Quora Answers by Monica Rogati

Monica Rogati, a legend in the data science space, recently provided some answers on Quora that are sheer internet gold.

Quora Answers by Monica Rogati

She answers questions involving:

  • What is a data science advisor?
  • Challenges of Building a data science team?
  • Characteristics of a good data scientist?
  • and more

They are filled with great advice.

Get me Sum ‘dat Big Data

I teach data science courses thoughout the US. I enjoying asking attendees why they are in class. I get many good answers, but occassionally I get some funny answers. Here is a story with one of the more humorous answers.

While chatting with an attendee before class, I asked why he chose to attend this class. Here was his answer.

Well, my boss attended a conference and heard a talk on Big Data. Then, he came back to the office and bought hadoop for some of our systems. Next he heard about this training and told me to attend. When preparing to leave, the boss said, “Get me sum ‘dat big data”.

After a slight chuckle from both of us, I mentioned we would talk more about that in class.

While this story is somewhat humorous, it is not all that uncommon. Companies want to start using data science, they often just do not know where to start. If you are looking for a starting point, check out this post, You Want Data Science, Now What?.

Do you have a funny “data science” or “big data” story? If so, please share in the comments.

Best Practices for Machine Learning Engineering

Martin Zinkevich, Research Scientist at Google, just compiled a large list (43 to be exact) of best practices for building machine learning systems.

Rules of Machine Learning:
Best Practices for ML Engineering

If you do data engineering or are involved with building data science systems, this document is worth a look.

You Want Data Science, Now What?

I am often confronted by people or organizations whom have heard about data science but don’t know where to start. It is a valid concern. Data science is a broad topic with different meanings to different people.

Here are the common questions I hear. Should I hire a data scientist? Should I hire some consultants? Should I build a data science team? There is no perfect answer for those questions because it depends upon your organization and situation. I would like to suggest a different approach. At first, don’t worry about the titles and organizational structure. Worry about the problems you want to solve. First, start out with 2 questions.

1. What is the goal (be specific)?

This question might seem obvious, but it is often overlooked. Don’t start with data science just because you have heard about others using it. A bad goal for data science is: be data-driven to increase profits. While that might be a high-level strategy, it is much too broad. Better goals are:

  • identify which customers are likely to leave
  • identify which products a customer might buy next
  • determine what cities would be best for expansion
  • find the most profitable type of marketing for your organization
  • predict if a person will get cancer in the next year

These are examples of specific goals that data science can help to address. Work hard to narrow your goals to something specific. If you can get enough specific goals, then you might be able to increase profits.

2. What action can be taken?

This is very important. All the predictions and fancy data science does you no good if your organization cannot take any action. For example, sticking with the previous examples. Suppose you can predict if a person will get cancer in the next year. What do you do with that information? Do you send the person an email? What if you are wrong? Do people really want to know that? That is a tricky situation to handle and any action you take has an ethics component.

Other situations have simpler actions, such as identifying the products a customer might be next. Common actions might be: sending a coupon, displaying an add, or suggesting the item be added to the cart.

Another factor to consider with the action is cost. How much will it cost to perform some action. In certain businesses, it might be more profitable to attract new customers than retain existing customers. Thus, there is little advantage to identifying which customers are likely leave.

Conclusion

Data science is very exciting, and it has many positives. However, when done with incorrect expectations, it can lead to nowhere but headaches. Thus, before you start building a team or hiring some consultants, make sure you are clear on your goals and actions.