Brainstorm is also known as a Mind-map.A Brainstorm is a diagram used to map associated ideas, words, images and concepts together. Brainstorms are also a tool and method for idea generation, finding associations, classifying ideas, organising information, visualising structure and a general aid to studying. Brainstorms are often used at the initial stage of a project and work as a form of note-taking. They can also be useful in collaboration work and team-building morale. The structure of a Brainstorm is as follows: major categories extend out from a central node. Lesser categories branch out of the major ones as subcategories, which can also develop their own related subcategories. Here’s a simple guide to creating a Brainstorm:1. Start in the center of a page and write the title of the project or topic by encapsulating it in a shape (typically a circle or cloud). ![]() The post Brainstorm in Data Visualization appeared first on Data Science PR. Via https://datasciencepr.com/brainstorm-in-data-visualization/?utm_source=rss&utm_medium=rss&utm_campaign=brainstorm-in-data-visualization For the second time, astronomers detected a consistent pattern in a fast radio burst from somewhere in deep space. The first such pattern was discovered in a different FRB back in February. Now scientists say they have details on the habits of FRB 121102, which repeats every 157 days, according to a study published in the journal Monthly Notices of the Royal Astronomical Society. This FRB was discovered in 2012, and researchers noted its repeating nature in 2016. The source is a dwarf galaxy more than 3 billion light years away. The pattern might be the result of a massive star, a neutron star or black hole’s orbital motion. “This is an exciting result as it is only the second system where we believe we see this modulation in burst activity,” the University of Manchester’s Kaustubh Rajwade, who led the research, said in a release. “Detecting a periodicity provides an important constraint on the origin of the bursts and the activity cycles could argue against a precessing neutron star.” FRBs remain mysterious — they were first discovered in 2007 — but each new one offers astronomers a better sense of their origins. We know what you’re thinking… aliens, right? It seems unlikely that would be the case, according to Swinburne University of Technology astrophysicist Adam Deller. “I think in all likelihood we’ll work out a natural explanation for these events, but I like to keep an open mind and follow wherever the evidence leads me,” he said of the first repeating burst. When it comes to the origins of FRB 121102, Rajwade told CNET that one good guess is a neutron star. “Based on the short durations and the high luminosities of the bursts themselves, a good guess would be a neutron star with a very high magnetic field that is orbiting a companion object,” he said. Source: CNET The post Second mysterious, repeating radio burst from space keeps scientists guessing appeared first on Data Science PR. Via https://datasciencepr.com/second-mysterious-repeating-radio-burst-from-space-keeps-scientists-guessing/?utm_source=rss&utm_medium=rss&utm_campaign=second-mysterious-repeating-radio-burst-from-space-keeps-scientists-guessing The ecommerce industry has greatly benefited from the explosion of customer-centric data analytics. Industry leaders have been able to leverage it to great effect, fueling their various marketing, advertising and website content personalization efforts, yielding game-changing sales conversion lift. Contemporary online shopping companies have even been rampantly gathering, tracking, and even buying data regarding individuals’ shopping intent, cross-platform browsing behavior and owned media interactions to compile dynamic, frighteningly detailed customer profiles. These profiles are then used to create targeted marketing campaigns that can effectively hook and reel in customers. One only need to look at how Facebook Ads and Amazon product recommendations seem to accurately coincide with a person’s needs and wants to see how effective and even eerie these methods can be. However, the consumer backlash against these practices has been building momentum. An increasing number of users have now become more discerning of how their personal data is being used. In addition, privacy regulations such as the GDPR and CCPA now have stringent provisions on how companies can gather, store and use consumer data, making it difficult to now use these “intrusive” marketing methods. Third-party cookies are now being phased out, and web browsers that block all data collection are rising in popularity. As a result, merchants are now at a critical juncture where they have to find a balance between maximizing their data investments and still effectively engaging customers while making sure that consumer privacy is respected. WHY DATA PRIVACY BECOME AN ISSUE IN THE FIRST PLACEData privacy regulations are now regarded as a critical safeguard to public good and a pillar of an individual’s rights in the digital age. A great deal of this trend can be attributed to the number of headline-grabbing episodes of data blunders involving businesses, such as:
