why should you join innomatics

Why should you join Innomatics?

At Innomatics we believe in looking at the larger picture and forecasting which direction the industry is moving. We have been a part of the Edtech revolution that promises quality online training and so we are passionate about quality online learning technologies and hence invested in them. We strive to form a comprehensive curriculum and design it in a way that is accessible to all. 

Innomatics style of education focuses on having easy access to knowledge without compromising on the quality of training provided. We focus on catering for the needs of many students seeking an exciting & lucrative career opportunity. During Pandemic, without active college classes generally low employment numbers, it is important that individuals up skill themselves. And here’s where we step in.

It is common knowledge that Data Science, Digital Marketing & Full Stack Development are some of the hottest trends currently. While Data Science has topped LinkedIn US Emerging Job Data 2020 for three years straight, FSD is the most in-demand job at the present. With a hefty salary and a lucrative future, the Digital Marketing Industry is growing by leaps and bounds. With a large number of businesses embracing digital our curriculum focuses on both theoretical and practical knowledge and has been designed by industry experts and trainers, hailing from Fortune 500 companies. Our team of over 70 young expert trainers are passionate, with an immense understanding of this dynamic industry and will help you steer through the sharp edges & rough terrain. Our training is top-notch with round the clock mentoring for resolving doubts and queries of the students. Along with expert trainers we also have a team of full-time mentors. Our in-house team of Data Scientists, Full Stack Developers & Digital Marketers double as mentors for our students and help them understand the subject better. Our round the clock mentoring system will clear doubts, resolve queries, allow you to revisit theories that seem doubtful. Mentoring session gives you an opportunity to interact with experts who have experience in teaching and have themselves been learners in the past. perations, many individuals are entering the space to create and promote their brands. It is not surprising that India observed a 45% increase in AI adoption rate since the pandemic.

Therefore take a stroll with us on a path of career transformation where we bring futuristic technical education to your doorstep! You may ask why pick Innomatics ONLY.. So, here are your reasons:

1)Curriculum based on intensive practical training

At Innomatics you will find live online and offline courses on futuristic technologies. Our courses include Data Science, Full Stack Development and Digital Marketing. All the courses are curated in a way that they are industry-aligned, making you placement ready. Our courses imbibe principles of theoretical knowledge and hands-on practical experience, hence giving you a taste of a professional life even at the training stage. In fields such as Data Science, Digital marketing and Full Stack Development, it is not just the educational qualification, but also training in real-life use cases and practical knowledge that helps one make a mark in the industry. Our quizzes, projects and assignments will give you the chance to get your hands dirty & experience a professional environment, even during the training period.

2) Industry Experts from Fortune 500 companies & dedicated team of mentors

Our curriculum focuses on both theoretical and practical knowledge and has been designed by industry experts and trainers, hailing from Fortune 500 companies. Our team of over 70 young expert trainers are passionate, with an immense understanding of this dynamic industry and will help you steer through the sharp edges & rough terrain. Our training is top-notch with round the clock mentoring for resolving doubts and queries of the students. Along with expert trainers we also have a team of full-time mentors. Our in-house team of Data Scientists, Full Stack Developers & Digital Marketers double as mentors for our students and help them understand the subject better. Our round the clock mentoring system will clear doubts, resolve queries, allow you to revisit theories that seem doubtful. Mentoring session gives you an opportunity to interact with experts who have experience in teaching and have themselves been learners in the past.

3) A trusted name, that has built a network of students, trainers and associates

With over 120 batches and around  2000 students, we have established ourselves as a trusted name in the Edtech space. We are working hard and constantly building trust by providing cutting edge education in Data Science, Digital marketing & Full Stack Development. We offer more than 250 hours of theoretical training & 200 hours of practical hands-on training. With over 30 POCs and use cases to work on and learn from, get the opportunity to attend meet-ups, seminars, webinars and participate in hackathons. With us, you also have the opportunity for an internship where you will learn on the go. We build a network of like-minded ambitious individuals who chart their own successful career path.

4) Flexibility in terms of online or live classes

Our courses are designed to suit both the online & offline mode of teaching:Enrolling with Innomatics is a good idea for working professionals, students, freshers and everyone alike due to the flexible course design structure. Once you enrol in Innomatics, you gain lifetime access to the Learning Management system for a  self-paced style of learning. Therefore, you will get a chance to revisit concepts, re-read theories that you are unclear about or have forgotten. Along with this, you will be made a part of a larger Discord community of students, mentors and trainers. This will give you an opportunity to discuss doubts, problem areas or just keep in touch with a like-minded group of budding Data Scientists. Digital Marketers or Full Stack Developers

5) Extensive Placement Assistance

With over 300 hiring partners, Innomatics provides Placement Assistance that is unparalleled to our competition. We understand that students wish to enter the fields of Data Science, Digital Marketing and the Full Stack Development industry because of the massive availability in the number of jobs and we are not in the business of selling false promises. Our placement assistance cell will equip you with mock tests, interviews, presentation skills etc. Not only will there be technical evaluation but also HR rounds where there will be a special focus on building your professional social media presence, a good resume, communication and interpersonal skills.

