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Big Data & Digital Marketing
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Data analytics as the key to know your customers and offer them what they really want.
Curated by
Luca Naso
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Data science is the new thing! How to be a data scientist? [originally written by the team behind DataCamp]
Luca Naso's insight:
Becoming a Data Scientist IS NOT like cooking a recipe, and a data scientist IS NOT supposed to be able to solve all of your Big Data issues.
This being said, here is a list of 8 categories of skills very useful to any data scientist: 1: Stats, Math, Machine Learning 2: Coding 3: Database 4: Visualisation and reports 5: Big Data 6: Meet peers 7: Get a job 8: Be social 
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The Data Scientist’s Toolbox is a free online class taught by Jeff Leek, Brian Caffo and Roger D. Peng of Johns Hopkins University
Luca Naso's insight:
For those who want to learn about Big Data, Johns Hopkins University offers a "Data Science" Specialization on Coursera, a series of 9 free* courses and a final project (4 weeks each, total 40 weeks): 1. The Data Scientist's toolbox 2. R Programming 3. Getting and Cleaning Data 4. Exploratory Data Analysis 5. Reproducible Research 6. Statistical Inference 7. Regression Models 8. Practical Machine Learning 9. Developing Data Products 10. Capstone Project *there is a $49 fee to pay (for each course and project) if you want a certified signature track. Signature Track is optional. You can still participate in the course for free and earn a Statement of Accomplishment.
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What is Big Data? Why is Big Data so important? Who are your Big Data key players? How do you lead with Big Data? Which Big Data solutions do you need? When do you start with Big Data? Explore the Universe of Big Data.
Luca Naso's insight:
My first scoop of the year is an interactive tool to know more about Big Data. This is a web app that allows you to explore lots of resources such as articles, videos and infographics.
Here is a summary of the topics that you can find: 1. What is Big Data?
2. Why is Big Data so important?
6. When do you start with Big Data?
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Learn about the power of big data, and how businesses need to come up with ways to manage and make sense of all the information
Luca Naso's insight:
Lots of information about Big Data from New Jersey Institute of Technology.
The upper half of the infographics introduces Big Data with the usual buzz words. The remaining has a lot of interesting statistics. My 6 takeaways from the infographics: 1. $300 billion, what US could save in Healthcare 2. +60% in operating margin (retail) 3. 73% of companies have already increased revenues thanks to big data 4. 56% of IT decision makers believe that finding the right staff is the biggest challenge 5. "Query and reporting" is the Top 1 capability currently available 6. "Transactions" and "Log Data" are the most common source of data currently collected and analysed (88% and 73% respectively).
FLYONIT's curator insight,
May 30, 2015 1:44 AM
Big data and mobile platform, will do wonders for consumers!
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Blindly trusting it can lead you to the wrong conclusions.
Luca Naso's insight:
Big Data can be extremely dangerous without a Big Brain to analyse them properly. Huge data sets ALWAYS contain some relations: some of them are right (causation), others are simply wrong. It pertains to data analysis to uncover the truth.
"As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already existing inequities will be further entrenched. [...] This goes beyond merely conducting focus groups to confirm what you already want to see in a big data set. It means complementing data sources with rigorous qualitative research."
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Research and ideas shared at recent INSEAD alumni panel discussions shed light on the elements required to capture and effectively use big data.
Luca Naso's insight:
1. Data-driven business
The main benefit of “big data” is the ability to make better strategic decisions.
2. The right set of skills The biggest reason why some organisations are not considering the use of big data is the lack of capabilities and skills. it is particularly difficult to find people that understand both worlds – the technical and the business world – and that can also connect the dots between these two worlds.
3. Avoid Silos, foster integration The competitive edge is held by those able to efficiently share and reuse data analytics internally. Too many companies still think in silos.
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A large number of businesses are recruiting data scientists in a bid to build up the right skills to support board-level big data initiatives.
Luca Naso's insight:
42% of UK companies are looking to recruit data scientists in big data analytics roles.
But 58% of the executives asked said they were struggling to find candidates with the right mix of skills.
Although technical experience remains of greatest importance, other essential attributes include 1. problem-solving skills (cited by 43%, rising to 55% in Germany), 2. analytical skills (42%) 3. and creativity (35%).
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There is a lot of content in this (long) article published by the Harvard Magazine.
Here are my main 3 takeaways:
1. "The Big Data revolution lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity" by Gary King
2. Big Data isn't everything: "We had petabytes of data and yet we were building models that were fundamentally flawed, because we didn't have insights about what was happening" by Nathan Eagle
3. "No matter how much data exists, researchers still need to ask the right questions to create a hypothesis, design a test, and use the data to determine whether the hypothesis is true." by Nathan Eagle