HOW ECOMMERCE BENEFITS FROM CUSTOMER DATAEcommerce companies use data to drive personalization and targeted advertising and marketing to boost their conversion and sales in a highly competitive market. In fact, 40% of executives report that their personalization efforts have had a direct impact on maximizing sales, basket size, and profits in direct-to-consumer channels such as ecommerce. Data also helps businesses identify certain groups of people that are most likely to buy their products. As such, they will be able to target their marketing to interested customers and won’t waste resources on groups that are less likely to buy from their brand. In addition, companies can also track consumer activities to provide them with better customer experience and recommend products and services based on their context. Around 75% of today’s buyers expect businesses to anticipate their needs and make relevant suggestions. For example, online fashion retailer ASOS has a dynamic interface that changes based on user browsing and search history. If a user has previously searched for men’s clothing, for instance, the user will automatically be redirected to the men’s section the next time they browse to ASOS’s homepage. COMPLIANT DATA USE IS JUST TABLE STAKESThe emergence of data privacy regulations has prompted many businesses to rethink their data efforts as these laws carry the real threat of litigation and very hefty fines. And given the importance of data for a business’ success, they have little choice but to adjust their methods and adopt tools that make their data efforts compliant. But compliance with regulations is just the beginning. Nowadays, it seems that additional regulations are constantly under deliberation at trade organizations and legislative bodies around the world. It’s also possible to remain compliant while still freaking out your customers, so ecommerce data managers would do well to always err on the side of less intrusiveness rather than more. Seeking permission from visitors and providing transparency into data use policies are key in all this. This means that companies must always ask for consent before they collect user data. Websites must now contain a cookie permission notice that explains what data will be collected and for what purposes. It’s important that customers have a choice to accept or decline. Companies even have to keep records of which customers have actually given consent. Likewise, customers must also be given the option to opt in or opt out of promotional materials. They must first affirm that they’re willing to receive marketing emails before organizations can send them one. In addition, companies must only ask for relevant information and be transparent in the data they collect in their sign-up forms. Under privacy laws, consumers can also withdraw their consent, ask for their data records, and request that their histories be deleted. Companies must provide their customers with options to unsubscribe from mailing lists, deny access to data collection, and delete their histories and records on a website. Businesses must also be able to provide a copy of collected consumer data that includes information about which parties the data was shared with and the purpose of the collection – within 30 days after the request was made. Achieving compliance is no easy feat. Fortunately, the emergence of compliance tools and platforms can help companies implement all these necessary changes to their channels. Other compliance applications can even automatically generate data reports to help companies fulfill data requests from users. Privacy regulations also make companies more accountable for the protection of consumer data. The adoption of security solutions such as antivirus apps, firewalls and access controls can safeguard sensitive information from being intercepted. Overall, companies must also review the impression that their user experiences create. Customers must feel safe throughout their journey and that they aren’t creeped out by any of the website or application’s behavior at all. User testing and feedback are critical in building such experiences. FINDING THE RIGHT BALANCEBeing respectful of people’s privacy is indeed a matter of doing the right thing and regulatory compliance, but it’s also understandable that business leaders would aim to optimize interfaces and paid media activity for sales conversions. They are running businesses after all. Ecommerce companies need to perform their due diligence to remain compliant while using careful personalization to drive their sales. Ultimately, the most important thing is that your customers always feel that their privacy is being respected, that you’re looking after their best interests, and no lines