6) Awards and accolades for our achievements

Awards and accolades have come our way and our success continues to soar. Our founders, with over 20 years of experience, are constantly working on making Innomatics the best in the Edtech space. Our efforts have been recognized a number of times. We are a two-time awardee of the Times of India award for the ‘Best institute in Data Science & Digital Marketing in Hyderabad’. We have also been featured as the best Data Science & Big Data Analytics Institute in India by the Knowledge Review Magazine. The high standards of technical knowledge imparted by us in our courses has fetched us an IBM certification in a Data Science course. So, if you wish to add an IBM tag to your resume, Innomatics is the ideal destination for you.

One of our key features is that we are not in a hurry to sell our courses. When students approach us with their interest in the courses we offer, we ask them detailed questions about their qualifications, interests, dreams, ambitions and passions. We try to understand their requirements and then suggest a course that is best suited to their requirements. We invest in a robust counselling system so our students are aware and informed about the choices that they make. 

If the above points still don’t convince you, call us on +91 7670993443 or +91 6302521724 to receive in-person counselling, or walk-in for a session between 10 am to 7 pm from Monday to Saturday.

The corona virus pandemic has changed the world’s priorities. In the past year, the world has changed more than we can ever imagine. As we transitioned to work from home, the world rediscovered the potential of the internet and how despite the Pandemic, life could still go on. As the world embraces digitalisation, the demand for Data Scientists, Full Stack Developers and Digital Marketers continues to grow. Often in such areas, it is not the educational qualification but real-life experience that matters, and at Innomatics you are provided with a work-like environment, even in your training period.

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    7 Things to know before you start your career in Data Science

    Around 82,000 job openings are present worldwide in 2021 that require skills in Data Science and around 92% of the world’s organizations gain effective marketing insights using Data Analysis!

    As we encounter the digital transformation of businesses and organisations, the demand and need for Data Scientists become unquenchable. Therefore it was not surprising when Harvard Business Review called Data Science ‘the sexiest job of the 21st century.’

    A Data Scientist sounds like he/she is on an endless adventure. Not only does the individual enjoy a higher average income than the rest of the occupations, but is also an organisation’s sure shot at problem-solving. A Data Scientist can help in forming key decisions for a business or an organisation. 

    But can anyone be a Data Scientist? Is a career in Data Science is as lucrative as people claim? What makes it a popular choice amongst students? How does one chart a career path in Data Science? In this blog, we walk you through the important determinants that will guide your way in this big and exciting world of Data Science.

    Before we begin, we must acknowledge that the discipline of Data Science is broad and dynamic. Within the discipline, there are several areas of specialization and an abundance of opportunities depending on your educational qualifications! If you are a student or a fresher who aims to enter the world of Data Science and Analytics.

    Here are a few tips, tricks and hacks to have a successful career as a Data Scientist

    1) Data Science is an intriguing field

    According to LinkedIn US Jobs Data, Data Science has emerged as the most popular career option for three years in a row. Other than a high average pay scale and promising career opportunities, it has various roles in this field like Data Engineer, Data Analyst, Data Scientist, Business Intelligence Analyst, Database Administrator, Data Architect, Machine Learning Engineer and many more. Before you take a plunge into the world of Data Science, understand its various functions and narrow it down to an area of interest that excites you. Depending on your educational background, some roles might be easier for you to adapt to than others.

    2) Go beyond theoretical concepts

    Get your hands dirty with practical training. You must gain comprehensive knowledge about Data Science. A common but grave mistake while designing Data Science course is overloading it with information and making it theory-heavy without providing a follow-up practical experience to execute the things learned. If theory and practice are made to go hand-in-hand, it helps students apply their concepts and master the subject. One important aspect of being a successful Data Scientist is to have hands-on experience with working on real-life cases and projects. It is paramount that the courses you choose to enroll in have a unique blend of theory and practical, to make you job-ready!

    3) Find an internship opportunity

    One must read this in extension to the point made in the above paragraph. One way to gain experience that makes you job-ready is by looking for internship opportunities in the area of Data Science. An internship will help you climb the rungs of this industry efficiently and effectively. It is a definite way to test, practice and apply the knowledge gained during your enrollment in a course. An internship will give you first-hand experience of tackling a problem, along with advice and assistance from experienced people in the field. Find the opportunity to work on open data sets and apply your learning to test yourself. An internship is a good way to gain ‘learning by experience’.

    4) Understanding the business problem

    Before we plunge into the data, one must grasp the business problem that organisations aim to solve. This will encourage communication between the management and the Data Scientist, which in turn will help them reach a suitable conclusion and resolve critical problems. It is important that a Data Scientist can understand and communicate clearly with other teams, this will ensure that all doubts, confusion and miscommunication are eliminated.

    5) Don’t underestimate the power of soft skills

    Soft skills are an asset and will help you stand out in a crowd. A good Data Scientist must be curious, should be willing to get his/her hands dirty with the problems at hand. Communication plays a key role in understanding and conversing with non-technical people. One also needs to build a team spirit for effective communication and work on increasing efficiency at the workplace. Other than these aforementioned characteristics, analytical skills, critical thinking & a strong business acumen can catapult a Data Scientist into key decision-making roles, within an organisation.

