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Here's the step by step guide for Data Science Interview Process The Interview process begins directly from the point you begin investigating the various job positions that allure you. Furthermore, it goes up to the stage of in-person (face to face) interviews. Remember that this is a crucial interview procedure. You probably won't need to experience every single step in your interview procedure. Comprehend and follow the Different Roles, Skills and Interviews Update your Resume and Start Applying! Telephonic Screening Clearing the Assignments In-Person Interaction(s) Post-Interview Steps The above-mentioned steps will be helpful all through your Interview preparation! Know about different Roles, Skills and Interviews in Data Science The main thing you need to comprehend is that there are many jobs in the data science environment. An average data science project has a life cycle. A data scientist is just one part of an effective data science project. Let's check out a quick run-through of different data Scientist job roles. Data Scientist Business Analyst Data Analyst Data Visualizer Analyst Data Science Manager Data Architect AI Engineer PC Vision Engineer You need to have great correspondence and critical thinking skills. You need not know Python and technicals skills. A data architect will probably be tested on his/her programming skills. Get prepared as per the company's expectations. Prepare for the interviews- Create your Digital presence Over 80% of employers we addressed revealed that they check an applicant's LinkedIn profile. Recruiters need to crosscheck and assure the claims made in the resume are genuine or not. You ought to have a LinkedIn profile. It ought to be updated and enhanced by the role(s) you're applying for. Make a GitHub account. Writing computer programs is a crucial task in the data science job role. Transferring your code and ventures to GitHub helps the recruiters see your work directly. Regularly

How to Prepare for a Data Scientist Interview? Are you struggling to get into data science and wondering what the interviews will be like? You might even know data science. You might also know about Data Science tools and techniques but still getting rejected in the interviews. It is essential to brush up your skillset to become a Data Scientist in demand. These are some times where many of us are hoping to change or find some job. Interview preparation has become an important step to land a good job. What's more, interviews are a serious thing for everybody. Uncertainty, randomness, and human blunders make an interview damn unnerving. Preparing for an interview is the only way to limit your misfortunes during an interview. Here's the step by step guide for Data Science Interview Process The Interview process begins directly from the point you begin investigating the various job positions that allure you. Furthermore, it goes up to the stage of in-person (face to face) interviews. Remember that this is a crucial interview procedure. You probably won't need to experience every single step in your interview procedure. Comprehend and follow the Different Roles, Skills and Interviews Update your Resume and Start Applying! Telephonic Screening Clearing the Assignments In-Person Interaction(s) Post-Interview Steps The above-mentioned steps will be helpful all through your Interview preparation! Know about different Roles, Skills and Interviews in Data Science The main thing you need to comprehend is that there are many jobs in the data science environment. An average data science project has a life cycle. A data scientist is just one part of an effective data science project. Let's check out a quick run-through of different data Scientist job roles. Data Scientist Business Analyst Data Analyst Data Visualizer Analyst Data Science Manager Data Architect AI Engineer PC Vision Engineer

Home > Data Science > How to learn Data Science in 2021? Data Science (DS) is a great, promising, and most demanding career. But are you confused about being a beginner and wanna know the Right Way to Learn DS? Technology is more into digitisation and due to this extensive transformation, huge data is expected to be produced in the coming future. To make use of such big data we need data scientists who can layout, design and filter the data in an organized way. All things considered, with a huge number of options available choosing the correct curriculum program and admission to the right institute is important. Besides all this, the course and institute to be suitable for your requirement are extremely difficult. While online courses are an incredible path for some to upskill, good opportunities from top colleges and universities are the platform for data scientists to test their knowledge in their field. Here are the five essential guidelines to turn into an expert in Data Science: Get a Data Science certificate or similar degree To get an opportunity as an entry-level data researcher, one would require a four-year certification in DS, math, insights and software engineering. Degrees can likewise give temporary job openings. All things considered if you have just secured a 4 yr certification in a different background through online e-courses or basic boot camps and workshops. The focus should be given to mastering the skills required for DS. Take a look at this course from AntWak on Data Engineering delivered by top global experts. Take comprehensive courses and devote time to learning You can become a top-class data researcher by taking a full course at a time. It will be the skill and instruments needed to turn into a data researcher, many critical projects and coaching help. A variety of such courses are available online that offer top to bottom analysis of subjects, for example, data scratching, AI, big data analysis and many more. To begin your learning

This is a great video site showing the life and careers of famous scientists. Find out about Richard Feynman, Rosalind Franklin, Gregor Mendel and even Mister Spock in interviews with scientists in their field today. http://ictmagic.wikispaces.com/Science

Nairobi 2010 Conference Reoprt  Climate Change and Natural Resource Use in Eastern Africa: Impacts, Adaptation and Mitigation Report of the 3rd Scientific Conference of the Ecological Society for Eastern Africa (ESEA) and co-hosted with the Pan African Climate Justice Alliance (PACJA) at Multimedia University College, Nairobi, 19 - 21 May 2010 By Nicholas Oguge, Caroline Lumosi, Teddy Odindo, Joseph Ngondi and Philista Malaki October 4, 2010 Summary While the Earth's climate has changed throughout history, the current warming trend has been of particular concern because most of it is human-induced and proceeding at a rate that is unprecedented.  One of the greatest concerns of this global warming is climate variability and change.  The risks associated with climate change add to development challenges such as food and water insecurity. Although climate change is only one of the many drivers negatively affecting biodiversity and ecosystem services, it certainly exacerbates the other factors such as land degradation and unsustainable natural resource use. There is general concern on the accelerating deterioration of the human environment and natural resources. This would widen poverty levels confronting eastern African countries and threaten gains made towards achieving the Millennium Development Goals. Thus, the Ecological Society for Eastern African organised its 3rd annual scientific conference with a theme addressing this global challenge.  The idea was to bring together researchers, policy makers and the general public together to discuss the climate change phenomena, its impacts, mitigation strategies and adaptation measures with regard to natural resource use in Eastern Africa. In order to address above issues and to reach out to a wider audience, we designed the conference strategically on three tiers:   1. Plenary talks: to provide policy information and direction, science on climate change, ecological and socio-economic effects, how to commun

