At Dataquest, we strongly advocate portfolio projects as a means of getting a first data science job. In this blog post, we’ll walk you through an example portfolio project.

Multi-Objective Optimization Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making, that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives. For a nontrivial multi-objective optimization problem, no single solution exists that simultaneously optimizes each objective. In that case, the objective functions are said to be conflicting, and there exists a (possibly infinite) number of Pareto optimal solutions. A solution is called nondominated, Pareto optimal, Pareto efficient or noninferior, if none of the objective functions can be improved in value without degrading some of the other objective values. Without additional subjective preference information, all Pareto optimal solutions are considered equally good (as vectors cannot be ordered completely). Researchers study multi-objective optimization problems from different viewpoints and, thus, there exist different solution philosophies and goals when setting and solving them. The goal may be to find a representative set of Pareto optimal solutions, and/or quantify the trade-offs in satisfying the different objectives, and/or finding a single solution that satisfies the subjective preferences of a human decision maker (DM). …

Forecasting: Principles and Practice

Artificial intelligence and machine learning can be used to predict physicians likely to overprescribe opioids and patients likely to come back for more, with the help of historical data. So, why don’t organizations use these anticipatory tools?

If you want to do statistical analysis or machine learning with data in SQL Server, you can of course extract the data from SQL Server and then analyze it in R or Python. But a better way is to run R or Python within the database, using Microsoft ML Services in SQL Server 2017. Why? 

Thomas Ball writes:

StanCon 2018 Helsinki tutorial videos are now online at Stan YouTube channel

In any capital-dependent industry, be it manufacturing, fleet management, or heavy machinery, the biggest headaches come when things stop working. With predictive maintenance, it’s possible to address these pain points before they ever see the light of day.

$$\def\aa{\boldsymbol a} \def\bb{\boldsymbol b} \def\cc{\boldsymbol c} \def\xx{\boldsymbol x} \def\zz{\boldsymbol z} \def\uu{\boldsymbol u} \def\vv{\boldsymbol v} \def\yy{\boldsymbol y} \def\ss{\boldsymbol s} \def\TT{\boldsymbol T} \def\CC{\boldsymbol C} \def\RR{\mathbb R} \DeclareMathOperator*{\argmin}{{arg\,min}} \DeclareMathOperator*{\argmax}{{arg\,max}} \DeclareMathOperator*{\minimize}{{minimize}} \DeclareMathOperator*{\Fix}{\mathbf{Fix}} \DeclareMathOperator{\prox}{\mathbf{prox}} \def\defas{\stackrel{\text{def}}{=}}$$

Jordan Anaya pointed me to this post, in which Casper Albers shared this snippet from a recently-published paper from an article in Nature Communications: