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Erik Marsja liked Howard Friedman's blog post Critically Reading Scientific Papers
Jul 18
Howard Friedman posted a blog post

Critically Reading Scientific Papers

Critically reading scientific papers is critical for Data Scientists working some areas - especially those working in health. With that in mind, here are some key considerations in reading scientific (peer-review, grey literature) papers:Theory: Is the theory sound? Are there theoretical issues in the design that cause problems? Implementation: Are there concerns about the implementation that cause you to question the conclusions?Methodology best practices: Consider the best practices for doing…See More
Jul 14
Howard Friedman's blog post was featured

Critically Reading Scientific Papers

Critically reading scientific papers is critical for Data Scientists working some areas - especially those working in health. With that in mind, here are some key considerations in reading scientific (peer-review, grey literature) papers:Theory: Is the theory sound? Are there theoretical issues in the design that cause problems? Implementation: Are there concerns about the implementation that cause you to question the conclusions?Methodology best practices: Consider the best practices for doing…See More
Jul 14
Cristian Vava commented on Howard Friedman's blog post Private Equity and Data Science: Due Diligence Stage
"Sharing data, models, and assumptions gives analysts the insights and confidence required by the investors. However, my opinion is that sharing too much might be dangerous. On one hand the rate of acquisition / investment following the due diligence…"
Jul 5
Howard Friedman posted a blog post

Issues with Random Experiments: Attrition

Continuing my thoughts on random experiments and what can go wrong:Another common problem is Attrition, especially situations where the attrition rate is not randomly distributed (if the attrition rate is randomly distributed then you have lost power in your study).After random assignment and administration of treatment/program/test, some subjects will not be available and so you won't have information about the impact of the treatment/program/test.Bias to the treatment effect estimate can be…See More
Jun 30
Howard Friedman's blog post was featured

Issues with Random Experiments: Attrition

Continuing my thoughts on random experiments and what can go wrong:Another common problem is Attrition, especially situations where the attrition rate is not randomly distributed (if the attrition rate is randomly distributed then you have lost power in your study).After random assignment and administration of treatment/program/test, some subjects will not be available and so you won't have information about the impact of the treatment/program/test.Bias to the treatment effect estimate can be…See More
Jun 30
Howard Friedman posted a blog post

Private Equity and Data Science: Due Diligence Stage

An emerging trend in the private equity space is an enhanced focus on data science.This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and…See More
Jun 27
Howard Friedman's blog post was featured

Private Equity and Data Science: Due Diligence Stage

An emerging trend in the private equity space is an enhanced focus on data science.This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and…See More
Jun 27
Howard Friedman posted blog posts
Jun 22
Howard Friedman posted blog posts
Jun 20
Howard Friedman posted a blog post

Data Science: Lifecycle approach to data-driven value creation

Data science had broad applications across many different industries. If we focus on industries that are in the business of buying (some or all) of a company, then trying to improve the operations before selling then we can identify at least three critical stages for data science to play a significant role.Early Exploration: Mining databases for trend and customer insightsEnhanced Pre-acquisition Analysis: Linking early exploration insights with company data. Testing growth, customer value and…See More
May 31
Howard Friedman's blog post was featured

Data Science: Lifecycle approach to data-driven value creation

Data science had broad applications across many different industries. If we focus on industries that are in the business of buying (some or all) of a company, then trying to improve the operations before selling then we can identify at least three critical stages for data science to play a significant role.Early Exploration: Mining databases for trend and customer insightsEnhanced Pre-acquisition Analysis: Linking early exploration insights with company data. Testing growth, customer value and…See More
May 31
Howard Friedman posted a blog post

6 Reasons for Investing Some Time to Learn Tableau

I have seen a few mentions of Tableau in my feed and wanted to offer some thoughts on why I strongly suggest data scientists investing a few hours to learn the basics of Tableau.(1)   Tableau is widely used. Many people that have reporting functions rely on Tableau so knowing the basics is helpful to your business and clients.(2)   Tableau is great for quick data visualizations and for generating some insights into the data and variable relationships.(3)   Tableau as Extract, Transform and…See More
Apr 17
Howard Friedman posted a blog post

10 things to consider when purchasing business intelligence software

Background: Business intelligence software casts a wide net. Software for site selection, customer segmentation, marketing tests, employee productivity, operational metrics, sentiment analysis, profitability sectors and mapping tools all fall in this category. Businesses are constantly challenged with business intelligence software questions such as:·      Should we “Build or Buy”?·      Should we “Retain, Upgrade, or Replace” software?·      What value are we getting from the…See More
Mar 7
Howard Friedman's blog post was featured

10 things to consider when purchasing business intelligence software

Background: Business intelligence software casts a wide net. Software for site selection, customer segmentation, marketing tests, employee productivity, operational metrics, sentiment analysis, profitability sectors and mapping tools all fall in this category. Businesses are constantly challenged with business intelligence software questions such as:·      Should we “Build or Buy”?·      Should we “Retain, Upgrade, or Replace” software?·      What value are we getting from the…See More
Mar 7
Howard Friedman posted a blog post

Adding Program Evaluation to the Data Science Curriculum

We tried to do XYZ. Did it make a difference?”Whether you are in the for-profit world or the not-for profit world, this is a very basic question that many people try to answer.  You could be working at a bank trying to figure out which offer is most appealing to customers, at an online retailer figuring out which ad display gets the most clicks, at the Department of Education trying to test the effect of smaller class sizes, at the city government office trying to see if the new bike lane…See More
Feb 20

Profile Information

Short Bio
Statistician, health economist and writer at the United Nations Population Fund (UNFPA) and Columbia University. Developer of SAS Business Knowledge Series Courses, former Director of Data Modeling in Banking, consultant for health industry.
My Web Site Or LinkedIn Profile
http://howard-friedman.com
Professional Status
Professor
Years of Experience:
15+
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Twitter
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Networking, New venture, Other

Howard Friedman's Blog

Critically Reading Scientific Papers

Posted on July 13, 2018 at 9:11am 0 Comments

Critically reading scientific papers is critical for Data Scientists working some areas - especially those working in health. With that in mind, here are some key considerations in reading scientific (peer-review, grey literature) papers:

Theory: Is the theory sound? Are there theoretical issues in the design that cause…

Continue

Private Equity and Data Science: Due Diligence Stage

Posted on June 27, 2018 at 9:30am 1 Comment

An emerging trend in the private equity space is an enhanced focus on data science.

This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and…

Continue

Issues with Random Experiments: Attrition

Posted on June 26, 2018 at 6:00am 0 Comments

Continuing my thoughts on random experiments and what can go wrong:

Another common problem is Attrition, especially situations where the attrition rate is not randomly distributed (if the attrition rate is randomly distributed then you have lost power in your study).

After random…

Continue

Contamination of the Control Group

Posted on June 22, 2018 at 3:55am 0 Comments

Continuing my thoughts on random experiments and what can go wrong:

One common problem is Contamination of the Control Group

The only difference between the treatment and control groups should be the treatment.  That said, this isn't always true.  How the treatment is administered can affect the control group.  Think about health examples where, for example, deworming…

Continue

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