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What are the best data science degrees to get hired in 2020? We did a lot of research and we can now share with you the best data science degrees in 2020!To get into Data Science, you need a degree that signals potential employers you are the qualified candidate they’re looking for. We have conducted several studies on this topic to determine what are the best degrees for an aspiring Data Scientist. So, in this article, we’ll go over the level, discipline and university rank you should be looking at when deciding what degree is worth pursuing or if your current degree is suitable for the field. Our results show that virtually all data scientists have graduated from an institution of higher education. This includes Bachelors, Masters, MBAs, and Ph.Ds. However, some degrees seem to be much more popular than others. In fact, only around 2% of all data scientists in our sample owned an MBA, but that’s not entirely surprising. If you decide to do an MBA, chances are you’re not aiming at the hands-on technical data scientist role on the team. Bachelors, Masters, and Ph.Ds round up roughly 95% of the data, with 75% being split among Masters and PhDs. This means that roughly 3 out of every 4 data scientists have at least a master’s degree. So, yes, going for a graduate program is highly recommended. Of course, if you think a B.A. is as high as you want to go, there is no need to be discouraged. Nearly 20% of the data scientists in our sample had only completed an undergraduate prior to entering the field. And while this number is not high, the percentage of data scientists holding only a Bachelor’s degree has been steadily growing over the last three years. This is a refreshing indicator that shows employers are starting to value skills over years of schooling. In other words, a qualified candidate today has a higher chance of breaking into the field, compared to two years ago. The post What Are The Best Data Science Degrees to Get Hired in 2020? appeared first on Data Science PR.
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How to Become a Data Scientist in India in 2020? Let’s find out! In this article we’ll look at the latest research to help you assess your chances of landing a data scientist job in India.We’ll focus on the education and qualifications you need to become a data scientist eligible for the job at any company. To learn all that and some extra tips that will help you get a data science job in India, watch this video! A recent report by The Hindu states that there are an estimated 97,000 data analytics job openings in India. Bengaluru accounts for 24% of these job openings, while Delhi/NCR for 22%. Since the data science field is progressing at an extremely fast rate, the huge demand for data science talent, especially in the Technology/IT and Industrial domain, can hardly catch up with the supply of skilled data scientists. That’s why it doesn’t come as a surprise that a significant part of the data scientists in India (more than 40%) have received a ‘data scientist’ job title within the last 24 months. So, becoming a data scientist in India seems like a truly golden opportunity. Let’s see what that means in terms of salaries! According to upgrad.com, “data scientist” is the highest paying job in India. To be more precise, the average data scientist salary listed on Glassdoor is above 1,000,000 rupees per year and it could go up to 2,000,000 rupees for more experienced candidates. Of course, corporations based in large cities like Mumbai and Bangalore offer higher salaries. But keep in mind that this may soon change. Many international companies prefer to open an office in Hyderabad, as it’s much more affordable. No wonder giants like Facebook, Microsoft, Google, Amazon, and P&G already call Hyderabad their home. And that translates into even more career opportunities for data scientists in India… The post How To Become A Data Scientist In India in 2020? appeared first on Data Science PR.
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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! 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… The post Is Data Science a Rising Career in 2020? appeared first on Data Science PR.
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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.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. o 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. The post How to Get an Entry-Level Data Scientist Job in 2020? appeared first on Data Science PR.
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Pandas 1.0.0 is the Python’s primary library for data analysis and manipulation.Although at first sight this latest version is not much different for the user than the previous release starting with a 0: 0.25.3, there are plenty of enhanced features that boost performance and lay a better foundation in the long run. They represent 1.0.0 as a stable version of pandas with a strengthened API, which has also been cleaned of many prior version deprecations. Here are the most notable improvements that come with 1.0.0.
The post Pandas 1.0.0 – key features in the new version in 2020 appeared first on Data Science PR.
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In these series of articles, we will provide you with a brief dictionary of terms surrounding data science including AI, machine learning, and deep learning. Now, what is Tidyverse in Data Science?The tidyverse is a very well thought-out collection of R packages for data manipulation, exploratory data analysis, and visualization that share a common design philosophy. The tidyverse was primarily developed by data science luminary Hadley Wickham, but is now being expanded by several other contributors. The goal for the tidyverse is to make data scientists more productive by providing a path through workflows that facilitate concise communication, and results in reproducible work products. The post What is Tidyverse in Data Science? appeared first on Data Science PR.
