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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. 
10 Mandatory Skills to Become an AI & ML Engineer
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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. This information is then used to create business goals or agendas that are aligned to the end-user/customer’s needs.  This outlines that a data scientist is someone with sound technical knowledge, interpersonal skills, strong business acumen, and most importantly, a passionate data enthusiast. Listed below are some mandatory skills that an aspiring data scientist must develop. 10 Mandatory Skills to Become a Data Scientist Technical Skills  1. Programming, Packages, and Software Since the first task of data scientists is to gather all the information or raw data and transform this into actionable insights, they need to have advanced knowledge in coding and statistical data processing. Some of the common programming languages used by data scientists are Python, R, SQL, NoSQL, Java, Scala, Hadoop, and many more.  2. Machine Learning and Deep LearningMachine Learning and Deep Learning are subsets of Artificial Intelligence (AI). Data science largely overlaps the growing field of AI, as data scientists use their potentials to clean, prepare, and extract data to run several AI applications. While machine learning enables supervised, unsupervised, and reinforced learning, deep learning helps in making datasets study and learn from existing information. A good example is the facial recognition feature in photos, doodling games like quick draw, and more. 3. Big Data Data Scientists are the best bridge between the vast pool of big data and emerging businesses. Big data analytics uses Hadoop or Spark to gather, distribute, and process various datasets. This is an important business trend that companies are using to predict customer tendencies and create a competitive edge.  4. NLP, Cloud Computing and othersNatural Language Processing (NLP), a branch of AI that uses the language used by human beings, processes it, and learns to respond accordingly. Several apps and voice-assisted devices like Alexa and Siri are already using this remarkable feature. As data scientists use large amounts of data stored on clouds, familiarity with cloud computing software like AWS, Azure, and Google cloud will be beneficial. Learning frameworks like DevOps can help data scientists streamline their work, along with several other such upcoming technologies. 5. Database management and visualizationWhile all the above skills deal with gathering and reading data, database management is related to data manipulation. In database management, the data clusters are edited, indexed, and manipulated to yield desirable outcomes or information. The next step to this transformed raw data is to present it in a visually comprehensible manner, which is nothing but data visualization. It includes graphical representation and other elements to make the data easily understandable even by a layman.  Non-technical Skills 6. Communication skills As explained above, once the raw data is processed, it needs to be presented understandably. This does not limit the job to just visually coherent information but also the ability to communicate the insights of these visual representations. The data scientist should be excellent at communicating the results to the marketing team, sales team, business leaders, and other stakeholders. 7. Team player This is related to the previous point. Along with effective communication skills, data scientists need to be good team players, accommodating feedback, and other inputs from business teams. They should also be able to efficiently communicate their requirements to the data engineers, data analysts, and other members of the team. Coordination with their team members can yield faster results and optimal outputs. 8. Business acumenSince the job of the data scientist ultimately boils down to improving/growing the business, they need to be able to think from a business perspective while outlining their data structures. They should have in-depth knowledge of the industry of their business, the existing business problems of their company, and forecasting potential business problems and their solutions. 9. Critical thinking Apart from finding insights, data scientists need to align these results with the business. They need to be able to frame appropriate questions and steps/solutions to solve business problems. This objective ability to analyse data and addressing the problem from multiple angles is crucial in a data scientist. 10. Intellectual curiosityAccording to Harvard Business Review, data scientists spend 80% of their time discovering and preparing data. For this, they must always be a step ahead and catch up with the latest trends. Constant upskilling and a curiosity to learn new ways to solve existing problems quicker can get data scientists a long way in their careers. Taking data-driven decisionsData science is indisputably one of the leading industries today. Whether you are from a technical field or a non-technical background, there are several ways to build up the skill to become a data scientist. From online courses to boot camps, one should always be a step ahead in this competitive field to build up their data work portfolios. Additionally, reading up on the latest technologies and regular experimentation with new trends is the way forward for aspirants. 
