This page contains resources used to support TU/e post-master course on Data Analytics (lecturer Zeljko Obrenovic).
Free Resources on Basic Analytic Techniques
- Statistics and Visualization for Data Analysis and Inference (MIT Open Courseware)
- Probability Cheatsheet
- Free Data Science Books
- Data Analytics Handbook
- Customer Analytics
- How to Cheat With Statistics
- Little Crash-Course in Big Data – Collecting Data
- Little Crash-Course in Big Data – Analysing Data
- Little Crash-Course in Big Data – Visualising Data
- Seeing Theory – A Visual Introduction to Probability and Statistics
- Web Analytics Overview
- Digital Dashboards: Strategic & Tactical: Best Practices, Tips, Examples
- Google Analytics Academy
- 2IID0. Web Analytics (2015-2016, Semester A, Quartile 2)
- Platforms and Tools
Telling stories with data
- Story-based inquiry: a manual for investigative journalists, The publication focuses on the hypothesis-based inquiry approach, which takes the basic assumption that a story is only a hypothesis until verified. The methods and skills applying to every step of the investigative process, from conception to research, writing, quality control and dissemination, have been thoroughly analyzed and are well illustrated by case studies in each chapter.
- Data Journalism Handbook
- Analytics Meets Mother Goose, Want to get your point across about data? You’d better learn to tell stories. The “last mile problem” of analytics.
- The Dark Side of Applying Analytics to Journalism, AOL has “designed a system called Seed, a hybrid of journalism and engineering. Seed is based on the idea that editors can figure out what stories to assign by mining data from search engines like Google and social networks like Facebook. If algorithms can tell you what people are talking about, and what they’re searching for, then you know what they want to read.
- Better Decision Making with Objective Data is Impossible, Data is not the same thing as facts. Data requires analysis — which is replete with subjective interpretation. The objectivity of data is a myth. Modern analytical methods afford creative and flexible uses of data that can support multiple perspectives and competing analyses about the same data sets.
- Detecting Bias in Data Analysis, How you handle your data — from cleanup through presentation — affects the results you’ll get.
- Minding the Analytics Gap, a gap between an organization’s capacity to produce analytical results and its ability to apply them effectively to business issues
- The Emergence of the Extra-Rational Manager, just as revolutions in science are preceded by revolutions in measurement, so, too, are revolutions in business preceded by revolutions in measurement. Could managers improve their ability to manage if they knew who is talking with whom, and how often, and where these conversations are taking place and what the tone of these interactions are?
- Raising the Bar With Analytics, We can take our technology and put it on partner sites [to help them get] a deeper understanding of what’s happening with customers — what’s working and why.
- Winning With Data, giving companies “radically improved measurement” capabilities
- New opportunities for data analysis, the rapid growth of “digital data” — everything from public records in digital form to information generated from social network traffic — creates all kinds of new opportunities for statistical analysis — and for statisticians.
- New Horizons in Data Analytics, IT Professional, vol.17, no. 4, pp. 20-22, July-Aug. 2015, Analytics has become a hot topic, putting increased pressure on IT organizations to provide new ways of making sense of customer and operational data. The theme of data analytics with application in four areas related to knowledge development and utilization: the institution of education itself, optimizing knowledge sharing in communities, gaining and applying business insights, and the state of the knowledge and skills gap in China.
- Data Analytics Makes the Transition From Novelty to Commodity, What happens when the use of analytics in business stops being new and different?
- The Four Traps of Predictive Analytics, Magical thinking, starting at the top, building cottages not factories, seeking purified data.
- Revisiting Complexity in the Digital Age, Imagine a retailer that has 10 million products and hundreds of variations for each product yet keeps it simple for customers to make a choice. Impossible? Not today. Amazon.com Inc. creates value from its product complexity with simple customer-facing processes, such as search, ratings, reviews and suggestions.
- A New, Analytics-Based Era of Banking Dawns at State Street, State Street Global Exchange (SSGX), a new business launched in April 2013, which enables the organization to partner with its clients to apply a wrapper of information, insights and analytics around the investment process.
