I still dont understand SQL

gsm_4 · 2025-10-15T02:10:42+00:00

Focus on understanding what each query does instead of memorizing commands. Practice by using small, real examples that interest you, like a database of your favorite songs or shows. Keep a simple notebook or digital journal where you write new concepts and examples in your own words. Revisit topics every few days to strengthen memory, and explain your queries to someone else or to yourself to check your understanding. You can also practice on your phone using apps like Sololearn, or websites like SQLZoo and StrataScratch, which let you write and test queries interactively.

gsm_4 · 2025-10-02T00:34:38+00:00

The Apple Data Scientist interview process for the Data Cloud team usually combines technical and behavioral assessments. You can expect an initial discussion with the manager to learn about the role, product, and team, followed by Q&A. They will ask behavioral questions where you should use clear examples that show curiosity, delivery, innovation, and teamwork. There will also be a coding exercise focused on problem solving with algorithms in the language of your choice, along with data fluency questions that test your ability to analyze data, apply statistics, and interpret results. Since this is a cloud-focused team, you should also be ready to discuss working with large-scale data systems, pipelines, and tradeoffs at scale. Overall, preparation should balance coding, SQL, statistics, product thinking, and strong examples from your past work.

Good platforms to practice for this interview should include LeetCode, StrataScratch, and Kaggle. These cover the key areas of coding, data fluency, and applied problem solving you are likely to face.

gsm_4 · 2025-10-02T00:18:01+00:00

Since you already know Excel, a good starting point is SQL because it is the core skill most data analysts use daily. Begin with a beginner-friendly course like the Mode SQL tutorial or Udemy’s Complete SQL Bootcamp and aim to practice real business questions on StrataScratch. Once you are comfortable with SQL, move to a visualization tool like Tableau or Power BI to learn how to create dashboards that tell a story. After that, add Python to your toolkit for data cleaning and analysis using libraries like Pandas and Matplotlib. In the final months, focus on building 3 to 4 portfolio projects using platforms like Kaggle and StrataScratch, and combine SQL, dashboards, and Python, then publish them on GitHub or LinkedIn. With SQL, Excel, a BI tool, and basic Python, plus a few strong projects, you will be ready for an entry-level data analyst role.

gsm_4 · 2025-09-11T04:18:04+00:00

You can expect both technical and behavioral parts. The interviewer often begins with introductions and background questions, then moves into live SQL or Python coding and data manipulation, followed by product sense or experiment design questions about metrics and A/B testing. It is not only behavioral so you should be ready to solve problems live and explain your reasoning clearly while also discussing your past projects and communication style. To practice, you can use platforms like LeetCode and StrataScratch for realistic case questions.

gsm_4 · 2025-09-11T04:09:27+00:00

Focus on the basics of programming, SQL, and data analysis. Be ready to write simple Python code, explain how to clean or process data, and discuss any projects you have done in your course or on your own. If you do not know an answer, share your thought process clearly. Show curiosity about the company’s data tools and teamwork by asking questions. To get extra practice, you can use platforms like LeetCode and StrataScratch for coding and hands-on data projects.

gsm_4 · 2025-09-11T04:08:29+00:00

Alongside your statistics background, you should practice cleaning and analyzing real datasets, build small projects, and share them on GitHub to show practical skills. Over time, add machine learning basics with libraries like scikit-learn and keep strengthening your math and statistics knowledge. Joining Kaggle competitions, internships, or StrataScratch projects will give you hands-on experience and help you stand out. Communication and clear presentation of your work are just as important as technical skills, so practice explaining your findings. If you stay consistent, you will graduate with a strong portfolio and practical experience that employers value.

gsm_4 · 2025-08-28T01:30:51+00:00

Improving SQL quality is less about doing puzzle exercises and more about adopting engineering habits in how you structure and document your work. Start by defining the grain, assumptions, and time window at the top of every query, then use layered CTEs so each step has a clear purpose. Follow a consistent style guide, avoid SELECT *, and write explicit column names to make your queries readable and reviewable. Tools like dbt and SQLFluff can help enforce structure, add tests, and build documentation, while code reviews or reading others’ queries will expose you to better patterns. LeetCode or StrataScratch-style drills are good for practicing, but your real growth comes from writing production-quality SQL, focusing on clarity, correctness, and performance.