Know about different Roles, Skills and Interviews in Data Science The main thing you need to comprehend is that there are many jobs in the data science environment. An average data science project has a life cycle. A data scientist is just one part of an effective data science project. Let's check out a quick run-through of different data Scientist job roles.

Know your Einstein from your Eddington with this fascinating site that profiles some of the greatest scientists. http://ictmagic.wikispaces.com/Science

Willing to get started as a data scientist? Then enrol for ScholarsPro's 3 days Bootcamp in Dubai where you will be equipped with all the know-how to make your mark in the data science industry. Url: https://www.scholarspro.com/data-science-and-machine-learning-bootcamp-training-dubai

Willing to get started as a data scientist? Then enrol for ScholarsPro's 3 days Bootcamp in Dubai where you will be equipped with all the know-how to make your mark in the data science industry. Url: https://www.scholarspro.com/data-science-and-machine-learning-bootcamp-training-dubai

Willing to get started as a data scientist? Then enrol for ScholarsPro's 3 days Bootcamp in Dubai where you will be equipped with all the know-how to make your mark in the data science industry. Url: https://www.scholarspro.com/data-science-and-machine-learning-bootcamp-training-dubai

Are you struggling to get into data science and wondering what the interviews will be like? You might even know data science. You might also know about Data Science tools and techniques but still getting rejected in the interviews. It is essential to brush up your skillset to become a Data Scientist in demand. These are some times where many of us are hoping to change or find some job. Interview preparation has become an important step to land a good job. What's more, interviews are a serious thing for everybody. Uncertainty, randomness, and human blunders make an interview damn unnerving. Preparing for an interview is the only way to limit your misfortunes during an interview.

Most business Entrepreneurs and Data Scientists can disclose how to triumph with (AI) and ML, yet rarely anyone can share to fail with such technologies. While the innovation is solid and publicised   there is a lot of ways to fall flat with AI. Let's talk about nine innovative approaches to censure any AI startup to bankruptcy. #1 Cut R&D expenses AI requires heavy expenditure in cutting-edge research, experimentation, advanced computing, and computing infrastructure. Any AI startup willing to create helpful AI innovations needs to spend a lot of money on innovative work (R&D). To scale down expenses in this area, cutting R&D expenses will rapidly make way to failure. #2 Technology Bubble operation Technology is confined to the social condition in which it is created. Technology never sustains itself but other various important aspects. AI has failed a few times since the commencement of computer science not for technical reasons but as a result of an absence of social need and interest at that point. Experience has taught that AI advancements can't be made in isolation from the social conditions that make them important (like medical care, Health analysis, and money). It is quite crucial to first engineer people to persuade them. Before designing the actual technology, visionaries and business visionaries convince them to suspend their questions and embrace the novelty and utility of disruptive ideas. Working in a bubble and overlooking the current necessities of society is a certain way to failure. #3 Prioritize Technology over business technique Only technology isn't enough to make progress, regardless of how strong it is. In the end, Tech startups also need a great strategy to succeed in being a business entity. Any startup that comes up short on a technique for recognizing objective business sectors, generating sales, and viably allotting and spending resources, yet gives need only to their technical resources, is destined to fail rapidly.

A role-playing game which highlights what we can do to make a better world by being citizen scientists. Take the character on a science adventure and follow the clues to complete the missions. http://ictmagic.wikispaces.com/Science

The 350-year-old mystery of why pendulum clocks hanging from the same wall synchronize over time may finally be solved, scientists say.

Merrilea Mayo, a materials scientist and serious games expert, brings a wide range of public policy, research, fiscal management, and academic experience to the Kauffman Foundation. Prior to joining the Foundation she was the director of the Government-University-Industry-Research Roundtable (GUIRR), National Academies in Washington, D.C. Merrilea led GUIRR in its efforts to define national strategic issues for action by federal science agency heads, industry CEOs and university presidents. During her career she has also served as the founding director of the Alliance for Science and Technology Research in America, and as the Congressional Science and Engineering Fellow for the Office of Senator Joseph Lieberman. Merrilea is a long-time member and past-president of the Materials Research Society and has led workshops and been an advisor for the serious games community. She received her undergraduate degree from Brown University and went on to earn both her Masters and Doctorate in materials science and engineering from Stanford University. She has been an assistant professor and associate professor with Pennsylvania State University. Her work has been published in numerous professional journals, books, and conference proceedings and has earned a U.S. patent and a variety of awards in the materials science field.

This is a wonderful citizen science project where users are asked to classify animals on the Serengeti from camera traps to help real scientists survey and track populations of important and endangered wildlife. http://ictmagic.wikispaces.com/Science

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Multiverse at the University of California, Berkeley Space Sciences Laboratory provides earth and space science educational opportunities and resources for a variety of audiences, especially for those who are underrepresented in the sciences. Our audiences include teachers, students, education and outreach professionals, and the public. We partner with NASA, the National Science Foundation, scientists, teachers, science center and museum educators, park interpreters, and others ...