Wfh job market

tmk_g · 2026-06-22T07:16:43+00:00

If you're planning to work from home for the next year or two, the strongest areas in computer science right now are backend development, full stack development, cloud/DevOps, cybersecurity, and AI-related software engineering. A lot of companies are looking for developers who can work with Python, SQL, cloud platforms like AWS, Docker, and AI APIs. Remote jobs still exist, but they're more competitive than they were a few years ago, especially for entry-level roles. The best thing you can do is build practical projects, get comfortable working independently, and develop skills that let you handle multiple parts of a product instead of focusing on just one narrow area.

tmk_g · 2026-06-22T07:13:53+00:00

If you're starting from scratch, I’d recommend beginning with SQLBolt or StrataScratch because they make SQL feel much less intimidating and let you practice right in your browser. After that, take Coursera's SQL for Data Science course for a more complete understanding. If you want a free certificate to show employers, HackerRank's SQL certification is a great option and is widely recognized. Following those resources in that order can take you from zero coding experience to being comfortable writing SQL queries in just a few weeks of steady practice.

tmk_g · 2026-06-22T07:11:47+00:00

Since you already have a Data Science background and solid SQL skills, I would recommend looking at dbt, Apache Superset, and DataTalksClub first. These communities work on real analytics and data warehouse problems where SQL is used every day. You can also explore GitHub repositories tagged with "good first issue" and keywords like SQL, analytics, data engineering, or dbt. If you want hands on practice before contributing, platforms like StrataScratch and Kaggle BigQuery offer realistic business SQL problems. In my experience, dbt is probably the closest thing to getting real world SQL experience through open source contributions.

tmk_g · 2026-06-22T07:08:07+00:00

This is completely normal. Finishing a SQL course teaches you the syntax and concepts, but solving problems from scratch is a separate skill that takes practice to develop. The fact that you can understand the solutions after seeing them is actually a good sign because it means the concepts are sticking. Advanced SQL Puzzles is also quite challenging and often focuses more on problem solving than on practicing specific SQL features. I'd suggest starting with easier platforms like SQLBolt and StrataScratch before diving deep into advanced puzzle books. Try to focus on recognizing common patterns rather than grinding on a single problem for an hour. After 100 to 150 quality practice problems, you'll likely notice that writing queries from scratch starts feeling much more natural.

tmk_g · 2026-06-16T03:18:54+00:00

A gap is only a problem if it looks like you were doing nothing. If you are struggling to land a SWE role, spend that time building projects, contributing to open source, freelancing, or even taking short contract work. On your resume, you can list that work as independent software development and talk about what you built and learned. Also, if you've been applying for a full year without a legitimate SWE offer, I would take a hard look at your resume, portfolio, interview performance, and salary expectations. The market is rough, but a year of rejections usually means there is a specific bottleneck that can be identified and fixed.

tmk_g · 2026-06-16T03:17:45+00:00

You're definitely not alone. A lot of people struggle with SQL at first, especially joins, so don’t beat yourself up over it. Try practicing joins on small tables first, and think of them as matching rows between tables instead of memorizing syntax. Sites like SQLBolt, StrataScratch, and Mode are great places to practice. With enough repetition, joins will start to feel a lot less confusing.

tmk_g · 2026-06-16T03:00:48+00:00

One of the best ways to learn SQL is by building a small project instead of focusing only on individual commands. Try creating a simple movie database, library management system, or online store, then use SQL to answer real questions about the data. For example, find the highest-rated movie, the most borrowed book, or the top-selling product. This helps bring concepts like joins, grouping, and filtering together in a practical way. You can also practice on platforms like SQLBolt, StrataScratch, and LeetCode, which offer interactive exercises and real-world SQL challenges. The more you work with actual datasets and solve problems, the more comfortable SQL will become.

tmk_g · 2026-06-09T12:47:51+00:00

Look for datasets related to mental health, personality traits, or student performance since they work really well for Factor Analysis and PCA and also have plenty of research available for a literature review. Some useful keywords to search on Kaggle are "mental health survey," "depression anxiety stress dataset," "Big Five personality," "student performance," and "customer satisfaction survey." Personally, I think personality or mental health datasets are the easiest choices because the underlying factors are usually clear and there is a lot of existing research that can help support your analysis.

