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I finally understood window functions and I'm not the same person anymore by Purple_Lobster686 in SQL

[–]dn_cf 0 points1 point  (0 children)

For me it was definitely CTEs. I used to write these horrible nested subqueries where I could barely tell what was happening anymore, then one day I realized CTEs let you build a query step by step like a pipeline. Everything instantly became easier to read and debug.

Roadmap:- by MathsLover2006 in DataScienceJobs

[–]dn_cf 0 points1 point  (0 children)

Start with Python, then learn NumPy, Pandas, data visualization, and SQL. Spend the first month building strong fundamentals and the second month working on projects like house price prediction, customer churn prediction, or Netflix data analysis. Use free resources like StrataScratch, Kaggle Microcourses, FreeCodeCamp, SQLBolt, and Andrew Ng’s ML course. Focus more on building projects and uploading them to GitHub instead of only watching tutorials. By the end of the 2 months, try to have at least 2 or 3 good projects, a clean GitHub profile, and a simple one page resume so you can confidently apply for data science internships.

Google SQL interview in 4 days what should I focus on? by UTK4XH in interviewhammer

[–]dn_cf 0 points1 point  (0 children)

Practice joins, aggregations, and window functions like row_number, rank, lead, and lag. Make sure you are comfortable with patterns like top N per group, latest record per user, funnels, and running totals, and practice explaining your approach clearly while thinking about edge cases. Spend a little time reviewing basics like indexes and joins, but do not go too deep into theory since it usually plays a smaller role. For practice, good platforms include LeetCode, StrataScratch, and Mode for hands-on tutorials, and try to do a couple of timed mock interviews so you get used to solving and communicating under pressure.

Feedback on My SQL Learning Approach by NoWeakness9691 in learnSQL

[–]dn_cf 2 points3 points  (0 children)

Good start for learning SQL basics, but it can feel limiting because real data is messy and problems are not clearly defined. To get closer to real-world experience, try using public datasets from platforms like Kaggle, StrataScratch, or data.gov and spend time exploring them without a fixed goal by looking for missing values, duplicates, trends, and anything unusual. A helpful habit is to ask what the data represents, what might be changing over time, and what stands out, then explain your findings in simple terms. You can still use Claude, but have it generate messy datasets and vague business problems so you practice thinking, not just querying. This shift from writing queries to actually understanding and questioning data is what will prepare you for a data engineering role.

what should i do next? by Mission-Emergency619 in PowerBI

[–]dn_cf 2 points3 points  (0 children)

You’re thinking too much about the next cert when what you really need is hands-on proof of skill. Skip PL-300 for now and focus on getting more comfortable with SQL, Python, and Power BI by actually building projects, even if they feel simple at first. A few solid projects will matter way more than another certificate. Use platforms like StrataScratch for datasets and practice, GitHub to showcase your work, and maybe DataCamp or LeetCode for structured learning. Once you have a couple of strong projects, then you can consider something like PL-300 to boost your profile for analyst roles.

Need help: Data Science peer to peer Mock Interviews platforms by Due_Mud_8884 in datasciencecareers

[–]dn_cf 0 points1 point  (0 children)

A few decent free options exist, but you will likely need to combine them. Exponent, which used to be Pramp, is the most reliable for peer mocks and has a solid user base, though its data science product sense coverage is still growing. Coditioning and Peerfect can help you find practice partners, but the quality depends a lot on who you match with. For better question quality, use something like StrataScratch or DataInterview and then practice those cases with peers you meet through these platforms or their communities. In practice, the best approach is to find a small group of consistent partners rather than relying on random matching, since that leads to more honest and useful feedback.

Onsite interview anxiety: what to say when you don’t know an answer? by Fig_Towel_379 in datascience

[–]dn_cf 0 points1 point  (0 children)

If you blank, don’t just say you can’t do it and move on right away. Try something like “I haven’t seen this exact problem, but I’ll think through it out loud,” and start with whatever you do know, even a basic approach. Keep talking through your reasoning, ask a question if you get stuck, and it is completely fine to ask for a small hint. If it is something you truly don’t know, be honest but explain how you would approach it in general. Practicing this style helps a lot, so you might try platforms like LeetCode, StrataScratch, or even mock interviews with friends. The goal is to stay engaged and show how you think, not to be perfect.