are being crossed. Source: DataConomy The post How eCommerce Companies Can Be Less Intrusive When Using Customer Data in 2020? appeared first on Data Science PR. Via https://datasciencepr.com/how-ecommerce-companies-can-be-less-intrusive-when-using-customer-data-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=how-ecommerce-companies-can-be-less-intrusive-when-using-customer-data-in-2020 TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. In this deep learning with TensorFlow tutorial, we will introduce TensorFlow, the second machine learning framework created by Google. The post Introduction to TensorFlow Syntax in 20 Minutes appeared first on Data Science PR. Via https://datasciencepr.com/introduction-to-tensorflow-syntax-in-20-minutes/?utm_source=rss&utm_medium=rss&utm_campaign=introduction-to-tensorflow-syntax-in-20-minutes Despite the potential challenges, it’s clear that there is significant potential for blockchain in the energy industry. Research by Global Market Insights predicts the blockchain energy segment to grow from $200 million in 2018 to $3 billion in 2025.While it’s uncertain exactly what those blockchain solutions will look like, research by Renewable & Sustainable Energy Reviews found that 60% of analyzed projects are currently based on Ethereum. This number perhaps distorts the number of projects that are being built on energy-use specific blockchains or private, permissioned systems. One of the biggest questions is how these projects go from small-scale pilots to reach a critical mass, and we may see VeChain’s recent success with Shanghai Gas as a template. Shanghai Gas has the largest energy customer size, storage and transportation capacity in China. The joint project began in 2018 with a pilot handling quality assurance, including classification, order information, tanker IoT equipment information and transportation information. On March 31, it was announced that the project would be continued and expanded following the success of the pilot, with plans to incorporate a comprehensive Energy-as-a-Service ecosystem that includes logistics management, energy trading, and financial products for both upstream and downstream stakeholders. VeChain also aims to make its blockchain more attractive to enterprise operations than other public blockchains by utilizing a fee delegation feature as well as their VeChain ToolChainTM Blockchain-as-a-Service platform, which has helped over 50 corporate partners integrate blockchain solutions. This longer-term vision seems to encapsulate many of the opportunities presented by blockchain into one ecosystem. What is abundantly clear is how well blockchain and energy mesh together to create a more efficient and transparent system, though only time will tell if the value proposition is strong enough for these systems to gain widespread adoption within the industry. The post What’s next for blockchain in energy? appeared first on Data Science PR. Via https://datasciencepr.com/blockchain-energy-industry/?utm_source=rss&utm_medium=rss&utm_campaign=blockchain-energy-industry 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. 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… The post How to Become a Data Architect in 2020? appeared first on Data Science PR. Via https://datasciencepr.com/data-architect/?utm_source=rss&utm_medium=rss&utm_campaign=data-architect Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data. Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies. Here is an essential list of tips to kick start your data science career:
The post How to Get a Data Science Internship in 2020? appeared first on Data Science PR. Via https://datasciencepr.com/how-to-get-a-data-science-internship-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-get-a-data-science-internship-in-2020 How IBM does data science consulting? In 2020 IBM does data science consulting by following their vetted best-practice framework.We will focus on a fascinating topic – the step-by-step process IBM’s data science team applies when working on a consulting project. We believe this overview can be highly beneficial for both experienced professionals and data science beginners. We will explore a best-practice framework applied by one of the pioneer and leading companies in the field. This way, you’ll get an insider’s look at how a consulting project that involves data analysis and data science unfolds. In addition, we will examine the results achieved in IBM’s data science consulting projects with major clients from different industries. Why is that important? Well, each of these initiatives serves as an invaluable lesson to the rest of the companies in the respective industry. If, for example, Carrefour managed to leverage AI to improve its supply chain processes, the rest of the global hypermarket chains would basically be obliged to follow, if they want to keep up. Let’s get right in and outline the five stages of a data science consulting project.
