- 17 More Must-Know Data Science Interview Questions and Answers, Part 3 - Mar 15, 2017.
The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.
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- Bad Data + Good Models = Bad Results - Jan 26, 2017.
No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.
- Ten Simple Rules for Effective Statistical Practice: An Overview - Jun 23, 2016.
An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.
- Hyundai: Quality Data Analytics Manager - Jun 1, 2016.
Seeking a Quality Data Analytics Manager to apply deep analytical skills to blend, process, and explore complex datasets for the Product Quality department to aid in knowledge discovery, and to assist with performing root-cause analysis, survival analysis, time series forecasting and other analytical activities.
- Webcast: Tech expert Phil Simon on exploring data - Jun 17, 2015.
Phil Simon, award-winning author, talks about how data visualization can help improve data quality, promoting the exploratory mindset, telling good stories with data, and more. On demand webcast.
- In Machine Learning, What is Better: More Data or better Algorithms - Jun 17, 2015.
Gross over-generalization of “more data gives better results” is misguiding. Here we explain, in which scenario more data or more features are helpful and which are not. Also, how the choice of the algorithm affects the end result.
- Interview: Michael Lurye, Time Warner Cable on Key Lessons from Shifting to Hadoop - Apr 14, 2015.
We discuss the key lessons from shifting to Hadoop, data management in today’s world, future of Data Science, advice and more.
- Interview: Josh Hemann, Activision on Why the Tolerance for Ambiguity is Vital - Mar 12, 2015.
We discuss handling bias in data, other data quality concerns, advice, desired qualities, and more.
- Top KDnuggets tweets, Sep 26-28: Any data scientist worth their salary will say you should start with a question - Sep 29, 2014.
CNN embarrassing lack of "Data Quality" - this #Scotland Independence poll adds; Statistical & Machine learning with R; Any data scientist worth their salary will say you should start with a question; Automotive Customer Churn Prediction using SVM and SOM.
- Interview: Pallas Horwitz, Blue Shell Games on Why Data Science is So Critical for Gaming Studios - Aug 14, 2014.
We discuss the role of data science at Blue Shell Games, the importance of "Lean Data", key metrics for online games, cross-product projects and optimizing meeting the data needs across an organization.
- Lavastorm Sun Seekers Caribbean Challenge 2 - Aug 5, 2014.
Use Lavastorm Analytics Engine Public Edition to overcome data quality issues and consolidate the lists. Step-by-step instructions make completing the task a snap! Submit your entry by August 31, 2014.
- Interview: Christophe Toum, Talend on Why Big Data Needs Big Governance - Aug 2, 2014.
We discuss the priority order of data governance for Big Data initiatives, impact of increasing shift towards Hadoop and NoSQL, data quality, current trends, talent crunch, advice and more.
- Interview: Aparna Pujar, eBay on Evolution of Behavior Analytics for User Engagement - Jul 25, 2014.
We discuss Behavior Analytics vs. Web Analytics, important metrics for user engagement, challenges of behavior insights domain, future of multi-screen analytics, key soft skill and more.
- Lynn Goldstein, Chief Data Officer, NYU on the Need for Data Governance - Jun 3, 2014.
We discuss the role of Data Governance, establishing Big Data accountability, impact of Data Governance on Data Quality, and assessing the education available for Data Governance.
- Forrester Research: Build Trusted Data with Data Quality - Apr 1, 2014.
Key takeaways of the report include: How managing data quality brings IT and the business closer together, Different data quality definitions, and advantages of transparency in data quality.