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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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Population vs sample - The first step of every statistical analysis you will perform is to determine whether the data you are dealing with is a population or a sample.
A population is the collection of all items of interest to our study and is usually denoted with an uppercase N. The numbers we’ve obtained when using a population are called parameters.
A sample is a subset of the population and is denoted with a lowercase n, and the numbers we’ve obtained when working with a sample are called statistics.
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Populations are hard to define and observe. On the other hand, sampling is difficult. But samples have two big advantages. First, after you have experience, it is not that hard to recognize if a sample is representative. And, second, statistical tests are designed to work with incomplete data; thus, making a small mistake while sampling is not always a problem.
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What is segmentation, targeting and positioning? Learn customer analytics, data science, and how the two work together!
Leading companies are always on the lookout for savvy data scientists to join their fast-growing Customers Analytics teams. In that sense, considering a career as a data scientist in customer analytics is a super smart choice. But here’s why exactly:
First, companies need people who know how to use data to understand their customers' needs. Once they understand their needs, they can provide the products customers want to buy.
Second – and that’s a bit more technical – companies need people who have the skills to build the analytics capabilities that will help them provide these innovative customer experiences.
In these videos, we’ll be focusing on the customer part of customers analytics. Why? Because even if you know how to do the technical analyses well, unless you understand the customer, you won’t be able to meaningfully help your company. So let’s build those foundations!
Enjoy watching!
***
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Why cloud computing is critical for data scientists? If small companies want to level the playing field, cloud computing is critical for their data science teams. ✅Get 20% OFF all learning plans! http://bit.ly/2TVaJ0O
To understand the advantages cloud computing provides when it comes to data science, let’s imagine a world with as much data as we have today, but without servers. In such an unfortunate scenario, firms would need databases that run locally, right?
So, every time when you, as a data scientist, want to engage in new analyses or refresh an existing algorithm, you’d have to transfer information to your machine from the central database, and then proceed to operate locally. This unfortunate world would have several main drawbacks...
For example, manual intervention would be necessary to retrieve data... Your machine becomes a single point of failure for the analyses you have worked on locally... Processing speed would be equivalent to the computing power of your computer... Chances are you will be able to work with a limited amount of data due to the limited computing resources at your disposal... Moreover, under this setup, you wouldn’t be able to leverage real-time data to build recommender systems or any type of machine learning algorithms that require ‘live’ data.
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Doesn’t sound like the perfect scenario, does it? Well, that’s why we invented servers. And then these servers had drawbacks of their own.
Fortunately, we now have clouds. They overshadow local servers in almost every conceivable aspect. And, in fact, data scientists should be focused on developing great algorithms, testing hypothesis, taking advantage of all available data without having to wait hours to see the results of the tests they are performing and certainly without having to worry how much memory space they have left on their computer. And yes, sometimes data scientists do end up waiting for long hours for an algorithm to train, but with a cloud, they have the option to pay more and get the job done faster. That’s yet another advantage of cloud computing over servers.
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This tutorial will help you understand Binomial Distribution.
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Did data science change the finance industry? And if it did -- how? Well, of course, data science in finance changed the industry massively! ✅Get 20% OFF the data science training! http://bit.ly/2GgsyAM
From informing banks how to give low risk credits to stock exchange through machine learning algorithms -- data science is reinventing finance! To learn more about all the other ways data science changed finance, though, watch this video!
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Ever since its genesis, Data Science has helped transform many industries.
For decades financial analysts have relied on data to extract valuable insights, but the rise of Data Science and Machine Learning has brought upon a new era in the field. Now, more than ever, automated algorithms and complex analytical tools are being used hand-in-hand to get ahead of the curve.
So, let’s explore the 5 ways in which financial institutions use these methods to their advantage!
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Number 5: Fraud Prevention
Fraud prevention is a part of financial security that deals with fraudulent activities, such as identity theft and credit card schemes. Abnormally high transactions from conservative spenders, or out of region purchases often signal credit card fraud. Whenever such are detected, the cards are usually automatically blocked, and a notification is sent out to the owner.
