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Trending Specialization Courses in Data Science

Data scientists, today are earning more than the average IT employees. A study estimates a need for 190,000 data scientists in the US alone by 2021. In India, this number is estimated to grow eightfold, reaching $16 billion by 2025 in the Big Data analytics sector. With such a growing demand for data scientists, the industry is developing a niche market of specialists within its fields.  Companies of all sizes, right from large corporations to start-ups are realizing the potential of data science and increasingly hiring data scientists. This means that most data scientists are coupled with a team, which is staffed with individuals with similar skills. While you cannot remain a domain expert in everything related to data, one can be the best at the specific skill or specialization that they were hired for. Not only this specialization within data science will also entail you with more skills in paper and practice, compared to other prospects during your next interview. Trending Specialization Courses in Data ScienceOne of the biggest myths about data science is that one needs a degree or Ph.D. in Data Science to get a good job. This is not always necessary. In reality employers value job experience more than education. Even if one is from a non-technical background, they can pursue a career in data science with basic knowledge about its tools such as SAS/R, Python coding, SQL database, Hadoop, and a passion towards data.  Let’s explore some of the trending specializations that companies are currently looking out for while hiring data scientists: Data Science with RA powerful language commonly used for data analysis and statistical computing; R is one of the best picks for beginners as it does not require any prior coding experience. It consists of packages like SparkR, ggplot2, dplyr, tidyr, readr, etc., which have made data manipulation, visualization, and computation faster. Additionally, it also has provisions to implement machine learning algorithms. Data Science with Python Python, originally a general-purpose language, is open-source code and a common language for data science. This language has a dedicated library for data analysis and predictive modelling, making it a highly demanded data science tool. On a personal level, learning data science with python can also help you produce web-based analytics products.  Big Data analytics Big data is the most trending of the listed specializations and requires a certain level of experience. It examines large amounts of data and extracts hidden patterns, correlations, and several other insights. Companies world-over are using it to get instant inputs and business results. According to IDC, Big Data and Business Analytics Solutions will reach a whopping $189.1 billion this year. Additionally, big data is a huge umbrella term that uses several types of technologies to get the most value out of the data collected. Some of them include machine learning, natural language processing, predictive analysis, text mining, SAS®, Hadoop, and many more.  Other specializationsSome knowledge of other fields is also required for data scientists to showcase their expertise in the field. Being in the know-how of tools and technologies related to machine learning, artificial intelligence, the Internet of Things (IoT), blockchain and several other unexplored fields is vital for data enthusiasts to emerge as leaders in their niche fields.  Building a career in Data ScienceWhether you are a data aspirant from a non-technical background, a fresher, or an experienced data scientist – staying industry-relevant is important to get ahead. The industry is growing at a massive rate and is expected to have 2.7 million open job roles by the end of 2020. Industry experts point out that one of the biggest causes for tech companies to lay off employees is not automation, but the growing gap between evolving technologies and the lack of niche manpower to work on it. To meet these high standards keeping up with your data game is crucial.
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Trending Specialization Courses in Data Science

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Trending Specialization Courses in Data Science

Data scientists, today are earning more than the average IT employees. A study estimates a need for 190,000 data scientists in the US alone by 2021. In India, this number is estimated to grow eightfold, reaching $16 billion by 2025 in the Big Data analytics sector. With such a growing demand for data scientists, the industry is developing a niche market of specialists within its fields.  

Companies of all sizes, right from large corporations to start-ups are realizing the potential of data science and increasingly hiring data scientists. This means that most data scientists are coupled with a team, which is staffed with individuals with similar skills. While you cannot remain a domain expert in everything related to data, one can be the best at the specific skill or specialization that they were hired for. Not only this specialization within data science will also entail you with more skills in paper and practice, compared to other prospects during your next interview. 

Trending Specialization Courses in Data Science

One of the biggest myths about data science is that one needs a degree or Ph.D. in Data Science to get a good job. This is not always necessary. In reality employers value job experience more than education. Even if one is from a non-technical background, they can pursue a career in data science with basic knowledge about its tools such as SAS/R, Python coding, SQL database, Hadoop, and a passion towards data.  

