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This tutorial provides and introduction to K-means Clustering. It also looks at the pros and cons of K-means Clustering.
00:11 K-means Clustering
20:38
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This probability tutorial provides an introduction to the Discrete Uniform Distribution.
If you've ever wanted to dip your toes into probability distributions, starting off with the Discrete Uniform Distribution is the way to go.
Enjoy!
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Hi everyone and welcome to another episode of 365 Data Use Cases! In this series of super informative videos, we explore the diverse areas where data is widely used nowadays. Let's be honest, every industry relies on data for insights and business strategies. But you'll be amazed how fascinating some of these cases are!
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In this video, our friend Ken Jee will tell you more about his favorite data use case – Sports! He’ll share with you 3 applications of data science in the broader field of professional athletics and sports analytics – performance improvement, fan engagement and predicting player and game outcomes. In Ken’s own words, “We are just beginning to understand what is possible with all of this data, and maybe you can be a part of this discovery process”.
So, are you ready ? Watch the video and dive into the world of sports and data science!
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We will continue the series with plenty more exciting data use cases like this one.
Stay tuned to find out more!
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Discover the best data science startups to work for in 2020... or read the full article: http://bit.ly/38y6GN6
Before you start sending out your resume to Bain and McKinsey, consider our list of the Best Data Science Startups to Work For in 2020!
Why work for a data science startup?
Sure, big data science consultancies have the stability and the benefits every aspiring data scientist strives for. However, you may find yourself working on predictable and often repetitive tasks with little opportunities for growth. At least for the first few years.
Startups, on the other hand, allow you to develop your skillset by trying new things and handling a variety of challenges. Responsibilities there change quite frequently. So, within less than a year you could be doing something entirely different… And a lot more interesting for you than what you were initially hired for. In other words, the sky is the limit!
We based our research on insight from KDnuggets, LinkedIn, and Forbes to discover the best startups to watch in 2019. Then we picked our top 10 – the fastest growing machine learning and tech startup companies that give you both development opportunities, and a chance to work on inspiring projects.
So... watch our data science startups review to learn what they do, where to apply, and why you should consider working there. And don’t mind the order – these startups are so unique, that every single one of them could easily be number 1 on our list.
Enjoy watching!
***
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This tutorial for beginners will introduce you to Database Terminology.
00:16 Relational Database Essentials
4:47 Database Terminology
13:28 Relational Schemas: Foreign Key
18:09 Relational Schemas: Unique Key and Null Values
21:15 Relationships
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Elon Musk’s Neuralink plans on merging your brain with AI. But how exactly will this happen? Let’s find out!
Neuralink, Elon Musk’s neurotechnology company, made headlines in July of 2019 when they announced their brand-new product – a futuristic technology that would allegedly allow humans to merge with machines and become cyborgs. Everything was fine though, as it would save us from the otherwise imminent overtake of an ultra-smart AI Overlord.
In this video, we’ll try to look beyond the hype and hullabaloo of the presentation and reveal some groundbreaking information about Neuralink’s technology.
Enjoy watching!
***
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Hello everyone, and welcome to our 365 Data Science special dedicated to yet another exciting business career - the marketing analyst profession. In this video, we’ll learn who the marketing analyst is, what they do, how much they make, and what skills and degree you need to become one.
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So, who are the marketing analysts and why are they so important for business success?
Marketing analysts tend to be some of the most hands-on professionals in a company and their role is truly versatile, encompassing a wide range of activities:
• digital marketing
• traditional/offline marketing
• brand marketing
• market research
..and many more
A marketing analyst works closely with product owners and can be assigned to a particular product or, alternatively, to a specific channel. For example, digital marketing analysts are usually responsible for a company's social media accounts, as well as the communication with agencies to discuss ad spend, upcoming campaigns, and amount of promotional budget that can be allocated. A marketing analyst also collaborates frequently with the sales team and provides them with valuable insight when it comes to forecasting and resources that can be spent at a given time.
That’s the compact presentation of this exciting job role. To get a better idea of what it means to be a marketing analyst, do watch the video!
Once again, we just want to remind you that there are plenty of amazing career paths you can explore within the field of data science and data analytics, such as:
• Machine Learning Engineer;
• Data Analyst;
• Research Analyst
We’ll do a video just like this for each of these career opportunities and many more, so be sure to subscribe to our channel and check them out too!
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In this tutorial we are going to discuss another type of probability distributions- the Bernoulli distribution. Bernoulli events are the simplest we can have, since they consist of a single trial and only 2 possible outcomes. Examining them will provide us with the fundamental properties necessary to build more complex scenarios as we dive deeper into the field of probability.
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How to get an entry-level data scientist job is a question we get a lot. So, we discuss how to get an entry-level data scientist job in this video.
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We’ll go over education, experience, skills and finish up with a cohesive plan about the steps you need to take to start your journey as an entry-level Data Scientist. Of course, all the information is based on empirical research, statements by employers in data science, and a dash of our personal experience.
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In previous videos on our channel, we’ve discussed the best degree for an aspiring data scientist. To recap: any form of post-graduate degree in a quantitative field gives you a pretty good chance of success, with Computer Science being the most-represented major.
Apart from an education, you also need some sort of experience credentials to your name. To understand the methodology we used, you can check out the article linked in the description. For reference, the results suggest that roughly 35% of current Data Scientists have already had a job in the same position. This means that the remaining 65% had a different occupation prior to that. Therefore, roughly 2 out of every 3 data scientists are on their first data scientist job in the field.
However, don’t expect to become a data scientist right after school. As little as 2% of all data scientists started off with no previous position on their resume. And even this number in itself sounded suspiciously *high* to us. Either way, to land even an entry-level position, you still need some previous experience elsewhere.
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