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10 Mandatory Skills to Become a Data Scientist

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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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Fighting Covid-19 Using Data Science, AI, and Machine Learning

The world is suffering from a pandemic, the emergence of the novel Coronavirus has left the world in turbulence. COVID-19, the disease caused by the virus, has reached every corner of the world. As of April 24th, 2020, COVID-19 had taken the lives of 1,90,872, across at least 79 countries, including the United States and the United Kingdom. This makes the coronavirus’ total death toll more than that of its ‘cousin’ SARS (severe acute respiratory syndrome) virus in 2003 (774 total deaths) and ‘bird flu’ in 2013 (616 total deaths). So, how the world is handling such a critical condition? Let’s discuss how the world is fighting COVID-19 using Data Science, AI, and Machine Learning. We will look at the current trend of technology that the world is using to fight coronavirus.  Role of Technologies during corona pandemic:The coronavirus has spread across the world has affected more than 100 countries with more than 191K deaths. This resulted in nations across the world started fighting COVID-19 using AI and other technologies. Now, let us have a look at the use of Artificial Intelligence and various other technologies in tackling the pandemic.  Artificial Intelligence in Global Health Emergency Because of the wide-scale spread of the coronavirus, it has gotten important to screen traffic at open places, for example, air terminals, railroad stations, and other transportation centre points. It needs different observing apparatuses furnished with Computerized reasoning, AI, and warm sensors. These instruments can help check 200 individuals/minute. Also, they can perceive the internal heat level and can flag if it is more noteworthy than 37.3°. They can likewise be utilized to identify and isolate the presumes who may be COVID-19 positive. AI helps in the following ways: Automating Healthcare Processes Predicting the Survival Chances Using AI Drug Research Using AI Virus Research with Artificial Intelligence Let us look or glance at every single one of them in detail. Automating Healthcare ProcessesAs the instances of COVID-19 are expanding quickly, it gets important to play out the analysis of patients at the earliest opportunity. For COVID-19 positive patients, the normal side effect is pneumonia. It is normally distinguished by a CT sweep of the chest of the speculated patients.   Since there are a set number of clinical assets, machines outfitted with man-made consciousness and AI can help specialists to recognize the sickness rapidly and precisely and watch the patients with more consideration. For battling COVID-19 utilizing man-made consciousness viably, nations are robotizing their clinical procedures by utilizing machines furnished with man-made intelligence in all sections and leave focuses.Predicting Survival Chances Using AIFor dealing with such a basic circumstance, where a huge number of individuals are influenced, China has made a simulated intelligence instrument that predicts the endurance pace of patients. This computer-based intelligence instrument likewise helps in choosing the medicine to be given to the patient. Besides, it assists specialists with settling on better clinical choices for the treatment of COVID-19 patients. Additionally, researchers have assembled the AI frameworks to anticipate the infection of the patients. Thusly, alongside man-made reasoning, the world is battling COVID-19 with AI.Drug Research Using AI We are undependable from this novel illness until we create an immunization that can fix it. To locate an appropriate immunization or a viable medication for COVID-19, wellbeing organizations and researchers around the globe are investing their best amounts of energy into an investigation. It is in the testing of antibodies that computer-based intelligence comes into the image.    Through a huge number of tests directed with the assistance of simulated intelligence empowered instruments, scientists can demonstrate the viability of medication, and its results too. If it is prepared by people, at that point, it would take over 10 years and would include billions of dollars, which would be deadly in the present situation.Virus Research using Artificial IntelligenceAs of late, man-made brainpower has contributed a ton to innovative work in the social insurance area. Presently, in such a crisis, the need for man-made reasoning ascents all things get considered. To discover a remedy for the coronavirus, we must initially comprehend the conduct of the infection. For this, computer-based intelligence is helping us process many experiments on the infection in lesser time when contrasted with the time taken by manual preparing. It can recognize the malady and its degree of results. As of now, for battling COVID-19 utilizing Information Science, artificial intelligence, and AI, researchers and wellbeing scientists are working day and night.Big Data and Data Science The primary driver of the spread of the coronavirus is the absence of data about the beginning period indications. This has prompted a circumstance where individuals don't know that they are influenced. They venture out starting with one spot then onto the next with no piece of information that they are conveying the infection with them. Presently, the legislatures have begun gathering the data of residents, for example, their movement history and clinical records. This has brought about the assortment of colossal information of residents. Nations have just begun preparing this information with the assistance of Huge Information devices. The handling of the information of billions of residents includes expelling excess, scaling the information, and organizing it for additional utilization. This is just conceivable with the assistance of different basic devices of Large Information. After the assortment and preparation of such colossal information, the administration specialists examine and envision it. Here, by investigating the information and envisioning the patterns in it, Information Science enables the administrations to make appraises about the extent of further spread of the infection, the accessible clinical framework to concede influenced patients and the financial backing required for the entirety of this. With the assistance of these estimations, Information Science is helping the legislatures choose for clinical offices and money to spend on their residents. This is helping a ton in battling COVID-19 utilizing Information Science. To conclude This is the way the world is dealing with the worldwide health-related crisis and battling coronavirus with Information Science, Computerized reasoning, and huge information. Be that as it may, the endeavours of the legislatures and the wellbeing associations are still in a hurry as it is difficult to battle the coronavirus. Hence, if you are a specialist in Data Science,AI, or Information Science, this is the correct time to enter the field and help experts in battling COVID-19! 
