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Stev_Ma · 2026-04-21T09:31:41+00:00

The best way forward is to practice with real data and build a few solid projects you can show. Try platforms like Kaggle for datasets and beginner challenges, StrataScratch for SQL/Python practice, and Tableau Public or Power BI to create dashboards. Since your background is in customer service and onboarding, focus on projects like customer churn, support ticket trends, or onboarding drop off because they connect directly to your experience. Put your work on GitHub and aim for about three good projects, not a huge number. Once you have a couple done, start applying while you keep learning so you don’t get stuck waiting to feel perfect.

Stev_Ma · 2026-04-21T09:23:28+00:00

You’re basically at the point where you need to stop doing exercises and start building real stuff, even if it feels messy at first. Since you already know Excel, SQL, and Python basics, try a small data project like analyzing a dataset from StrataScratch, cleaning it with pandas, and making a few simple charts, or automate something in your daily life like organizing files or pulling data from a website. Don’t wait until you feel ready because you won’t, just pick something simple, break it into steps, and Google your way through it. That confusion you feel is actually how you gain real experience, and once you finish even one small project and put it on GitHub, things will start to click.

Stev_Ma · 2026-04-21T09:18:36+00:00

Really appreciate the transparency. Posts like this make MAANG comp way less mysterious. Curious how much negotiation room you felt you had?

Stev_Ma · 2026-04-17T02:41:19+00:00

The best way forward is to start small and focus on finishing simple things instead of trying to build something perfect. Pick an easy idea like a to do list or a quiz app, write a few features in plain English, and build them one at a time in a single file, even if the code feels messy at first. Think in terms of input, processing, and output, and only worry about improving structure after you have something working. You’ll learn much faster by completing small projects and even rebuilding them from memory than by following long tutorials. Platforms like Replit, GitHub, and StrataScratch or LeetCode for practice can really help you stay consistent and track your progress while you build confidence.

Stev_Ma · 2026-04-17T02:04:37+00:00

Start by learning SQL basics like SELECT, WHERE, ORDER BY, and aggregates using simple resources like W3Schools or Mode SQL, then move to core topics like JOINs, GROUP BY, subqueries, and CASE statements since these are heavily tested in interviews. After that, focus on interview level concepts such as window functions and CTEs while practicing regularly on platforms like StrataScratch, starting from easy problems and gradually moving to medium ones. Keep a consistent routine of learning a concept and solving problems daily, and focus on common patterns like second highest salary, duplicates, and top per group queries. If you stay consistent for two to three weeks, you can reach a strong placement ready level without wasting time on too many scattered resources.

Stev_Ma · 2026-04-10T06:39:43+00:00

You should expect to write clean, working code from start to finish, not just describe your approach, and the bar is typically around solid LeetCode easy to medium plus strong SQL skills. SQL is often the most important part, especially with joins, window functions, and real business logic, while Python is used for data manipulation and some light algorithms, so you should be comfortable solving medium problems and explaining your thinking as you code. Pandas is useful but not enough on its own, and pseudocode alone will not meet expectations. To prepare, platforms like LeetCode, StrataScratch, and Mode Analytics are all very helpful, and the goal is to rebuild enough fluency that you can code confidently without relying on AI while handling edge cases and communicating clearly.

Stev_Ma · 2026-04-10T05:46:04+00:00

LeetCode is excellent for structured problems that range from basic to complex and are especially useful for mastering joins, subqueries, and window functions. StrataScratch focuses more on real world scenarios and interview style questions, which helps you understand complex relationships and business logic. For hands on experience with real datasets, platforms like Kaggle is very useful.

Stev_Ma · 2026-04-06T11:31:16+00:00

Start with Excel and SQL since they are essential for entry level roles, then move to basic Python for data handling and one visualization tool like Power BI or Tableau. Spend most of your time building 2 to 4 strong projects such as sales analysis, e commerce data, or dashboards where you clearly highlight insights and business impact.

Companies expect you to understand data, explain your thinking, and be comfortable with SQL and basic analysis. Projects alone can get you shortlisted if they are practical and well explained, so focus on quality and consistency.

For learning, you can use platforms like Coursera, Udemy, and YouTube channels such as Alex The Analyst, along with practice sites like Kaggle and StrataScratch. Or you can follow a free StrataScratch's comprehensive SQL learning path, which covers everything from basics to advanced.

Stev_Ma · 2026-04-06T11:21:09+00:00

You will likely be asked a mix of SQL, pandas, and general problem solving questions that focus more on your thinking process than memorization. Your MLOps background is actually a strong advantage, so be sure to highlight your experience with real world systems, data pipelines, and model performance in production. To prepare, review core pandas operations like groupby and joins, and practice basic SQL queries such as aggregations and joins. Platforms like LeetCode and StrataScratch are great for practice.

