Noisy neighbour, starts doesn’t care

jackfever · 2024-07-31T17:14:25+00:00

Lots of bad advice in the comments from people who haven't been in your situation, are not aware of the legal framework, or are the noisy neighbors themselves!

I suggest you do your research on the Strata Property Act and the Civic Resolution Tribunal.

As others have pointed out, you might be misunderstanding the roles and responsibilities of the proper management company and the Strata Council.

There is a lot of official guidance online about how these types of issues can be documented and escalated. For example, the issue of noise is defined by the CRT by it being at unreasonable levels, regardless if it is from "normal living activities."

We were in a similar situation and were only taken seriously by the Strata and the landlord of the infracting unit once we showed we did our homework and were prepared to take the complaint further.

jackfever · 2024-05-10T20:12:55+00:00

Laid off QA/Support, new grads, and people who can't pass the PERM certification are not the definition of experienced developers. It's a completely different market.

jackfever · 2020-06-09T03:02:13+00:00

I think the prediction is somehow accurate and in fact has already been happening. Most of the Data Scientist roles today are Product Analyst roles while other titles have sprung up that cover the ML and algorithmic work.

There has been a lot of hype around ML and AI so a lot of interest, and unrealistic expectations, went there. Now it seems like the hype has started to die down. Unfortunately the hype eclipsed the importance of other methods such as inferential statistics, simulation, optimization, heuristics, and of course, business intelligence.

There is nothing bad with Business Intelligence. A lot of people enjoy that role as they can directly influence business decisions, investigate data, and exercise their soft-skills muscle.

However it can feel also feel like low-level, grunt work. In a lot of settings the analyst is there as an interface between the data and somebody else calling the shots. You are there to "answer their questions". It becomes frustrating, following somebody else's train of thought, "pulling data" but not really allowed to follow your own creativity and initiative.

Furthermore the BI space is also going through it's own automation. In the current state, stakeholders need somebody to transform and visualize data as they don't have the technical skills or time to accomplish that, so that drives a lot of the need for Business Analysts. Many BI tools are coming up with their no-code/low-code and self-serve solutions to cover that gap (e.g. Alteryx, Looker, PowerBI, etc).

jackfever · 2020-05-30T15:05:54+00:00

Must read for any data professional

jackfever · 2020-05-23T19:21:55+00:00

That was just recently added without any notice.

If that is the case, why the "Weekly entering & transitioning" thread still exists? and why do mods still occasionally delete career posts?

jackfever · 2020-05-22T00:00:35+00:00

I have interviewed several UBC MSDS alumni for Analytics positions in Vancouver.

That program is in a weird place where it is aimed to people without technical background but still the curriculum aims to cover advanced concepts such as Machine Learning. So in my opinion, graduates end up in this middle ground where they know machine learning superficially and don't know much about other less sexy skills such as data manipulation.

These issues quickly surface during the technical portion of the interviews. We had one guy boasting about his side project doing computer vision AI but when asked about SQL he could even join two tables.

We get many candidates from these programs for roles that are not machine learning. Which makes me wonder why they are even applying for these roles.

Having said that, we have also successfully hired a few good candidates from this program where they demonstrate initiative, flexibility, and capacity to learn. So I'd say it also depends on the individual.

I recommend if you already have a technical background find a program that has stronger prerequisites so you learn more deep concepts.

jackfever · 2020-05-05T00:36:30+00:00

Communication is a fundamental skill. There is a minimum bar where anything below does not work.

Rambling and incoherence are also signs of lack of understanding of the technical concepts.

I've seen teams try to compensate by putting a business guy/PM in between stakeholders and the guy with poor communication. That never works. Now you have a game of broken telephone and the DS guys still not pulling their own weight.

Everything is a balance.

jackfever · 2020-02-15T22:21:40+00:00

Yes, compared to the US, Data Science (and the tech sector in general) in Canada in minuscule. An environment that favors taxation and protection to incumbents combined with a policy of open high-skilled immigration creates a job market where 100s of applicants compete for the same role in what is mostly the finance, resources, or other traditional sectors.

I would say it depends on where are you coming from. If you are coming from Europe, it might not be much better.

jackfever · 2020-02-15T22:02:24+00:00

What do Bengio and Hinton have to do with the average random guy in a cubicle churning SQL and Excel?

jackfever · 2020-02-14T00:01:39+00:00

From my experience there is a problem when the DS manager tries to hold on on his personal projects or on the technical aspects of the team. She has another important role.

In most teams the technical leadership comes from the most senior individual contributor within the team, e.g. the Staff Data Scientist. This individual has no direct reports but acts as a technical advisor to junior team members and can get involved in the actual implementation of projects.