    6) Find a good mentor & maintain a robust peer group

     As mentioned above, the field of Data Science is dynamic. Although segments like BFSI and healthcare have been using Data Science for a long period, we are discovering the scope and extent of Data Science even today. A budding Data Scientist needs a good mentor who can help navigate your career in the best possible way. An aspiring Data Scientist might need help regarding the specialization best suited within the discipline, here a sound mentor-ship system can be of help. Similarly, a robust peer group can help when you are stuck with a problem. It is also important that you maintain this peer group to keep yourself informed about the latest trends, innovations within the field and to motivate yourself from time to time. Both these factors will make your experience within the field wholesome & help you chart a successful career path.

    7) Learn, practice, apply, repeat

    Data Science is dynamic. With each new day, we are finding innovative uses of Artificial Intelligence and Machine Learning. While we as a society adopt technology to accommodate a more fast-paced efficient lifestyle, we are still inventing new technologies to make our lives easier. Therefore it is important for anyone aiming for a career in Data Science to keep abreast with the latest developments in the field. A Data Scientist never stops learning and is constantly hungry for innovations, inventions and discoveries. This curious scientific rationality is an important determinant of your success as a Data Scientist.

    Now that you have all you need, what are you waiting for? Plan for a career in Data Science course today!

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      What skills do I need to become a data scientist?

      How To Start a Data Science Course to become a data scientist?

      Today, the value of data is revolutionary in the modern digital economy. The statistical methods and the discovery of patterns in large data sets play a significant role in discovering hidden information and unknown relationships between data. Based on this new information, companies and organizations can innovate with the help of Data Science. It includes developing new products/services, management information, and increasing services for the customer.

      Modern organizations are transforming their organization into a data-driven organization to optimize the business and operational processes. This change requires a type of employee with knowledge of data-intensive technologies. It is defined as the Data Scientist. The Data Scientist finds and interprets data, manages large amounts of data, and merges this data. The Data Scientist provides consistent data and creates visualizations to help others understand the data. Besides, he/she builds mathematical models, presents and communicates data insights and findings to specialists and colleagues. Besides, a Data Scientist naturally advises on solutions for applying the data.

      What skills do I need to become a data scientist?

      Completed education in data science is not the only indication that someone is suitable for a job or analytics project. Software maker Tableau identified five skills that are extremely useful in a data culture. See them as indicators of someone’s suitability to be hired or trained to be a data professional.

      ●      Critical thinking

      A critical thinker does not accept everything indiscriminately, nor does he question everything he hears or sees. Critical thinking can objectively observe and analyze issues, hypotheses, and results and form an opinion based on them. So regardless of your feelings or personal situation.

      Critical thinkers look at a problem from multiple angles and know which questions need to be answered to arrive at a possible solution. They also consider the sources of the data. They distinguish essential from side issues and relevant from irrelevant information.

      Critical thinking is of great value in many positions, but especially when working with data. After all, in that field, questions must be formulated, which contribute to the business objectives. For example: what is the next step for the organization? What questions should then be asked? And which data is relevant in finding useful answers?

      ●      Communicate effectively

      The questions analyze, and results are worthless if they are not shared within the organization. Effective communication is essential for people who work with materials that others find difficult or uninteresting. It is precious when someone can clearly explain which insights the data has led to, what the relevance is for the organization and its relation to the business strategy. It must be done for different audiences, whose knowledge of technology varies greatly.

      Tableau says effective communication is decisive in increasing the data skills of an organization. When a company wants to create or develop a data culture, everyone who works with data is also an ambassador and a spokesperson. Effective means being able to tell a story that makes results understandable. A pleasant way of presenting, distinguishing between what is fun to tell and what is good to hear. Nevertheless, also telling a complete story and not shy away from the disadvantages.

      ●      Problem-solving way of thinking

      Some people think in terms of problems, others in solutions. This last skill is useful for data professionals: having fun understanding issues and looking for the matter’s heart like a detective.

      Sometimes, however, wanting to solve problems is an inner urge that is difficult to control. Working in a problem-solving manner also requires making choices: which problems are for later, which ones need to be tackled now and with which method.

      Someone who thinks in a problem-solving way sees opportunities in problems and knows which resources to use to arrive at practical solutions. 

      ●      Intellectual curiosity

      The word intellectual indicates that curiosity as a skill is professional. Gossip and backbiting are not included. Curiosity as a skill drives people to delve into problems, analyze causes thoroughly, and ask interesting questions to get to the bottom of something. Tableau argues that ‘just enough’ is never enough for successful data professionals. They will keep looking for answers until the bottom stone is up.

      People with a healthy curiosity ask questions (and listen for the answers) and often show interest in a wide variety of topics.

      ● Business insight

      It has already been mentioned under critical thinking: results of data analysis must contribute to business goals. Someone with business acumen understands how a company functions and what an organization needs. Having business acumen means knowing what problems to solve so the business benefits. Think of finding the cause of a persistent problem, fine-tuning a market segment, and predicting sales trends.

      ●      Sheep with five legs

      Those looking for people who have all these skills equally have a long and frustrating journey ahead. After all, not everyone needs the same level of data skills for day-to-day work. A data scientist does not have to be an excellent communicator if a colleague links his work and the organization. And business acumen counts more heavily for those who formulate assignments for analytics than for an executive analyst.

