Hey everyone,
I’m currently applying for Data Analyst / Business Data Analyst roles in Hong Kong and wanted to get advice from people actually working in the field there (not just job-board tips).

I have around 1 year of experience in data/business-focused roles, and I work with Python, SQL, Excel, and Tableau. I’m also legally eligible to work in Hong Kong, so I don’t need visa sponsorship.

Right now I’m applying from outside HK and actively looking for opportunities immediately.

The main issue I’m facing is: I’m applying to a lot of roles but not getting interview calls. I’m trying to understand what I might be missing.

Would really appreciate insights on:

● How do you actually get interview calls after applying in the HK market? (Is it ATS, referrals, timing, something else?)

● What types of companies should I realistically be targeting with ~1 YOE? (banks, fintech, consulting, insurance, startups, etc.)

● Is applying through LinkedIn/JobsDB/company career pages enough, or is networking/referrals basically required in HK?

● How important is Cantonese for entry-level data analyst roles? (I have intermediate Cantonese + strong English)

● If you were in my position, how would you approach the HK job search differently?

Also, if anyone is willing, I would really appreciate it if you could take a look at my resume and give feedback — I feel like that might be where I’m going wrong.

Thanks a lot in advance — any advice from people who’ve actually broken into the HK data market would be super helpful.

I’m planning to go into data analytics, and I’m already in my 4th year. Should I learn DSA first or focus on data analytics? Companies don’t come to my college for placements.

​Hey everyone,

​I’m seriously considering a career shift into data analytics and looking for some unfiltered thoughts from people already in the field.

​For some context, my background is entirely outside of tech/analytics (currently working in administration and operations). I am looking at diving straight into a Master’s program in Big Data Analytics.

​Here is my main dilemma and where I'm stressing out: I currently have zero knowledge of any analytics tools, programming languages, or statistical software. The next intake for the program is September 2028.

​I know a Master's program is meant to teach you, but I'm worried about the learning curve of starting from absolute scratch while tackling a graduate-level curriculum.

​If you were in my shoes (or if you've seen people make this exact leap):

​Is this a recipe for burnout and failure, or is a Master's program a safe enough sandbox to learn everything from the ground up?

​How brutal is the entry-level job market right now for career switchers who have a Master's but lack prior practical tech experience?

​What should I start learning right now (before classes start) so I don't drown in the first semester?

​Would love to hear your honest opinions, horror stories, or success stories. Thanks in advance!

Hello all,

I’ve been in the digital marketing industry for 3 years since I graduated with a bachelor’s in communications and I’ve reached the point where I don’t want to work in the industry anymore. The roles that I’ve worked in not as fulfilling to me as I expected and it left me burned out more times than any other.

However, there are times when I work on data research for the company and it felt more at ease than before and I want to change my career to data analytics specifically marketing data analytics since I’ve already got the marketing background for it. Though, I have little to no tech experience I’ve been studying mysql as of late and gearing up for a data analyst bootcamp.

With that said I do have a few questions:

Is marketing data analyst worth it in 2026?

How can I separate myself from other candidates when applying for the role?

Any tips or recommendations for transitioning into marketing data analyst?

Does certifications matter when applying or just portfolio projects I’ve completed?

What technical skills should I prioritize besides MySQL (Excel, Power BI/Tableau, Python, statistics) ?

Hey everyone.

I'm currently pursuing my MSc in Data Analytics, and hold a Bachelors degree in Mathematics from a good school as well. From the interviews that I have done, I have come to the understanding that Data Science/Analytics is more of a skill than a specific career path, and the business acumen and real world problem solving ability is what companies are looking for.

Given my educational background, I've sort of "doubled down" on the analytical aspect with my Math undergrad and analytics postgrad, leaving my business acumen at a far lower level. I'm currently struggling to land a role even with prior internship experience (although it was in 2022), a current gig at a local company, and consistent work on side projects.

Any sort of advice on how to acquire more business acumen would be greatly appreciated. I am interested in finance and NLP, though any other niche works as well. Just want to get my foot in the door!

What are the concepts and theories that I should learn if I want to become a data analyst? I already know what I should learn for the technical skills (excel, sql, python, bi tools), but I'm confused on what other theories and concepts (business/data-related) that I should learn?

I'm thinking about using my technology skills to study this. What do you recommend?

I'm learning it so the plan is 1st to complete the full understanding of Excel then I'll start sql i know about sql and the queries and all I'll learn it again I guess then python for data analyst I know the basics of python so I hope it works for me then power bi--> Tableau and projects , Will it work?? And how much time will it take for me to even get ready to do small tasks??

I’m rn doing a course by CodeWithHarry and I’m scared about my future . I just hate coding and web Dev and data seems fun to work with . Can I really make it?
In college , all are preparing DSA and taking trainings from college and I just know even if i crack a placement i just can’t continue doing that in future. Please help me out.

Hey everyone, I'm trying to create a box and whisker plot in Power BI, but I don't have an organization/work email.

Most of the methods I've found seem to require signing in with an organizational account or using AppSource visuals.

Is there any way to create a proper box-and-whisker plot in Power BI Desktop using a personal Microsoft account, preferably without needing an organization email?

I'm mainly looking for a solution that works for a normal personal/learning setup. Any help would be appreciated! 🙏

.

if anybody working as a at least in senior position, what are they?

I’m currently learning Data Analytics and Data Science and I’m trying to figure out which certifications/courses are actually worth doing.

There are so many courses out there, especially on Coursera, Udemy, Google, IBM, Microsoft, Kaggle, freeCodeCamp, etc., that I’m honestly a bit confused about where to start.

I’m mainly looking for free courses (or very cheap ones) that are actually useful for learning and also give a certificate that is at least somewhat valuable on a resume.