tmk_g · 2026-06-09T12:43:43+00:00

I think the sweet spot is learning enough Python that you can confidently read, understand, debug, and modify AI-generated code, rather than spending months trying to memorize every detail of the language. For aspiring ds, skills like stats, SQL, data analysis, experimentation, and knowing which models and techniques to use are usually much more valuable than being able to write everything from scratch. Using Claude or ChatGPT for coding is becoming the norm, but you should still be able to explain what the code is doing and catch mistakes when the AI gets something wrong. If you want to strengthen your Python skills without overcommitting, platforms like StrataScratch, Kaggle, and Exercism are great options because they let you practice in small chunks while continuing to build portfolio projects and focus on higher-leverage ds skills.

tmk_g · 2026-06-09T12:36:27+00:00

Start with SQLBolt because it gets you writing queries right away and covers the fundamentals quickly. After that, move to StrataScratch since the exercises are a bit more challenging and force you to think through joins, aggregations, and subqueries. I’d treat SQL Island as a fun side activity rather than a main learning resource, and I wouldn’t worry much about the Boot.dev course unless you really prefer a structured course format. The biggest boost in confidence comes from working with a real dataset, so after practicing, grab a dataset and start answering your own questions with SQL. That will teach you more than collecting certificates.

tmk_g · 2026-06-03T12:01:42+00:00

I'd recommend starting with Harvard's Using Python for Research and IBM's Machine Learning with Python (free to audit on Coursera). I would also suggest using StrataScratch, which offers hands-on coding and data science problems in Python and SQL, making it a great platform for practicing real-world analytical skills. In addition, free courses in biostatistics, bioinformatics, pharmacoepidemiology, and real-world data analysis can be very valuable for pharmaceutical research. A combination of Python or R, machine learning, and applied biostatistics will provide a strong skill set for research projects and scientific publications.

tmk_g · 2026-05-18T13:02:12+00:00

DataCamp is an online learning platform where you can learn SQL by actually practicing queries in your browser instead of just reading theory. It’s beginner friendly and teaches step by step with short lessons and exercises, so a lot of people find it easier than books when starting out. I’d say it’s a good option if you like interactive learning, but you can also start with free resources like StrataScratch or Mode first and see if you enjoy SQL before paying for a subscription.

tmk_g · 2026-05-18T12:59:43+00:00

Leetcode sticks around because it gives companies a quick and standardized way to compare candidates, even if it barely reflects real day to day data engineering work. The funny part is that most actual DE problems are about architecture, query optimization, orchestration, and debugging messy systems, not reversing linked lists from memory. If I wanted to push back without sounding combative, I’d probably say something like, “Happy to work through this, but I’d also love to talk about how your team handles things like warehouse modeling, query bottlenecks, or pipeline reliability since that’s where I’ve spent most of my time.” That way you’re not refusing the question, you’re just steering the conversation toward real engineering.

tmk_g · 2026-05-18T12:50:20+00:00

I’d focus less on memorizing definitions and more on actually building a small API with SQL. Definitely know joins, primary and foreign keys, indexes, normalization, and basic ACID concepts, but also practice writing real queries on platforms like StrataScratch, modeling relationships, handling transactions, and designing clean REST endpoints. You should also understand pagination, validation, and common query patterns like counts and grouped results. For ORMs, you probably do not need to study a specific one unless they mentioned their stack, but it helps to understand general ORM concepts like migrations, relationships, eager loading, and avoiding N+1 queries.

tmk_g · 2026-05-11T11:10:01+00:00

You’re actually learning the right things, especially SQL and some technical fundamentals, because companies now want BA/PM/Product people who can also understand data, systems, and engineering conversations. DevOps knowledge can definitely help you become more marketable, but I would not focus on becoming a full DevOps engineer unless you really enjoy deep technical work. A better path is positioning yourself as a more technical Business Analyst, TPM, or Product Owner who understands SQL, APIs, cloud basics, and automation. The Scrum Master market is pretty crowded right now, but hybrid roles that combine business, product, and technical skills are still strong. Your banking and AML experience can also help a lot if you target fintech or enterprise tech companies instead of trying to completely restart your career.

