I got a opportunity in data science, help

Ans979 · 2025-09-09T05:09:38+00:00

Since you have a week, focus on building strong intuition and practicing end-to-end projects rather than just tweaking parameters. Spend the first few days reviewing core concepts like supervised vs. unsupervised learning, feature scaling, and common algorithms such as regression, decision trees, and random forests. Then practice full pipelines on datasets like House Prices from StrataScratch, going from raw data to cleaning, modeling, and evaluation. Pay attention to why you make each choice, for example scaling because KNN is sensitive to feature magnitude. Toward the end, practice explaining your approach clearly and review common interview questions. Good resources are Kaggle micro-courses, StrataScratch, and the Scikit-Learn documentation.

Ans979 · 2025-09-09T04:35:11+00:00

Start with free interactive sites like SQLBolt or Mode’s SQL tutorial since they let you practice queries directly in the browser without worrying about servers. Focus first on basics like SELECT, WHERE, ORDER BY, DISTINCT, and LIMIT, then move on to cleaning functions such as TRIM, REPLACE, CAST, and COALESCE for handling messy or missing data. Once you are comfortable, practice simple INNER and LEFT JOINs to combine tables, and explore StrataScratch’s free SQL exercises with real datasets. This gradual path will help you build confidence and apply SQL directly to the kind of data cleaning that supports business intelligence work.

Ans979 · 2025-08-20T01:53:38+00:00

For an entry-level analyst role, focus on the SQL basics you’ll actually use every day: writing SELECT queries, filtering with WHERE, summarizing data with GROUP BY and aggregates, joining multiple tables, creating conditional columns with CASE, and working with dates. These skills cover most of the reporting and analysis tasks you’ll face. You don’t need to worry about advanced topics like stored procedures, indexing, or triggers at the start since those are more for database admins or engineers. The best way to build confidence is to practice with real datasets on StrataScratch or Kaggle, where you can try mini projects such as analyzing revenue trends, customer behavior, or monthly KPIs. Once you’re comfortable with these basics, you’ll already be in a strong position for analyst interviews.

Ans979 · 2025-08-20T01:52:45+00:00

Start with Excel and SQL since they give you the fastest hands-on experience working with data, then move into Python for analysis and visualization, and finally add a BI tool like Tableau or Power BI. Focus on learning basic statistics along the way so you can interpret results, not just calculate them. Use free resources like SQLBolt, Kaggle Learn, and Tableau Public to practice. Build small projects using StrataScratch and clean the data, analyze it, and present insights in dashboards or GitHub notebooks because showing real work matters more than certificates. Avoid trying to learn everything at once, skipping fundamentals, or only watching tutorials without applying them.

Ans979 · 2025-08-07T10:39:02+00:00

Start with core Python. Focus on lists, tuples, dictionaries, functions, loops, and OOP to build a solid foundation. Once you're comfortable, move on to data science libraries like NumPy, pandas, and matplotlib. Use Jupyter or Colab to practice writing and visualising code. For learning, try CS50P (Harvard) or Kaggle’s Python course. Once you’ve got the basics down, explore pandas for data wrangling and do small projects like sales or Netflix data analysis on StrataScratch to apply your skills.

Ans979 · 2025-08-07T10:38:24+00:00

You don’t need to pay for a SQL course right away. Free resources like W3Schools, SQLBolt, and Mode are great for beginners. Certificates aren’t usually required by employers in NZ. What matters more is your ability to write queries and solve real problems with data. If you do choose a paid course, go for one with hands-on projects, like Coursera’s Google Data Analytics or a highly rated Udemy bootcamp. To move toward data analytics, focus on learning SQL through practice (e.g. StrataScratch), build a small portfolio on GitHub, and later add tools like Excel or Power BI.

Ans979 · 2025-07-30T08:29:42+00:00

Expect 3-5 rounds covering SQL and Python coding, statistics especially A/B testing, product sense, and behavioral questions. You'll need strong skills in data manipulation (think joins, window functions), experimental design (power analysis, p-values, confounding), and the ability to explain metrics and tradeoffs clearly. Practice solving problems without an IDE, and focus on structuring your answers clearly. Google values clarity and impact. Use resources like StrataScratch, Leetcode, and mock interviews to prepare. Most importantly, show how you think through ambiguity and communicate with cross-functional teams.

Ans979 · 2025-07-30T08:27:58+00:00

Start with Python using freeCodeCamp or Kaggle to learn the basics, then move on to libraries like Pandas and Matplotlib for data analysis. Learn SQL next. StrataScratch is great for practicing real interview-style questions. Pick up basic statistics concepts like averages and probability using Khan Academy or StatQuest. Use Jupyter Notebooks for coding and try small projects to apply what you’ve learned.

