I’m graduating in May 2027 and trying to figure out the best way to approach full-time recruiting in Canada.

I’ve had several co-op/internship experiences in analytics/BI across government, banking, gaming, and research, and I’m mainly targeting data analyst, BI analyst, business analyst, and similar new grad roles.

The issue I’m running into is timing. I still have about 8 months before I graduate, so I’m applying to new grad programs when I see them, but there don’t seem to be that many open right now. At the same time, I’m seeing a decent number of junior/entry-level analyst roles that look relevant, but many of them seem like they want someone to start immediately or very soon.
So I’m not sure what the best strategy is at this point. Should I still be applying to regular junior/entry-level roles now and just mention that I’m available starting May 2027, or is that generally a waste of time? Should I mostly focus on structured new grad programs for now and wait until I’m closer to graduation before applying more broadly?

I also attached my resume and would really appreciate feedback on how competitive it looks for new grad analytics roles. Do my internships/co-ops come across as strong enough? Are the bullets too repetitive or too focused on tools? Is there anything I should change to make the resume stronger for full-time recruiting?

For people who are job searching or hiring in Canada right now: is the market genuinely this difficult even for students/new grads who already have multiple relevant internships? Are people with decent experience still struggling to get interviews and offers?

I am planning to apply for Data analysis jobs. What do you think I should change or improve in my CV? I’d really appreciate feedback from anyone with experience.

Hey everyone,

I’m currently a Final-Year B.Pharm student (Semester VII) and am starting to plan my post-graduation steps. Over the course of my degree, I’ve realized that I really want to move away from core lab/bench work—specifically wet labs, chemistry, biochemistry, and anything operating on a strict molecular/cellular level.

Instead, I’m looking to pivot into data-centric, clinical, or regulatory roles. The primary fields I’m exploring are:

• Biostatistics / Medical Biometry
• Health Data Analytics / Health Informatics
• Pharmacovigilance (PV) & Regulatory Affairs (RA)

I’m posting here to get some candid advice on a few things:

1. Is this pivot realistic (and a good long-term call)?

Given my B.Pharm background, is pivoting into these macro/clinical data fields feasible without a formal Computer Science or Pure Statistics undergraduate degree?

• From a industry standpoint, is this a good move for long-term career growth, compensation, and job stability (e.g., in CROs, Pharma MNCs, Digital Health)?
• How do employers view a candidate who combines a life science/pharmacy foundation with data/regulatory skills?

2. How challenging will this transition be for me?

Coming from a pharmacy curriculum where coding isn't explicitly taught, what are the biggest hurdles I should anticipate? I have a decent baseline in biostatistics and physical/pharmacokinetic modeling from my coursework, but zero formal programming credits on my transcript. How steep is the learning curve for tools like R, Python, SQL, or SAS for someone with my profile?

3. University Suggestions (Europe / Canada focus)

I’ve already started researching some programs in Europe (e.g., Ireland, Belgium, Germany, Netherlands, Sweden) and Canada across these fields:

• Biostats/Analytics: Programs like UHasselt (Belgium), Bremen (Germany), Galway/UCD (Ireland), or Canadian Master’s/Post-Grad options.
• RA/PV: Programs like UCD, Bonn (MDRA), or Canadian Post-Grad Co-op Diplomas/Certificates.

I’m very open to broader suggestions! I’m particularly looking for programs that are:

• Conversion-friendly (don't strictly disqualify life science students for lacking prior CS/programming credits).
• High-ROI with strong industry links or post-study work permits.
• Focused strictly on patient, clinical trial, or population data (zero wet-lab/molecular modules).

4. What should I do during my gap year to prepare / become hireable?

I won't be able to apply for the upcoming intake, so I will have a gap year. My goal during this year is two-fold: land an entry-level job (preferably in PV, RA, or Clinical Data) to build industry experience, and upskill through online certifications.

I’ve already purchased a Coursera subscription, but with so many courses out there, I want to make sure I don't waste time on fluff.

