Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15
[ COVER OF THE WEEK ]
Complex data Source
[ AnalyticsWeek BYTES]
>> A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation by administrator
>> Jun 28, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin
>> Human-Centric Artificial Intelligence: What and Why? by analyticsweekpick
[ FEATURED COURSE]
R, ggplot, and Simple Linear Regression
![]() |
Begin to use R and ggplot while learning the basics of linear regression… more |
[ FEATURED READ]
Rise of the Robots: Technology and the Threat of a Jobless Future
![]() |
[ TIPS & TRICKS OF THE WEEK]
Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.
[ DATA SCIENCE Q&A]
Q:What is the life cycle of a data science project ?
A: 1. Data acquisition
Acquiring data from both internal and external sources, including social media or web scraping. In a steady state, data extraction and routines should be in place, and new sources, once identified would be acquired following the established processes
2. Data preparation
Also called data wrangling: cleaning the data and shaping it into a suitable form for later analyses. Involves exploratory data analysis and feature extraction.
3. Hypothesis & modelling
Like in data mining but not with samples, with all the data instead. Applying machine learning techniques to all the data. A key sub-step: model selection. This involves preparing a training set for model candidates, and validation and test sets for comparing model performances, selecting the best performing model, gauging model accuracy and preventing overfitting
4. Evaluation & interpretation
Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.
5. Deployment
6. Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold
7. Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model
Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.
Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.
Deployment
Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold
Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model
Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.
Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.
Deployment
Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold
Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model
Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.
Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.
Deployment
Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold
Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model
Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.
Source
[ VIDEO OF THE WEEK]
#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership
Subscribe to Youtube
[ QUOTE OF THE WEEK]
We chose it because we deal with huge amounts of data. Besides, it sounds really cool. Larry Page
[ PODCAST OF THE WEEK]
@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast
Subscribe
[ FACT OF THE WEEK]
Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.