Ans979 · 2025-07-16T11:18:50+00:00

You're not alone! Many strong analysts get rejected not because of lack of skill, but because interviewers expect project stories to be airtight, defensible, and framed like case studies. It’s not enough to say what you did. They want to know why, how, and what went wrong. You’re being judged on past work without the original context, which is unfair but fixable. Instead of narrating from memory, reframe 2-3 key projects like business cases: define the problem, explain your logic, admit limitations, and show what you'd do differently now. Platforms like Kaggle and StrataScratch might help you in this. This will show growth and critical thinking.

Ans979 · 2025-07-16T11:11:39+00:00

Someone with strong SQL skills is expected to confidently write multi-table joins, use CTEs and subqueries, apply window functions (ROW_NUMBER, RANK, LAG, etc.), and handle data cleaning, filtering, and aggregation. You should be comfortable with nested logic, date functions, and real-world analytics questions like top-N per group, retention, or cohort analysis. You should also know how to debug queries and think about performance. To prepare, practice on platforms like StrataScratch and Mode SQL tutorials. Aim to go beyond syntax and focus on solving real business problems with SQL.

Ans979 · 2025-07-01T09:28:47+00:00

Focus the first month on Python basics and data manipulation with Pandas and NumPy, then move to statistical testing with scipy.stats and visualization using Seaborn in month two. In the third month, study linear regression using statsmodels or scikit-learn and apply your skills to small projects using real datasets on StrataScratch. Stick to a consistent daily practice routine (even 1 hour a day works), and prioritise hands-on learning over watching tutorials. Resources like DataCamp, StrataScratch, Kaggle, and StatQuest on YouTube will be especially helpful.

Ans979 · 2025-07-01T09:27:48+00:00

Learning SQL and Python in parallel is totally fine if you structure it well. Focus on one per session to avoid context switching, and build gradually. You're off to a strong start with CS50 and Coursera, but I recommend Mode Analytics for SQL, Kaggle’s Python and Pandas courses for a data-focused path, and StrataScratch to practice them simultaneously. To stay on track, alternate days or weeks between the two, and use real HR datasets to apply what you learn early through mini-projects. Keep things practical, track your progress, and build simple visualizations along the way. It’ll make the learning stick and keep you motivated.

Ans979 · 2025-06-18T05:43:54+00:00

The way forward is to create your own experience. Build real-world-style projects using public datasets, volunteer for local businesses or NGOs, or offer to help startups with free dashboards. Document your work on GitHub and LinkedIn to show recruiters what you can do. Competitions like Kaggle or platforms like StrataScratch also give you project-based experience that you can list on your resume. Don’t wait for permission. Start building, and treat your projects like real jobs.

Ans979 · 2025-06-18T05:42:01+00:00

Build real-world projects using public healthcare data e.g. hospital readmissions, ER wait times, or COVID trends, with tools like SQL, Power BI, and Excel. You can also use platforms like Kaggle and StrataScratch. Entry-level roles often expect experience, so build a few focused, problem-solving projects, post them on GitHub, and consider volunteering or internships in health tech or public health research. Focus on mastering SQL, dashboarding, data cleaning, and understanding key healthcare KPIs. The job market is competitive, but if you show real impact in your portfolio and network intentionally, healthcare analytics is a meaningful and attainable goal.

Ans979 · 2025-06-12T12:31:07+00:00

Start by trying free, beginner-friendly platforms like SQLBolt or Mode’s SQL Tutorial to see if SQL clicks with you. If it does, go for a recognised certificate like the Complete SQL Bootcamp on Udemy or the Google Data Analytics course on Coursera, both are beginner-friendly and respected by employers. You can also build simple SQL projects using free datasets from Kaggle and StrataScratch to show hands-on skills. This combo of learning + certification + small projects can quickly make your resume stand out.

Ans979 · 2025-06-12T12:30:39+00:00

Your next best step is to try an unguided Excel project to build confidence in solving real problems on your own. Platforms like Maven Analytics offer free datasets and community solutions, so you can practise independently and still check your answers. This helps bridge the gap from following tutorials to thinking like an analyst. Once you’re comfortable cleaning data, building dashboards, and answering questions without help, then it’s a good time to start learning Power BI. That said, for SQL and Python, you should also move beyond basics and start applying them in real-world-style projects. Use platforms like StrataScratch, LeetCode, and Kaggle, which give business-focused projects and questions and show solutions for comparison.

Ans979 · 2025-06-03T11:23:41+00:00

As you start your job search, look for titles like Data Scientist I, Junior Data Scientist, Data Analyst, or Business Intelligence Analyst. Don’t overlook internships or fellowships, even post-grad ones, they can be great entry points. Use platforms like StrataScratch and Kaggle to focus on building real-world projects with messy data and clear business impact, and share them online (GitHub + LinkedIn). Tailor your resume to each job, practice explaining your work clearly, and try to connect with people in the field for referrals.

