North America BCGX DS OA has been released one after another. This version has four subjective questions, plus a set of three multiple choice questions. The overall feeling is a little more "engineered" than that in Europe, with more emphasis on data cleaning, unified field naming, and standard date processing. I have done a lot of EU/UK BCGX question banks before. After getting the North American version this time, it has been basically smooth. I will sort out the key points for you in one go.
The overall rhythm is still the familiar Pandas practical route: reading data, cleaning, filtering, merging, and aggregation. 90 minutes is plenty of time, as long as you don't get stuck by bugs, you can get through it safely.
Q1:Calculate the average property price, the proportion of waterfront properties, and the 30-day average page views.
This question is the most typical BCGX style:
The requirement itself has three parts:
- average house price
- Water view room ratio
- Average views over the last 30 days
Logic is an old friend:
- The average price is directly
priceFind the mean, and be careful to filter out obvious outliers (negative numbers, null values, etc.). - waterfront
waterfrontUsually 0/1, directly sum / count. - The number of views in 30 days needs to be calculated first
Dateparse into datetime, then findmax(date), filter forward 30 days, only forviewsFind the average.
The areas where points are easily deducted for this question are almost all format issues: dates are not converted,waterfront Find the average. dropna()Usually 0/1, directly sum / count.
Q2:Standardize inconsistent column names, perform time-based filtering, and merge the cleaned datasets.
The second question is the feature of the North American version of BCGX: unify the dirty column names into a standard format, and then perform date filtering and merge.
Usually you will see:
"Page Views""page-views"" pageviews "
All must be standardized when reading files, such as lowercase, underscore separation, spaces, and special characters. Just write a paragraph at the beginning:
df.columns = (
df.columns
.str.lower()
.str.strip()
.str.replace(" ", "_")
.str.replace(r"[^a-z0-9_]", "", regex=True)
)
All subsequent merges and filters are so smooth that they lose their temper.
The real difficulty of this question is not the logic, but whether you can realize that: the field name is wrong, and all subsequent groupby / join / filter are wrong.
The North American version emphasizes "listing rigor" more than the European version. Some students write very good logic, but they fail because of inconsistent join key names. This is also the pitfall that we usually remind us the most.
Q3 / Q4
The content types of the latter two questions belong to the old tradition of BCGX:
- Multi-condition data filtering
- Groupby aggregation of multiple indicators
- multi-key merge of two datasets
- Construct some derived features (rolling, lag, ratio)
- Do some lightweight quality checks (duplicates, missing patterns)
Basically, they are of medium difficulty. If you are familiar with Pandas, you can write it quickly; if you are not familiar with it, you will get stuck on merge, date, and derived columns.
Overall, experience can provide obvious advantages: you know how they like to take tests and where they like to make pitfalls, so you won't waste time on debugging.
Multiple Choice (3 questions)
The three questions are all basic skills:
- Statistical judgment (variance, sampling, correlation)
- Experimental logic (A/B controls, sources of bias)
- SQL / Python True or False Questions
The level of difficulty is moderate. As long as you have some contact with data work, it will not be strenuous at all.
Summary of practical difficulties in OA
The difficulty of BCGX North America OA has never been that "the logic is difficult", but rather:
- Column names, dates, and data types must be extremely clean
- Once the merge key is written incorrectly, the entire question will be scrapped.
- Have good habits: clean first, then calculate
- The code cannot be messy, and readers must be able to understand your ideas.
There is actually enough time, but once the details are missed, the results will be biased. If you do it more, you will find that their test points are very unified:
Real-world data will be dirty, and can you quickly turn it into an "analyzable format" in a short time?
ProgramHelp | Let you no longer be sent away by minor bugs in BCGX OA
The most feared thing about practical OA like BCGX is "the logic is correct, but the output is wrong", and these errors are almost all small details, such as inconsistent field names, omissions in date parse, and reversed merge order. Our VO assistance here is a full voice prompt, which does not touch your environment or interfere with your code. It is completely invisible and only reminds you not to step into pitfalls in key steps, making the entire OA writing more stable and faster.
Some students finished writing in 80 minutes and had a lot of bugs, but we adjusted it to 40 minutes to complete + full AC score. The essence of exams like OA is to have a steady hand and steady details.
