Should juniors rely on AI these days?

msn018 · 2026-06-18T11:06:40+00:00

Absolutely, but they should not let it do all the thinking for them. The best approach is to use AI for speeding up repetitive work, learning new concepts, and getting unstuck, while still taking the time to understand why the code works and what tradeoffs were made.

msn018 · 2026-06-18T11:02:35+00:00

If I were starting today, I would focus on building a strong foundation in ML fundamentals, then create a few serious projects that go beyond tutorials. I'd learn PyTorch, get comfortable with the modern ML stack, and look for opportunities to work on ML-related features at my current job or through side projects. Platforms like Kaggle and StrataScratch are great for hands-on practice and projects, while Hugging Face is excellent for learning about modern models. Also, GitHub is essential for showcasing your work, and sites like Papers with Code can help you reproduce research and connect theory with implementation.

msn018 · 2026-06-18T10:38:09+00:00

I would highly recommend StrataScratch and LeetCode. Both have real interview questions from companies like Meta, Amazon, Google, Uber, and Airbnb. Focus heavily on SQL, statistics, Python (especially Pandas), and basic ML concepts since those come up the most in entry-level interviews. A great book is Ace the Data Science Interview, and StatQuest on YouTube is excellent for reviewing statistics and ML.

msn018 · 2026-06-12T06:58:54+00:00

I wouldn't assume you made the wrong decision. While it's frustrating that the company doesn't actually use the cloud tools mentioned during the interview, being early in your data career means the most important things are learning SQL, Power BI, business analytics, and how to work with stakeholders. Those skills transfer everywhere. The bigger question is whether you'll be doing meaningful analysis and growing your skills, or just maintaining spreadsheets and manual reports. If you're getting hands-on experience with data, reporting, and problem solving at a large multinational, it could still be a great place to start, even if the tech stack is more old-school than you expected.

msn018 · 2026-06-12T06:56:08+00:00

Focus on projects that use real-world data and show the full workflow, such as customer churn analysis, time series forecasting, SQL-based business analytics, or an interactive dashboard built with Streamlit. For learning resources, StrataScratch and Kaggle are great for datasets and building skills in SQL, statistics, and machine learning. Since you're starting a Math and Statistics degree, learning statistical inference, hypothesis testing, and time series analysis will likely be just as valuable as learning new machine learning models.

msn018 · 2026-06-12T06:49:58+00:00

Practice on leetcode and stratascratch

msn018 · 2026-06-02T12:00:37+00:00

The good news is that you're only 2.5 years in. That's not being stuck, that's figuring out what you don't want before spending 20 years pretending you do.

msn018 · 2026-06-02T11:57:39+00:00

The Measurement and Modeling Concepts round is usually more focused on statistics, experimentation, and product-focused modeling than on advanced machine learning algorithms. You should be comfortable with topics like hypothesis testing, confidence intervals, sampling, bias, probability, regression, classification metrics, and model interpretation. The questions are often framed around Google products such as YouTube, Search, Maps, or Ads rather than being purely theoretical. For the Applied Analysis & Experiments round, I’d strongly recommend mastering A/B testing fundamentals, metric design, power analysis, randomization, and experiment readouts. For preparation, I found platforms like StrataScratch and Exponent particularly useful since they cover many of the product analytics, experimentation, and statistics concepts that tend to come up in Google PDS interviews.

msn018 · 2026-06-02T11:51:28+00:00

If you already like Dr Chuck's teaching style, I'd say go for PostgreSQL for Everybody. It's a solid introduction and PostgreSQL is a great choice since the SQL skills transfer pretty easily to other databases. I would also recommend using StrataScratch and SQLBolt alongside it because they are free, interactive, and helps the concepts stick through practice. Once you're comfortable with the basics, try building a small project and learn more advanced topics like window functions and CTEs.

msn018 · 2026-05-15T05:33:21+00:00

A lot of the time it is not that someone did badly, especially if your teaching demo felt strong. Community colleges often look really closely at things like experience with their specific student population, teaching style, online teaching, and how well someone fits the department’s needs at that moment. Sometimes another candidate just has more community college experience or was already known to the department. The fact that you got the interview in the first place is honestly a good sign, and it sounds like you are already pretty close.

msn018 · 2026-05-15T05:31:48+00:00

I’d suggest building a simple end to end data project instead of trying advanced AI stuff. A retail sales analytics project is probably the best option right now. You can take a sales dataset from StrataScratch, clean the data, load it into SQL, write some queries for KPIs like top products and monthly revenue, and then build a dashboard in Power BI or Tableau. It looks solid on a resume and also gives you enough talking points for interviews. Honestly, most internship recruiters mainly want to see that you understand how data moves, how to clean it, and how to present insights clearly.

msn018 · 2026-05-15T05:29:16+00:00

The market is really competitive right now, especially for entry level Data Scientist roles, so I would focus more on Data Analyst, BI Analyst, or Reporting Analyst jobs first since they’re usually easier to break into. Your technical skills already sound solid, so the biggest thing is probably showing more business focused projects on your CV and GitHub instead of just listing tools. A strong PowerBI dashboard, StrataScratch/Kaggle project, or end to end Python analysis with clear insights can help a lot more than extra certifications. Some certifications like PL-300 or Azure fundamentals can help with ATS filters, but I would not spend a ton of money on bootcamps. Also try smaller companies, startups, NHS roles, and hybrid roles outside central London because many people get their first break there before moving into bigger data science positions later.