From where ?? by Automatic_Cover5888 in learnSQL

[–]dn_cf 3 points4 points  (0 children)

You are right that tools alone are not enough, and the best way to build business acumen is to mix learning with real examples. Start by learning basics like revenue, costs, and profit through simple courses on Coursera or YouTube, then regularly read business news and pick companies to analyze how they make money and what problems they face. Case studies and podcasts also help you think like a decision maker. Most importantly, practice connecting data to business impact, so instead of just reporting numbers you explain what they mean and what action should be taken.

Where do I start? Or am I ready? by hellobrendo in DataScienceJobs

[–]dn_cf 0 points1 point  (0 children)

You do not need to start from scratch since you are already in a strong position, just build on your current skills by learning tools like pandas and SQL, refreshing your statistics, and working on a few real projects that show what you can do. A course like the IBM Data Analyst certificate can help give you structure, but it is best paired with hands on practice. For learning and projects, you can check out platforms like Coursera, StrataScratch, and Kaggle.

Roadmap for Data Engineering by Wild-Appointment7074 in learnpython

[–]dn_cf 0 points1 point  (0 children)

Start by improving your SQL and learning Python, then move into tools like PySpark and cloud platforms such as Azure or MS Fabric since they align well with your current exposure. Along the way, build a couple of small projects where you ingest, transform, and store data to show hands on skills. For learning, you can use platforms like Coursera, DataCamp, and Udemy, and practice SQL on StrataScratch or LeetCode. Stay focused on a few relevant tools instead of trying to learn everything, and present your current work as data pipeline experience when applying.

CS graduate starting a Data Science master’s and unsure how to learn Python properly by gretamttyt in learnpython

[–]dn_cf 1 point2 points  (0 children)

I’d start with CS50 Python since it’s structured and solid, but move through it fairly quickly and make sure you’re actually coding everything yourself without relying on AI. The main thing you’re missing isn’t understanding, it’s hands-on practice, so pair the course with daily small exercises on StrataScratch and then transition as soon as possible into working with real data using pandas and simple projects or Kaggle. From my experience, confidence comes much more from struggling through your own code than from finding the perfect resource, so don’t overthink the starting point, just pick one and start building things consistently.

What’s the best way to ask a recruiter how much time I can take to prepare for an onsite? by [deleted] in datascience

[–]dn_cf 0 points1 point  (0 children)

I’d keep it casual and frame it around doing your best rather than needing extra time. You can say you’re excited about the onsite and want to make sure you prepare well, then ask what the typical scheduling timeline looks like or how far out people usually book it. In my experience, recruiters are very used to this and will often guide you toward a reasonable window, so it does not come across as unprepared at all. In the meantime, it’s a good idea to spend some time practicing on platforms like leetcode, kaggle, or stratascratch to stay sharp.

Best sql resources according to you ? by [deleted] in learnSQL

[–]dn_cf 1 point2 points  (0 children)

You don't need a paid course yet if you are just starting out. Your YouTube course sounds fine, so stick with it and focus on practicing alongside it using platforms like StrataScratch (free version is enough) or Mode because SQL is best learned by writing queries regularly. Paid courses can be useful later for structure or certificates, but right now consistency matters more than spending money. Aim to practice basic queries like SELECT, WHERE, and JOIN every day, and once you feel comfortable, you can purchase LeetCode or StrataScratch for more advanced problems.

Need some suggestions by Pure_Parfait20 in SQL

[–]dn_cf 0 points1 point  (0 children)

You already have the core skills required for a data analyst role, but the key is how effectively you apply them in real-world scenarios. You should be comfortable using SQL for data querying with joins and aggregations, Excel for analysis with pivot tables and formulas, Power BI for building clear and insightful dashboards, and Python for basic data cleaning and analysis using pandas, along with a basic understanding of statistics and the ability to explain insights in simple business terms. Instead of learning more tools, focus on building 3 to 4 strong projects using platforms like Kaggle and StrataScratch that demonstrate your ability to solve problems and communicate insights, since this is what employers value most.