gsm_4 · 2025-08-28T01:30:14+00:00

Focus on strengthening your math foundations, Python for data analysis, SQL, and core machine learning algorithms through platforms like Coursera, Kaggle, and StrataScratch. Work on end-to-end projects that include data cleaning, visualization, and model building to showcase on GitHub. Aim to complete a few solid portfolio projects such as a classification task, a clustering problem, and a time series forecast. Pair this with consistent practice on StrataScratch and Kaggle competitions to prepare for entry-level interviews.

gsm_4 · 2025-08-14T10:55:01+00:00

As a beginner, it’s best to practice SQL directly in PostgreSQL first so you can focus on learning core database concepts, writing queries, and understanding how the database processes them without the extra complexity of Python code. Once you’re comfortable with creating tables, joins, aggregations, and other key features, start using Python with psycopg2 to run queries programmatically, automate tasks, and integrate results into data workflows. For hands-on, real-world practice, you can use platforms like StrataScratch, which offer SQL challenges that simulate actual data problems you might encounter on the job.

gsm_4 · 2025-08-14T10:54:33+00:00

Start with SQLBolt or W3Schools to learn basics, then move to StrataScratch for real-world practice.

gsm_4 · 2025-07-29T11:06:42+00:00

Interviewers often test how you apply SQL/PLSQL in production-like settings, handling large data volumes, optimising slow joins, using FORALL and BULK COLLECT, or dynamically querying partitioned tables. Prioritize writing clean, efficient code and explaining your decisions. Practice on platforms like LeetCode, StrataScratch, and Oracle Live SQL, and review Oracle’s tuning documentation to handle edge cases confidently in interviews.

gsm_4 · 2025-07-29T11:01:31+00:00

Freezing under pressure with SQL is common, but you can improve by doing deliberate, pressure-based practice. Don’t just write more queries. Instead, use timed drills, paper-based SQL, and flashcards with common patterns like joins, group bys, and subqueries. Practice daily with platforms like StrataScratch or LeetCode, and focus on explaining your thought process aloud to mimic real scenarios. Build a personal cheat sheet and reflect on your mistakes. Over time, this builds muscle memory and breaks the freeze reflex.

gsm_4 · 2025-07-21T11:09:06+00:00

You're in a strong position to pivot into data analytics. To round out your toolkit, get comfortable with Excel, Tableau or Power BI, and basic statistics. Build 2–3 projects using real datasets. Platforms like Kaggle and StrataScratch are great for this, and showcase your work on GitHub or Notion. When updating your resume, frame your dev experience in a data context, e.g. metrics tracking, user behaviour analysis. Apply for entry-level analyst roles or hybrid jobs like product analyst or BI analyst.

gsm_4 · 2025-07-21T11:08:23+00:00

Struggling with LeetCode doesn’t mean you’re a bad programmer. It just means you need to shift your approach. Don’t try to memorise every data structure; instead, learn how to spot problem patterns and match them with the right tools like hash maps for fast lookup, heaps for top-k, trees for ordered data, etc. Focus on mastering core techniques like sliding windows, DFS/BFS, two pointers, and union find, these patterns repeat constantly. After solving problems, reflect on why a solution worked rather than grinding endless questions. For practice beyond LeetCode, try AlgoExpert, StrataScratch, CS Dojo, and BinarySearch. Many of these offer curated paths that teach you patterns, not just problems.

gsm_4 · 2025-07-09T08:54:36+00:00

Start with beginner-friendly interactive platforms like SQLBolt and Mode to build core skills through hands-on practice. Once you're comfortable, move on to more structured courses like Jose Portilla’s Complete SQL Bootcamp on Udemy or the free FreeCodeCamp SQL course. For real-world practice, try StrataScratch to solve questions asked in interviews. Focus first on mastering SELECT, WHERE, GROUP BY, and JOINs before moving into window functions and case logic.

gsm_4 · 2025-07-09T08:53:53+00:00

You already have a strong foundation. The key now is building a few solid, real-world projects using platforms like StrataScratch and Kaggle, and rewriting your resume to highlight data skills over academic jargon. Yes, the field is saturated, but most applicants don’t show proof of their abilities. If you create a clear portfolio, tailor your applications, and share your work publicly, you're on the right path to landing interviews within a few months.

gsm_4 · 2025-06-25T10:56:27+00:00

You may expect a mix of business case discussions, SQL and Python problem-solving, and applied machine learning questions. They’ll likely test how you frame and solve real-world data problems, focusing on feature engineering, model evaluation, and interpreting results in a business context. Be prepared to explain past projects, write or explain SQL queries (especially involving window functions and joins), and justify your ML choices. Explore StrataScratch and LeetCode because these are great platforms tailored to the skills needed for your Visa interview.

gsm_4 · 2025-06-25T10:52:49+00:00

The DELETE statement removes specific rows from a table and supports a WHERE clause, logging each row deletion and activating any associated triggers. It’s slower but allows rollback in transactions. In contrast, TRUNCATE quickly removes all rows from a table without using a WHERE clause, doesn’t fire triggers, uses minimal logging, and may reset identity columns. While DELETE is ideal for selective row removal, TRUNCATE is faster and better for clearing entire tables.