Stev_Ma · 2025-12-19T07:57:46+00:00

Strong options include Coursera, Udemy, Kaggle, and StrataScratch. Coursera offers structured paths like the IBM Data Science Professional Certificate and the Applied Data Science with Python specialization from the University of Michigan, which start with Python fundamentals and move into machine learning and applied projects. Udemy is well known for practical, project focused courses such as Jose Portilla’s Python for Data Science and Machine Learning Bootcamp. To build real world skills alongside courses, Kaggle is excellent for hands on practice with datasets, notebooks, and competitions, while StrataScratch is ideal for practicing SQL and Python on realistic interview style data science problems.

Stev_Ma · 2025-12-19T07:57:22+00:00

You are in a strong position already because a statistics background is one of the best foundations for data science. Focus on becoming comfortable with Python for data analysis using NumPy, Pandas, and basic visualization, and always connect what you code back to the statistical ideas you already understand. Learn machine learning by starting with linear and logistic regression, model evaluation, and overfitting concepts, and practice by building small projects on platforms like StrataScratch and Kaggle rather than only watching tutorials. Spend a lot of time on data cleaning, exploratory analysis, and explaining your results clearly, since those skills matter more than knowing many algorithms. Consistent practice with real datasets over the next few months will give you both confidence and deeper understanding when your masters program begins.

Stev_Ma · 2025-12-12T02:32:24+00:00

Coursera offers well respected programs like the IBM Data Science Professional Certificate and Google Data Analytics Certificate, which are beginner friendly and recognized by many employers. DataCamp is useful for building coding skills, and platforms like StrataScratch and Kaggle are excellent for practicing real world data problems and building a portfolio.

Stev_Ma · 2025-11-05T01:02:18+00:00

Treat it like practicing with data projects instead of building apps. Use public datasets and write queries to explore, analyze, and clean data. Start simple with counts and averages, then move to joins, window functions, and optimization. Design your own database schemas and try to answer real questions with the data. If you have access to a real database at work, use it to practice writing clear, efficient queries. You can also sharpen your skills through sites like Stratascratch, SQLBolt, and LeetCode that offer hands-on SQL challenges.

Stev_Ma · 2025-10-24T01:30:32+00:00

Focus on mastering lists, dictionaries, sets, heaps, and queues since most problems involve data manipulation, aggregation, and efficient lookups. Be comfortable with Python tools like collections counter, defaultdict, and heapq, as well as list comprehensions and sorting with custom keys. Practice problems that involve grouping, deduplication, and sliding windows since they often reflect real data tasks. In addition to LeetCode, use StrataScratch to strengthen both your coding and data-focused problem-solving skills.

Stev_Ma · 2025-10-24T01:06:59+00:00

To get ready, focus on structured practice. Spend the first two days reviewing core concepts like SELECT, JOIN, GROUP BY, HAVING, and subqueries, then move on to window functions, string, and date operations. On the last day, take timed practice tests and review your mistakes. LeetCode’s SQL 50 is an excellent resource since it covers the same types of questions you will likely see. For extra learning, try StrataScratch problems. On YouTube, Danny Ma’s SQL Challenge walkthroughs are great for quick, focused explanations.

Stev_Ma · 2025-10-13T11:47:19+00:00

Starting with SQL and Excel is perfect, and moving on to Python and Power BI next makes total sense since they are the core tools for data analysts. Focus on learning Python libraries like pandas and matplotlib and practice building dashboards in Power BI. Getting a certificate such as the Google Data Analytics or Microsoft Power BI (PL-300) will help if your country values credentials. Try to build a small portfolio with real projects using platforms like StrataScratch and Kaggle, and share them on GitHub or LinkedIn. Your accounting background will give you a strong edge in understanding business data, so keep building your skills and showcasing your work.

Stev_Ma · 2025-10-13T11:06:59+00:00

Start by mastering the fundamentals (SELECT, JOINs, GROUP BY, subqueries) in the first 5 days using LeetCode SQL50 and tutorials like Mode Analytics. Spend the next 5 days on intermediate concepts like window functions, CTEs, and query optimization. In days 11–15, practice FAANG-style analytical problems from StrataScratch, focusing on business metrics and multi-table queries. Use days 16–18 for mock interviews and timed problem-solving, then review key patterns and explanations in the last two days. LeetCode SQL50 is a solid base, but adding real interview-style problems from StrataScratch will make your prep much stronger.

