HSD. W Business Analytics (M.Sc.) IT in Business Analytics. IT Applications in Business Analytics SS2016 / 01 Introduction Thomas Zeutschler

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1 Hochschule Düsseldorf University of Applied Scienses Fachbereich Wirtschaftswissenschaften W Business Analytics (M.Sc.) IT in Business Analytics IT Applications in Business Analytics SS2016 / 01 Introduction SS IT Applications in Business Analytics - 1. Introduction 1

2 Lecturer Head of Analytics Center of Excellence and Lead Architect for Business Intelligence at Henkel AG & Co. KGaA, Düsseldorf IT Applications in Business Analytics SS IT Applications in Business Analytics - 1. Introduction 2

3 Let s get started SS IT Applications in Business Analytics - 1. Introduction 3

4 Targets of Module and Lectures German Die Studierenden erlernen die Anwendung praxisrelevanter IT- Werkzeuge (für Business Analytics) anhand von Fallstudien. English Students will learn to apply analytical tools on business problems. American English We ll try make you a Bruce Willis in Analytics. SS IT Applications in Business Analytics - 1. Introduction 4

5 Scope of Module and Lectures Advanced Analytics Advanced Analytics is the autonomous or semiautonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. SS IT Applications in Business Analytics - 1. Introduction 5

6 In Scope / Out of Scope Data Science Data Mining, Text Mining Predictive Analytics, Simulation, Machine Learning Database Technologies Information Retrieval Data Analysis Text Analysis, Semantic Web, XML Data Warehouse, Data Mart, ETL In Memory Technologies Reporting, OLAP Data and Decision Modelling Data Visualization Data Quality Management, Data Protection Specific Business Applications Case Studies SS IT Applications in Business Analytics - 1. Introduction 6

7 Application of Analytics in Business Science Sensor Data Process Optimization Product Quality Fraud Detection Web & Social Analytics Customer Segmentation Customer Churn Basket Analysis Sentiment Analysis Predictive Maintenance Pattern Detection IoT Analytical Apps Price Prediction News & Alerts Deep Learning Text Mining Sales Forecasting Financial Simulation Next Best Offer Up-/Cross-Selling Data Visualization SS IT Applications in Business Analytics - 1. Introduction 7

8 Sequence of Lectures Introduction 1 st April 2016 Methodology and process model for analytics (CRISP DM) Tools, technologies and data sources The R Programming Language KNIME Case Study Case Study 2 Case Study 3 Wrap Up 8 th July 2016 Theory Tools Training Hands On Case Studies SS IT Applications in Business Analytics - 1. Introduction 8

9 Analytics Value Chain SS IT Applications in Business Analytics - 1. Introduction 9

10 Analytics Value Chain Data Information Insight Action identify, access, store and manage data data discovery understand, transform and structure data data understanding process and evaluate data, derive insights data modelling communicate, decide, take action leverage insights SS IT Applications in Business Analytics - 1. Introduction 10

11 Analytics Value Chain Technology Building Blocks Data Information Insight Action Data Acquisition Data Modelling & Analytics Automation (Big) Data Management SS IT Applications in Business Analytics - 1. Introduction 11

12 Technology Building Blocks Your lost SS IT Applications in Business Analytics - 1. Introduction 12

13 Module Focus Technologies Internet Data Information Insight Action not relevant Data Acquisition Data Modelling & Analytics Automation (Big) Data Management SS IT Applications in Business Analytics - 1. Introduction 13

14 Adventures in Analytics SS IT Applications in Business Analytics - 1. Introduction 14

15 The Beauty of Evidence Exercise Visualize the successive losses in men of Napoléon's French army troops in the Russian campaign SS IT Applications in Business Analytics - 1. Introduction 15

16 The Beauty of Evidence Map of the successive losses in men of the French army in the Russian campaign by Charles Jospeh Minard in 1869 Exercise Visualize the successive losses in men of Napoléon's French army troops in the Russian campaign SS IT Applications in Business Analytics - 1. Introduction 16

17 The Force of Analytics SS IT Applications in Business Analytics - 1. Introduction 17

18 The Force of Analytics SS IT Applications in Business Analytics - 1. Introduction 18

19 The Force of Analytics SS IT Applications in Business Analytics - 1. Introduction 19

20 The Force of Analytics SS IT Applications in Business Analytics - 1. Introduction 20

21 The Force of Analytics SS IT Applications in Business Analytics - 1. Introduction 21

22 Data Scientist SS IT Applications in Business Analytics - 1. Introduction 22

23 9 Skills You Need to Become a Data Scientist Technical Skills: Analytics 1. Education Data scientists are highly educated 88% have at least a Master s degree and 46% have PhDs and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). 2. Analytical Tools In-depth knowledge of at least one analytical tool, for data science R is generally preferred. SS IT Applications in Business Analytics - 1. Introduction 23

24 9 Skills You Need to Become a Data Scientist Technical Skills: Computer Science 3. Python Coding Python is the most common coding language I typically see required in data science roles, along with Java/C#, Perl, or C/C Hadoop Platform Although this isn t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 or Microsoft Azure can also be beneficial. 5. SQL Database/Coding Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL. 6. Unstructured data It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds or audio. SS IT Applications in Business Analytics - 1. Introduction 24

25 9 Skills You Need to Become a Data Scientist Technical Skills: Computer Science 7. Intellectual curiosity No doubt you ve seen this phrase everywhere lately, especially as it relates to data scientists. Frank Lo describes what it means, and talks about other necessary soft skills in his guest blog posted a few months ago. 8. Business acumen To be a data scientist you ll need a solid understanding of the industry you re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data. 9. Communication skills Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately. Check out our recent flash survey for more information on communication skills for quantitative professionals. SS IT Applications in Business Analytics - 1. Introduction 25

26 Lecture Summary & Homework SS IT Applications in Business Analytics - 1. Introduction 26

27 Lessons Learned Scope and lectures of module IT applications in business analytics. Analytics is a very wide and depth field of science, methodologies, technologies and professional business. Data and analytics is the #1 driver for new business models. Mastering data and analytics is a competition advantage. Analytics is Fun! Bruce Willis SS IT Applications in Business Analytics - 1. Introduction 27

28 Resources Analytics Site Analytics Literature (ebooks) SS IT Applications in Business Analytics - 1. Introduction 28

29 Get Prepared (Homework) Prepare your laptop / infrastructure Download and install R and R-Studio R >>> R-Studio >>> Register, download and install KNIME KNIME >>> Register for Azure For individual purposes: For course purposes: already done SS IT Applications in Business Analytics - 1. Introduction 29

30 Any Questions? SS IT Applications in Business Analytics - 1. Introduction 30