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Here’s Why so Many Data Scientists Are Leaving Their Jobs

When it comes to data science, the sky is the limit. The growing industry has several unexplored potentials and few roles, like data scientists and Artificial Intelligence (AI) engineers, that are taking over all other industries. According to Indeed, there has been a 29% increase in the demand for data scientists in just over a year. A survey conducted by IBM indicated that there will be 2,720,000 data science-related openings in 2020 alone! The Harvard Business Review calls it the “sexiest job” of the 21st Century. Yet, despite the growth in numbers and continuous demand, Glassdoor observes a dip by 1.2% in the salaries of data scientists since March 2019. Industry experts have also observed a mismatch in the supply and demand for data scientists. Here, we have analyzed why the budding industry is seeing a slump in its early stages. 5 reasons data scientists quit their jobs 1. Expectations vs. realityWith the growing demand and potential opportunities, it is not a surprise that individuals from different fields are looking out for opportunities in data science. Many take up these positions with a lot of expectations. Although the data science industry is touted as an open field where one can develop expertise irrespective of their non-technical backgrounds, the reality is quite different. According to IBM’s The Quant Crunch, 81% of data science jobs require workers with 3-5 years of experience or more. Aspirants are expected to know the basics such as collection and storage of data, version control, deploying models into production, and other key aspects. Not only does the lack of such training hamper the individual, but it also confuses them further about their job role. 2. Matching data with business goals One of the perils of being a part of a growing industry is the grey area of the unknown. Business leaders all over the world are expectant of the data’s results to revolutionize their businesses. With the data being collected, businesses intend to shape their goals to align with their customer/user behaviour or needs. However, a lot of data accumulated or being gathered is based on experimentation. This process of trial and error takes several months to arrive at the desired outcome, which delays the business requirements in due process.  It can be frustrating for individuals/teams to orient with such business needs and stress, causing them to quit.  3. Need for constant upskillingThe industry is entirely dynamic and demands new skills every day. Usually, individuals want to get their hands dirty to try the quickest and most effective method to get accurate data. But in most cases, this might lead to more delays in the data processes. Moreover, businesses expect data scientists to be in the know-how of their field, despite their workload. For example, Natural Language Processing (NLP) domain is Facebook AI's XLM/mBERT with a multilanguage model with 100 languages and many more features – a quick rise since its inception in 2017. Not all industries have a separate R & D wing to accommodate this or the flexibility for their data scientists to explore more. According to O'Reilly's 2019 artificial intelligence survey, almost 18% of its respondents said that the lack of skilled people in Artificial Intelligence is slowing its adoption in companies. 4. Compromising salaries While the reports show high figures for data scientists, not every company or job allows for an exact pay-out. Top companies like Pinterest and Uber shed over $212,000 per year but the median salary lies at $80,265. As many individuals are open to opportunities in the mid-level and small companies, the salary pay-out is considerably lesser. With the glaring gap in these figures and no proper demarcation/standardization of salaries, individuals find it frustrating to cope up with the industry.  5. Short term projectsMost of the small to mid-sized companies tend to look for data scientists only for short-term business requirements. By tweaking business protocols to match consumer trends, businesses are merely looking to get offshore instead of finding permanent solutions. This jeopardizes the careers of data scientists in these companies are they are usually employed only for an interim period. Sometimes, this can also mean hiring large teams and eventually reducing its size to fit the company requirements and budgets.  An added advantage to this is that individuals will develop a portfolio of projects instead of a single skillset. Freelancers in data science are also on the rise and expertise in multiple fields within data science is in demand.  The reality of data scientistsAccording to a report, Data scientist jobs remain open 45 days – 5 days longer than the average market. Specific job roles such as Director of Analytics and Systems Analysts tend to remain open for well over 50 days. With salaries hitting a roadblock and expectations of the aspirants unmatched by the industry standards, the gap in the demand and supply is expected to grow. If you are a data scientist or an aspirant, being open to new opportunities and exploring different projects is vital for growth. Industry leaders advise freshers to constantly engage with seniors or organization alumni to bridge the gap between expectations and reality. Despite rampant workplace politics and siloed work, individuals are encouraged to actively research and gain industry insights. Learning new skills in technologies like AI, ML (machine learning) are also some ways to bridge this gap. With a broader approach to data, data can also broaden a data scientist’s career.
