Chapter 176: Chapter 93: What on earth do this child’s parents do?! Chapter 176: Chapter 93: What on earth do this child’s parents do?! In Gu Jiuyue’s view, her wording had been precise, sincere, and bulletproof.
But to everyone else, her response was obviously hiding a massive amount of unspeakable information.
For example…
Passed on, Gu Jiuyue felt that the man she got along with best was Su Huai!
Passed on, Gu Jiuyue currently liked Su Huai the most!
Passed on, Gu Jiuyue was super into Su Huai!
Passed on, Gu Jiuyue thought Su Huai was a soulmate, perfectly aligned in values and filled with harmony…
What?!
Were Su Huai and Gu Jiuyue an item?!!!
Oh my god, since when?!!!
Worldly rumors and gossip often start like this. Lu Xun once said: “See a short sleeve and you think of a bare arm, and then you think of nudity… Chinese people’s imagination can make such leaps only in this aspect.”
He was wrong, facts have proven that everyone in the world’s imagination leaps just as far when it cos to gossip.
And now the most troubled person was, no one talked to Gu Jiuyue to her face; everyone was holding back their excitent, preparing to gossip quietly after they got back.
Gu Jiuyue hadn’t realized what kind of rhythm her statent was going to create.
If Su Huai had known earlier, he would have had a way to calm things down, but he was up on stage organizing the discussion, working tirelessly for everyone’s academic pursuits…
Amidst the undercurrents, Su Huai was making a new round of summary.
“Through everyone’s discussions, we have reached an initial consensus—data science has great potential. In the visible next ten years, the shortage of talent will be long-term, systemic, and on a large scale.
So in this class, we can try to discuss which direction seems most promising, among the various paths?
Huang ngting, stop your chattering down there, co on, you’re the first one to co up and share your thoughts!”
Little Miss Huang, stunned by Gu Jiuyue’s shockwave, was unexpectedly called out by Su Huai. She stared at Su Huai with an odd look, stamring out a question.
“Class leader, do you think compatibility or feelings are more important in love?”
“What the hell?!”
Su Huai was bewildered, leading to a burst of raucous laughter from the crowd.
But since it wasn’t a serious occasion to begin with, and Su Huai wasn’t the type to unnecessarily wield authority, although bewildered, he still answered her.
“If we deduce with big data, love is definitely about feelings first. It requires the surge of intense passion in the short term to evolve from liking to loving.
After the passion, the next stage would probably place greater importance on compatibility.
One is a starting condition, the other a sustaining condition; they can’t be stored in the sa category, so your question is considered quite amateurish and ineffective in our field. How did you beco a product manager?”
Su Huai’s joke triggered another wave of laughter, and this ti, even Gu Jiuyue couldn’t help but smile faintly.
Realizing she had made a fool of herself, Huang ngting quickly ducked her head down on the table, not daring to ss around anymore.
The incident didn’t disrupt Su Huai’s control of the situation, and the sisters of room 511 didn’t continue to discuss this topic, instead joining actively in everyone’s analysis.
During this, Su Huai incorporated his “judgnts” about the future into the summaries several tis, helping everyone to set up an overall frawork.
Finally, he made a conclusion that dazzled everyone.
“For us in the data science field, there are essentially only two career paths worth putting effort into—data engineer and data analyst.
This is because any computer science graduate can easily master database managent, visualization tools, and even future AI tools after a period of self-study.
Back-end maintenance positions are too competitive and easily replaceable.
Engineers and analysts face high barriers to entry and are less likely to be replaced.
Furthermore, this isn’t a profession confined to the internet industry; technology, finance, retail, and other industries all need professional data officers, so these two professions may not be counter-cyclical, but they can certainly survive across cycles.
So, if we think about it from another angle, such as: how do we build a moat of knowledge in data science?
Then we’ll understand where we should focus our learning.
To summarize in one sentence: we study what others don’t; what others do study, we combine and learn.
The most important areas are mathematics, followed by statistics, then computer science fundantals, data fraworks and algorithm problems, Java’s lower levels and frawork source code, data warehouse projects, and so on and so forth.
I looked through our university’s four-year curriculum and felt that it was designed with care, but there are also so omissions and delays that we need to cover ourselves.
How do we cover them?
I personally believe we should pursue self-study and research targeted towards the needs of major corporations and top-tier technology and finance companies.
By sophomore year at the latest, I’ll carefully study and discuss with the faculty and departnt heads to see if we can secure additional training or even new courses.
As society develops, the demand for general IT talent has dropped to rock bottom, but data science is heating up like oil on fire.
Brothers and sisters, at this grand mont, we have already taken more than a step ahead of our peers in our choice of track. What remains is to study diligently, making them fall further behind.
After today’s discussion, our goals and paths have beco quite clear—
Starting salaries over ten thousand, striving to beco the backbone of the data departnt, then aiming for a data officer role. Those inclined towards data can continue to aim for Chief Data Officer, while those leaning towards business can switch to head of the product departnt.
After another year or two of experience, those with the necessary resources can jump to finance to beco a chief analyst, and the next step would be to aim for hedge fund manager…
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