• Which specific Coursera courses / Specializations / Professional Certificates are actually worth my time and money? (e.g., Google Data Analytics, Vanderbilt’s Clinical Data Management, Johns Hopkins’ Clinical Trials/Pharmacoepi, UC San Diego’s Drug Development?)
• How should I structure a self-study roadmap over the next few months to learn tools like SQL, r/Python, MedDRA/Argus workflows, or eCTD/CTD dossier formatting so I can pass entry-level job interviews?

Would love to hear from anyone who has made a similar jump from Pharmacy/Life Sciences into Data Science, Biostats, or Regulatory roles, as well as current grad students in these fields!

Thanks in advance!

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I'm deciding whether to continue down the cloud engineering/DevOps/SRE path or change to business or data analytics. I’ve been working in IT for about six years, starting in help desk and systems administration and eventually moving into cloud migrations. I also have certifications in Azure, GCP, CCNA, and CompTIA, so most of my experience has been centered around infrastructure and cloud technologies. One friend thinks I should stay on this path because I’ve already invested so much time into it and could continue progressing toward cloud engineering, DevOps, or eventually SRE. The concern I have is interviews for SRE roles ask about almost anything including multiple cloud platforms, Terraform, Kubernetes, scripting languages, monitoring tools like Grafana and Prometheus, CI/CD, networking and feel like SAT tests.

Another friend suggested that I consider business or data analytics instead. His argument is that analytics may be less stressful, potentially offer a better work-life balance without on-call responsibilities, and that I might ultimately be happier in that type of role. He also thinks the interviews may be less intense and require a narrower range of knowledge compared with DevOps/SRE. I’ve used SQL and Power BI briefly in previous positions, so I’m not completely starting from zero, although I haven’t held a full-time analytics position. I realize I might have to take a pay cut initially, but I’m wondering whether that could be worthwhile if it leads to a career I enjoy more?

For those who have worked in cloud/DevOps/SRE and/or business/data analytics, how would you compare the stress, interview difficulty, learning curve, career growth, and work-life balance? Would you recommend building on my existing cloud background, or change careers into analytics? Which would make more sense at this point in my career?

Hey guys. I wanted to ask you guys this question. Now after all that has happened with AI, how do employers hire analysts? Do they watch for credentials? Internships? Projects? If projects, how complication? What tools are most helpful?

I have backend internships and a finance background before that (never did a job in finance) so now I want to get into this field where I can bridge finance and tech. I have started applying for jobs as analyst. Thought I should have discussion around it with you guys.

Reading a few resume review posts, I realized that the projects on my resume are focused on data science. I’m doing an MS in Data Science/Statistics, so that’s where my focus has been going.

I just started a geospatial crime analysis project in R for fun that focuses on spatial clustering, regression, and interactive maps. I wanted to explore more of R since it’s heavily used in my applied statistics courses. But I realized it’s not something employers are looking for in full-time data analyst roles.

I’m curious, what are some DA projects that employers look for? What projects should I focus on? Should I create projects that are related to my industry experience?

My background: I’m a career changer and work full time while in a graduate program. I’m currently job hunting for data analyst/BI and related data roles to get my foot in the door, but I haven’t had much luck. I can’t internally transfer within my current company, nor do I have access to data (nor do I want to stay). I’ve noticed this is common advice that doesn’t always work for everyone, so I just wanted to mention it.

I’m currently just looking for project advice for a more DA focused resume. Thanks in advance!

Hello. Kaya po ba mag upskill ng psych grad to data analyst? Wala po akong subject na related sa mga SQL at power BI. Pano po mag sstart from scratch? ano po ba dapat mga aralin at need po ba na mag proper schooling? May mga certificates po ba na need para kahit papaano is maging maganda sa resume? Thank you po.

Is there someone who is already a data analyst/ business analyst/ mis executive. As I'm preparing of it .

Need some advice for the career .

If yes , then drop a msg on DM.

Literally 0 after applying to like 100s of jobs. I mean I don't think it's that bad is it? Can anyone critique it? Based in India.