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        Activities required for Data Scientist

        You have already read a bit about a data scientist’s activities, but in this chapter, we will go into that a little more deeply.

        As a data scientist, you can work at (large) organizations or companies. There you will work with Big Data to predict the future based on specific patterns in the data. If you can predict the future well, a company or organization will benefit significantly from it. A company or organization makes decisions based on these future predictions. A data scientist has a vital role in this.

        Companies and organizations will also expect that as a data scientist, you know about Machine Learning. You can get challenging assignments to make computers smarter. To do that, you write algorithms that make that possible.

        A good example is an app that predicts when a patient will become ill. This prediction can be made by analyzing previous patient information. This analysis is automatic by the algorithm that a data scientist has made.

        Some other activities of a data scientist are:

        ● Identify data quality issues.

        ● Structuring and cleaning data sets

        ● Explain complex solutions to a customer

        ● Report and present conclusions from data

        ● Combining internal and external datasets

        ● Apply mathematical techniques

        The salary of a data scientist

        As a data scientist, you have nothing to complain about money. According to DataJobs, a data scientist earns on average between $ 50,000 and $ 70,000 gross per year. Most data scientists, however, have a university degree in their pocket.

        The salary is so good because there is a significant shortage of data scientists. The demand is greater than the supply, and many companies are therefore lagging. Money is not the primary driver for most data scientists, They love their profession and enjoy taking on challenging jobs, There is no challenge. Nowadays, data comes in all shapes and sizes, and there are many interesting issues.

        Difference between a data scientist and a data analyst

        The activities of a data scientist and a data analyst are very similar. Yet, there are differences. Below the critical differences at a glance:

        ● A data scientist is more concerned with future predictions, and a data analyst provides meaningful insights from data.

        ● A data scientist has Machine Learning skills, and a data analyst is (usually) not involved with this.

        ● A data scientist often works with unstructured data from multiple sources, while a data analyst usually works with structured data from one source.

        Become a (Junior) data scientist

        You already know what the meaning of a data scientist is, what kind of activities are part of the profession, and that you can earn a high salary as a data scientist.

        Have you become enthusiastic, and do you want to become a (junior) data scientist yourself? If so, read on quickly. As a starting data scientist, you will start as a junior data scientist.

        To become a junior data scientist, it is advisable to follow a specific training course. For example, Computer Science or Physics at HBO or WO level would be suitable. However, it is not easy to become a data scientist, and the matter can be problematic.

        To start quickly, it is wise to first follow courses without spending much money. If you have already started a course and think it is too challenging or not fun, you have already spent a lot of money, and you have lost it. However, according to the highly regarded Harvard Business Review, being a data scientist is the sexiest job of the 21st century. It earns well, and the work is enjoyable and challenging—enough reasons to become a data scientist.

        Why is Data Science a Good Course?

        Outstanding courses to get acquainted with the programming languages ​​and technologies that a data scientist must master can be found on Innomatics. Through online or offline courses, you can learn the theory and also apply it yourself. 

        In “Innomatics Institute Demo Class,” you can see an extensive review and description of the platform. You can even try the platform utterly free in Demo. Also, on coursera are great learning opportunities to learn to be with data science and data scientists. 

        Recommended programming languages ​​and technologies to learn Some programming languages ​​and technologies that are useful to learn for a data scientist are: Python and R. Machine Learning SQL Implement predictive analytics with TensorFlow Hadoop. These are some examples, but there are even more programming languages ​​and technologies that can be very useful for a data scientist. If you are motivated to become a data scientist, it is perfect for your future. 

        Now the task is to turn that motivation into action. A data scientist is a very cool profession with many possibilities. Also, you earn well. Don’t wait any longer and get started!

        Advantages of doing Data science

        IT executives know that the big data hype has been around for a while, but few understand how to handle the big data and what value it can represent to their organization. If you still doubt starting your first big data initiative, these are five reasons to take the step now.

        1. Manage data better

        Many modern data processing platforms allow data scientists to collect, rearrange, and analyze different data types. While it takes some technical know-how to work out how data is collected and stored, many current big data and BI tools allow users to play the driver and work with the data without performing complicated technical operations.

        The extra level of abstraction has enabled various usage scenarios in which data in different formats can be successfully collected and used for multiple purposes. Examples are video surveillance during significant events and medical patient files.

        2. Make optimal use of the speed, capacity, and scalability of the cloud

        Organizations that want to work with vast data sets should consider the use of third-party cloud providers. They can provide both the storage and computing resources needed to process a lot of data in a specific time.

        The cloud offers two clear advantages. First of all, it allows companies to analyze large data sets without requiring large hardware investment. Also, hosting a big data platform yourself requires the necessary skills and training. A hosted model partly removes this complexity and enables faster implementation of big data technology. The cloud developers also have a sandbox environment set up to start to test applications immediately.

        3. Let end-users visualize data

        The BI software market is already reasonably mature, and big data requires new data visualization tools that can capture BI data in easy-to-read graphs, tables, and slideshows. These applications must be able to let users quickly browse through data and manipulate it in real-time. Applications must provide APIs that can connect to external sources for additional data sets.