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It is demanding to know where to begin once zoućve decided that, yes, you wish to dive into the fascinating world of data and AI. Just having a look at all the technologies you need to understand all the tools you’re supposed to master is enough to make you confused.Well, luckily for you, creating your first data project is actually not difficult as it seems. Becoming data-powered is first and most foremost about having to learn the basic steps and following them to go from raw data to create a machine learning model, and in the end to operationalization. Let’s jump into the following steps that will help you in successfully delivering a data science project. 1. Understanding the businessHaving an understanding of the business or activity that your data project is part of is one of the major keys to ensuring its success. To motivate different participants necessary to get your project from design to creation, your project must be the answer to a clear organizational need or problem. So before you even think about the data, venture out and talk to the people in your organization whose processes you aim to improve with data. Afterward, sit down and define a timeline and concrete key performance indicators. 2. Gather your dataOnce you’ve figured your goal out, it’s time to start looking for your crucial data. Mixing and merging data from as many sources as possible is what defines a great project, so reach out as far as possible. Here are a few ways to gather some data:
3. Explore and clean your dataOnce you’ve gathered your data, it’s time to get to work on it. Start digging to see what you’ve got and how you can merge everything together to answer your original goal. Start writing notes on your first analyses, and ask questions to business and people, or the IT guys, to understand what these variables mean. 4. Enrich your datasetNow that you’ve got somewhat clean data, it’s time to manipulate it in order to get the most value of it. You should begin by joining all your different sources and group logs to specify your data down to essential features. An example of that is to enrich your data by creating a time-based feature like:
5. Get predictiveThis is when the actual fun starts. Machine learning algorithms can help you go a step further into acquiring insights and predicting trends of the future. Also using a data science platform is one of the easiest methods in automating your machine learning pipeline. By working with clustering algorithms, you’re able to create models to uncover trends in the data that were not easily seen in graphs and stats. These create groups of similar events, also known as clusters, and more or less explicitly express which feature is decisive in these results. In conclusionIn order to successfully finish your first data project, you need to be aware that your model will never be fully “finished” – for it to remain useful and accurate, you need to constantly reevaluate, retrain it and create new features. A data scientists’ job is never actually done, but that’s what makes working with data all the more interesting! Source: SmartCollectiveData The post How to Deliver a Data Science Project Successfully in 2020? appeared first on Data Science PR.
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In these series of articles, we will provide you with a brief dictionary of terms surrounding data science including AI, machine learning, and deep learning. What is Natural Language Processing in Data Science in 2020?Natural Language Processing is a branch of Artificial Intelligence that provides a vehicle for computers to understand, interpret, and manipulate natural human language. Natural Language Processing is composed of elements from a number of fields including computer science and computational linguistics in order to bridge the separation between human communication and computer understanding. The post What is Natural Language Processing in Data Science in 2020? appeared first on Data Science PR.
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In these series of articles, we will provide you with a brief dictionary of terms surrounding data science including AI, machine learning, and deep learning. So, what is Explainable AI in Data Science in 2020?Explainable (interpretable) AI models strive to solve the recognized problem that as we generate newer and more innovative applications for neural networks, the question “How do they work?” becomes more and more important. Opening the black box to enable transparency is becoming more important as we realize that we don’t really know why AI models make the choices they do. As models become more complex, the task of producing an interpretable version of the model becomes more difficult. The post What is Explainable AI in Data Science in 2020? appeared first on Data Science PR.
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In these series of articles, we will provide you with a brief dictionary of terms surrounding data science including AI, machine learning, and deep learning. So, what is Long Short Term Memory in Data Science in 2020?Recurrent neural networks, of which long short-term memory units are the most powerful and well known subset, are a type of artificial neural network designed to recognize patterns in sequences of data, such as numerical times series data emanating from sensors, stock markets and government agencies, but also including text, genomes, handwriting and the spoken word. A long short-term memory network is a special kind of recurrent neural network which is optimized for learning from and acting upon time-related data which may have undefined or unknown lengths of time between relevant events. Long short-term memory networks work very well on a wide range of problems and are now widely used. They were introduced in 1997 by Hochreiter & Schmidhuber, and were refined and popularized by many subsequent researchers. The post What is Long Short Term Memory in Data Science in 2020? appeared first on Data Science PR. |
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