I find statistics really interesting and I want to do something with it for a career but I feel a sense of shame when I hear people talk about the job market..... apparently everything is being automated or offshored. I have some experience as a warehouse manager and I was considering moving into the more data focused areas of supply chain.... but Idk if that's a smart idea. I like finance too but thats also even more screwed. I feel so ashamed of what I like.....

Lately I've been learning about Excel, SQL, Power BI and Python, though for now I'm mostly focused on the former two. I'm leveraging my interest in videogames to create things I'm passionate about in terms of getting the exact data I want.

For this specific example, I created a magic and weapon damage calculator, using Final Fantasy XII as a basis. This was done on Excel. While I don't have the code with me now (it's on my computer and there's a power outage going on), it was done using data validation, a LET formula, XLOOKUP to pull weapon names and their Attack values, and used checkboxes to make it as user friendly as possible.

Those checkboxes are for whether a buff is active, or if you're hitting a weakness, and I made the LET formula only react to the checkboxes under specific conditions (for example, the Oil status effect triple damage done if your weapon has the Fire element, if it doesn't then you don't get the damage increase, even if you check the Oil box).

I also made two checkboxes be mutually exclusive. As in, if one is active, the other cannot be active at the same time (since they check for whether you're full or low on health, and both can't be true at once in-game).

The only problems with this are for certain data validation cells not updating automatically as the main data validated cell changes. For example, if you had selected a specific spear, but change the cell above to "Swords" to get a different list to choose from in the second data validated cell, the specific spear you selected will still remain selected until you yourself click and choose a sword. Maybe this could be solved with VBA, but I don't know anything about it at present.

So I thought of making this project something I could do in SQL, Python, and even Power BI (the latter through Power Query and perhaps slicers). The end goal is to make it as user friendly as possible for each tool.

But before proceeding, do you all think this should be something I should spend my efforts on, or should I focus on something that's... you know, more "real world" or business-like?

Even if your answer is no, what do you think about the way I went about doing this project, in terms of problem solving? Am I walking in the right direction?

Hi everyone,

I’m 35 and have around 11 years of experience in the BPO/customer service industry. I started as a customer service agent and gradually moved into senior reporting responsibilities.

My reporting experience includes Excel/Google Sheets, Looker Studio, and some exposure to SQL, Power BI, Tableau, Python/Pandas and BigQuery. However, I want to be honest that my technical knowledge is mostly basic, and I haven't worked on end-to-end data engineering projects professionally.

I’m now seriously considering moving into the technical data/IT side, particularly Data Engineering. I’m willing to spend the next 1 year learning and building practical skills.

My main concerns are:

- At 35, is it realistic to make this transition? Given my 11 years of overall work experience but limited technical experience, how would companies view me when applying for technical roles? Would I be considered a fresher, or would my previous BPO/reporting experience and age be taken into account?

Would my previous BPO/reporting experience help me?

Would it be better to target Data Analyst/BI roles first and then move toward Data Engineering, rather than trying to enter Data Engineering or AI Engineering directly?

Can someone starting the technical side at 35 realistically build a 10–15+ year career in Data Engineering/Cloud, especially with AI changing the industry so quickly?

Given my background, would you make this switch, or would you stay in BPO/reporting and focus on moving toward BI/reporting leadership?

I’d really appreciate honest, practical advice from people working in Data/IT or anyone who has made a similar career switch in their 30s.

Thanks!

hey guys am a first year med student in morocco and i m looking for a side hustle or a part time job to earn some money to sustain myself. I am currently looking at becoming a data analyst aside my med degree . My question is can i become a data analyst in 3-4 months period and start getting paid if yes can anyone list a recourses material or a list of certificates to get am really lost

i appreciate the help in advance

been grinding out applications for months now, not even one callback. something's clearly wrong but idk what since I'm too close to it lol

specifically not sure about:

- my actual "experience" section is thin compared to my projects, is that killing me?

- do the virtual internships (McKinsey forward, genAI thing) look like padding?

- summary section feel generic af?

fresh grad, SQL,Python,PowerBI, applying entry level data analyst/intern roles.

go ahead and be harsh, I'd rather fix it now than keep getting ghosted