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Who is a Data Engineer and What Do They Do in Data Science?

With over 50 billion connected smart devices collecting, sharing, and analyzing data, it is undeniable that Data Science is ruling over the world and is here to stay. Not just this, big data is big money too. The industry is expected to generate over 274.3 billion U.S. dollars by 2022. Simply put, data science obtains large amounts of data from the internet and other smart devices and makes use of modern scientific methods, algorithms, processes, and systems to analyze this and predict customer behavior. This information is then leveraged in making major business decisions for growth and increased revenues.  The incredibly large industry has multiple people performing different tasks, right from cleaning data, implementing predictive models, to creating comprehensible business strategies. While data scientists are the most sought-after in this industry, there are many other stakeholders involved in generating the data, such as data analysts, data architects, and data engineers. On average, data scientists are earning more than the typical software jobs, with an annual income of $1,13,000 annually. While the salaries of data architects and data engineers range from $103000 to $108000 respectively. Who is a data engineer? Data engineers are the foundation for the data science industry as they convert the raw data into a useful format for the data scientists. They also find the trends in data sets that are in turn used to convert raw data. While a data scientist is more concerned with the end-user, a data engineer is the one interacting in the back end to collect vast amounts of data. Typically, a data engineer is concerned with building pipelines that convert the data into formats that data scientists can use.  Data Engineering RolesThe three major categories of a data engineer based on the company size and roles are as follows:  1. GeneralistTypically, generalists are found in small teams or companies where their role is broad. They are the one-man-army for data and are responsible for every step of the data process, from streamlining data, managing it, to analyzing it from time to time. Since these companies do not have numerous users, the systems architecture knowledge required for this role is also less.  2. Pipeline-centric Pipeline-centric data engineers are found in mid-sized companies and convert huge data into a useful format for analysis. They are usually required in companies with complex data needs and work closely with data scientists. A pipeline-centric data engineer is expected to have an in-depth knowledge of computer science and distributed systems.  3. Database-centric Database-centric engineers are responsible for setting up and populating the analytics of databases. They go beyond creating pipelines and adjust the database into bite-sized formats for quicker analysis. They are concerned with ETL (extract, transform, load) work and creating table schemas, and are required in large companies with data distributed across databases.   Data Engineer Responsibilities Some common responsibilities of a data engineer include: Developing and constructing architectures; testing and maintaining them Strategizing architecture to align it with business requirements, conducting relevant industry research and providing updates/solutions to business questions/stakeholders Data acquisition and dataset process development; utilizing this data to address business issues Identifying ways to improve the reliability of data, its efficiency, and quality Deploying advanced analytics programs, optimizing machine learning tools and statistical methods; identifying tasks that can be automated with the same Using different programming languages and tools Finding hidden patterns of customer behavior using data Data Engineering SkillsData engineers are architects at heart working on large-scale systems or huge amounts of data. Technical knowledge of software such as Apache Hadoop, NoSQL, Apache Spark is highly in demand today. Expertise in setting up cloud clusters and machine learning is also highly beneficial for aspiring data engineers.  Without data engineers, data scientists will not function, making them a critical-first member of the data science team. It was found that bad data is costing US businesses alone $600 billion annually, which shows the growing need for organized data – and data engineers are vital for this process. If data is something that excites you, a variety of online courses, right from boot camps to introductory practice modules are available to get you started.
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Who is a Data Engineer and What Do They Do in Data Science?
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Who is a Data Engineer and What Do They Do in Data...

With over 50 billion connected smart devices coll... Read More