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"content": "Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on statistics, programming and machine learning.<br /><br />Before we get started, there’s one tip I’d like to share.<br />I’ve noticed that there are several types of data science interviews that companies conduct.<br /><br />Some data science interviews are very product and metric driven. These interviews focus more on asking product questions like what kind of metrics would you use to show what you should improve in a product. These are often paired with SQL and some Python questions.<br />The other type of data science interview tends to be a mix of programming and machine learning.<br /><br /><a href=\"https://www.minds.com/search?f=top&t=all&q=datascience\" title=\"#datascience\" class=\"u-url hashtag\" target=\"_blank\">#datascience</a> <br /><a href=\"https://www.minds.com/search?f=top&t=all&q=programming\" title=\"#programming\" class=\"u-url hashtag\" target=\"_blank\">#programming</a><br /><a href=\"https://www.minds.com/search?f=top&t=all&q=python\" title=\"#python\" class=\"u-url hashtag\" target=\"_blank\">#python</a><br /><br />We recommend asking the recruiter if you aren’t sure which type of interview you will be facing. Some companies are very good at keeping interviews consistent, but even then, teams can deviate depending on what they are looking for. Here are some examples of what we have noticed about some companies data science interviews.<br /><br /><a href=\"https://medium.com/better-programming/the-data-science-interview-study-guide-c3824cb76c2e?source=friends_link&sk=b4c98b0ee20b58e5b026ff39d30ba661\" target=\"_blank\">https://medium.com/better-programming/the-data-science-interview-study-guide-c3824cb76c2e?source=friends_link&sk=b4c98b0ee20b58e5b026ff39d30ba661</a>",
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"published": "2020-11-23T16:11:36+00:00",
"source": {
"content": "Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on statistics, programming and machine learning.\n\nBefore we get started, there’s one tip I’d like to share.\nI’ve noticed that there are several types of data science interviews that companies conduct.\n\nSome data science interviews are very product and metric driven. These interviews focus more on asking product questions like what kind of metrics would you use to show what you should improve in a product. These are often paired with SQL and some Python questions.\nThe other type of data science interview tends to be a mix of programming and machine learning.\n\n#datascience \n#programming\n#python\n\nWe recommend asking the recruiter if you aren’t sure which type of interview you will be facing. Some companies are very good at keeping interviews consistent, but even then, teams can deviate depending on what they are looking for. Here are some examples of what we have noticed about some companies data science interviews.\n\nhttps://medium.com/better-programming/the-data-science-interview-study-guide-c3824cb76c2e?source=friends_link&sk=b4c98b0ee20b58e5b026ff39d30ba661",
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"content": "Automation. It’s supposed to remove work. So, why not help it along?<br /><br />Truth be told, automating even simple tasks robustly takes time and a lot of dependency management which can become very complex.<br /><br />Nevertheless, it can be very rewarding to automate some simple tasks. Even if it was more for fun.<br /><br />For those that are Python fans already, you know how friendly Python is for interacting with just about anything. From sending HTTP requests, interacting with APIs, to loading and managing databases.<br /><br />Python is a great choice for automation.<br /><br />Here are five simple things you can automate that can help your various workflows and projects.<br /><br /><a href=\"https://www.theseattledataguy.com/4-simple-python-ideas-to-automate-your-workflow/\" target=\"_blank\">https://www.theseattledataguy.com/4-simple-python-ideas-to-automate-your-workflow/</a>",
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"url": "https://www.minds.com/newsfeed/1091023489461362688",
"published": "2020-03-29T15:37:50+00:00",
"source": {
"content": "Automation. It’s supposed to remove work. So, why not help it along?\n\nTruth be told, automating even simple tasks robustly takes time and a lot of dependency management which can become very complex.\n\nNevertheless, it can be very rewarding to automate some simple tasks. Even if it was more for fun.\n\nFor those that are Python fans already, you know how friendly Python is for interacting with just about anything. From sending HTTP requests, interacting with APIs, to loading and managing databases.\n\nPython is a great choice for automation.\n\nHere are five simple things you can automate that can help your various workflows and projects.\n\nhttps://www.theseattledataguy.com/4-simple-python-ideas-to-automate-your-workflow/",
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"content": "One of the main roles of a data engineer can be summed up as getting data from point A to point B.<br /><br />We often need to pull data out of one system and insert it into another. This could be for various purposes. This includes analytics, integrations, and machine learning.<br /><br />But in order to get that data moving, we need to use what are known as ETLs/Data pipelines.<br /><br />These are processes that pipe data from one data system to another.<br /><br />One question we need to answer as data engineers is how often do we need this data to be updated. This is where the question about batch vs. stream comes into play. These are the two main types of ETLs/ELTs that exist.<br /><br /><a href=\"https://www.theseattledataguy.com/data-engineering-101-writing-your-first-pipeline/\" target=\"_blank\">https://www.theseattledataguy.com/data-engineering-101-writing-your-first-pipeline/</a>",
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"url": "https://www.minds.com/newsfeed/1091022872685789184",
"published": "2020-03-29T15:35:23+00:00",
"source": {
"content": "One of the main roles of a data engineer can be summed up as getting data from point A to point B.\n\nWe often need to pull data out of one system and insert it into another. This could be for various purposes. This includes analytics, integrations, and machine learning.\n\nBut in order to get that data moving, we need to use what are known as ETLs/Data pipelines.\n\nThese are processes that pipe data from one data system to another.\n\nOne question we need to answer as data engineers is how often do we need this data to be updated. This is where the question about batch vs. stream comes into play. These are the two main types of ETLs/ELTs that exist.\n\nhttps://www.theseattledataguy.com/data-engineering-101-writing-your-first-pipeline/",
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"content": "This is a software engineering study guide that you can use to help prepare yourself for your interview. This was developed by people who have interviewed and gotten jobs at FAANGs and several other tech companies. We hope these help you get great jobs as well!<br /><br />We have also created a checklist with the same problems listed below that can help you keep track of your progress that can be found here.<br /><br /><a href=\"https://www.minds.com/search?f=top&t=all&q=technology\" title=\"#technology\" class=\"u-url hashtag\" target=\"_blank\">#technology</a> <a href=\"https://www.minds.com/search?f=top&t=all&q=bigdata\" title=\"#bigdata\" class=\"u-url hashtag\" target=\"_blank\">#bigdata</a> <a href=\"https://www.minds.com/search?f=top&t=all&q=software\" title=\"#software\" class=\"u-url hashtag\" target=\"_blank\">#software</a> <a href=\"https://www.minds.com/search?f=top&t=all&q=interviews\" title=\"#interviews\" class=\"u-url hashtag\" target=\"_blank\">#interviews</a><br /><br /><a href=\"https://www.coriers.com/the-interview-study-guide-for-software-engineers/\" target=\"_blank\">https://www.coriers.com/the-interview-study-guide-for-software-engineers/</a>",
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"url": "https://www.minds.com/newsfeed/976222575144701952",
"published": "2019-05-17T20:39:59+00:00",
"source": {
"content": "This is a software engineering study guide that you can use to help prepare yourself for your interview. This was developed by people who have interviewed and gotten jobs at FAANGs and several other tech companies. We hope these help you get great jobs as well!\n\nWe have also created a checklist with the same problems listed below that can help you keep track of your progress that can be found here.\n\n#technology #bigdata #software #interviews\n\nhttps://www.coriers.com/the-interview-study-guide-for-software-engineers/",
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