Test Post from 365 Data Science

Test Post from 365 Data Science
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Data Science Career Tips: Can You Become a Data Scientist?

So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/38Kt3PN Data science is a super-hot topic and the data scientist is one of the most illustrious jobs of the 21st century. But how does one actually become a data scientist? You can ask around, read Quora answers, or talk to someone in the industry, sure, these methods will supply you with information, but there’s no doubt that this information will be biased towards someone else’s personal experience. How others became data scientists is of little importance to you, I bet. What you’re interested in is whether YOU can become one. Are your skills appropriate for this field? What steps do you need to take to become a successful data scientist? Will your background affect the chances of becoming a data scientist? All valid questions. *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/2TZDriF If you want to become a data scientist, you should answer questions like one. A data scientist wouldn’t take the experience and background of just one or two other data scientists and accept them as a quintessential guide. So, how much data would be statistically adequate to give us an idea of what it takes to become a data scientist? 100 profiles? 500? How about 1,001? Well, that’s exactly what we did. You can find the whole study here: https://bit.ly/2NLGO7G Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #science #datascientist

What Do You Need to Become a Data Scientist in 2020?

So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/2RUfXca Data science is a super-hot topic and it is the most promising job of 2020. But what do you need to become a data scientist in 2020? Along with this question, you probably want to find out whether your skills are appropriate for this field, what steps you need to take to become a successful data scientist, and if your background will affect the chances of becoming a data scientist. All valid questions! So, to help you answer your questions, we examined 1,001 linked LinkedIn resumes of people who are currently working as data scientists to see where exactly they come from and what skills they’re using in their day-to-day activities. The main messages we extracted from our study both last and this year, is that if you have the skill base that makes a data scientist, you can be a data scientist. It will be interesting to see how the data science profession changes in the next 2-5 years, but right now, a universal data scientist profile appears to be taking shape: a unique programming language toolbox desired across industries and locations; preferably a Master’s degree, or a Bachelor’s and proof of practical abilities; and a confident learning-on-the-go attitude are the currencies of the field. *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/2U0Rwwg YOU CAN FIND THE WHOLE STUDY HERE: http://bit.ly/2SYd38R YOU CAN ALSO COMPARE THE FINDINGS WITH LAST YEAR’S STUDY: https://bit.ly/2NLGO7G Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #science #datascientist

Data Science Career: How to Become a Data Analyst in 2020

How to Become a Data Analyst in 2020? | Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/3aR5ZRe to find out or watch this video, in which we’ll talk about an alternative way of getting into data science. 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. 5:25 LINK TO ‘’STARTING A CAREER IN DATA SCIENCE: THE ULTIMATE GUIDE’’ http://bit.ly/2L0blNX 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! *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/2RU68Lg Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #analyst #career

Data Science: How to Become a Business Intelligence Analyst in 2020

How to Become a Business Intelligence Analyst in 2020? | Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/36yICsg to find out or watch this video, in which we’ll talk about becoming the BFF of business performance- the Business Intelligence Analyst! More specifically, we’ll look at who the BI analyst is, what they do, and how much zeroes are tacked on the end of their salary. Finally, we’ll discuss what skills and academic background will help you become one. 2:05 ENROLL THE 365 DATA SCIENCE PROGRAM WITH 20% OFF DISCOUNT https://bit.ly/2O5WYdm 8:20 LINK TO ‘’STARTING A CAREER IN DATA SCIENCE: THE ULTIMATE GUIDE’’ https://bit.ly/36yICsg So, who is the BI analyst and what makes them so special? BI analysts are fierce business performance ninjas who possess a blend of business vision, consultant abilities and profound understanding of data. They join forces with senior management to shape and develop a data strategy. Analysis of Key Performance Indicators (KPIs), accurate overview of business performance and identifying areas that need improvement are also specialties in the BI analyst’s domain. And what exactly do BI analysts do? Well, they focus primarily on analyses and reporting of past historical data. Once the relevant data is in the hands of the BI Analyst (that’s… monthly revenue, customer, sales volume, etc.), they must quantify the observations, calculate KPIs, and examine the measures to extract insights. Of course, the most important aspect of a BI analyst’s job is to continually improve their company’s competitive positioning. Therefore, they examine their competitors, data trends, seasonality, and other random effects to quickly identify issues and best practices. On top of that, they create killer graphs and dashboards to review major decisions and measure effectiveness. So, in a word, if you want to have an impact on the business world, become a BI analyst. Well, maybe that’s easier said than done. But let’s see how much a BI analyst makes per year, maybe that’ll have some inspirational effect on you… Enjoy watching! Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #business #intelligence #analyst

