Course5 Intelligence R Programming Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Course5 Intelligence hiring for R Programming roles
Course5 Intelligence is a Mumbai-based analytics and AI company that offers market research, digital analytics, customer analytics and AI-driven insights to global brands. It works with clients in technology, retail, consumer goods, media and pharma. In India it hires data analysts, research analysts, data scientists and engineers who turn survey, digital and business data into insights.
For R Programming roles, this Course5 Intelligence practice set focuses most on Modelling and Machine Learning in R (6), Reports, Shiny and Production R (6), Data Visualization with ggplot2 (5), Data Structures in R (4). It has 6 easy, 20 medium, 4 hard questions, so you can start easy and work up to the harder ones.
Company type
Analytics
Headquarters
Mumbai, India
Main India offices
Mumbai, Bengaluru
Course5 Intelligence R Programming interview process
Online assessment (aptitude + analytics basics)
Technical interview
Case or managerial round
HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for Course5 IntelligenceStart practicing
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Indian files often store dates as dd/mm/yyyy, but as.Date() expects yyyy-mm-dd by default, so values become NA. I specify the format, as.Date(x, format = '%d/%m/%Y'), or use lubridate::dmy(x), which handles different separators automatically. Then I count NAs before and after conversion to confirm nothing failed silently. If the column came from Excel as serial numbers, I use as.Date(x, origin = '1899-12-30') instead.
purrr's map() applies a function to each element of a list or vector and always returns a list. Typed variants return specific types: map_dbl(), map_chr(), map_lgl() and map_int(), and they error if a result has the wrong type, which is safer than sapply(). map2() iterates over two inputs, pmap() over many, and walk() is used for side effects like saving files. Formula shortcuts such as ~ .x * 2 keep code short.
Two variables can move together because of a third confounding variable, reverse causation or pure chance. For example, ice cream sales and drowning incidents are correlated because both rise in summer. To claim causation you need a randomized experiment such as an A/B test, or careful causal methods like difference-in-differences or regression with controls. In interviews, mention that observational correlations are useful for hypotheses but not proof of impact.
07
Describe a mistake you made in an analysis and how you handled it.
Situation: I shared a retention analysis showing improvement, but a week later I noticed I had excluded customers with missing signup dates, who were mostly churned users. Task: correct it before decisions were made. Action: I informed my manager immediately, reran the analysis with proper handling, explained the impact clearly and added a missing-value check to my template. Result: the corrected numbers were used, and my manager appreciated the transparency.
08
You receive 60 monthly CSV files with the same structure. How do you combine them into one data frame?
I list the files with list.files(pattern = 'csv$', full.names = TRUE), then read them all at once with readr::read_csv(files, id = 'file'), which also records the source file. Alternatively I use purrr::map() with read_csv and dplyr::bind_rows(). Before combining, I check that column names and types match, because one file with text in a numeric column will fail or coerce the whole column. I then derive the month from the file name for traceability.
09
How do you generate the same report for each of 20 regions automatically?
Use a parameterized Quarto or R Markdown report and render it in a loop.
# report.qmd header:
# params:
# region: 'North'
# then use params$region in the code
regions <- unique(sales$region)
purrr::walk(regions, function(r) {
quarto::quarto_render(
'report.qmd',
execute_params = list(region = r),
output_file = paste0('report_', r, '.html')
)
})
10
Write a tidymodels workflow to train and evaluate a random forest.
Each coefficient is the expected change in the outcome for a one-unit increase in that predictor, holding others constant. The p-value tests whether the coefficient differs from zero. R-squared is the share of variance explained, and adjusted R-squared penalizes extra predictors. Residual standard error shows typical prediction error in outcome units. The F-statistic tests whether the model beats an intercept-only model. For factors, coefficients are differences from the reference level.
13
Your R script works on your laptop but fails on a colleague's machine with 'could not find function'. What do you check?
First I check that the package providing the function is installed and loaded with library() at the top of the script, not only in my interactive session. Then I compare R and package versions using sessionInfo(), because functions get renamed or deprecated. To prevent this, I use renv to snapshot exact package versions into a lockfile, so the colleague runs renv::restore() and gets the same environment. Namespacing calls like dplyr::filter() also avoids masking conflicts.
Tidy data follows three rules: each variable is a column, each observation is a row and each type of observational unit forms a table. For example, a sales sheet with separate columns for Jan, Feb and Mar is not tidy, because month is a variable spread across columns. Reshaping it into month and sales columns makes it tidy. Tidy data works directly with dplyr, ggplot2 and modelling functions, reducing reshaping effort later.
15
Your manager says your charts are confusing. How do you respond?
I would thank them and ask which part was unclear, such as too many colours, missing labels or an unclear message. In my internship this happened with a crowded chart of twelve product lines. I simplified it by highlighting the top three, greying the rest, adding a clear title stating the finding and labelling values directly. My manager found the new version easy to present, and I now review every chart for one clear message.
A factor stores categorical data as integer codes with a set of labels called levels. Factors are used for variables like gender, city or rating, and modelling functions such as lm() turn them into dummy variables automatically. Ordered factors, created with factor(x, levels = c('Low', 'Medium', 'High'), ordered = TRUE), support comparisons. A common pitfall is as.numeric() on a numeric factor, which returns codes; use as.numeric(as.character(f)) instead.
