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In this lesson, we’ll create our first visualization and it is going to be awesome.
As you can see the workspace area is empty right now. We’ve already loaded the GDP data file and we can see that here.
The way data is organized here is rather interesting. Our attention should be focused on the ‘dimensions and measures’ part of the screen.
First off, we should notice that Tableau has been very smart and managed to organize our data – categorical variables are right here under “dimensions”, while numerical data such as the countries’ actual GDP is under “measures”. “Dimensions” have been colored in blue, and “measures” are in green.
Another important remark we have to make is that some of the fields we see here are in italics and others aren’t. The distinction between the two is that Tableau generates certain fields based on the data it finds. When Tableau generates its own fields such as the “Measure names” field we see here, these are fields that are not contained in our original data source, but Tableau deems that these can be useful and creates them for us. The same thing is true for “Latitude”, “Longitude”, “Number of records”, and “Measure values” we see in green under “Measures”. The rest of the fields written without Italics are the ones we saw in the Excel file we loaded – “Country name”, “Indicator name”, and the years from 2002 to 2016, where we have countries’ GDP figures.
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Student's T Distribution – we would like to tell you a story!
William Gosset was an English statistician who worked for the brewery of Guinness. He developed different methods for the selection of the best yielding varieties of barley – an important ingredient when making beer. Gosset found big samples tedious, so he was trying to develop a way to extract small samples but still come up with meaningful predictions.
He was a curious and productive researcher and published a number of papers that are still relevant today. However, due to the Guinness company policy, he was not allowed to sign the papers with his own name. Therefore, all of his work was under the pen name: Student.
Later on, a friend of his and a famous statistician, Ronald Fisher, stepping on the findings of Gosset, introduced the t-statistic, and the name that stuck with the corresponding distribution even today is Student’s t.
The Student’s t distribution is one of the biggest breakthroughs in statistics, as it allowed inference through small samples with an unknown population variance. This setting can be applied to a big part of the statistical problems we face today and is an important part of this course.
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What Is a Data Warehouse? Data warehousing is one of the hottest topics both in business and in data science. But if you’re new to the field, you’re probably wondering what a data warehouse is, why we need it, and how it works. Don’t worry because in 4 minutes you’ll know the answers to all these questions.
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First, let’s start with a definition: what is the meaning of the phrase: ‘Single source of truth’. In information systems theory, the ‘single source of truth’ is the practice of structuring all the best quality data in one place.
But what if you knew that there is one single place where you would always have the single source of information? That would be quite helpful wouldn’t it? Well, a data warehouse exists to fill that need.
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So, what is a data warehouse exactly? It is the place where companies store their valuable data assets, including customer data, sales data, employee data, and so on. In short, a data warehouse is the de facto ‘single source of data truth’ for an organization. It is usually created and used primarily for data reporting and analysis purposes.
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This tutorial will introduce the INDIRECT Excel Function, and will explain how it works, and when we can use it. Enjoy watching!
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Do you want to bring your product management and data science skills to the next level and be a part of the fast-developing industry of big data and AI? Just over the past 4 years, the adoption of AI by organisations has grown by 270% and now they are looking for YOUR potential to manage their products and systems.
We have the perfect course that will prepare you to become a leader in the AI and Data Science Product Management field! Join us on this exciting journey with Danielle Thé, a senior product manager with experience in working for world-renowned companies like Google and Deloitte! She will teach you how to understand, develop and implement data science products into the market and lead organizations into the big data and AI future. What are you waiting for? Sign up for the course now: ?? https://bit.ly/3dRfnX5
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This Introduction to Probability Distributions tutorial serves as an overview of what a probability distribution is and what main characteristics it has.
Before we dive deep into the different types of probabilities, we will introduce a few important terms that we will use for the remainder of our YouTube course.
Enjoy!
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How to Become a Data Architect in 2020? | We talk about an alternative way of getting into data science by becoming a data architect! More specifically, we’ll look at who the data architect is, what they do, how they fare in terms of salaries, and what skills and academic background you need to become one. Our free step by step guide will walk you through how to start a career in data science ✅https://bit.ly/3a3HOzj
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Who is the data architect exactly?
If you’ve seen the 1999 cult movie The Matrix, you probably recognize the Architect as the creator of the utopian world for human minds to inhabit. Much like their blockbuster counterpart, data architects create the database from scratch. They design the way data will be retrieved, processed, and consumed.
Data architects are technical experts who adapt dataflow management and data storage strategy to a wide range of businesses and solutions. They’re in charge of continually improving the way data is collected and stored. In addition, data architects control access to data. So, all you corporate spies out there – now you know who to look for.
Data architects are also responsible for design patterns, data modeling, service-oriented integration, and business intelligence domains. They often partner with fellow data scientists and IT guys to reach the company’s data strategy goals.
A data architect constantly seeks out innovations to provide improved data quality and reporting, eliminate redundancies, and provide better data collection sources, methods, and tools...
We'll talk about this and so much more in the video, so enjoy watching!
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You're just starting your career in data science? But how can you get that data science internship you want to apply for? Watch this video to find out!
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So... can YOU become a Data Scientist? Probably -- we did our own research on 1,001 data scientists to find out.
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In this Introduction to Probability video, we’ll talk about the Student’s T Distribution and its characteristics.
For starters, we use the lower-case letter “t” to define a Students’ T distribution, followed by a single parameter in parenthesis, called “degrees of freedom”.
As we mentioned in the last video, it is a small sample size approximation of a Normal Distribution. In instances, where we would assume a Normal distribution were it not for the limited number of observations, we use the Students’ T distribution.
For instance, the average lap times for the entire season of a Formula 1 race follow a Normal Distribution, but the lap times for the first lap of the Monaco Grand Prix would follow a Students’ T distribution.
Now, the curve of the students’ T distribution is also bell-shaped and symmetric. However, it has fatter tails to accommodate the occurrence of values far away from the mean. That is because if such a value features in our limited data, it would be representing a bigger part of the total.
Another key difference between the Students’ T Distribution and the Normal one is that apart from the mean and variance, we must also define the degrees of freedom for the distribution…
Enjoy Watching!
***
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