Hello :) Today is Day 128!
A quick summary of today:
- attended IBM Consulting Insights: Virtual Careers event
- watched a few more lectures of Stanford’s CS109 Probability for computer scientists
Firstly, for the IBM consulting event
It was an introduction to the different positions that IBM offers in their Consulting department. It was through webex, and many ‘IBMers’ showed up. From interns to graduates, to permanent employees and employees from different backgrounds. Below are some pics I took of the presentation.
They presented how IBM thinks of consulting
And the importance of putting clients first, embracing new perspectives, open ecosystems, and collaborative approaches, and restless reinvention and innovation. They shared some habits of success to flourish in our careers:
- build client trust
- collaborate to succeed
- grow with endless curiosity
- embrace diverse perspectives
- innovate with purpose
- deliver with impact
What skills do we need for consulting? Here is what IBMers think
They also shared about their project development process.
And how they employ modern techniques like Agile using retrospectives, standups, work breakdown, and planning walls.
And very important: they want all IBMers to want and be excited to learn. Because they have all of these support systems established for continuous learning.
We were also introduced to IBM’s learning platform called SkillBuild. And invited to join a specially crafted learning path for the Consulting virtual event
Covering the below courses
I will put this on my to-do list ^^
And ~ I got a certificate
Secondly, about CS109
Lecture 17: Adding Random Variables
Talked about adding two distributions, adding random vars, or convolutions (if you want to sound fancy, as professor Piech said haha).
Sum of 2 uniforms looks like a triangle
What if we add many of any distribution?
The Central Limit Theorem
The professor’s teaching method is so nice, I wish I found this earlier in my journey haha.
Lecture 18: Central Limit Theorem
Finding sample mean and variance, and the variance of the sample mean. We saw how the distribution of the sample means from a population, regardless of the original distribution of the population, will tend to follow a normal distribution as the samples increase.
Lecture 19: Bootstraping and p-values
Talked about how to estimate the sampling distribution of a statistic - in this case mean and variance. And once we have some values, how to test if they are good estimaters - p-values.
Lecture 20: Algorithmic analysis
Learned about the law of total expectation
Tomorrow, the lectures begin with maximum likelihood estimation and ML. I am excited to see how professor Chris Piech will introduce these. I really wish I found these lectures at the start of my journey, but just watching them now and confirming my knowledge still feels good. And also the professor is amazing!
That is all for today!
See you tomorrow :)