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Boris Gorelik

Data science, machine learning & data visualization consultant and lecturer

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Tag: data science

35 (and more) Ways Data Go Bad — Stats With Cats Blog

Posted on July 14, 2020July 14, 2020No Comments on 35 (and more) Ways Data Go Bad — Stats With Cats Blog
Online data science conference on May, 28

Online data science conference on May, 28

Posted on April 30, 2020April 30, 2020No Comments on Online data science conference on May, 28
Why is forecasting s-curves hard?

Why is forecasting s-curves hard?

Posted on April 19, 2020April 19, 2020No Comments on Why is forecasting s-curves hard?
Not a wasted time

Not a wasted time

Posted on February 19, 2020February 19, 2020No Comments on Not a wasted time
Career advice. A research pharmacist wants to become a data scientist.

Career advice. A research pharmacist wants to become a data scientist.

Posted on January 9, 2020January 10, 2020No Comments on Career advice. A research pharmacist wants to become a data scientist.
Staying employable and relevant as a data scientist

Staying employable and relevant as a data scientist

Posted on December 23, 2019January 25, 20201 Comment on Staying employable and relevant as a data scientist
Cow shit, virtual patient, big data, and the future of the human species

Cow shit, virtual patient, big data, and the future of the human species

Posted on November 28, 2019November 28, 2019No Comments on Cow shit, virtual patient, big data, and the future of the human species
Data science tools with a graphical user interface

Data science tools with a graphical user interface

Posted on November 5, 2019November 5, 20191 Comment on Data science tools with a graphical user interface
Bootstrapping the right way?

Bootstrapping the right way?

Posted on October 6, 2019October 6, 20191 Comment on Bootstrapping the right way?
The third wave data scientist – a useful point of view

The third wave data scientist – a useful point of view

Posted on April 8, 2019No Comments on The third wave data scientist – a useful point of view
To specialize, or not to specialize, that is the data scientists’ question

To specialize, or not to specialize, that is the data scientists’ question

Posted on March 14, 2019March 14, 2019No Comments on To specialize, or not to specialize, that is the data scientists’ question
The data science umbrella or should you study data science as a career move (the 2019 edition)?

The data science umbrella or should you study data science as a career move (the 2019 edition)?

Posted on March 7, 2019March 7, 20194 Comments on The data science umbrella or should you study data science as a career move (the 2019 edition)?
Against A/B tests

Against A/B tests

Posted on December 12, 20181 Comment on Against A/B tests

I wonder how this analysis of remained unnoticed by the social media

The recent election of Doug Jones […] got me thinking: What if the Black populations of Southern cities were to experience a dramatic increase? How many other elections would be impacted?

via Back to Mississippi: Black migration in the 21st century — Charlescearl’s Weblog

Posted on September 4, 2018September 2, 2018No Comments on Back to Mississippi: Black migration in the 21st century. By Charles Earl
Evolution of a complex graph. Part 1. What do you want to say?

Evolution of a complex graph. Part 1. What do you want to say?

Posted on July 23, 2018July 23, 2018No Comments on Evolution of a complex graph. Part 1. What do you want to say?

I will host a data visualization workshop at Israel’s biggest data science event

Posted on May 1, 2018May 1, 20181 Comment on I will host a data visualization workshop at Israel’s biggest data science event
Whoever owns the metric owns the results — don’t trust benchmarks

Whoever owns the metric owns the results — don’t trust benchmarks

Posted on April 13, 20181 Comment on Whoever owns the metric owns the results — don’t trust benchmarks
On algorithmic fairness & transparency

On algorithmic fairness & transparency

Posted on February 28, 2018February 28, 2018No Comments on On algorithmic fairness & transparency

Five misconceptions about data science

Posted on February 27, 2018No Comments on Five misconceptions about data science
Overfitting reading list

Overfitting reading list

Posted on February 22, 2018No Comments on Overfitting reading list

Once again on becoming a data scientist

Posted on February 19, 20181 Comment on Once again on becoming a data scientist
Is Data Science a Science?

Is Data Science a Science?

Posted on February 14, 20181 Comment on Is Data Science a Science?
What is the best way to collect feedback after a lecture or a presentation?

What is the best way to collect feedback after a lecture or a presentation?

Posted on February 4, 2018No Comments on What is the best way to collect feedback after a lecture or a presentation?
Data is the new

Data is the new

Posted on February 3, 2018February 2, 2018No Comments on Data is the new

Don’t take career advises from people who mistreat graphs this badly

Posted on January 4, 2018December 27, 2017No Comments on Don’t take career advises from people who mistreat graphs this badly

The Keys to Effective Data Science Projects — Operationalize

Posted on December 20, 2017December 13, 2017No Comments on The Keys to Effective Data Science Projects — Operationalize

Buzzword shift

Posted on December 18, 2017December 13, 2017No Comments on Buzzword shift
On alert fatigue 

On alert fatigue 

Posted on December 17, 2017December 16, 2017No Comments on On alert fatigue 
What’s the most important thing about communicating uncertainty?

What’s the most important thing about communicating uncertainty?

Posted on December 14, 2017December 10, 2017No Comments on What’s the most important thing about communicating uncertainty?

The fastest way to get first N items in each group of a Pandas DataFrame

Posted on November 27, 2017November 26, 2017No Comments on The fastest way to get first N items in each group of a Pandas DataFrame

On machine learning, job security, professional pride, and network trolling

Posted on November 21, 2017November 21, 2017No Comments on On machine learning, job security, professional pride, and network trolling

Good information + bad visualization = BAD

Posted on November 14, 2017November 8, 2017No Comments on Good information + bad visualization = BAD
What are the best practices in planning & interpreting A/B tests?

What are the best practices in planning & interpreting A/B tests?

Posted on November 13, 2017November 8, 20171 Comment on What are the best practices in planning & interpreting A/B tests?

Data Science or Data Hype?

Posted on November 8, 2017November 8, 2017No Comments on Data Science or Data Hype?

Although it is easy to lie with statistics, it is easier to lie without

Posted on October 30, 2017October 25, 2017No Comments on Although it is easy to lie with statistics, it is easier to lie without
Gartner: More than 40% of data science tasks will be automated by 2020. So what?

Gartner: More than 40% of data science tasks will be automated by 2020. So what?

Posted on October 25, 2017October 26, 2017No Comments on Gartner: More than 40% of data science tasks will be automated by 2020. So what?
Why is it (almost) impossible to set deadlines for data science projects?

Why is it (almost) impossible to set deadlines for data science projects?

Posted on October 19, 2017October 19, 20171 Comment on Why is it (almost) impossible to set deadlines for data science projects?
What is the best thing that can happen to your career?

What is the best thing that can happen to your career?

Posted on October 19, 2017October 26, 20172 Comments on What is the best thing that can happen to your career?

Advice for aspiring data scientists and other FAQs — Yanir Seroussi

Posted on October 15, 2017October 26, 2017No Comments on Advice for aspiring data scientists and other FAQs — Yanir Seroussi
What you need to know to start a career as a data scientist

What you need to know to start a career as a data scientist

Posted on October 11, 2017October 26, 20171 Comment on What you need to know to start a career as a data scientist

Identifying and overcoming bias in machine learning

Posted on October 8, 2017No Comments on Identifying and overcoming bias in machine learning
Fashion, data, science

Fashion, data, science

Posted on August 16, 2017August 16, 2017No Comments on Fashion, data, science
Boris Gorelik

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