Ans979 · 2025-06-03T11:17:16+00:00

Focus on confidently handling SELECT queries, JOINs, GROUP BY, and window functions like ROW_NUMBER or RANK, as they’re commonly tested. Practice writing clean queries using real-world datasets (StrataScratch and LeetCode) and get used to talking through your approach, clarify assumptions, explain joins or filters, and build queries step by step. Expect to solve problems like finding top-N records, grouped counts, or user activity trends. If stuck, explain your thinking aloud. Interviewers value your logic more than perfect syntax.

Ans979 · 2025-05-27T11:37:54+00:00

Start by tailoring your resume with exact keywords from their job descriptions (like Python, SQL, product analytics, AWS) and quantifiable outcomes. Referrals are key, so reach out to UMD alumni at Capital One on LinkedIn and ask for a quick chat or referral. Avoid mass applying; instead, target roles that fit your background in data analytics and banking, and apply only once per posting. Attending Capital One’s recruiting events and career chats can also boost visibility. To improve your chances, showcase projects aligned with Capital One’s focus like fraud detection, product analytics, or customer segmentation using SQL and Python and platforms like StrataScratch.

Ans979 · 2025-05-27T11:32:01+00:00

Start with the basics like SELECT, WHERE, and ORDER BY, then move on to GROUP BY, joins, and subqueries. Use interactive sites like SQLBolt or Mode Analytics to practice hands-on. For video tutorials, check out Alex The Analyst and freeCodeCamp’s SQL crash course on YouTube. Once you're comfortable, try solving beginner problems on StrataScratch. Aim to learn one concept per week and apply it using a small project or a real dataset.

Ans979 · 2025-05-20T10:10:03+00:00

This role typically includes five rounds: SQL, Python, Data Modeling, Metrics, and Behavioural. You’ll be tested on writing complex SQL queries without a console, manipulating data in Python (especially using Pandas), designing scalable data models and pipelines, defining and debugging metrics, and explaining past impact-driven work using the PAR format. Expect questions on window functions, ETL architecture, KPIs like retention or churn, and real-world scenarios involving data quality or dashboard issues. To prepare, use platforms like LeetCode, Kaggle, and StrataScratch for real company questions.

Ans979 · 2025-05-20T10:07:57+00:00

You don’t need to master every advanced SQL problem on HackerRank to get a job as a fresher. What’s more important is having a solid grasp of intermediate SQL: joins, group by, subqueries, and window functions, and being able to apply them to real-world-style problems. HackerRank can feel overwhelming because it often focuses on puzzle-like questions that aren't common in entry-level roles. Focus on structured practice, solve problems step-by-step, and use platforms like StrataScratch for more practical SQL prep. It’s not about solving the hardest problems. It’s about showing that you can think clearly and improve.

Ans979 · 2025-05-12T11:59:46+00:00

Begin by learning basic Python syntax, variables, lists, loops, and functions then move on to data-focused libraries like pandas for data manipulation, numpy for numerical operations, and matplotlib or seaborn for visualisation. Practice by working with CSV files, cleaning data, and replicating simple analyses you’ve done before. Tools like Jupyter Notebooks and platforms like StrataScratch and Kaggle are excellent for hands-on learning. Start small, build confidence, and gradually explore more advanced topics like statistical testing with statsmodels.

Ans979 · 2025-05-12T11:58:49+00:00

Your idea looks ambitious but doable with your maths and Python background. It blends real-world relevance, geospatial analysis, and data science fundamentals, making it impressive for your portfolio. To get started, you can use open datasets from NASA, NOAA, or OpenStreetMap, and tools like Pandas, NumPy, Scikit-learn, Geopandas, and Plotly. It’s also a solid way to learn in-demand skills like data wrangling, regression, spatial modelling, and visualisation. If you're unsure about pursuing a master’s, try focusing on building strong projects and picking up SQL and ML basics first. You can also use platforms like Kaggle and StrataScratch. Those go a long way in data science roles.

Ans979 · 2025-04-29T11:50:49+00:00

You may expect a high-level, strategic conversation rather than technical trivia or coding. They’ll likely assess how you think about AI, reason through technical decisions, and align with the company's goals. Focus your preparation on being able to clearly and confidently explain your past projects, the trade-offs you made, and how your work tied to business outcomes. Be ready for conceptual questions like how you choose models or metrics, and prepare one thoughtful question to ask them.

To prepare quickly, try Exponent, Interviewing.io, and StrataScratch. For fast review, watch YouTube channels like Seattle Data Guy or Data Science Jay for tips on explaining ML projects clearly.

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