Let’s explore some of the trending specializations that companies are currently looking out for while hiring data scientists: 

Data Science with R

A powerful language commonly used for data analysis and statistical computing; R is one of the best picks for beginners as it does not require any prior coding experience. It consists of packages like SparkR, ggplot2, dplyr, tidyr, readr, etc., which have made data manipulation, visualization, and computation faster. Additionally, it also has provisions to implement machine learning algorithms. 

Data Science with Python 

Python, originally a general-purpose language, is open-source code and a common language for data science. This language has a dedicated library for data analysis and predictive modelling, making it a highly demanded data science tool. On a personal level, learning data science with python can also help you produce web-based analytics products.  

Big Data analytics 

Big data is the most trending of the listed specializations and requires a certain level of experience. It examines large amounts of data and extracts hidden patterns, correlations, and several other insights. Companies world-over are using it to get instant inputs and business results. According to IDC, Big Data and Business Analytics Solutions will reach a whopping $189.1 billion this year. 

Additionally, big data is a huge umbrella term that uses several types of technologies to get the most value out of the data collected. Some of them include machine learning, natural language processing, predictive analysis, text mining, SAS®, Hadoop, and many more.  

Other specializations

Some knowledge of other fields is also required for data scientists to showcase their expertise in the field. Being in the know-how of tools and technologies related to machine learning, artificial intelligence, the Internet of Things (IoT), blockchain and several other unexplored fields is vital for data enthusiasts to emerge as leaders in their niche fields.  

Building a career in Data Science

Whether you are a data aspirant from a non-technical background, a fresher, or an experienced data scientist – staying industry-relevant is important to get ahead. The industry is growing at a massive rate and is expected to have 2.7 million open job roles by the end of 2020. Industry experts point out that one of the biggest causes for tech companies to lay off employees is not automation, but the growing gap between evolving technologies and the lack of niche manpower to work on it. To meet these high standards keeping up with your data game is crucial.

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KnowledgeHut

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KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.
Website : https://www.knowledgehut.com

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10 Mandatory Skills to Become an AI & ML Engineer

