# Level 1 – Introduction for Data Scientists

## Why is mathematical optimization important to data scientists?

A growing number of data scientists are adding mathematical optimization (MO), which is also known as mixed-integer programming (MIP), into their analytics toolbox – as they are discovering that MO solves problems, answers questions, and delivers insights that machine learning (ML) cannot. Incorporating MO into your data science repertoire gives you the opportunity to utilize a broader range of applications, maximize the business value of your data, and increase your overall impact on your organization.

Although the techniques of MO were invented more than 70 years ago, recent advances in computing power, algorithms, and data availability and quality have made it possible for MO technologies to rapidly and effectively handle the world’s most complex business problems and automatically generate optimal solutions. As a result, MO has had an immense impact on a wide variety of business areas – including finance, manufacturing, mining, electrical power, and logistics – and has enabled companies in these and many other industries to boost their operational efficiency and overall profitability.

## Why should you use mathematical optimization?

In this video, Gurobi CEO and Co-founder Ed Rothberg explains how mixed-integer programming (MIP) combines expressiveness and robustness to produce high-quality, reliable solutions.

## How does mathematical optimization work in concert with machine learning?

While machine learning (ML) can enable you to make predictions, mathematical optimization (MO) empowers you to make decisions. When your problem involves complex tradeoffs among various (and often conflicting) business objectives and has an astronomical number of possible solutions, only MO has the power to find the best or optimal solution – which can be used to make optimal business decisions.

Actually, MO and ML are complementary technologies, and more and more companies are developing and deploying applications that combine MO and ML. MO technologies can leverage ML-generated predictions by using them as input for MO-based solutions and decisions. A good example of this is predictive maintenance: ML can enable manufacturers to predict when machine failures are likely to occur, and then MO can use these ML-based predictions to help create optimal maintenance schedules that minimize resource costs and production disruptions. Also, MO-based solutions can be utilized to help shape, retrain, and improve ML models – which can decay over time.

## Webinar: Mathematical Optimization + Machine Learning

In this webinar, you will hear the results of the 2019 Mathematical Optimization Survey commissioned by Gurobi and conducted by Forrester and gain insights on how data scientists can use tools such as MO in tandem with ML technologies to drive optimal decision-making and business outcomes.

Discover how Blue Yonder uses machine learning and mathematical optimization to enable daily, automated retail pricing decisions.

## Now that you have explored Level 1, you are ready to move on to the next level to continue your education.

### Commercial Evaluation Trial

Gurobi allows you to try a free, full-featured, commercial evaluation license for 30 days. During that time, you’ll also get:

• Free benchmarking services
• Free model tuning services