        Usability is another consideration. In addition to the CIO, the CFO, CMO, and other non-IT executives are also looking for ways to make better use of data, so they too need access to charts, infographics, and dashboards. Many BI vendors are working towards a self-service analytics model that puts users in control. It will accelerate product adoption and increase ROI as analytics will no longer be used only by rapporteurs and tech-savvy end users.

        4. Company discovers new opportunities

        As big data analytics tools mature, more CIOs also realize the benefits of a data-driven organization. Political parties are discovering how to target their target audience better, while social networks learn to sell ads based on users’ interests. New research into customer behavior within retail ensures that (online) stores use offers in new ways and advertise products differently.

        5. Methods to analyze data are evolving

        Data is no longer about simple numbers in a database. Text, audio, and video can also provide insights; advanced tools can discover patterns based on specified criteria. Widely used with processing tools capable of text mining, sentiment analysis, language analysis, and entity recognition. This call center tool can match an incoming caller to the correct agent using predictive routing and other analytics technology. It also applies audio analysis based on predefined criteria to determine what score a call gets. These advanced possibilities will only increase in the coming years.

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        Top Emerging Jobs on LinkedIn 2021 - Data science, Big Data, Artificial Intelligence, Machine Learning Engineer

        Data Science: Top Emerging Jobs on LinkedIn

        There’s no point in arguing that LinkedIn has become one of the most excellent places for job hunters. And the best thing above all is that you can interact over LinkedIn, get to know your prospects, and then proceed with a job. After getting your Data Science course done, you might be in better luck. Recently, LinkedIn has released its list of all the top emerging jobs for the year 2020. And you will be surprised to know that all these areas have gained exponential growth in the last five years. 

        Within the entire list, Data Science is all over all the place. So, if you are looking forward to seeking out a career in Data Science, down below are some of the job descriptions that are worth noting: 

        1) Artificial Intelligence Specialist

        During your Data Science training, you will get to know that Artificial Intelligence has tremendously helped the domain of Data Science and is one of the leading professions these days. According to the LinkedIn reports, there has been a hiring growth of about 74% in the last four years. Further, with such great opportunities, one needs to understand that the job requires skill sets such as Machine Learning, Python Programming Language, Deep Learning, and Natural Language Processing. Some of the industries that are hiring these individuals include IT, software companies, and consumer electronics. 

        2) Data Scientist

        Do you know that Data Science is already termed as the sexiest job of the 21st century? LinkedIn’s data shows that there has been a 37% rise in the hiring rate in the last 3 years. The individual roles of Statisticians and Analysts are gradually evolving into that of Data Scientist. To become a successful Data Scientist, you need to have a very profound stronghold on Machine Learning, Data Science, Python, R, and Apache Spark. And since the market is not saturated enough, you can always get your higher salary compared to that of your friends and peers. 

        3) Robotics Engineer

        We all can agree to the fact that automation and Robotics is going to be the new future. That being said, LinkedIn’s hiring growth for Robotics Engineer has increased over 40% in the last four fours. This leaves us with the promising future. That further down the line, the market seeks for more demanding jobs like the same. Based on hardware and software, these roles generally vary due to which you can always seek out a more flexible option. Robotics is gradually becoming a mainstream job, as more businesses are shifting towards robotics process automation. 

        4) Full Stack Engineer

        A full-stack engineer is not a new job role, yet it has seen a significant increase of 35% in the hiring department for over four years. It is gradually becoming precious for all the developers who are looking forward to full-stack. Some of the unique skills to this full-stack engineer job role include React.js, JavaScript, Note.js, and AngularJS. If you are willing to spare some of your time to learn these, you can always leverage the same and get a higher paying job. 

        5) Site Reliability Engineer

        Have you ever come across the same term? If yes, then you are certainly on the right track. With an increased hire rate of 34%, a site reliability engineer’s job has started to show growth. Typically, the job revolves around the idea of a cloud engineer and that of a full-stack engineer. At the end of the day, your work requires running and maintaining apps efficiently. Some of the critical skills necessary for a site reliability engineer include having a better understanding of Docker, AWS, Kubernetes, and Terraform.

        Other Positions among the LinkedIn Emerging Jobs Includes: 

        While we have already discussed the plethora of opportunities that are put forward by LinkedIn in the fields related to Data Science, down below are also some of the other emerging jobs that are worth considering: 

        • JavaScript Developers: Over the last five years, JavaScript Developers have seen a growth of 25%; that being said, the number is quite intimidating on its own. Skills required for this job include JavaScript, React.js, AngularJS, and Node.js. 
        • Cloud Engineer: Being a cloud engineer in the 21st century is yet another lucrative job. With an annual growth of 27% in the past five years, one should consider applying for it. You need to have a deep understanding of Cloud Computing, AWS, and Docker Products to become a cloud engineer. 
        • Chief Revenue Officer: Lesser on the technical side, still there is a growth of 28% for the last five years for the Chief Revenue Officer. Being the head of the revenue department is not an easy job, indeed. For that matter, one needs to have a deep understanding of strategic partnerships, how a start-up works, and above all, SAAS and go-to marketing strategies. 
        • Back-end Developers: The market is booming with back-end developers’ requirements, and the significant increase of 30% annual growth signifies the same. Back-end developers are some of the most demanding jobs in the market. With skills like AWS, JavaScript, and Git, you can always secure a position as a back-end developer in a reputed farm. 
        • Customer Success Specialists: Yet another sales job is a promising career. The customer success specialist has seen a significant increase of 34% annual growth in the last five years. 