The world has been evolving rapidly with technological advancements. Out of many of these, we have AI (Artificial Intelligence) and ML (Machine learning). The era of machines and robots are taking center stage and soon there will be a time when AI and ML will be an integral part of our lives. From automated cars to android systems in many phones, apps, and other electronic devices, AI and ML have a wide range of impact on how easy machines and AI can make our lives. The future of these technologies is quite promising; it is beyond our wildest imagination. So, there is already and will be a lot of demand for AI and ML professionals, known as AI and ML engineers. Before understanding the essential skills required to become an AI and ML engineer, we should understand what kind of job roles these two are. AI Engineer vs ML Engineer: Are they the same?Although they look the same, there are some subtle differences between AI and ML engineers. It boils down to the way they work and the software and languages they work on, to reach one common goal: Artificial Intelligence. Simply put, an AI engineer applies AI algorithms to solve real-life problems and building software. On similar terms, an ML engineer utilizes machine learning techniques in solving real-life problems and to build software. They enable computers to self-learn by giving them the thinking capability of humans. Like mentioned earlier, these two job roles get the same output using different methods. However, many top companies are hiring professionals skilled in working both on AI and ML. The capability of an astounding AI and ML engineer is reflected by both the technical and non-technical skills. Let us see what it takes to be one of these two professionals. Common skills for Artificial and Machine Learning Technical Skills 1. Programming Languages A good understanding of programming languages, preferably python, R, Java, Python, C++ is necessary. They are easy to learn, and their applications provide more scope than any other language. Python is the undisputed lingua franca of Machine Learning. 2. Linear Algebra, Calculus, Statistics It is recommended to have a good understanding of the concepts of Matrices, Vectors, and Matrix Multiplication. Moreover, knowledge in Derivatives and Integrals and their applications is essential to even understand simple concepts like gradient descent. Whereas statistical concepts like Mean, Standard Deviations, and Gaussian Distributions along with probability theory for algorithms like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models are necessary to thrive in the world of Artificial Intelligence and Machine Learning. 3. Signal Processing TechniquesA Machine Learning engineer should be competent in understanding Signal Processing and able to solve several problems using Signal Processing techniques because feature extraction is one of the most critical aspects of Machine Learning. Then we have Time-frequency Analysis and Advanced Signal Processing Algorithms like Wavelets, Shearlets, Curvelets, and Bandlets. A profound theoretical and practical knowledge of these will help you to solve complex situations. 4. Applied Math and AlgorithmsA solid foundation and expertise in algorithm theory is surely a must. This skill set will enable understanding subjects like Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential equation, and Summations. As tough as it may seem, Machine Learning and Artificial Intelligence are much more dependable on mathematics than how things are in, e.g. front-end development. 5. Neural Network ArchitecturesMachine Learning is used for complex tasks that are beyond human capability to code. Neural networks have been understood and proven to be by far the most precise way of countering many problems like Translation, Speech Recognition, and Image Classification, playing a pivotal role in the AI department. Non-Technical and Business skills 1. Communication Communication is the key in any line of work, AI/ML engineering is no exception. Explaining AI and ML concepts to even to a layman is only possible by communicating fluently and clearly. An AI and ML engineer does not work alone. Projects will involve working alongside a team of engineers and non-technical teams like the Marketing or Sales departments. So a good form of communication will help to translate the technical findings to the non-technical teams. Communication does not only mean speaking efficiently and clearly.2. Industry KnowledgeMachine learning projects that focus on major troubling issues are the ones that finish without any flaws. Irrespective of the industry an AI and ML engineer works for, profound knowledge of how the industry works and what benefits the business is the key ingredient to having a successful AI and ML career. Channeling all the technical skills productively is only possible when an AI and ML engineer possesses sound business expertise of the crucial aspects required to make a successful business model. Proper industry knowledge also facilitates in interpreting potential challenges and enabling the continual running of the business. 3. Rapid PrototypingIt is quite critical to keep working on the perfect idea with the minimum time consumed. Especially in Machine Learning, choosing the right model along with working on projects like A/B testing holds the key to a project’s success. Rapid Prototyping helps in forming an array of techniques to fasten building a scale model of a physical part. This is also true while assembling with three-dimensional computer-aided design, more so while working with 3D models Additional skills for Machine Learning 1. Language, Audio and Video ProcessingWith Natural Language Processing, AI and ML engineers get the chance to work with two of the foremost areas of work: Linguistics and Computer Science like text, audio, or video. An AI and ML engineer should be well versed with libraries like Gensim, NLTK, and techniques like word2vec, Sentimental Analysis, and Summarization 2. Physics, Reinforcement Learning, and Computer VisionPhysics: There will be real-world scenarios that require the application of machine learning techniques to systems, and that is when the knowledge of Physics comes into play. Reinforcement Learning: The year, 2017 witnessed Reinforcement Learning as the primary reason behind improving deep learning and artificial intelligence to a great extent. This will act as a helping hand to pave the way into the field of robotics, self-driving cars, or other lines of work in AI. Computer Vision: Computer Vision (CV) and Machine Learning are the two major computer science branches that can separately work and control very complex systems, systems that rely exclusively on CV and ML algorithms but can bring more output when the two work in tandem. 
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10 Mandatory Skills to Become a Data Scientist

The data science industry is growing at an alarming pace, generating a revenue of $3.03 billion in India alone. Even a 10% increase in data accessibility is said to result in over $65 million additional net income for the typical Fortune 1000 companies worldwide. The data scientist has been ranked the best job in the US for the 4th year in a row, with an average salary of $108,000; and the demand for more data scientists only seems to be growing. Who is a Data scientist?A data scientist is precisely someone who collects all the massive data that is available online, organizes the unstructured formats into bite-sized readable content, and analyses this to extract vital information about customer trends, thinking patterns, and behavior. 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