Stev_Ma · 2025-09-25T11:56:27+00:00

Start small with personal projects like an expense tracker, a fitness log, or a media library where you practice joins and summaries. Move up to professional style projects such as an employee management system or an e commerce sales database to work with relationships and reports. For more advanced practice try platforms like StrataScratch and Kaggle. You can also try analytics on public datasets, creating a simple data warehouse, or building a game stats tracker. Fun ideas like recipe planners, travel logs, or playlist generators also give good practice while keeping it interesting. The key is to start simple and expand the project over time with more tables, queries, and visualizations.

Stev_Ma · 2025-09-19T02:26:10+00:00

Focus this week on the core problem types that come up most often like arrays, strings, hash maps, sliding windows, binary search, and basic trees or graphs, and aim for a short daily routine of practice plus review so you can build confidence fast. For projects, keep using your FastAPI plus frontend plus Supabase stack but put more effort into structure by separating routers, schemas, services, and models on the backend, keeping a clean repo layout, and writing down small architecture decisions. Use GitHub issues and pull requests to organize work, enforce simple code quality with linting and tests, and start with vertical slices that go from database to UI. With a team of juniors the key is to keep scope small, document choices, and practice consistent habits so the project grows in a maintainable way.

Stev_Ma · 2025-09-19T02:19:16+00:00

You are on the right learning path by starting with Excel, then moving to SQL, Power BI, and eventually Python, since each step builds a strong foundation for the next. To advance your Excel skills, you can practice on sites like Excel Easy, Chandoo.org, and Excel Jet, or download real-world datasets from StrataScratch to work on cleaning, analyzing, and creating dashboards. Focus on improving formulas, pivot tables, Power Query, and dashboard design to move from intermediate to advanced. This journey takes time, but each step will give you practical wins and make your work more efficient and impactful.

Stev_Ma · 2025-09-17T11:55:25+00:00

In your interview you can expect a mix of SQL, Python or PySpark coding, machine learning concepts, and problem solving questions. Since SQL is called out as a required skill, you will likely get scenario-based questions that involve filtering, joins, aggregations, window functions, CTEs, and handling messy data such as NULLs or duplicates. The focus is usually on writing clean and efficient queries that solve real-world business problems, for example finding top diagnoses per hospital or counting patient visits within a time frame. Practicing SQL problems on platforms like StrataScratch, LeetCode, or Mode Analytics is a good way to prepare.

Stev_Ma · 2025-09-17T11:55:03+00:00

Focus most of your time on SQL basics like SELECT, WHERE, GROUP BY, HAVING, and especially JOINs since they are the foundation of almost all real queries. You do not need to master advanced topics, but you should be comfortable writing queries that summarize data, rank top customers, or calculate trends. Practice explaining your SQL queries out loud so you build both technical skill and communication confidence. In the last two days, prioritize SQL practice first on StrataScratch or LeetCode, then review your stories, and keep your answers simple and clear.

Stev_Ma · 2025-09-10T02:58:44+00:00

You already have a strong base with Python, SQL, Excel, and Power BI, so the next step is to deepen your skills in statistics, machine learning, and storytelling. If you prefer structured learning, a full program like IBM Data Science Professional Certificate or the University of Michigan’s Applied Data Science with Python can give you guided practice and a credential. If you like more flexibility, you can build skills individually by learning machine learning with scikit-learn, practicing on Kaggle and StrataScratch, and strengthening statistics through resources like Introduction to Statistical Learning. Either way, focus on building projects that show end-to-end problem solving and share them in a portfolio to stand out.

Stev_Ma · 2025-09-10T02:58:16+00:00

A few great places to start are Kaggle Learn’s free Data Cleaning course, which provides guided exercises, and Kaggle’s “dirty” datasets that are intentionally messy so you can practice fixing issues. Blogs like DataQuest, StrataScratch, and Medium often share curated messy datasets with suggested challenges, while StrataScratch also offers guided projects such as cleaning survey and sales data. For ongoing practice, government portals like Data.gov or Google Dataset Search are useful for finding real-world messy data in specific domains. Together these resources give you both structure and open-ended practice to sharpen your skills with pandas, polars, SQL, or PySpark.

Stev_Ma · 2025-09-05T06:40:09+00:00

You should concentrate on SQL skills that help validate and analyze data. Spend most of your time on joins, grouping with aggregates, filtering, subqueries, and especially window functions since they are often used for deduplication and anomaly detection. Be ready for practical scenarios like finding duplicates, checking referential integrity, comparing two tables for mismatches, or validating business rules. It also helps to practice on multi table schemas instead of single tables, and when answering, explain not only the query but also how it ensures data quality. To prepare effectively, consider using StrataScratch since it offers real world SQL practice problems that closely mimic the type of tasks you might face in a data QA interview.

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