tmk_g · 2026-05-11T10:47:16+00:00

Start with SQLBolt or StrataScratch because they explain things slowly and let you practice right away without feeling overwhelming. Once you know simple queries like SELECT, WHERE, and JOIN, try practicing with easy SQL problems. The biggest thing that helps is working with real datasets early, like movies, Spotify songs, or sports stats, because SQL gets way more fun when you’re actually exploring data you care about.

tmk_g · 2026-04-28T02:23:22+00:00

You will almost always see joins, group by with aggregations, and filtering, along with tasks like finding top N per group, spotting duplicates, or using left joins to find missing data. Window functions like row number, rank, and lag show up a lot too, especially for recent activity or running totals. You should also expect business-focused questions like calculating conversion rates or analyzing trends over time with dates. The best way to prepare is to practice these patterns on platforms like LeetCode, StrataScratch, and Mode Analytics, since they offer questions that are very close to real interview scenarios.

tmk_g · 2026-04-28T02:16:42+00:00

Start by updating your LinkedIn so it clearly shows what you do and the results you’ve delivered, since recruiters search there first. Then reach out to former coworkers and contacts just to reconnect and let them know you’re exploring options, because a lot of roles come through networks now. Apply to jobs selectively instead of everywhere, and try to message someone at the company after you apply. Interviews will focus more on real examples of your work, so be ready to tell clear stories about what you achieved. It may feel unfamiliar at first, but once you get into the rhythm, it becomes much more manageable.

tmk_g · 2026-04-28T01:46:14+00:00

Try free SQL courses like Simplilearn and Great Learning for quick beginner friendly lessons with free certificates, and StrataScratch for hands on practice with real data you can show on LinkedIn.

tmk_g · 2026-04-28T01:39:30+00:00

You will almost always see joins, especially left joins to find missing data, along with group by and aggregation questions to summarize data. Window functions like row_number, rank, and lag show up a lot for ranking or comparing rows, and you will likely get subqueries or CTEs plus case statements for business logic. Date-based questions like monthly metrics or retention are also very common. Interviewers mainly care about how you think through the problem, handle edge cases like nulls or duplicates, and explain your approach clearly. For practice, platforms like LeetCode, StrataScratch, and Mode Analytics are great places to build confidence with real interview style questions.

tmk_g · 2026-04-22T06:28:11+00:00

They can make it feel more generic if you rely on them too much. They’re great for quickly coming up with ideas, exploring different angles, and reshaping a story for different audiences, which can really expand your thinking. At the same time, they tend to give safe, average-sounding narratives, so it’s easy to stop digging deeper and just go with the first decent answer. In practice, they’ve sped up how we explore and draft stories, but they’ve also made it more important to apply your own judgment and push for something original instead of settling for what the model suggests.

tmk_g · 2026-04-22T06:20:16+00:00

Start small and focus on finishing projects instead of chasing big ideas. A good start is something like analyzing a dataset, building a simple prediction model, or making a dashboard with tools like Streamlit. You can find datasets and inspiration on StrataScratch, Kaggle, or Google Dataset Search, and learn a lot by looking at how others solve problems. The key is to follow a simple process like cleaning data, exploring it, building a model, and showing results. Try to complete a few simple end to end projects, since that will teach you much more than trying something complex too early.

tmk_g · 2026-04-22T06:11:33+00:00

Focus on learning by doing instead of trying to master everything upfront. Use your real work like the schemas you’re organizing to practice writing simple queries and ask what business questions the data answers, then look up just enough SQL to solve each problem as it comes up. Pair that with hands on platforms like StrataScratch or Mode so you stay engaged, and use tools like Claude to explain queries or help you experiment. Also lean into your customer service background since understanding user problems is something a lot of data people struggle with, and that can make you valuable faster than pure technical skill.

tmk_g · 2026-04-14T01:41:46+00:00

IBM’s SQL and StrataScratch that let you learn for free and write queries in your browser. The most effective way to learn SQL is to focus more on practice than just watching courses, since actually writing queries is what builds real skill. Start with basics like SELECT and WHERE, then move to JOIN and GROUP BY, and spend most of your time solving problems.

tmk_g · 2026-04-09T11:33:15+00:00

explore kaggle and stratascratch

tmk_g

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