        Final Takeaway

        With all the data mentioned above, LinkedIn has become one of the most lucrative platforms to get a promising job for your future. If you are looking forward to building a career out of data science, you can always reach out to Innomatics Research Labs. You will get the best in class Data science course training in Hyderabad. Taught by the industry professionals, you will get to know all the ins and outs of Data Science there is to learn about.

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          Which career is more promising: data scientist or software developer? Which is better in terms of salary and long term growth?

          Who has the most promising future? Data Scientists or Software Engineers?

          Data Science is the great buzzword these days, and to be honest, we all know how all the businesses are using data. There is much confusion for the graduates, working professionals about the career path. They are not sure about which way to take and what pays more.

          The question which runs in their minds currently is should I go for a Data Science? Or software engineering? To answer this question, one has to understand the difference between both.

          What is the work that both Data Scientist and Software Developer do?

          A software developer is an individual who writes lines of codes, designs and develops complete software architecture for complex systems. In contrast, a Data Scientist is the one who solves complex data problems with various elements such as mathematics, computer science, etc.

          What is the payscale of a Data Scientist and Software Engineer?

          It depends on the skillset; the average base salary of a Data Scientist in the United States is $122,839 per year. A person with no experience can earn around $103,884, whereas an individual with four years of experience can earn $141,550 per year.

          The average salary of a software engineer is $109,335 per year in the U.S. If a person is senior and has the experience, he can earn $120,052 per year.

          Scope of Data Scientists and Software Developers

          Software Engineers mainly create products that create data, while data scientists analyze the same data. Data Scientists convert useful information into the user data which a business can use, and developers work on developing the Apps and mobile Apps. 

          Business direly needs data scientists to extract useful data for various purposes, such as marketing, financing, banking, etc. Data never ends, software engineering is the current trend, but data science is the future. Many businesses have been crunching data to make decisions, predict the stock market, trade, weather conditions, and whatnot for decades now, and this is what data science is all about! Today data is growing like a can of worms. Almost every company is dealing with a million bytes of data. Shortly, every enterprise will deal with 50 times more data which is being generated now. That is, most being unstructured requires data scientists to extract, make it in a useful format, and consume for business use.

          Can a software engineer become a data scientist?

          Yes ! it is easier for a software developer to become data scientists. A better understanding of data science concepts and hands-on experience in a data science project is all required. Many organizations and industries are using data Science. 

          Software Developers have to know how to develop the algorithms and use statistical models for the data. Becoming a data scientist is an incredible journey. Once you are, you will love the life of being a data scientist. 

          One should be familiar with languages like SQL, R, Python, SPSS, and SAS. It will be an added advantage if one knows the statistical models to improve algorithms based on the work.

          It’s time to take a quick decision and upgrade your future. Click here to know more about data science.

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            Top 5 reasons to know why AI is trending today. The role of AI in future.

            Why Artificial Intelligence is trending today and the role of it in the future

            Today, artificial intelligence is a very popular and demanding subject that is widely discussed among all such as in business circles. In fact, many scientists, research, analyst claim that Artificial Intelligence and Machine Learning is the future. A lot of smart people out there who think developing artificial intelligence to human level is a dangerous thing to do but this statement is partially wrong because we create a machine, we develop the machine, so in short, humans are the creator and a machine always needs a human who can fix them whenever it gets destroyed and without humans, there would be no existence of Artificial Intelligence and Machine Learning. Technology is so vast that we all are connected to artificial intelligence in many ways like Siri, Alexa, Google assistant etc.

            Technology is in its initial pass and many companies are investing resources or money in Artificial Intelligence so that they can develop more and more products and apps in the coming few years. AI is now so trending in market that recently IIT Guwahati announced Mtech program in Data Science, which includes AI as well as ML so things are changing now everyone showing their interest in AI and getting deep to learn something and us aware of this that analytics as a discipline becomes more established across domains, especially in India financial services sector, organisations and high-growth startups are going on a hiring exultancy. As researchers stated that the total number of analytics and data science job positions available are 97,000 where, it mentioned that out of these, 96% job openings in India are on a full-time basis while 4% are part-time.

            We all know Siri is one of the most popular famous assistants which is using by several people and people are liking it too. Siri guides us to find route, direction, information, send messages, etc, only on our one command or order. Today 15% of Apple users using series voice recognitions. One more example is the Echo which was launched by Amazon and it is also getting smarter and they are adding up new features, facilities as well. It is a product which helps you to search, control, play music etc.

            Artificial intelligence is achieving popularity, attracting people, improving customer experiences and leaving an impact on humans life. We all are using Artificial Intelligence all day, every day as I told before Siri, Google, Alexa etc, it can be found in cars, videogames, E-Commerce software etc. In the coming few years it will bring a huge revolution among us and change the mindset of many people. Artificial Intelligence is developing faster than we think as well as it helps people and makes their life easier.

