSPCPSPS2PS3PS4PSPX360XBXOne51252212295711902127130639311931232803247 Visualizations Pareto charts are graphical representations showing both cumulative and individual frequencies (Georgina, 2015; Chambers, 2017; Roles, Baeten, & Signer, 2016). Below is a Pareto chart of global sales by platform. 4 Visualizations Scatterplots are dots plotted for corresponding data points of the two variables (Kirk, 2016). Chart below indicates presence of linearity between North American and Global Sales. Area Charts are modified line graphs with area under the graphs shaded as shown below (Martinez, Martinez, & Solka, 2010). 5 NA Sales vs Global_Sales Scatterplot Global_Sales11.289.05000000000000079.7100000000000009157.029.434.746.389.668.413.435.516.859.03999999999999916.039.69999999999999935.284.998.258.525.546.995.035.993.962.57.974.344.34999999999999963.016.764.01999999999999964.88999999999999972.964.994.765.016.733.667.043.016.653.884.09999999999999965.935.72.02999999999999984.40000000000000045.051.12000000000000016.821.062.792.910.992.992.27999999999999982.762.92.810.663.783.274.594.80999999999999964.463.272.852.52999999999999983.272.983.682.712.933.234.15000000000000042.773.113.270.841.662.790.782.644.98000000000000042.54999999999999983.644.343.70.083.114.053.923.540.82.45000000000000023.182.631.882.412.81.880.632.25999999999999982.49000000000000023.072.543.282.990.472.712.812.06999999999999982.731.851.741.623.131.923.222.02999999999999983.112.29999999999999980.970.651.211.09000000000000012.441.983.223.813.591.631.961.850.612.04999999999999981.73.422.793.363.491.833.42.313.9802.571.912.742.572.832.362.991.881.733.051.942.292.06999999999999981.82.084.183.193.632.782.710.21.612.712.660.12.822.02999999999999981.561.742.20000000000000022.091.482.770.579999999999999962.50999999999999982.24000000000000021.461.412.50999999999999980.922.45000000000000021.70.780.881.282.250.842.02999999999999983.793.364.030.712.571.10000000000000012.31999999999999980.122.662.351.42.27999999999999981.681.12999999999999992.082.671.021.191.532.150.92.290.681.15999999999999992.49000000000000020.931.531.440.162.132.121.780.861.351.822.232.42.02999999999999981.41.371.31.931.330.5799999999999999602.132.041.581.652.52999999999999982.080.052.080.61.932.04999999999999982.021.352.18000000000000021.62.00999999999999981.571.561.231.661.91.981.960.712.142.662.272.112.851.32.20000000000000022.152.11.212.10.5902.41.551.12999999999999991.09000000000000011.750.461.831.560.812.042.142.47000000000000020.881.6301.451.440.271.471.41.991.91.50.092.611.890.60.50.890.952.340.721.911.721.761.481.781.352.151.272.060.681.871.72.141.210.550000000000000041.520.91.691.40.881.992.172.51.720.5111.891.751.911.941.291.541.941.851.450.611.41.382.61.14999999999999991.670.121.361.581.011.041.361.841.332.22000000000000021.451.821.761.551.880.791.831.540.081.750.991.512.02999999999999981.30.092.52999999999999982.091.071.371.982.521.321.651.870.021.280.711.321.320.289999999999999981.961.191.281.980.462.191.681.441.050.891.51.411.451.741.611.451.251.20.410.991.521.492.421.850.892.381.2301.630.280000000000000031.481.441.991.780.431.591.14999999999999990.690.860.7821.472.151.542.310.760.591.560.91.72.02999999999999980.480.621.41.531.312.140.370.931.042.111.760.642.211.12999999999999990.262.10.132.291.841.811.031.14999999999999991.381.730.861.14999999999999991.470.931.562.121.071.520.51.462.081.261.560.60.871.630.289999999999999982.06999999999999980.810.691.442.12.29999999999999981.491.672.041.15999999999999991.482.27999999999999980.962.18000000000000021.251.742.02999999999999981.681.730.921.14999999999999991.081.951.380.790.851.580.961.651.12000000000000010.511.070.9101.021.380.30.381.071.270.947.0000000000000007E-21.081.340.61.280.621.521.931.231.41.480.511.981.09000000000000011.050.881.240.550000000000000041.011.062.00999999999999981.211.781.031.41.451.061.681.540.341.171.920.61.291.281.231.671.51.280.810.591.20.521.041.371.621.680.110.631.440.940.631.541.170.711.631.20.9901.841.260.960.251.15999999999999991.771.11000000000000010.892.040.890.891.411.771.540.920.361.480.460.680.631.21.061.040.61.770.630.891.740.710.731.450.481.211.571.821.14999999999999990.751.471.250.90.579999999999999960.731.051.480.480.951.060.790.750.730.740.710.50.821.09000000000000010.741.12000000000000011.261.09000000000000011.690.950.781.011.940.590.730.491.09000000000000011.411.421.2211.530.781.310.761.030.620.940.261.10000000000000010.730.720.660.860.741.561.14999999999999991.370.920.250.891.251.420.470.221.60.531.051.751.12000000000000010.9400.910.470.621.330.5900.771.321.531.330.610.920.920.511.050.841.731.810.971.09000000000000011.051.10000000000000010.780.420.8811.6611.410.730.550000000000000040.321.251.12000000000000010.390.851.190.231.271.071.060.861.230.860.520.611.681.240.061.250.450.670.3510.850.90.720.630.560000000000000051.050.411.660.610.850.431.480.20.680.850.350.611.371.610.80.411.060.990.881.090000000000000100.71.13999999999999991.14999999999999990.230.831.410.590.630.681.220.720.940.491.10000000000000010.790.480.790.821.580.880.380.980.811.670.90.931.581.221.09000000000000010.970.961.12999999999999991.350.810.831.080.450.780.811.011.250.90.960.650.760.5699999999999999501.09000000000000010.930.81.440.540.81.540.521.181.350.711.320.860.450.81.020.590.90.680.30.7900.820.981.491.30.590.711.260.010.710.610.040.880.880.920.880.60.579999999999999961.060.10.770.920.90.840.569999999999999951.310.980.170.630.771.070.621.051.320.569999999999999950.80.760.910.950.569999999999999951.550.470.850.370.850.830.579999999999999961.40.51.260.850.181.440.720.850.641.070.361.431.450.881.430.921.260.850.9400.231.410.351.180.560000000000000050.020.60.60.661.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