- Harnessing Quant Power, Framing the problem. Solving the problem. Communicating and acting on results. This new era of computational prowess does not obviate the need for intuition and creativity, and that is especially true in the important first step of framing a problem. Half the battle in problem solving and decision making is framing the problem or decision in a creative way so that it can be addressed effectively.
- When an IT Project “Goes Red”, Declaring to your whole company that the project everyone is excited about is in trouble can be demoralizing. But it’s exactly what can turn things around.
- In Experiments We Trust: From Intuit to Harrah’s Casinos, the company’s continued focuses on data analysis and small-scale testing that can scale into company-wide initiatives. These tests run from the use of coupons to offers of free meals or hotel stays, all designed to get customers to spend more money during their playtime.
- Webcast Recap: Get Started Today Using Analytics, As data floods into the company, as we go from an information desert to an information jungle, the bottleneck at the tops of organizations gets more and more constraining.
- The Science of Managing Black Swans, “black swan” phenomena are highly unlikely events that have massive impacts on a business or society on the rare occasions they occur. By exploiting many types of data, managers can help prevent (or at least contain) the damage related to black swan events and other risky blind spots. The caveat: organizations should rely less on management experience and intuition and rely more on integrated data to point to potential risks (see Managing Risks with Data).
- From the Editor: Decision Making in the Digital Age, even when you have plenty of data, making wise decisions about topics like strategy can be challenging. And no matter how much data we collect and analyze, our perspectives are still colored by human foibles.
- Overheard at MIT, The limitations of data. Not every decision should be data-driven.
- Lessons From Analytical Innovators, engendering the beliefs, practices, and outcomes characteristic of Analytical Innovators; enabling the success factors required to excel in today’s analytics revolution; creating a framework that shows how your company — regardless of analytical sophistication — can become more like Analytical Innovators; and succeeding in action
- Innovating With Analytics, Our competition uses a psychological-based methodology and they work closely with psychologists. Match.com believes that every psychological theory is different, so it becomes difficult to have something that is concrete as opposed to a mathematical equation. We haven’t seen much in the market quite like it. Plus the unique thing about Match.com is that we have billions of data points from the last 17 years to analyze.
- Why Our Minds Swap Out Hard Questions For Easy Ones, When faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution.
- Tim Harford on Trial, Error and Our “God Complex”, companies that have a God complex look for smart people (what he calls “little Gods”) to solve complex problems — when what they should really be doing is establishing systematic processes of trial and error.
- Location Analytics: Bringing Geography Back, The relatively new market of location analytics is expanding the uses of more traditional geographic information system (GIS) technology to include social, geographic, physical and emotional indicators that help organizations better predict trends, according to ABI Research, which forecasts that the location analytics market will grow to $9 billion by 2016.
- The Secrets to Managing Business Analytics Projects, project managers’ most important qualities can be sorted into five areas: (1) having a delivery orientation and a bias toward execution, (2) seeing value in use and value of learning, (3) working to gain commitment, (4) relying on intelligent experimentation and (5) promoting smart use of information technology.
- “Mapping the TV Genome” at Bluefin Labs and Big Data’s Big Stats, Software engineers who understand analytics algorithms are in huge demand, and “65% percent of data professionals expect a deficit in expertise in the field over the next five years, according to a report from EMC Corp. The data and analytics marketplace is now worth $64 billion, according to a McKinsey Global Institute estimate.
- Creating Business Value with Analytics, by David Kiron and Rebecca Shockley, MIT Sloan Management Review, Fall 2011
- How Analytics Can Transform Business Models, MIT Sloan Management Review, April 2013
- What Is Analytics Amplifying in Your Organization?
- Mining Data at PayPal to Guide Business Strategy
- Risky Business: How Data Analytics Can Help
- Rent The Runway: Organizing Around Analytics
- Reading Global Clients’ Signals
- Analyzing Performance in Service Organizations
- Which Strategy When?