The post How IBM Does Data Science Consulting in 2020? appeared first on Data Science PR. Via https://datasciencepr.com/how-ibm-does-data-science-consulting-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=how-ibm-does-data-science-consulting-in-2020 The coefficient of variation, variance, and standard deviation are the most widely used measures of variability. We’ll discuss each of these in turn, finishing off with the coefficient of variation.Variance measures the dispersion of a set of data points around their mean value. Population variance, denoted by sigma squared, is equal to the sum of squared differences between the observed values and the population mean, divided by the total number of observations. Sample variance, on the other hand, is denoted by s squared and is equal to the sum of squared differences between observed sample values and the sample mean, divided by the number of sample observations minus 1. While variance is a common measure of data dispersion, in most cases the figure you will obtain is pretty large and hard to compare as the unit of measurement is squared. The easy fix is to calculate its square root and obtain a statistic known as standard deviation. In most analyses you perform, standard deviation will be much more meaningful than variance. Alright. The other measure we still have to introduce is the coefficient of variation. It is equal to the standard deviation, divided by the mean. Another name for the term is relative standard deviation. This is an easy way to remember its formula – it is simply the standard deviation relative to the mean. As you probably guessed, there is a population and sample formula once again. The post Variance, Standard Deviation, Coefficient of Variation in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/variance-standard-deviation-coefficient-of-variation-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=variance-standard-deviation-coefficient-of-variation-in-2020 ‘Data Scientist’ is one of the fastest-growing jobs in recent years. It’s an exciting and highly paid career, that presents you with tons of opportunities for development. So, what are the skills you need to become a data scientist in 2020?We’ve been doing this research for 3 years now, and in this video, we’ll share the top skills that will make you successful in this super-competitive field. In 2020, our study portrays a data scientist’s collective image as a male (71%), who is bilingual, has been in the workforce for 8.5 years (3.5 years of which has worked as a data scientist). He or she works with Python and/or R and has a Master’s degree. You can’t become a data scientist without a strong programming skillset. And in 2020, general-purpose languages are used more extensively by data scientists than ever before. According to our own annual research, 74% of current data scientists are proficient in Python, 56% use R, and 51% – SQL. If you know that you want to become a data scientist, it will be beneficial to study the career path of others who have taken the data scientist career path and learn from their experience. We hope that this video was useful and will guide you in the right direction if you decide to pursue a data scientist career path! The post Skills Needed to Become a Data Scientist in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/skills-needed-to-become-a-data-scientist-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=skills-needed-to-become-a-data-scientist-in-2020 The market for Data Science has been growing extensively over recent years. As a result, the position of data scientist has emerged as a truly attractive career path option with an abundance of rewarding job opportunities. So, to help you stay at the forefront, we have conducted an in-depth study on job offers in the field of data science. We have extracted valuable information of the company offering the position, the required educational credentials, and sought-after work experience, as well as desired skills and techniques involved. That was our compelling look at a sample of 1,170 job offers for the position of data scientist. Hopefully, you have found some of this information useful and advantageous for you in your path to landing your dream data scientist job. The post Data Scientist Job Descriptions in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/data-scientist-job-descriptions-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=data-scientist-job-descriptions-in-2020 How to Become an SQL Developer in 2020? We’d love to explain, so let’s discuss how to become an SQL developer in 2020.We’ll describe an SQL developer’s role in a company. Then, we’ll focus on the technical and soft skills you need to be successful on the job. We’ll also discuss the education and working experience hiring companies are looking for. To top things off, we’ll provide information regarding an the expected salary for SQL developers in different parts of the world. So, what does an SQL developer actually do?In short, we can say that this position requires you to build, maintain, and manipulate database systems. And, very often, you’ll have to use the data stored in the databases you created to develop ad-hoc and recurring reports. To this end, you will need to write and test SQL code, as well as create stored procedures, functions, and views. Let’s discuss some of the technical skills an SQL developer needs on the job. Naturally, you need to be proficient in SQL. I’m sure you didn’t see this one coming. Some of the most popular database management systems that allow you to work with versions of the Structured Query Language are MySQL, SQL Server, and PostgreSQL. What formal qualifications do you need to apply as an SQL developer?This is a position that is a suitable position for junior professionals. However, in most cases, you need some initial experience. Almost all job ads we analysed required 1 or 2 (and sometimes more) years of experience with SQL and relational database tools in a professional environment. The post How to Become an SQL Developer in 2020? appeared first on Data Science PR. Via https://datasciencepr.com/how-to-become-an-sql-developer-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-become-an-sql-developer-in-2020 Data Science or Computer Science degree is best for data science? Here we’ll discuss whether a Data Science or Computer Science degree is the best choice for you. Well, both D-S and C-S are fantastic choices for a concentration, so please don’t feel discouraged if you’ve already chosen one over the other. That being said, we here at 365 have conducted research to determine which one is better for a successful career as a data scientist. We’ll begin by weighing the pros and cons of earning either degree, starting with D-S. Then, we’ll do some evaluation and head-to-head comparison before picking a winner.