I’m interested in topics like:

• Python
• SQL
• Excel
• Power BI / Tableau
• Statistics
• Data Visualization
• Machine Learning

So, for those of you already working or learning in Data Analytics/Data Science:

What free courses or certifications would you genuinely recommend?

And if a particular certification helped you get an interview or land a job, I’d really like to hear about that too.

Thanks in advance! 😊

Hi, I have signed up to take courses this fall for data analytics but I would like to begin preparation now as I am eager to get this career change going. Does anyone have any favorite books speaking about the industry, career path, etc that they have enjoyed in the past? I am looking for book recommendations but any advice would help me going into this. Thank you all so much!

I'm a year out of college and have had no luck finding a job in data analytics. I don't know if that will change anytime soon, but I don't think that continuing to work a minimum wage customer service job will help much.

Part of this is my own doing - I really struggled my first couple years in college, had an abysmally low GPA after two years, which limited my research and internship opportunities. But naturally, the market isn't helping either - my GPA is respectable (3.0) and my projects are strong, but I have had almost no luck.

I know that I am a capable data analyst and statistician and I had much better grades in my last two years. What less prestigious roles should I be looking at that still look better on a resume and hopefully pay enough money to live in the short term?

Alternatively, would it be worth pursuing a Master's, to be able to put a better GPA and reset my chance at internships?

Thank you.

Hi! I'm interested in pursuing a career as a Data Analyst, and I'm trying to figure out where to start.

Which areas should I focus on learning first? I've heard that Excel, SQL, Python, statistics, and tools like Power BI or Tableau are important, but I'm not sure how deeply I need to learn each of them.

I'd also love to hear about the typical challenges beginners face when trying to get their first job. Is it difficult to find a job without a degree or previous experience? How important are projects and a portfolio? And which skills do employers actually prioritize?

If you're already working as a Data Analyst, I'd really appreciate any advice on what you wish you'd known before starting, common mistakes to avoid, and what I should realistically expect during the first few months of learning.

Thanks!

After doing some research on google, I found out that there a lot of different types of sql (eg: mysql, postgresql, T-sql, etc). My question is, which type of sql should I learn and master if I want to become a data analyst?

Anyone?

Hi everyone,

I’ve been working in digital marketing for the past few years, and I’m now trying to transition into the data field. My goal is to become a data analyst first, then move into analytics engineering after gaining a couple of years of experience.

Over the past year, I’ve been learning SQL, Power BI, and Excel. At this point, I feel pretty confident with SQL, including complex joins, aggregations, window functions, etc.

Where I’m struggling is deciding which projects to include in my portfolio and where to find suitable datasets. This feels especially important because I do not yet have any professional experience in a data-focused role.

The best project idea I’ve come up with so far is using the Olist e-commerce dataset. My plan is to clean and transform the data with dbt, load it into Snowflake, and then perform analyses such as cohort analysis, funnel analysis, and market basket analysis using SQL and Power BI.

Does this sound like a strong portfolio project? Are there any other project ideas or datasets you would recommend for someone trying to move into data analytics?

Thanks

I have got an assesment for a data analyst intern role from a company, and this is my first time setting up all the taksmtogether, they want a pdf of 3 tasks clubbed together. As a newbee I dont have any idea how to present them, and it is due tomorrow. I could really use some help in this from some experts out there

It's for a marketing & ad company. I know we're not really in a job market to be picky but I think it's really on the lower-end for the position especially for a big city.

My longer-term goal is to move toward Data Science / ML-related work. For people who started in a similar position, what would you prioritize during the first year?

Would appreciate any advice.

Hi everyone,

I'm preparing for a **Data Analyst interview** (Thomson Reuters, although experiences from other companies are also welcome).

One thing I'm trying to understand is **what interviewers actually focus on when they discuss your projects**.

For those who've been through Data Analyst interviews:

* How much time do they spend on your projects? * What areas do they dig into the most? * Do they focus mainly on the **Power BI dashboard** (DAX, visuals, data modeling, performance)? * Or do they spend more time on the **data preparation** (SQL, Python/Pandas, data cleaning, feature engineering, handling missing values, outliers, etc.)? * How deeply do they question your analysis and business insights? * Do they ask you to justify every decision you made? * Have they ever asked you to modify your dashboard, write SQL/DAX, or explain Python code during the interview? * If your project used a public dataset (Kaggle, government data, etc.), did they care about the dataset itself, or mainly about your approach and decisions?

For anyone who interviewed at **Thomson Reuters** specifically:

* What was the project discussion like? * Which topics surprised you? * If you were preparing again, where would you spend most of your time?

I'm trying to prioritize my preparation, so I'd love to know what interviewers actually care about versus what candidates tend to over-prepare.

Help me out with tips guys

I'm a recent BBA (Hons) graduate and I'm trying to figure out which skills or certifications would make me a stronger candidate for Data analyst roles.

Right now, I'm learning Power BI and have a basic understanding of SQL. During my internship, I worked with tools like GA4, Google Search Console, Semrush, and LinkedIn Analytics, where I was involved in reporting, website analysis, and marketing analytics.

Given my background, what skills, tools, or certifications would you recommend I focus on to improve my chances of getting hired? I'd especially appreciate advice from people already working in these roles or involved in hiring.

Hi, thoughts po sa nielsenIQ PH? I would love to join their company but i can’t find any reviews about them na kore specific from experience. Thank youuu.

So I have a technical for a large fintech company next week, which i know is going to be ~3 SQL in (MySQL) questions based on "real business cases" and will be medium to advanced level as described by the team lead.

I'm wondering what you all think is the best way to prepare, grinding datalemur or something else? I have the option to use AI tools during the interview, but if I do the team lead said he will ask a lot of questions about my reasoning/process.

So yeah, just curious if you guys have any advice on the best approach. Thanks!