- The Gap Between the Vision for Marketing and Reality
- At This Education Nonprofit, A Is for Analytics, Social services agencies are turning to data to find the practices that get the best results.
- The Talent Dividend: Interactive Infographic, MIT Sloan Management Review 2015
- How to Build (and Keep) a World-Class Data Science Team
- Why Managing Data Scientists Is Different
- Once You Align the Analytical Stars, What’s Next?
- How to Hire Data-Driven Leaders
- Data Scientist In a Can?
- Catching Up with Scantily Clad Analytics Emperors
- Short on Analytics Talent? Seven Tips to Help Your Company Thrive
- Seven Reasons Why Big Data is Worth Shifting a Career For
- At Amadeus, Finding Data Science Talent Is Just the Beginning
- Are Data Scientists Really a Breed Apart?
- When Health Care Gets a Healthy Dose of Data
- Coming Soon: Doctors As Data Analysts
- How Analytics Can Get You Better Medical Treatment
- Using Big Data for Better Health Outcomes
- What Businesses Can Learn From Sports Analytics
- In Sports, It’s Quants Versus Managers
- The 2012 Olympic Games: Will Data Save the Day?
- What Baseball, Wine and Health Care Have in Common
- Boston Red Sox Baseball Team Now Mirrors MIT Professor’s Prediction
- Wimbledon, Big Data and Business Intuition
- Basketball Hot at MIT Sloan Sports Analytics Conference
- How Facebook is Delivering Personalization on a Whole New Scale
- Increasing the ROI of Social Media Marketing
- Facebook’s Bet: “Wisdom of Friends” Will Beat Out “Wisdom of Crowds”
- The Pitfalls of Using Online and Social Data in Big Data Analysis
- Chief Marketing Officers See True Value in Social Media
- Business Quandary? Use a Competition to Crowdsource Best Answers
- The Obama Election: Analytics Makes the Call
- Big Data and the U.S Presidential Campaign
- How Finding “Exceptions” Can Jump Start Your Social Initiative
- Is Your Organization Ready for the Impending Flood of Data?
- Why Detailed Data Is As Important As Big Data
- Online “Chatter Data” is Big Data Gold
- Does Your Company Collect Data — Or Hoard It?
- ComScore: The Art and Science of Big Data, From the Inside
- Gone Fishing — For Data
- Do You Have Too Much Data?
- What’s Your Information Footprint?
- Does Data Have a Shelf Life?
- Big Data That’s Good for the Public
- All Fired Up in Massachusetts: The State’s New Wave of Big Data Companies
- Is Your Information Diet Full of Junk Food?
- General Mills Builds Up Big Data to Answer Big Questions
- The Art of Customized Data and “Content Multiplication”
- Which Trumps: Info That’s Timely or Info of Consistent Quality?
- Big Data’s Travails Don’t Mean It’s Derailed
- Do You Need a Data Dictator?
- Software Analytics
- Multimedia Analysis + Visual Analytics = Multimedia Analytics
- Predictive Analytics in a Higher Education Context
- iBeacons, beacons and their use in the transportation section
- Revealed, what happens in just ONE minute on the internet: 216,000 photos posted, 278,000 Tweets and 1.8m Facebook likes
- Text Analytics for Predicting Question Acceptance Rates
Business Intelligence (BI)
- Business Intelligence: Concepts, Components, Techniques And Benefits, Journal of Theoretical and Applied Information Technology, 2009
- Introduction to Business Intelligence, IBM Software Group, 2007
- Introduction to Business Intelligence,Mykola Pechenizkiy, 2006
- A Step-by-Step Approach to Successful Business Intelligence, Gartner
- Dashboard Design (for at-a-glance monitoring), Stephen Few, 2010
- Why Most Dashboards Fail, Stephen Few, 2007
- A Guide to Creating Dashboards People Love to Use, Juice, 2009
- Making Data Visible So You Can Act On It