Of course, the main reason is that potential employers believe you have a great interest in the job. They don’t have to worry about programming skills, analytical understanding of statistical results, or your data-storytelling abilities. This is crucial because some great statisticians lack the coding pedigree, while some programming wonderkids lack the knowledge to extract insights from a dataset. With a Data Science degree, you’re sure to possess all the necessary qualities, without needing outside validation, like extra certification. However, currently, there is 1 major con when it comes to a Data Science degree – availability. Since the field is relatively new, a Data Science program can sometimes be hard to come by, regardless of whether we’re talking undergraduate or graduate programs. The scarcity has resulted in many students having to pick alternative concentrations and, as a result, losing interest in the field prior to graduating… The post Data Science vs Computer Science Degree for Data Science Career in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/data-science-vs-computer-science-degree-for-data-science-career-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=data-science-vs-computer-science-degree-for-data-science-career-in-2020 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! 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. The post List of The Top 10 Best Data Science Startups to Work for in 2020 appeared first on Data Science PR. Via https://datasciencepr.com/data-science-startups-2020/?utm_source=rss&utm_medium=rss&utm_campaign=data-science-startups-2020 Since we are talking data science, the only logical way to approach the question is to ask the data. And that’s what we’ve done for 3 consecutive years. Since 2018 we have explored 1001 data scientist LinkedIn profiles to uncover the most interesting trends in the data science field. In this video we will go through the most important findings from the last 3 years. In fact, we have created a very cool and interactive PowerBI dashboard which you can use to analyze the data yourself here. According to the data, the average data scientist from 2018 to 2020 is a male with a second-tier degree, coming from a quantitative background, which is not necessarily data science or computer science. Their preferred programming language is Python, but they’d often know R and SQL. Many of the new data scientist positions are being filled by people who are already data scientists, so the field feels much more saturated. Getting into data science still looks like a great opportunity, but the ‘data scientist’ position becomes more and more exclusive. Our sample shows that at least 80% of the people held a minimum of a Master’s degree. This isn’t as surprising, considering data science is a field that expects advanced know-how from the person — usually achieved by graduate or postgraduate types of education, or independent advanced research in other cases. The post What Do You Need to Become a Data Scientist in 2020 vs 2019 vs 2018? appeared first on Data Science PR. Via https://datasciencepr.com/what-do-you-need-to-become-a-data-scientist-in-2020-vs-2019-vs-2018/?utm_source=rss&utm_medium=rss&utm_campaign=what-do-you-need-to-become-a-data-scientist-in-2020-vs-2019-vs-2018 The evolution of technology has totally changed the world we live in, but is it moving too fast? We built a timeline to find out! The post Evolution of Technology And the Inventions that Changed the World appeared first on Data Science PR. Via https://datasciencepr.com/evolution-of-technology-and-the-inventions-that-changed-the-world/?utm_source=rss&utm_medium=rss&utm_campaign=evolution-of-technology-and-the-inventions-that-changed-the-world 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. Via https://datasciencepr.com/data-science-degrees/?utm_source=rss&utm_medium=rss&utm_campaign=data-science-degrees Who’s the data engineer and what do they do?Data engineers are the Jedi Knights of data science. They rely on a blend of analysis, wisdom, experience, and judgment to make key decisions for a company’s success. A data engineer is a