That way, banks can protect their clients, as well as themselves and even insurance companies, from huge financial losses in a short period of time. The opportunity costs far outweigh the small inconvenience of having to make a phone call or issue another card.
The role data science plays here comes in the form of random forests and other methods that determine whether there are sufficient factors to indicate suspicion.
Number 4: Anomaly Detection
Unlike Fraud Prevention, the goal here is to detect the problem, rather than prevent it. The reason is that we can’t classify an event “anomalous” as it happens but can only do so in the aftermath. The main application of this anomaly detection in finance comes in the form of catching illegal insider trading...
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In this Python Programming Bootcamp, you will be introduced to all concepts you need to learn how to code in Python. We will also give you practical examples so you can best understand each new skill you gain! Click here: https://bit.ly/2XyVLxa to download the exercises (with solutions).
In our previous tutorial (https://www.youtube.com/watch?v=1QDvkkdyGw0&t) we provided an introduction to programming for those of you who have not used Python or another coding language so far. In this video, we will continue expanding our knowledge in Python by covering the most important concepts that will help you start off your programming journey. In other words, this Python Programming Bootcamp is for those who are already familiar with Python.
The concepts we will cover:
0:01 Introduction to the If Statement
2:56 Add an Else Statement
5:28 Else if, for Brief- Elif
10:59 A note on Boolean Values
12:57 Defining a Function in Python
14:56 Creating a Function with a Parameter
21:17 Using a Function in another Function
22:58 Combining Conditional Statements and Functions
26:03 Creating Functions Containing a Few Arguments
27:17 Notable Built-In Functions in Python
31:05 Lists
34:39 Help Yourself with Methods
37:59 List Slicing
42:24 Tuples
45:35 Dictionaries
49:32 For Loops
51:55 While Loops and Incrementing
54:19 Create Lists with the range() Function
56:32 Use Conditional Statements and Loops Together
59:29 All In
1:01:56 Iterating over Dictionaries
Enjoy!
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How to Become a Data Analyst in 2020? That’s right – we’ll talk about becoming a data analyst in 2020! More specifically, we’ll look at who the data analyst 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/2DGvmt2 to find out or watch this video, in which we’ll talk about an alternative way of getting into data science.
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Who is the data analyst exactly?
Data analysts are the real troopers of data science. They’re the ones who are involved in gathering data, structuring databases, creating and running models, and preparing advanced types of analyses to explain the patterns in the data that have already emerged. A data analyst also overlooks the basic part of predictive analytics.
That’s the “elevator pitch of the data analyst”. But to really get an idea of what it means to be part of a team like that, we need to look at what a data analyst does.
As it turns out, quite a lot. A data analyst is both a thinker and a doer who doesn’t hesitate to roll up their sleeves and dig into the numbers. Data analysts extract and analyze data with a “can do” approach and then present data-driven insights to underpin decision making. They also develop and build analytics models and approaches as the basis for a company’s strategy and vision. On top of that, they are often responsible for identifying and extracting key business performance, risk and compliance data, and converting it into easy-to-digest formats. So, as you can see, agility to shift between strategic projects and operational activities a must.
If you think that sounds a bit lonely… Think again! Data analysts are great team players and work closely with various departments and leaders within the organization. That’s super important if they want to be effective in this role. So, the ability to communicate well and influence is critical here…
Enjoy watching!
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What is Marketing Mix? Learn customer analytics, data science, and how the two work together!
Leading companies are always on the lookout for savvy data scientists to join their fast-growing Customers Analytics teams. In that sense, considering a career as a data scientist in customer analytics is a super smart choice. But here’s why exactly:
First, companies need people who know how to use data to understand their customers' needs. Once they understand their needs, they can provide the products customers want to buy.
Second – and that’s a bit more technical – companies need people who have the skills to build the analytics capabilities that will help them provide these innovative customer experiences.
In these videos, we’ll be focusing on the customer part of customers analytics. Why? Because even if you know how to do the technical analyses well, unless you understand the customer, you won’t be able to meaningfully help your company. So let’s build those foundations!
Enjoy watching!
***
1:45 ENROLL THE 365 DATA SCIENCE PROGRAM WITH 20% OFF DISCOUNT
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