Data Science & Statistics: Hypothesis testing. Null vs alternative

In this tutorial we'll introduce hypothesis testing. There are four steps in data-driven decision-making. First, you must formulate a hypothesis. Second, once you have formulated a hypothesis, you will have to find the right test for your hypothesis. Third, you execute the test. And fourth, you make a decision based on the result. Let’s start from the beginning. What is a hypothesis? Though there are many ways to define it, the most intuitive I’ve seen is: “A hypothesis is an idea that can be tested.” This is not the formal definition, but it explains the point very well. So, if I tell you that apples in New York are expensive, this is an idea, or a statement, but is not testable, until I have something to compare it with. For instance, if I define expensive as: any price higher than $1.75 dollars per pound, then it immediately becomes a hypothesis. Alright, what’s something that cannot be a hypothesis? An example may be: would the USA do better or worse under a Clinton administration, compared to a Trump administration? Statistically speaking, this is an idea, but there is no data to test it, therefore it cannot be a hypothesis of a statistical test. Actually, it is more likely to be a topic of another discipline. Conversely, in statistics, we may compare different US presidencies that have already been completed, such as the Obama administration and the Bush administration, as we have data on both. Generally, the researcher is trying to reject the null hypothesis. Think about the null hypothesis as the status quo and the alternative as the change or innovation that challenges that status quo. In our example, Paul was representing the status quo, which we were challenging. *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/30YeGom So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/2TZF0gx Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #hypotesis #testing #tutorial

Data Science & Statistics: Type I error vs Type II error

In general, we can have two types of errors - type I error and type II error. Sounds a bit boring, but this will be a fun lecture, I promise! First we will define the problems, and then we will see some interesting examples. Type I error is when you reject a true null hypothesis and is the more serious error. It is also called ‘a false positive’. The probability of making this error is alpha – the level of significance. Since you, the researcher, choose the alpha, the responsibility for making this error lies solely on you. Type II error is when you accept a false null hypothesis. The probability of making this error is denoted by beta. Beta depends mainly on sample size and population variance. So, if your topic is difficult to test due to hard sampling or has high variability, it is more likely to make this type of error. As you can imagine, if the data set is hard to test, it is not your fault, so Type II error is considered a smaller problem. Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2HnysSC Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you!

Data Science & Statistics: Population vs sample

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. *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/2RvBCbv 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. So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/2TZF0gx Follow us on YouTube: ✅https://www.youtube.com/c/365DataScie... Connect with us on our social media platforms: ✅Website: https://bit.ly/2TrLiXb ✅Facebook: https://www.facebook.com/365datascience ✅Instagram: https://www.instagram.com/365datascience ✅Q&A Hub: https://365datascience.com/qa-hub/ ✅LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: ✅https://bit.ly/2HnysSC Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #science #datascience #statistics #population #sample

Database vs Spreadsheet - Advantages and Disadvantages

In this video, we will focus on the advantages and disadvantages of spreadsheets vs databases. What is a spreadsheet? It is an electronic ledger, an electronic version of paper accounting worksheets. It was created to facilitate people who needed to store their accounting information in tabular form digitally. So, it is possible to create tables in a spreadsheet. This is one reason some people believe spreadsheets and databases are interchangeable, while, in reality, they aren’t. There are similarities between the two. Both can contain a large amount of tabular data and can use existing data to make calculations. Third, neither spreadsheets nor databases are typically used by a single person, so many users will work with the data. The differences between the two forms of data storage lie in the way these three characteristics are implemented. Ok. Imagine a spreadsheet. Every cell is treated as a unique entity. It can store any type of information – a date, an integer value, a string name. And then, not only can we have different types of values in various cells, but we can also apply a specific format to these cells. This is not inherent to databases. They contain only raw data. Each cell is a container of a single data value. It is the smallest piece of information there is. You must pre-set the type of data contained in a certain field. This feature prevents inadvertent mistakes – for example, in a field containing date values, should the user try to insert a string, the software will show an error and she will have the chance to correct herself. This won’t happen in Excel – if you insert a string in the column with date values, you wouldn’t obtain an error message, and Excel will store the string value. Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #database #spreadsheet #excel

Is Data Science Really a Rising Career in 2020 ($100,000+ Salary)

Is data science really is a rising career in 2020? And if it is – why and for how long? The answer to the first question is simple: yes, data science is without a doubt a rising career, even in 2020. The reason is simple: data science operates under the same supply and demand economic principles as the rest of the business world. To learn more about how that secures the data scientist career and high salary ($100,000+) in 2020, watch this video! ► 7:32 LINK TO COURSES AT *** 20% OFF *** http://bit.ly/2NHFeFh

So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/2RWqqUs According to Glassdoor, 2016 was the first year in which “data scientist” was the ‘Best job’ on the market. And after that? Well, it was in the lead in 2017, 2018, and 2019 as well! With a mean base salary of more than $100,000, being a data scientist seems like the dream job of this century. But why is that? Of course, like any other business-related phenomenon, it follows the basic laws of economics – supply and demand. The demand for data science professionals is very high, while the supply is too low. Think about computer science years ago. The internet was becoming a “thing” and people were making serious cash off it. Everybody wanted to become a programmer, a web-designer or anything, really, that would allow them to be in the computer science industry. Salaries were terrific and it was exceptional to be there. As time passed by, the salaries plateaued as the supply of CS guys and girls started to catch up with the demand. That said, the industry is still above average in terms of pay. The same thing is happening to the data science industry right now. Demand is really high, while supply is still low. And, as stated in an extensive joint research performed by IBM, Burning Glass Technologies, and Business-Higher Education Forum, this tendency will continue to be strong for the years to come. This, by itself, determines that salaries will be outstanding. Consequently, people are very much willing to get into data science... Enjoy the video! ► Connect with us on our social media platforms: Website: https://365datascience.com/ Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... ► Prepare yourself for a career in data science with our comprehensive program: http://bit.ly/2NHFeFh ► Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #science #career #salary