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              A Crucial role of a Data scientist in Stock market for the growing business profits

              How Data Science is playing a crucial role in the Stock Market for the growing business profits

              Data Science is everywhere In Stock markets, analyzing products, etc. Stock investments analysis is a theme that can be deeply explored in programming. If we include R language, which already has a big literature, packages and functions developed/import in this point.

              Someone says that “In the short term, a market is a voting machine. But, in the long term, the market is a weighing machine”. — Ben Graham

              Nowadays, predicting how the stock market will perform is one of the most difficult things to do. There are so many features involved in the prediction.

              (a). physical factors vs. psychological,

              (b). rational and irrational behavior, etc.

              Working with historical data about the stock prices of a publicly listed company and implementing a mix of machine learning algorithms to predict the future stock price of this company, and starting with simple algorithms like (1)averaging and (2)linear regression. All these aspects link up to make share prices volatile and very difficult to predict with a high degree of accuracy.

              We all are in the era of online discount brokerages and super-fast connections for both wireless and wired and Combining this with companies taking away pension plans is a huge thing to make up. One software name as Stock-Forecasting software which helps traders to make decisions to buy a favourite stock and sell it at the right
              a moment in maximum profit.

              Let’s discuss the Basics of a Stock Market a long time ago, we humans ran businesses with their money. The businesses which they ran were small and they grew the businesses only with their own profits. However, not all businesses can be built or raise up with your own money.

              We all are aware that in the 16th century as the Europeans started exploring Asia and Americas, the big explorers felt they needed a lot of money and their kings were not providing them anymore. The wealthy guys demanded a lot of interest. Thus, they sensed they need to raise money from a bunch of common people. Thus, in 1602, the
              Dutch East Indian company became the first company to issue shares of its company in the Amsterdam Stock Exchange and get traded on a continuous basis.

              In every Company Stocks provides you with a share of the company’s future profits in return for the capital invested. For instance, if a person buy 1 stock of Mercedes now, then you will be assured one-billionth of Mercede’s profits in the future as there are almost a billion such stocks that Mercedes has issued now.

              In Time-series analysis it is a basic concept within the field of statistical learning that allows the user to find meaningful information in data collected over time. The power of this technique can be applied to the Stock Index in order to find the best model to predict future stock values.Big data is also used in optimization problems, e.g. trade execution, portfolio optimization, etc. This class of problem is usually solved via reinforcement learning, Big Data helps in stock trading.

              Big Data, or extremely large data sets, are being extensively used to identify patterns, trends and predict the outcome of certain events. The data can be both structured and unstructured that overwhelm a business on a day-to-day basis. Although it’s not the amount of data that’s important. It’s what the organizations do with the data that matters. Big data analytics for insights that lead to better decisions and strategic
              business moves, how to play with data and take out the relevant outcome. However, you can learn the fundamental and technical analysis form innomatics.
              We offer quality-driven Big Data courses in Hyderabad, India. We at Innomatics provide very comprehensive courses at Hyderabad. People from different part of cities come across here to get knowledge in a specific domain. We Innomatics provide hands-on experiment with 100% of placement once you are done with your course.

              A commonly associated definition of Big Data is in its Volume, Velocity and Variety. Based on this 3 Vs of big data, financial organisations and retail traders can extract a great deal of information and which help them in their trading decisions. Nowadays, Google applies analytics in markets and checks the behaviour of the users and to identify the trends. For an example, with the help of big data, it is easy to analyze the hot stocks as per how other people approach what stocks just similar to what Trump used to check interests of people in the US and how important the issues are.. with the help of big data and Hadoop we have a map and reduce system with
              which we are able to analyze the interests of other people in respective stocks.

              Sentiment Analysis is also one field which is very popular in Automated Trading. The output of the algorithm implemented is sentiment indices, based on the presence and the position of words in the text. Feature Selection is quite important as it is selecting the right stocks. We know Thomson Reuters has developed a language recognition algorithm. For every starting of main learnings, there would be little need to know what stocks mean, which stocks are the data provided for or what do the features mean and aware how to work with Time Series Data is sufficient.

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                8 Best Python algorithms that you must know in Machine learning

                8 Python Machine Learning Algorithms you can’t miss

                Python is most popular and powerful interpreted language, as well as Python, is very light weighted programming language it is created by Guido Van Rossum and first released in 1991.

                Python is designed in a way which facilitates data analysis and visualization. Python has very fewer lines of code compared to any other programming language. It works on different platforms i.e Windows, Mac, Linux, etc). It has a very simple syntax. It gives you the platform where you can use for both types of research as well as development and developing production system.

                To understand machine learning in Python you don’t need to understand everything on your first phase try to learn syntax in Python and start implementing it. Get to know about the platform how it works then know how the algorithm work it is important to know about the limitations. Python language is very easy if and only if you learn with all your honesty solely no need to mug up things. You don’t need to be a machine learning expert just try to learn about the benefits and limitations of the various algorithm. it is a high level interpreted and general-purpose dynamic programming language which focuses on code readability. It helps the programmer to do coding in fewer steps as compared to Java or C++.

