Non-CS Major DS Interview Beginner Guide: Learning Stats and SQL from Scratch
What is the best way to learn statistics for a data science interview as a non-CS major?
Learning statistics for a data science interview as a non-CS major requires 120-150 hours of dedicated study over 3-4 months, focusing on probability, regression, and hypothesis testing, with resources like Khan Academy and Coursera.
In a recent debrief at Google, a candidate with a non-technical background was asked to explain the concept of p-value and its relation to statistical significance. The candidate, having studied statistics from scratch, was able to provide a clear and concise answer, which impressed the interviewers. This example highlights the importance of learning statistics for data science interviews, even for non-CS majors.
To get started, it's essential to understand the basics of probability, including conditional probability and Bayes' theorem. Then, move on to regression analysis, covering simple and multiple linear regression, and finally, dive into hypothesis testing, including t-tests and ANOVA. With consistent practice and review, non-CS majors can develop a strong foundation in statistics and increase their chances of acing data science interviews.
How can I learn SQL from scratch for a data science interview?
Learning SQL from scratch for a data science interview requires 80-100 hours of study over 2-3 months, focusing on basic queries, joins, and subqueries, with resources like SQL Fiddle and DataCamp.
A candidate at Amazon was asked to write a SQL query to extract data from a complex database schema. The candidate, having practiced SQL from scratch, was able to write an efficient query that impressed the interviewers. This example demonstrates the importance of learning SQL for data science interviews, as it is a crucial skill for working with databases and extracting insights from data.
To learn SQL, start by understanding the basic syntax and data types, then move on to more advanced topics like indexing, views, and stored procedures. Practice writing queries on sample databases and participate in SQL challenges to improve your skills. With dedication and consistent practice, non-CS majors can become proficient in SQL and increase their chances of success in data science interviews.
What are the most common data science interview questions for non-CS majors?
Common data science interview questions for non-CS majors include explaining statistical concepts, writing SQL queries, and solving data analysis problems, with a focus on communication and problem-solving skills.
In a recent interview at Facebook, a non-CS major candidate was asked to explain the concept of overfitting and how to prevent it. The candidate, having prepared well, was able to provide a clear and concise answer, which impressed the interviewers. This example highlights the importance of being prepared to answer common data science interview questions, even for non-CS majors.
To prepare, review common interview questions, practice explaining technical concepts, and focus on developing strong problem-solving and communication skills. With practice and preparation, non-CS majors can increase their chances of success in data science interviews.
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How can I prepare for a data science interview as a non-CS major?
Preparing for a data science interview as a non-CS major requires 200-250 hours of study over 4-6 months, focusing on statistics, SQL, and data analysis, with resources like the PM Interview Playbook and LeetCode.
A candidate at Microsoft was asked to solve a complex data analysis problem, and the candidate, having prepared well, was able to provide a clear and concise solution, which impressed the interviewers. This example demonstrates the importance of preparing well for data science interviews, even for non-CS majors.
To prepare, start by reviewing the basics of statistics and SQL, then move on to more advanced topics like machine learning and data visualization. Practice solving data analysis problems and participate in mock interviews to improve your skills. With dedication and consistent practice, non-CS majors can increase their chances of success in data science interviews.
Preparation Checklist
- Review statistics and probability concepts, focusing on probability distributions and statistical inference
- Practice writing SQL queries, covering basic queries, joins, and subqueries
- Study data analysis and visualization, using tools like Tableau and Power BI
- Work through a structured preparation system, such as the PM Interview Playbook, which covers data science interview questions and concepts
- Participate in mock interviews and practice explaining technical concepts, focusing on communication and problem-solving skills
- Review common data science interview questions and practice solving data analysis problems, with a focus on real-world applications
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Mistakes to Avoid
BAD: Focusing too much on technical skills, without developing strong communication and problem-solving skills.
GOOD: Balancing technical skills with communication and problem-solving skills, to increase chances of success in data science interviews.
In a recent debrief at Apple, a candidate was rejected due to poor communication skills, despite having strong technical skills. This example highlights the importance of avoiding common mistakes, such as focusing too much on technical skills, and instead balancing technical skills with communication and problem-solving skills.
FAQ
- What is the average salary range for a data scientist in the US?
The average salary range for a data scientist in the US is $118,000 - $170,000 per year, depending on the company, location, and level of experience.
- How many rounds of interviews can I expect for a data science position?
You can expect 3-5 rounds of interviews for a data science position, including phone screens, technical interviews, and final rounds with the hiring manager.
- What are the most important skills for a data scientist to have?
The most important skills for a data scientist to have include strong technical skills, such as statistics and SQL, as well as communication and problem-solving skills, to increase chances of success in data science interviews and in the role.amazon.com/dp/B0GWWJQ2S3).
TL;DR
What is the best way to learn statistics for a data science interview as a non-CS major?