internprep/resources.html at main · ronakdm/internprep · GitHub

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    <title>UW Statistics InternPrep</title>
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                <li class="home"><a target="_self" href="index.html">InternPrep</a></li>
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            <div class="title">Resources</div>
            <div class="under-text">Coding</div>
            <p>
                A great website to practice coding exercises is <a target="_blank"
                    href="https://leetcode.com/">LeetCode</a>. There are
                three difficulty levels: easy, medium, and hard. Most of the coding
                exercises in interviews will be roughly of the level easy and medium.
                If you are new to coding exercises, we recommend that you start
                practicing by categories (e.g., array, string, etc.) using the “Tags”
                feature on the website. Another recourse to learn the basic concepts behind
                coding interview questions is <a target="_blank"
                    href="https://www.crackingthecodinginterview.com/">Cracking the Coding Interview</a>.
                Because these interviews are technical and statistics students may not have a background
                in computer science, it is recommended to start preparing the summer before.
            </p>
            <div class="under-text">Statistics and Machine Learning Basics</div>
            <p>
                <a target="_blank" href="https://statquest.org/">StatQuest</a> is a Youtube channel with short videos
                reviewing basic
                statistics and machine learning concepts such as logistic regression
                and neural networks. The videos are of high quality with lots of
                graphical illustrations. The introductory chapters of <a target="_blank"
                    href="https://books.google.com/books/about/Statistical_Inference.html?id=0x_vAAAAMAAJ">Casella &
                    Berger</a> can also
                be helpful to revisit basic probability. Finally, a short guide to understanding machine learning
                concepts
                specifically for interviewing is collected in this <a target="_blank"
                    href="https://github.com/ronakdm/ml-interviews">ml-interviews repo</a>.
            </p>
            <div class="under-text">Quantitative Questions</div>
            <p>
                While it is not usually necessary to have a deep background in finance for quantitative finance roles,
                one type of question in these interviews that are not covered in big tech (MAMAA) interviews is the
                so-called "quantitative brainteaser" question.
                These questions aim to test your quantitative abilities which only
                involves basics of mathematics, probability, statistics, and logic.
                Two classical reference books are <a target="_blank"
                    href="https://www.amazon.com/Heard-Street-Quantitative-Questions-Interviews/dp/0994103867">Heard on
                    the Street: Quantitative Questions from Wall Street Job Interviews</a>
                and <a target="_blank"
                    href="https://www.amazon.com/Practical-Guide-Quantitative-Finance-Interviews/dp/1438236662">A
                    Practical Guide To Quantitative Finance Interviews</a>.
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