Jul
2015 Aug Opinions, Interviews, Reports
All (97) | Courses, Education (10) | Meetings (13) | News, Features (18) | Opinions, Interviews, Reports (27) | Publications (5) | Software (3) | Top Tweets (4) | Tutorials, Overviews, How-Tos (7) | Webcasts (10)
- The Present and the Future of the KDD Cup Competition
- Aug 31, 2015.
KDD cup is the first and among most prestigious competitions in data science, Among key takeaways from KDD Cup 2015: XGBoost – Gradient Boosted Decision Trees package works wonders in data classification, feature engineering is the king, and team work is crucial.
- Big Data Influence on Data Driven Advertising
- Aug 31, 2015.
More and more companies relying on big data for their data driven initiatives. In a survey conducted by BlueKai, we are trying to capture what its impact on advertising strategies.
- Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is in
- Aug 28, 2015.
Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade!
- What is the success rate in Hadoop adoption?
- Aug 28, 2015.
Hadoop is no more an unknown term for the big data analytics, it’s to find its value return. Here, we tried to explore on the popular opinions of the Hadoop adopters, we also talk about current challenges for adoption.
- Data Hierarchy of Needs
- Aug 28, 2015.
Data Hierarchy of Needs helps understand the steps in Big Data processing. Before going to advanced data modeling (top of the pyramid), organizations need to fill huge holes they frequently have in the base of the pyramid, lacking reliable complete data flow.
- 5 questions to decide if you need a data scientist
- Aug 26, 2015.
Here are 5 questions to answer if you are thinking about hiring a data scientist. It depends not only on a person, but on the company culture, business problem and understanding its potential.
- Data Marts as an indispensable analytical tool
- Aug 26, 2015.
An analytical Data Mart is in effective and user-friendly tool for reporting, analyses and modeling. Explore, how data marts could provide time saving, less error prone and streamline solution for your business problems.
- OpenText Data Driven Digest Aug 21: College Majors, Hacking Glory, Innovation Performance
- Aug 25, 2015.
The simple beauty of X-Y coordinates belies the power they hold; indeed, many of the best data visualizations created today rely on, and build upon, on the Cartesian plane concept to show complex data sets. Here are three examples.
- Paradoxes of Data Science
- Aug 21, 2015.
There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.
- OpenText Data-Driven Digest, Aug 14
- Aug 15, 2015.
In three data visualizations, we dive into what you would see looking west or east across the ocean; the contours and makeup of the seabed; and the width of rivers throughout North America.
- Recycling Deep Learning Models with Transfer Learning
- Aug 14, 2015.
Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.
- 11 things to know about Sentiment Analysis
- Aug 13, 2015.
Seth Grimes, a text analytics guru, shares 11 key observations on what works, what is past, what is coming, and what to keep in mind while doing sentiment analysis.
- Overcoming Overfitting with the reusable holdout: Preserving validity in adaptive data analysis
- Aug 12, 2015.
Misapplication of statistical data analysis is a common cause of spurious discoveries in scientific research. We demonstrate a new approach for addressing the challenges of adaptivity based on insights from privacy-preserving data analysis.
- Predictive Analytics as an Engine Of R&D and New Product Launches
- Aug 12, 2015.
Predictive analytics is not only the way to discover the underlying patterns, but it can also help you with innovation. Here, we discuss the ways to innovate by combining it with business logic, marketing and bridging demand supply factors.
- 3D Data Sculptures: a New Way to Visualize Data
- Aug 11, 2015.
3D printing can go beyond printing products like iPod cases, or butterfly earrings, and can offer a sustainable way to understand strategic DATA by printing decision support landscapes.
- R Programming: Who, Where and What
- Aug 11, 2015.
The “sexiest job” has the sexiest demand, and R is one of their leading weapons. Here, we are trying to capture how these unicorns are distributed, and also where you can move if you want to have great opportunities.
- Three Essential Components of a Successful Data Science Team
- Aug 10, 2015.
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,
- World Economic Forum Tech Pioneers & Analytics Winners
- Aug 8, 2015.
World Economic Forum selected its 2015 Tech Pioneers, which included quite a few companies on the cutting edge of Analytics, Big Data, and Machine Learning.
- How Long Should You Stay at Your Analytics Job?
- Aug 7, 2015.
Considering the huge demand for the data scientists many are pondering to switch for a better profile and salary. But, there some things to be pondered about like what should be the interval between two switches, acquiring new skills and your loyalty.
- Big Data Analytics Pain Points
- Aug 6, 2015.
Big data analytics is still in infancy, and we haven't yet embraced a data-driven decision making. Here, we discussed the current pain points in it and how you can deal them in better ways.
- Interview: Stefan Groschupf, Datameer on Why Domain Expertise is More Important than Algorithms
- Aug 6, 2015.
We discuss large-scale data architectures in 2020, career path, open source involvement, advice, and more.
- Patterns for Streaming Realtime Analytics
- Aug 5, 2015.
Design patterns are well-known for solving the recurrent problems in software engineering, on similar lines we can have Streaming Realtime Analytics patterns and avoid reinventing the wheel. Here, you can see the major patterns we found out for it.
- The Big ‘Big Data’ Question: Hadoop or Spark?
- Aug 5, 2015.
With a considerable number of similarities, Hadoop and Spark are often wrongly considered as the same. Bernard carefully explains the differences between the two and how to choose the right one (or both) for your business needs.
- Interview: Stefan Groschupf, Datameer on Why SQL on Hadoop is a Bad Idea
- Aug 5, 2015.
We discuss the startups landscape in Big Data, valuation of Big Data companies, recognition earned by Datameer, and why SQL on Hadoop is a bad idea.
- Interview: Stefan Groschupf, Datameer on Balancing Accuracy and Simplicity in Analytics
- Aug 4, 2015.
We discuss common pain points in Big Data projects, evolution of Datameer technology, department specific solution – Datameer Professional, Datameer 5.0 Smart Execution, tacking over-simplicity and more.
- New Standard Methodology for Analytical Models
- Aug 3, 2015.
Traditional methods for the analytical modelling like CRISP-DM have several shortcomings. Here we describe these friction points in CRISP-DM and introduce a new approach of Standard Methodology for Analytics Models which overcomes them.
- Data is Ugly – Tales of Data Cleaning
- Aug 1, 2015.
Whether you want to do business analytics or build the deep learning models, getting correct data and cleansing it appropriately remains the major task. Find out experts opinions on how you can make efficient data cleansing and collection efforts.
7 Steps to Mastering SQL for Data Science — 2019 Edition