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Michael Hochster, PhD in Statistics, Stanford; Director of Research, Pandora – As a data scientist, how do you answer when non-technical people ask you “is your analysis result statistically significant?”

Michael Hochster, PhD in Statistics, Stanford; Director of Research, Pandora – As a data scientist, how do you answer when non-technical people ask you “is your analysis result statistically significant?”

Answer They are asking whether you have enough data to trust your results. I would try to answer the real question and not worry too much about whether the technical jargon is being used correctly (that’s my job, not theirs).

Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning

Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning

You don’t need a PhD or even a masters to do machine learning. On taking calculated risks and especially calculated exits from one’s comfort zone. Some notes on soul searching and how to choose a career that is also a passion. Reading list.

How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future

How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future

Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his [...]

Select Features and Target in Scikit Learn

Select Features and Target in Scikit Learn

To do Machine Learning in SKlearn, as a first step we need to import following import pandas as pd import numpy as np Step 1. We read the file in Panadas Dataframe by pd.read_csv. In jupyter Note book we defined dataframe as df=pd.read_csv('C:\Data\glass.csv') In order to select features and target for machine learning we will use [...]

Pre-Modeling: Data Preprocessing and Feature Exploration in Python

Pre-Modeling: Data Preprocessing and Feature Exploration in Python

In my recent seach on building dummy variables for a loan dataset which I downloaded from kaggle I came across this tutorial on Data preprocessing and feature exploration - step critical building machine learning models models. Though there are still more information I am searching on creating dummy variable, however I like the way presenter [...]

Loading External Data into Scikit lear AKA SKLEARN

Loading External Data into Scikit lear AKA SKLEARN

Scikit learn comes with some toy datasets such as iris. We can load these with from sklearn import neighbors, datasets iris = datasets.load_iris() print(iris) However if we try to load any other external, for example I downloaded the wines data from we have to We have to import pandas into SKlearn and then use the following [...]

Full Titanic Example with Random Forest

Full Titanic Example with Random Forest

I found this presentation of Random Forest quite well explained and learned the parameters tuning of optimizing the model as well as Reducing dimensions. That is fairly simple example but helpful in directing us to more advancxed features. Enjoy..

Advanced Machine Learning with scikit-learn

Advanced Machine Learning with scikit-learn

An excellent present on some advanced features of Scikit Learn. This video explore an d present the example on Handwritten digit classification using SVM , Find best best parameters using grid search. This Video also explored doing machine learning on computer network. Lastly a great exaple on Text Data. I selected this video based clarity, easy [...]

Excellent presentation on how to perform predictive analytics on text data. It was presented in Pycon 2016. In this video an example on how to identify spam using Naive Base and regression analysis are used. Very clear presentation by Kevin Markham, Enjoy...Although numeric data is easy to work with in Python, most knowledge created by humans is [...]

Can Machine Learning Decode Depression?

Can Machine Learning Decode Depression?

An interesting article published in Huffington post by Arshya Vahabzadeh, M.D. Harvard Trained Subspecialist Psychiatrist. Director & Researcher, Brain Power. Academic Faculty, Massachusetts General Hospital. Chair, Council on Communications, American Psychiatric Association. Machine learning already being utilised in healthcare and have [...]

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