17
How do you calculate ROC AUC and plot an ROC curve in R?
I profile with profvis and reactlog to find slow reactives. I move heavy data loading outside the server function so it runs once per process, pre-aggregate data instead of computing on raw rows, and cache results with bindCache(). I use bindEvent() so expensive work runs only on a button click. Long computations can go to background tasks with ExtendedTask and promises. Finally I scale out with multiple R processes on Posit Connect or Shiny Server Pro.
Facets split a plot into small multiples, one panel per category, sharing the same axes and geoms. facet_wrap(~ region) wraps panels for one variable, and facet_grid(rows = vars(segment), cols = vars(year)) creates a grid for two variables. scales = 'free_y' lets each panel have its own y-axis. Facets are often clearer than one chart with many overlapping coloured lines, for example comparing sales trends across ten states.
Calling rbind() inside a loop copies the growing data frame each iteration, so time grows roughly quadratically. I instead store each piece in a preallocated list, results[[i]] <- piece, and combine once at the end with dplyr::bind_rows() or data.table::rbindlist(), which is the fastest. Better still is to vectorize the computation or use purrr::map() followed by list_rbind(). On 10,000 iterations this can cut runtime from minutes to seconds.
22
How do you create a column with multiple conditions in dplyr?
dbplyr is a dplyr backend that translates dplyr verbs into SQL and runs them inside a database. You connect with DBI, reference a table with tbl(con, 'orders') and write normal dplyr code. Queries are lazy: nothing runs until you call collect(), and show_query() prints the generated SQL. This lets analysts work with large warehouse tables without pulling all rows into R, though some R functions have no SQL translation.
24
Your scatter plot of 200,000 points looks like a solid blob. How do you fix it?
This is overplotting. I reduce point size and add transparency with geom_point(alpha = 0.1, size = 0.5). Better options are geom_hex() or geom_bin2d(), which show density as colour, or geom_density_2d() contours. I could also plot a random sample for shape and a smoothed trend with geom_smooth() on the full data. If x values are discrete, geom_jitter() spreads points. The goal is to make density visible, not every point.
25
Bars in your chart appear in alphabetical order instead of by value. How do you fix it?
ggplot2 orders discrete axes by factor levels, which default to alphabetical. I reorder the factor by the value with forcats: aes(x = fct_reorder(city, sales), y = sales), and add coord_flip() or put the category on the y-axis for long labels. For a fixed custom order, such as months or ratings, I set levels explicitly with factor(x, levels = c(...)). fct_infreq() orders by frequency for count bars.
26
In your regression, coefficient signs look wrong and standard errors are huge. What might be the problem?
This is a classic sign of multicollinearity, where predictors are highly correlated, for example total spend and online spend. The model cannot separate their effects, so coefficients become unstable. I check correlations and the variance inflation factor with car::vif(); values above 5 or 10 are a concern. Solutions are removing or combining correlated variables, using principal components or ridge regression. Prediction accuracy may be fine, but interpretation is unreliable.
27
How do you customise scales, labels and themes in ggplot2?
Packages like sf, xml2 or RPostgres need system libraries such as GDAL, libxml2 or libpq, which differ between machines. I would containerize the pipeline with Docker, starting from a rocker image like rocker/r-ver pinned to the R version, installing system dependencies in the Dockerfile and restoring packages with renv. The same image then runs on laptops, servers and the cloud, eliminating environment differences between development and production.
30
Your team wants to share a Shiny app with 200 internal users. What deployment options exist?
Options include shinyapps.io, a hosted service that is quickest to start but may not suit sensitive internal data, Posit Connect, a commercial on-premise or cloud server with authentication, scheduling and scaling, the free open-source Shiny Server on a Linux machine, and a Docker container behind a reverse proxy like Nginx, often with ShinyProxy for authentication. For internal data, I would choose Connect or containers inside the company network with single sign-on.
Tips to crack the Course5 Intelligence R Programming interview
Revise Excel, SQL and a visualisation tool such as Power BI or Tableau, which are widely used in analytics roles.
Learn basic market research concepts like surveys, sampling and segmentation if you apply for research and insights positions.
Practise presenting a short insight story from a dataset, explaining what the numbers mean for a business decision.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Course5 Intelligence interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Course5 Intelligence R Programming interview?
Candidates usually go through 4 stages: Online assessment (aptitude + analytics basics); Technical interview; Case or managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Course5 Intelligence R Programming interview?
This practice set leans on Modelling and Machine Learning in R (6), Reports, Shiny and Production R (6), Data Visualization with ggplot2 (5), Data Structures in R (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Course5 Intelligence interviewers ask you to walk through real work.
Are these the exact questions asked at Course5 Intelligence?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Course5 Intelligence's. They are not a leaked or verified list of past questions.
Where does Course5 Intelligence hire R Programming professionals in India?
Course5 Intelligence has large teams in Mumbai, Bengaluru. Openings and locations change often, so check the Course5 Intelligence careers page and filter by city and experience.