Speaking on Leadership and Data Science

This week, I spoke twice at this year’s Los Altos High School STEAM Week event. The school is a two-thousand student public school in the heart of Silicon Valley with many families from big tech companies. Nvidia CEO Jen-Hsun Huang and White House Chief Data Scientist DJ Patil spoke at this same event in prior years.

For 20 years, the school has brought in industry speakers to their STEAM Week. STEAM is like STEM with careers in science, technology, engineering, and math, but also includes arts. I spoke on two different days on:

• Keynote on The Leadership Mindset: Keys to a Fulfilling Career and Life​
• Classroom session for the Advanced Data Science class

I’ll share my keynote content on leadership with you next week after the video recording gets published.

In the meantime, today, I’ll share about A) keynote speeches for those of you who are preparing for your own speaking engagements, and B) key takeaways from my classroom session for the Advanced Data Science class.

My Keynote Speech on Leadership Mindset

In my keynote speech, I encouraged the audience to develop their leadership and communication skills in order to future-proof their careers in STEAM or anywhere.

Leadership and communication skills have been essential in growing my own career. I see these skills as a path for people to stay ahead of AI disruptions in the workforce.

To be a good personal example, I had agreed to give this keynote speech at the STEAM Week event. Giving a good keynote speech takes a lot of preparation!

Keynote speeches typically need the following prep time for each hour of speech.

• 30 hours to research
• 30 hours to create the deck
• 30 hours to practice delivery

All that time is needed to research the audience and to prepare the proper visuals, stories, and supporting evidence. The preparation is needed to make sure the flow to the call-to-action is cohesive and compelling.

Someone told me that a keynote speech is not a regular “how-to” speech. A keynote speech is supposed to challenge how the audience thinks. But of course, still with actionable takeaways.

Standing in front of an entire theater of high schoolers at 8:30 am in the morning, I needed to make sure that my 30-minute keynote was particularly relevant and engaging. Frankly, keynotes should be more like performance and entertainment, rather than a lecture.

Practicing for several years in Toastmasters had helped me tremendously with public speaking and leadership skills. I continue to hone my confidence even further with each new event.

My keynote speech this week encouraged the audience themselves to also develop their leadership and communication skills for future-proofing their careers. I would give you, my readers, the same advice.

The event organizers will publish my keynote speech to YouTube next week. I’ll share the video with you after it’s available in case the material would be helpful for you too.

In any case, if your business needs a keynote speaker, please reach out to me as others have. Or, if you have an opportunity to be a keynote speaker yourself, you can also reach out to me for coaching.

From Data Science Class to Data Science Careers

I returned to Los Altos High School the next day to speak to the Advanced Data Science class. Yes, the high school math department teaches data science!

After talking about the LinkedIn economic graph, here’s what I shared for those aspiring to become new data professionals…

Businesses have a growing need for people with data skills for data science and analytics. Not all companies are building AI foundational models, but all companies collect data and need workers to guide the company using the data.

Data science for business analytics fulfills 4 categories of business questions.

• Descriptive Analytics: WHAT happened?
• Diagnostic Analytics: WHY did it happen?
• Predictive Analytics: What WILL happen?
• Prescriptive Analytics: What SHOULD happen?

Almost every department in any business needs people to analyze data.

The job prospects for data professionals remain bright for the future.

In addition to learning R in class, I advised the students to get a jump in their careers by getting hands-on experience with real data and using SQL.

How can learners to get hands-on experience with real data for free? Here are my recommendations:

• Use GitHub Pages to create a new website
• Use Google Analytics for web analytics on your new website
• Use Google BigQuery database to import Google Analytics data
• Use Colab Notebooks to analyze and graph your data

The above systems are all free. Online tutorials are freely available. The aspiring data professional will get valuable hands-on experiences with HTML, Google Analytics, SQL, and data visualizations.

The ambitious students who get the above valuable experiences onto their resumes will get a tremendous head-start to accelerate entry into the competitive job market.

Feel free to download my handful of slides that I shared with this data science class in case you're also aspiring to grow your career in data science and analytics.

Data Leadership For You

With over 27 years of industry experience, I offer various services to individuals, leaders, and groups to help them become better data professionals.

Ready for your business or career to thrive with my help? Contact me for a free consultation to learn how I can help you in your specific situation.

Jimmy Wong Coach, speaker, and entrepreneur enabling people to thrive in the age of AI. Data science leader with 12 years experience at the LinkedIn company and 27 years in the industry. Visit aijimmy.com​ ​​​​​​

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