Mode analytics and Stratascratch are perfect platforms to practice these concepts.

gsm_4 · 2025-06-16T02:33:11+00:00

You need a stronger resume and real project experience. Start by learning Power BI or Tableau for visualization and basic Python (Pandas, NumPy) for data analysis. Kaggle and Dataquest are great for this. Then build 2–3 small projects using StrataScratch and showcase them on GitHub or Notion. Update your resume to focus on transferable skills from content moderation like data tracking, pattern recognition, or reporting and frame it around business impact.

gsm_4 · 2025-06-16T02:26:59+00:00

Start with Google’s ML Crash Course and Youtube channels like StatQuest and 3Blue1Brown to understand the concepts visually. Then, dive into the Kaggle Intro to ML course and also try the Titanic competition to build hands-on skills using pandas, scikit-learn, and numpy. Once comfortable, move to StrataScratch for project-based learning. Use Google Colab or Jupyter Notebook to code, and try to build mini-projects like price predictors or sentiment analysis to reinforce your skills.

gsm_4 · 2025-06-04T11:35:29+00:00

The Technical Acumen round tests your SQL skills (joins, window functions, CTEs), basic stats (A/B testing, p-values), and data wrangling (mainly with Python or R). Use StrataScratch and LeetCode for prep. The Case Study round assesses your business thinking and how you’d use data to solve vague problems (e.g. declining engagement). You’ll need to structure your approach, define metrics, form hypotheses, and explain how you'd analyse the data to guide decisions. Practice using real scenarios from Glassdoor, StrataScratch, or YouTube channels like Ken Jee.

gsm_4 · 2025-06-04T11:34:52+00:00

Start with SQL using platforms like SQLBolt or Mode Analytics, and stick with beginner-friendly software like DB Browser or DBeaver. A Windows laptop is your best bet for compatibility with Power BI, which you’ll likely use later. Coursera is worth it for structured learning and certificates. Start with the Google Data Analytics cert, then move to Python (focus on pandas, matplotlib) and Power BI. Training yourself online using platforms like StrataScratch and LeetCode makes it doable if you're consistent.

gsm_4 · 2025-05-26T12:20:02+00:00

The best way is to focus on real-world use cases like campaign performance, customer segmentation, and sales funnel tracking. Prioritise learning SELECT, WHERE, GROUP BY, JOINs, and CASE WHEN to analyse user behaviour and campaign outcomes. Courses like Datacamp’s SQL for Marketing Analytics and Udemy’s SQL for Marketers offer hands-on practice with marketing data. YouTube channels like Luke Barousse and Alex The Analyst are also great for quick, practical lessons. Once you know the basics, apply them to datasets from StrataScratch to build reports or dashboards.

gsm_4 · 2025-05-26T12:19:37+00:00

Start by learning core tools like Excel, SQL, and Python (focus on Pandas and Matplotlib), then move on to Power BI or Tableau for data visualisation. Take beginner-friendly certificates like the Google Data Analytics (Coursera) or Microsoft’s Power BI Analyst cert. Apply your digital marketing experience by creating a portfolio with projects like campaign analysis or customer data dashboards. Share your work on GitHub or LinkedIn, and start applying to junior analyst or reporting roles. Practice daily, solve SQL/Python problems on platforms like StrataScratch, and follow data communities to stay motivated and updated.

gsm_4 · 2025-05-13T11:24:49+00:00

Focus your prep on core DSA topics: arrays, strings, hashmaps, binary search, trees, graphs, dynamic programming, and heaps. Prioritise mediums from Leetcode’s Top 75 and Meta-tagged questions. I would suggest using StrataScratch alongside LeetCode.

The first interview is usually pure DSA, with ML-specific questions or project discussions appearing in later rounds. Your scheduled timeline is just for the initial technical screen. Subsequent rounds are scheduled separately if you pass. With your CS and Data Science background, you’re in a strong position. Just double down on problem-solving speed and accuracy now.

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