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Elder Research
United States
เข้าร่วมเมื่อ 29 ม.ค. 2018
Elder Research Inc. is an artificial intelligence and data science consulting firm that, since 1995, has consulted hundreds of clients across a multitude of industries on analytics strategy and executive consulting, data science model development and deployment, and leadership and practitioner training.
Our 100+ data scientists and engineers use a holistic framework to understand each unique business challenge, define high-ROI objectives, create robust data pipelines, and deliver innovative and custom analytic solutions that empower organizations to make trustworthy decisions and maximize business value.
Our 100+ data scientists and engineers use a holistic framework to understand each unique business challenge, define high-ROI objectives, create robust data pipelines, and deliver innovative and custom analytic solutions that empower organizations to make trustworthy decisions and maximize business value.
What Wastes Money in Analytics?
When it comes to data projects, there are 2 common pitfalls:
1️⃣ Disconnected Data: Building the “perfect” data analytics environment without aligning it to real business challenges.
2️⃣ One-Stop Software: Searching for an “easy button” solution that promises to fix everything but often falls short.
Listen as Elder Research CEO Gerhard Pilcher shares why it's so important to starting with the business problem in the beginning of analytics initiatives.
1️⃣ Disconnected Data: Building the “perfect” data analytics environment without aligning it to real business challenges.
2️⃣ One-Stop Software: Searching for an “easy button” solution that promises to fix everything but often falls short.
Listen as Elder Research CEO Gerhard Pilcher shares why it's so important to starting with the business problem in the beginning of analytics initiatives.
มุมมอง: 5
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We have collected raw data using specific sensors, and we are currently in the preprocessing stage. At this stage, we are focusing on identifying and extracting outliers. The question is: what is the best approach to handle outliers? Should we use algorithms such as the Interquartile Range (IQR) method and others, or rely on the sensor specifications to define the minimum and maximum values it can record, considering any values outside this range as outliers?
amazing video. Do you have any practical example with python??
cheers bro
Fantastic video!
Thanks for this example! I was looking for a case like this and you help me a lot.
Very cool quote. Graphs are king.
Nice analogy mom! Great, quick overview, thanks!
Thanks so much. I am currently working on this project and i hope to get more videos. Please. Do you have any material with respect to this particular video?
nice example. It looks like we need to "mine" the graph relations to find out abnormal relations. Is there any recommendation to do it at scale?
This was super interesting. Great job explaining your thinking.
Glad you enjoyed the video, Mathieu!
Super cool!
'Promosm'
how are you getting the numbers -0.05, 0.10 and so on?
Thanks for your question! Here's what Jericho had to say about how he got those numbers: Isolation forests use a large number of randomized attempts to separate the data and count how many cuts it takes in each attempt to separate each datapoint. From that collection of counts for each record, scores are calculated. Since this is not straightforward to show by hand, I used the scikit-learn Python package and the wine dataset to calculate the scores, limiting the wine dataset to flavonoids and malic acid features. Then I took some example points from the outer edges and one from the middle of the real results and illustrated them as closely as possible in the whiteboard example. --- Here are the links to the scikit-learn and Python resources: scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html archive.ics.uci.edu/dataset/109/wine
Great video, explained in a very intuitive way!
Glad you enjoyed the video!
Good stuff.
Thank you! Glad you enjoyed the podcast!
That's a great explanation, thank you!
You're welcome! Glad it was helpful!
Great job, Marco!
Heck yes!
Thank you a lot go ahead!
So nice to hear about the path you took over the last 30 years Gerhard! And wonderful to see you!