self-starter who is inspired to complete more than your usual number of tasks. Data engineers are the ones to take things further up the data science pipeline. They use the data architects’ work as a steppingstone and then pre-processes the available data. These are the people who ensure the data is clean and organized and ready for the analysts to take over. Data engineers also implement complex, large scale big data projects with a focus on collecting, managing, analyzing and visualizing large data sets. They are also the ones who turn raw data into insights using various tool sets, techniques, and cloud-based platforms. That said, for a data engineer, knowledge of data modeling for both data warehousing and Big Data is a must, along with experience in the Big Data space (Hadoop Stack like M/R, HDFS, Pig, Hive, etc.). Of course, the ability to write, analyze, and debug SQL queries will helps the aspiring data engineer score high on any employer’s recruitment list. In terms of soft skills, they are great team-players. A data engineer knows how to actively collaborate with data scientists and executives to build solutions and platforms that meet, or even exceed a company’s business needs… The post How to Become a Data Engineer in 2020? appeared first on Data Science PR. Via https://datasciencepr.com/become-a-data-engineer/?utm_source=rss&utm_medium=rss&utm_campaign=become-a-data-engineer 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. Via https://datasciencepr.com/become-data-scientist-india/?utm_source=rss&utm_medium=rss&utm_campaign=become-data-scientist-india 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. Via https://datasciencepr.com/data-science-rising-career/?utm_source=rss&utm_medium=rss&utm_campaign=data-science-rising-career 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. Via https://datasciencepr.com/how-to-get-an-entry-level-data-scientist-job-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-get-an-entry-level-data-scientist-job-in-2020 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. Via https://datasciencepr.com/pandas-1-0-0/?utm_source=rss&utm_medium=rss&utm_campaign=pandas-1-0-0 Shares of cloud services company Cloudera jumped as much as 22% Tuesday on a Bloomberg report that the company received takeover interest from private equity firms. The stock was briefly halted after the news was reported. Shares closed up 18.58%. A Cloudera spokesperson declined to comment on the report. The company last year merged with rival Hortonworks in a stock swap that valued the combined companies at $5.2 billion. Today, even as it has achieved cost synergies stemming from the merger, Cloudera’s market capitalization is below $4 billion. Hewlett Packard Enterprise recently acquired the assets of another company in the category, MapR, though Cloudera CEO Rob Bearden said it was able to acquire worried MapR customers. “We’ve had a number of the MapR customers reaching out to us, and as they learn more about CDP [the Cloudera Data Platform], they’re very aggressively embracing us,” said Bearden, who was named chief executive in January. Cloudera shares fell about 13% on Thursday after the company issued full-year revenue guidance of $825 million to $845 million, which fell short of the $858.4 million Refinitiv consensus. Source: CNBC The post Shares of Cloudera jump on report private equity firms want to acquire it appeared first on Data Science PR. Via https://datasciencepr.com/shares-of-cloudera-jump-on-report-private-equity-firms-want-to-acquire-it/?utm_source=rss&utm_medium=rss&utm_campaign=shares-of-cloudera-jump-on-report-private-equity-firms-want-to-acquire-it 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. Via https://datasciencepr.com/what-is-tidyverse-in-data-science/?utm_source=rss&utm_medium=rss&utm_campaign=what-is-tidyverse-in-data-science 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. Via https://datasciencepr.com/how-to-deliver-a-data-science-project-successfully-in-2020/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-deliver-a-data-science-project-successfully-in-2020 |
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