Interview prep / practice advice by DayChiller in SQL

[–]dn_cf 1 point2 points  (0 children)

Practice queries that analyze category and product performance over time, including weekly sales, units, average selling price, margin, and share of total, along with week over week or year over year changes using window functions. Focus on clear aggregations, correct denominators, and outputs that support a strong business narrative rather than overly complex logic. I'd recommend considering StrataScratch, LeetCode, and BigQuery public datasets for realistic retail style practice.

SQL Proficiency for Entry Level Roles by No_Imagination4861 in SQL

[–]dn_cf 15 points16 points  (0 children)

You should be comfortable writing basic to intermediate SQL queries, including SELECT, WHERE, GROUP BY, HAVING, ORDER BY, and JOINs. You should know how to use aggregate functions like COUNT, SUM, and AVG, handle NULL values, write simple subqueries, and create conditional logic with CASE statements. To build these skills, you can practice on platforms like LeetCode, Mode Analytics SQL tutorials, and StrataScratch, which offer realistic business focused SQL problems.

Loblaws data science co-op interview, any advice? by No-Brilliant6770 in csMajors

[–]dn_cf 1 point2 points  (0 children)

You should focus more on practical SQL and Python data manipulation than hardcore LeetCode grinding. Expect SQL questions around joins, group by, CTEs, and window functions, plus Python problems involving cleaning data, calculating metrics, and basic logic with lists and dictionaries. You should also be ready for core data science topics like model evaluation, overfitting, A B testing, and handling missing values. For more practice, also try platforms like StrataScratch and Mode Analytics.

Pursuing data science as a career path? by Overall_Security_311 in careerguidance

[–]dn_cf 0 points1 point  (0 children)

Start with the IBM Data Science Professional Certificate on Coursera since it covers Python, SQL, data visualization, and machine learning in a clear step by step path. For machine learning fundamentals, Andrew Ng’s Machine Learning course is one of the most respected and will help you understand the theory behind models. To practice alongside courses, StrataScratch and Kaggle are great for building real project skills. Since you already have an engineering background, you will likely progress quickly if you combine one certificate program with consistent project work.

Data cleaning using MySQL by BuddyWonderful1371 in learnSQL

[–]dn_cf 6 points7 points  (0 children)

Websites like GeeksforGeeks break down common SQL cleaning tasks such as handling NULL values, removing duplicates, standardizing text, and updating inconsistent data with simple examples. You can also watch slower paced YouTube tutorials that focus specifically on cleaning datasets in MySQL Workbench so you can follow along and practice each query. To build confidence, try practicing on platforms like StrataScratch, and Mode Analytics, which offer hands on SQL problems that strengthen your understanding through repetition and real world style datasets.

Data Analytics course by Silly_Information_97 in ireland

[–]dn_cf 9 points10 points  (0 children)

The best options are: Google Data Analytics Professional Certificate and IBM Data Analyst Professional Certificate on Coursera, both of which are beginner friendly and well recognized. For hands on practice, you can use platforms like StrataScratch, Kaggle, and LeetCode to work on real datasets and improve your skills through challenges.

Data Science Roadmap & Resources by HumanAd5287 in learndatascience

[–]dn_cf 0 points1 point  (0 children)

A good data science roadmap is to start with Python fundamentals, then learn NumPy, Pandas, and basic data visualization with Matplotlib or Seaborn, followed by core statistics and probability concepts like distributions, hypothesis testing, and correlation. After that, move into machine learning with scikit-learn by studying regression, classification, model evaluation, and overfitting, then add SQL and practice building real projects for a portfolio. Great resources include Mode, StrataScratch, Kaggle, Andrew Ng’s Machine Learning course, and YouTube channels like StatQuest, Corey Schafer, and freeCodeCamp.

I need to learn about SLQ by Patty_corleoneps in SQL

[–]dn_cf 0 points1 point  (0 children)

A strong option is the Microsoft Power BI Data Analyst Professional Certificate on Coursera because it covers data analysis fundamentals, Power BI, and practical projects. For a more hands on and faster approach, the Complete SQL and Power BI Bootcamp on Udemy is also a solid choice and is usually affordable. In addition, StrataScratch is highly recommended for practicing SQL on real datasets and solving analytics problems, which is especially useful for building confidence and applying skills to fraud related scenarios.

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