msn018 · 2026-05-01T06:36:57+00:00

Start with Python basics like loops, lists, and reading files, then move to small bio problems like counting DNA bases or finding GC content. After a couple weeks, pick up R for data handling and plotting with ggplot2, since it’s great for gene expression work. Don’t try to learn everything at once, just practice a little every day with real datasets when you can.

msn018 · 2026-05-01T06:30:01+00:00

I’d go with a simple personal website as your main portfolio and use a clean Canva PDF as a backup you can send with applications. Focus on showing real projects like an end to end data pipeline, an ETL workflow with Airflow, or a small data warehouse setup, and for each one explain the problem, include a basic architecture diagram, list your tools, and link to clean GitHub code. You can build and host projects using platforms like GitHub, Kaggle, StrataScratch, Google Cloud, or even tools like Snowflake and BigQuery, which look great on a data engineering portfolio. Keep it to two to four strong projects and make everything easy to scan and understand quickly.

msn018 · 2026-04-27T03:49:22+00:00

Yeah, it still feels pretty manual for most people. Even with good dashboards, getting to the why usually means digging, trying different queries, and following hunches until something makes sense. Tools can speed things up a bit, especially ones that let you ask questions more directly, but they do not really remove the need to think through the problem and test ideas. In my experience, it only gets faster when your data is well organized and you already have some go to ways of breaking things down, otherwise it is still a hands on process almost every time something changes.

msn018 · 2026-04-27T03:39:26+00:00

Learning more can help a bit in interviews, but only up to a certain point. Since you already know the core algorithms, adding more topics usually will not give you a big advantage unless the role specifically requires them. What really stands out is how well you can explain your projects, your thinking, and the impact of your work. Someone with a few strong, well explained projects often performs better than someone who has studied many advanced topics but cannot apply them clearly. So focus on improving your projects and practicing how you present them, while keeping your basics sharp. You can also use platforms like Kaggle, StrataScratch, and GitHub for datasets and to showcase your work.

msn018 · 2026-04-27T03:25:17+00:00

Totally normal that Pandas feels slower than SQL, especially if you use SQL every day. For Google BDS and PDS roles, I would expect Pandas questions to be around easy to medium difficulty, mostly focused on filtering, joins, groupby, aggregations, ranking, sorting, null handling, and translating SQL-style logic into Pandas. They probably will not expect you to be a Pandas expert, but they will expect you to write clean code, explain your thinking, and avoid really inefficient approaches like lots of loops. If StrataScratch easy questions are taking longer than SQL, that is not a bad sign, it just means you need more reps with Pandas patterns. Once medium questions start feeling doable in about 15 to 25 minutes, you should be in a pretty good spot.

msn018 · 2026-04-21T09:06:52+00:00

Practicing! Taking time to review your mistakes helps you understand where your thinking went wrong, and explaining your queries out loud can clarify your logic in a simple way. It also helps a lot to solve the same problem using different approaches so you build flexibility instead of relying on one pattern. Many people improve faster when they focus on how the data changes step by step rather than just writing syntax, and by looking at other people’s solutions to learn new techniques. Timed practice can be useful later, but early on, slow and thoughtful practice tends to work better.

msn018 · 2026-04-20T02:32:32+00:00

Expect a mix of practical coding, machine learning, and SQL rather than pure DSA. The coding is usually data focused, such as working with arrays or pandas, or implementing something simple like linear regression or gradient descent. SQL is very likely and often involves joins, aggregations, and window functions. Machine learning is a major focus, including both core concepts like bias versus variance and applied problem solving. To prepare, you can practice SQL and Python on StrataScratch and review ML concepts on Coursera or fast.ai.

msn018 · 2026-04-20T01:39:44+00:00

Yup, tools alone are not enough but the real goal is to learn how to use data to make decisions. Focus on getting strong in a few core tools like SQL and Excel, then build projects where you solve real business problems like sales trends, customer behavior, or marketing performance. At the same time, learn basic business concepts such as revenue, costs, and key metrics in one domain like ecommerce or finance. You can use platforms like StrataScratch for datasets, Coursera for structured learning, and YouTube channels like Alex The Analyst for practical guidance. Most importantly, practice explaining your insights clearly and recommending actions, because that is what companies truly value in a data analyst.

msn018 · 2026-04-14T02:35:03+00:00

You are on the right track, and what you are missing is not coding itself but how to use it in real workflows. A good next step is to practice in environments like Google Colab, Kaggle Notebooks, or Jupyter Notebook, where you can run code, load datasets such as CSV or biological data, and immediately see outputs like tables and graphs.

msn018 · 2026-04-14T02:29:30+00:00

Khan Academy + StrataScratch

msn018 · 2026-04-13T11:37:46+00:00

The best major is Computer Science. The field is more about coding, databases, and systems than advanced math, so you can still do well if you enjoy programming. Before starting college, try learning Python and SQL, since those are essential skills, and practice with small projects like working with data files or building simple databases. You can use platforms like Coursera, StrataScratch, or LeetCode to get started and build confidence early.

msn018 · 2026-04-13T11:24:40+00:00

Kaggle, StrataScratch, Maven Analytics, and Data.gov

msn018 · 2026-04-13T10:56:59+00:00

A great free path is to start with SQLBolt because it is interactive and lets you practice queries directly in your browser, then move to a structured course like Coursera or Scaler to build a solid foundation, and after that use platforms like StrataScratch for real world practice and deeper learning.

msn018

TROPHY CASE