Data Science |Bringing it Alive| Scaling | Soft Skills & Approach (Part 2)

It was immediately clear that the biggest successes stemmed not simply from technical excellence but from softer factors such as a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address dozens of concerns among those impacted. Conversely, otherwise excellent technical work died on the vine when we failed to connect with the right people, at the right times, or in the right ways.

(Reference: https://hbr.org/2019/05/do-your-data-scientists-know-the-why-behind-their-work )

Data Science Requires Rigor

Thomas C. Redman in his HBR article “Do Your Data Scientists Know the ‘Why’ Behind Their Work?” covers the following relevant points.

• First, clarify your business objectives and measure progress toward them.?
• Second, hire data scientists best suited to the problems you face and immerse them in the day-in, day-out work of your organization.
• Third, demand that data scientists take end-to-end accountability for their work.?
• Finally, insist that data scientists teach others, both inside their departments and across the company.

Soft Skills to be a good Data Scientist

• Ability to get into problem identification and data depth
• How will the model help in the problem solving?
• How will the model be used and consumed?
• How will the model get integrated?

?Orientation Snippet

Data science is a team sport, but it’s not a game. Managers must make clear that the goal is to improve the business, and they must hire those that can help them do so. They must do all they can to integrate data scientists into their teams and they must insist that data scientists contribute in every way possible — before, during, and after the technical work.

(Thomas C. Redman article)

Scale Orientation

Data Science took a next level representation with?Dhanurjay "DJ" Patil is an American mathematician and computer scientist who served as the Chief Data Scientist of the United States Office of Science and Technology Policy from 2015 to 2017 (under President Obama). His books Data Driven, Building Science Teams, Data Jujitsu & Ethics and Data Science are very valuable books to peruse. His famous LinkedIn Series “Ask Me Anything” are also worth the perusal and reference.

As the first-ever US Chief Data Scientist appointed to Obama’s White House, D.J. Patil faced high expectations and considerable challenges. One of the tools he used throughout?that time was a simple?handwritten note (link) to himself.?

• Dream in years; plan in months; evaluate in weeks; ship daily.?
• Prototype for 1x; build for 10x; engineer for 100x.
• Find what’s required?to cut the timeline in half; what needs to be done to double the impact.

Soft Skills

Communication skills

It is important that you can communicate your insights from your data science projects in a clear and understandable way to people who have little to no technical knowledge.

Empathy

As a data scientist, you need empathy to understand and reason with the people directly affected by the problem you may be trying to solve. Understanding what matters to the stakeholders, helps you understand what variables and insight matter most to be able to drive the change you want to make.

A Business Mindset

One main goal of data science is to leverage business data to reach a specific goal. It is imperative, therefore, as a data scientist to have a good level of business acumen. Understanding the domain plays a critical role as well.

Critical Thinking

Gathering, analyzing and making sense of data is not as easy as it seems. There are points where at each stage in the data science process, you may have to make decisions.

Logical reasoning

When it comes to data science, thinking logically is paramount. It really is all about the numbers and facts. To be able to draw accurate insights from data, you need to stick to insights that the data you have produced.

?The Machine Learning Process in 7 Steps

Vincent Granville is a data science pioneer and mathematician who co-founded the “Data Science Central”. In chronological order, here are the main steps. Sometimes it is necessary to recognize errors in the process and move back and start again at an earlier step. This is by no mean a linear process, but more like trial and error experimentation.?

• Defining the problem?
• Defining goals?and types of analyses to be performed
• Collecting the data
• Exploratory data analysis
• The true machine learning / modelling step
• ·Creation of end-user platform
• Maintenance

?Reference

1.???????https://benjaminobi.medium.com/dont-become-a-data-scientist-576cb9100262

2.???????https://hbr.org/2019/05/do-your-data-scientists-know-the-why-behind-their-work

3.???????https://modelthinkers.com/mental-model/patils-project-principles

4.???????https://www.datasciencecentral.com/profiles/blogs/the-machine-learning-process

5.???????Data Science |Bringing it Alive| Scaling(Part 1) link?

6.???????https://www.dhirubhai.net/pulse/important-soft-skills-have-data-scientist-ivy-barley/