Data science is a large field and includes many different types of data processing. The most common types of data processing are: Data mining, Data modeling, Machine learning, Data visualization.
Data science is not just a buzzword. It’s a new way of solving problems, from predicting the best time to buy an airplane ticket to determining the risk of a heart attack.
Some topics you can read about here: Scatter plots, correlation, t-test, linear regression, logistic regression
The Decision Maker’s Handbook to Data Science has been a book I am particularly proud of. It is the culmination of years of work in data science, working with decision makers of all sorts, from small to big companies. The book has been largely based on my workshops I’ve done Read more…
I was recently reading an interesting report by O’Reilly called The Care and Feeding of Data Scientists. This report discusses various topics, such as how to hire data scientists, how to organise data science teams and how to help data scientists grow. Something which I found particularly interesting was a Read more…
Data science and AI: The new wave of HR disruption AI in human resources (HR) is currently one of the hot topics in the artificial intelligence industry, with many investing in AI solutions for HR: from predictive analytics to automation. Broadly speaking, applications of AI in HR falls into two Read more…
I’ve talked in the past about the different tribes of data scientists. Hiring and managing data scientists is not easy. It is very important to understand that data scientists can come from many different backgrounds, with different skillsets and ways of thinking. On this podcast I provide a quick intro Read more…
Dynamic pricing and the PAO framework Dynamic pricing is one of the most interesting application of data science. Using dynamic pricing you can find out the best price for a product or service by using historical data of past purchases. Dynamic pricing has a long history, starting with airlines. The Read more…
Data science is not always an easy matter. There can be many choices that you have to make, such as: What problem are you trying to solve? Do you have data of appropriate quality? Whom should you hire to do this for you? I’ve seen many mistakes happening in all Read more…
Today’s post is provided by Kite Table of Contents Introduction: balanced and imbalanced datasets What is data oversampling? What is SMOTE? How does SMOTE work? SMOTE tutorial using imbalanced-learn Base model Imbalanced model SMOTE’d model Recap and conclusion Introduction: balanced and imbalanced datasets Close your eyes. Now imagine a perfect Read more…
A very interesting article recently caught my attention. The article, called “You are not Google” explains how often companies get carried away with the newest, latest and shiniest technologies, without first thinking whether they are actually appropriate for their case. Some examples: Companies using NoSQL solutions, when a simple relational Read more…
Today’s post is coming from Kite. Kite is a plugin for your IDE that uses machine learning to give you useful code completions for Python. You can find the original post here. Table of Contents What is data science Positives: astrophysics, biology, and sports Negatives: gender bias and more Python, Read more…
We live in the era of personalisation. Through data science it becomes possible to suggest, discover and create products that are tailor-suited to each individual’s preferences. The first wave of personalisation through data science came in the form of recommender systems. Recommendation engines predict what you are going to like, Read more…
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