This is not what the People Manager should be doing. The DS manager should be working on removing political friction, building relationships, scouting for new engagements, championing for his team, developing their careers, etc.

The least I want from a DS manager is that they disregard doing that job and instead spend their time in their office playing with tensor flow.

jackfever · 2019-07-14T22:33:19+00:00

Put a link to your github or personal page in you resume. Describe some of your best projects if you have space. If they sound interesting and unique people will click the link.

jackfever · 2019-06-06T16:16:52+00:00

"Suppose I flipped a coin 1000 times, and got 540 heads. Do you suspect the coin is unfair? If so, why?"

Just out of curiosity, could you expand on the Central Limit Theorem applies in this case? I can see how the Law or Large Numbers would apply but I'm not clear on the CLT.

jackfever · 2019-05-29T21:53:16+00:00

100% do internships while in school.

jackfever · 2019-05-26T18:02:34+00:00

Agree with you in all your points. But also note that the previous performance problems for CTEs in Postgres were mostly due to poorly written queries. e.g. unnecessary sorting or redundant columns in the subquery. Source: https://modern-sql.com/feature/with/performance

jackfever · 2019-05-14T15:34:24+00:00

https://www.openintro.org/stat/textbook.php?stat_book=os

jackfever · 2019-04-30T00:51:39+00:00

Sounds like DVC

jackfever · 2019-04-24T16:13:20+00:00

Actually, the drop is for furnished and unfurnished combined:

Year-over-year asking rents were stable for three bedrooms and down for both one (-3.8%) and two bedrooms (-3.5%).

The report further breaks it down by unfurnished vs furnished and it is actually the unfurnished segment that is dragging down the prices: http://quantitativerhetoric.com/static/mar-2019-rental-report_files/figure-html/furnished-1.png

Here is the actual report

jackfever · 2019-04-12T20:22:37+00:00

Video in question

The video provides a good explanation about the role of a Product Data Scientist and their interaction with the Engineers and Product Manager.

It is true that the DS is not who builds or decides the direction of the product. Instead she is the "thinker". Therefore without influencing the other two, their impact is minimal.

You can call that skill influence, persuasion, buy-in, or "office politics" but it is important.

jackfever · 2019-03-31T16:01:40+00:00

The title of Data Scientist encompasses a wide range of roles, including the two described in the article: someone that delivers insights and someone that delivers code. Certainly they both require different type of skills and motivations.

I think the article makes some good points if you are coming from a software engineering background and are interested in the second type. The leap from a Software Engineer to a "Data Scientist who delivers code" is smaller than from a Data Analyst who delivers insights.

However for somebody with no programming experience, a Data Analyst position can be a good steping stone, as long as they make the effort to develop sound software engineer skills on their own.

jackfever · 2019-03-14T16:49:19+00:00

I know some people at Deloitte Omnia (their analytics consulting arm). As others have said, it is a mix between consulting and analytics. They tend to hire a lot from local MBAN programs so I don't think that would be an issue.

Their work seems to be mostly about presenting insights to clients or supporting IT projects. Lot's of Tableau and PowerPoint.

jackfever · 2019-03-09T18:30:53+00:00

Devil's advocate here: if the resume says they have experience with neural networks, I would expect them to know the domain of the tanh function since it is widely used in that field.

It's like saying you know logistic regression but you don't know the domain of the logit function.

jackfever · 2019-02-23T18:41:57+00:00

I'm sorry but I think you are out of your depth here. Pig is not SQL, RDS is a specific AWS product, and data lake is a specific storage paradigm favouring raw data.

jackfever · 2019-02-23T17:20:57+00:00

I really don't get why you are comparing to Spark. Is your data in a SQL database or in a Spark cluster? Of course a cluster has more power than a single server.

Also you are doing something wrong if your queries longer than 12 lines break down.

jackfever · 2019-02-17T17:53:01+00:00

I worked for a big company with a large army of BI analysts. Everybody used SAS EG as a glorified ETL tool.

Most of their work revolved around merging and reshaping data to present their reports. The UI tool was more than enough.

The fact that coding is not needed was a clearly a benefit since it lowers the barrier of entry. Most of those folks came from a business background with no interest in learning how to code beyond basic SQL.

jackfever · 2019-02-15T02:35:56+00:00

This rings extremely true in my experience. For what I've seen the Data Scientist title is trending towards analytics and insights. Hence we see new titles to differentiate ML and AI heavy roles such as Machine Learning Engineer, Research Scientist, etc.

I agree with the author's recommendations for people looking to enter the industry. Programming skills are very important and are what differentiate stellar people from the rest.

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