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This can be a dataset for you to analyze, a coding assessment, or a project that they would like you to present on. Companies may test you on your ability to work with ambiguity ([e.g. here](e.g. here)’s a dataset, find some insights and pitch to business  \nstakeholders) or focused on a more concrete deliverable (e.g. Here’s some data, build aclassifier) .  \nWhile all assessments may differ in their objectives, the common denominator is that you’ll be receiving data from the company. So regardless of what they’ve asked you to do, the first step will always be Exploratory Data Analysis. Luckily there are some automated EDA solutions, such as Speed ML.  \nPrimarily what you want to do here is investigate peculiarities in the data. More often than not, the company will have synthetically generated the data, leaving specific easter eggs for you to find (e.g. A power law distribution with customer revenue) .  \nThese are Ken Jee from Towards Data Science’s Top Tips  \n● If they don’t explicitly say you can’t bring in data from outside sources, consider appending other useful data.  \n● Using feature engineering or feature reduction (and being able to explain why) can improve the quality of your analysis greatly.  \n● Putting some time to making your work interpretable to business stakeholders (detailed visuals) can show that you understand how to drive business value.  \n● Pro-Tip: Make sure to save every challenge you do. You never know when you may need to reuse a component in future challenges.  \nTechnical Interview  \nExpect to be asked about the projects on your resume and the logic behind your methodology. You will likely be asked to explain some of the math behind the algorithms that you used so make sure you’re prepared with some revision of the theory. If they have any questions about the take-home test, they will generally ask them here as well. Since this is a project that they are familiar with (they may have taken the assessment themselves), be prepared for questions about the data cleaning, model choice, etc. Be prepared to reflect on what you did in detail and what result was achieved. Prepare to explain how you would improve on what you did.  \nThe most common question asked in a Data Science interview is one where the interviewer asks you to pic k an algorithm and explain how it works, what it tries to achieve and the pros and cons of using that particular algorithm. You can prepare an answer to this question ahead of the interview and adapt it to the question you get posed. Understand the performance metrics you would use to measure whether the algorithm is working and at what level.  \nReview general data science interview questions, these are generally statistics based. Without an advanced knowledge of statistics, it is difficult to succeed as a data scientist.  \nBe prepared to answer some fundamental statistics questions as part of your data science interview. For some common examples use the links under the resources heading below.  \nThere will likely also be a programming section of the interview- language-wise, Python, SQL and R are typically the three that you could be asked to write in, however, this can differ based on the role and company. You may be asked to solve a problem on a wh","cbCaiioLRC8G9K2F","https://ap.wps.com/l/cbCaiioLRC8G9K2F","pdf",118425,"English","# The Technical Assessment\n## Common assessment formats and goals\n## Exploratory Data Analysis (EDA)\n# Technical Interview\n## Algorithms and performance metrics\n## Statistics fundamentals\n## Programming section and practice\n# Commonly Asked Questions\n# Additional Resources\n# Technical Interview Nuances for Data Analysts","[{\"question\":\"What is the first step in most data science assessments once you receive the company’s data?\",\"answer\":\"Exploratory Data Analysis (EDA). The goal is to investigate peculiarities in the dataset and uncover useful patterns and insights.\"},{\"question\":\"What should you be prepared to discuss during the technical interview?\",\"answer\":\"Expect questions about projects on your resume, the logic behind your methodology, and details such as data cleaning and model choice. You should also be able to explain results and how you would improve the work.\"},{\"question\":\"Which topics are commonly covered in data science interview questions?\",\"answer\":\"Common topics include feature selection, differences between linear and logistic regression, decision trees, K-means clustering, credit risk model testing, bias-variance tradeoff, overfitting, Naive Bayes, cross-validation, and confounding variables.\"}]","Job_search_guide_Data_Scientist_Technical_Interview_Prep - prep guide | PDF",1790352492,13]