                And because of its multiple programming paradigms, it is widely used in bigger organisations. There is some best and simple or interesting way to learn and start machine learning by design in completing small projects. For beginners, we need to install Python on the SciPy platform.

                Some steps are like when you get into it you need to; Load the Datasets, Precise the Datasets, Visualize the Datasets, Designs of algorithms and making some productions. Our organisation will help you to build your career in data science and it will teach you machine learning using python. We will help you to download Python and SciPy. Those who are unaware of this platform we will help you to download this platform in your system.

                Our team will help you to download the latest version of python that is 3.5+ some key libraries need to be installed before moving further Scipy pandas NumPy learns matplotlib These are the five key libraries.

                We will help Mac user to install Python as well as these libraries by using MacPorts. Linux users can use the package manager. Windows user can install a free version of anacondas that includes everything you need.

                8 Python Machine Learning Algorithms
                After knowing this language you will be able to do things in machine learning on your
                own and they are;
                1. Collecting data.
                2. Load the data.
                3. Import libraries.
                4. Load Datasets.
                5. Summarize the Datasets.

                6. The dimension of data.
                7. Peek at the data.
                8. Summary of statistics or you can say statistical summary.
                9. Class Distribution.
                10. Data visualization.
                11. Box plotting.
                12. Build Model.
                13. Make a prediction.

                Feel free to contact us we are there and ready to help you 24*7. We will teach you about data science, machine learning how to download data how to load data how to make datasets, data evaluating with various algorithms and making some predictions as well.

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                  Why R programming plays crucial role in Machine Learning as a tool

                  How “R” plays a crucial role as a tool in Machine Learning

                  Machine learning is a branch of artificial intelligence and you can say it is a branch in computer science which deals with the design of algorithms, statistics that can learn. machine learning is a concept of pattern which basically focus on pattern and works on data and form it in the cluster (i.e collection of similar patterns)

                  Machine learning is of three types:
                  1. Supervised learning
                  2. Unsupervised learning and
                  3. Reinforcement learning

                  Supervised learning is basically the learning algorithm is presented with labelled example input where the level indicates the designed output Examples are; Classification and Regression Unsupervised learning no labels are provided and through which the learning algorithm focuses entirely on detecting structures in input unlabeled data. An example is; K means Reinforcement learning perform a task using feedback.

                  which they got from the environment

                  This is the small intro which is meant to give you some basic information before going in deep.


                  • R is created in the 1990s by Ross Inaka and Robert Gentleman.
                  • R language is an open-source platform which makes it highly cost-effective.
                  • As we know R is one of the major languages for machine learning language used for statistical calculation, graphical representation of data and to analyse data.
                  • According to the survey, most of the data scientists prefer R 49%, 30% prefer SQL and 35% prefer python.
                  • R provides you with outstanding visualization features and which is important to analyse the data before acknowledging it to any automated learning.
                  • Many packages for machine learning are implemented in R as a part of their development.
                  • This is remarkable as R is a specialized language for data analytics.

                  The IEEE 2016 ranking shows R is the most popular and helpful programming language for data science as we are aware of machine learning is a part of artificial intelligence.

                  Objectives and prerequisites

                  • Our aim is to provide you with knowledge about machine learning methods using R and application in R.
                  • This course focuses on the core part which is unsupervised and supervised learning/methods.
                  • Students need to download the following software and R packages before moving further Installation part.

                  R Studio
                  If you are not aware of this software we will surely go to help you and we will help you to download R packages as well,

                  • Students will get to know about R syntax and basic plotting functionality.
                  • There are numerous exercises and numerous projects which we will teach you during courses and also gives you hands-on practise with newly acquired material.
                  • At the end of the course, students will be able to apply what they have learnt as well as they feel enough confident to explore and apply new methods.

                  So if you guys are ready to be a data scientist then without any doubt choose this R language because R is a perfect choice to develop your career in data science it is an ideal choice for Big data, Data Science and Machine Learning, in fact, many researchers, scientist and scholars using R for their experiment and analyzing with data science.

                  We have many experts as well as Data Scientist who have lots of experience in this field and we would love to help you to build your career in data science. As well as there are lots of project in R. Some tasks are like Designing Algorithm, Data Visualizing, Image Processing, Analyses data etc. If you are looking for this course and want guidance to feel free to contact us we are ready to help you out and ready to build your career in this field.

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                  Importance of Machine Learning in Everyday Business

                  Why Machine Learning is important in everyday business?


                  Basically it is a branch of Artificial Intelligence. It is the field of study that gives the computer the capability to learn things and gather knowledge without being explicitly programmed. In short, your machine/system is learning on its own. It works on Data, then it analyzes the data and then response and gives you the feel of real human and It really makes work easier than your expectations.

                  So let’s get into more brief there are some key terminologies
                  1. Data converts into information
                  2. Data can be an image, video, text file, CSV file etc.
                  3. It is a problem-solving tools i.e through ML we can develop tools
                  which cal solve any problems.
                  4. for example; Traveling Salesman Problems
                  Chess Game and many more
                  5. It is a combination of Computer Science, Statistics
                  6. Learn from data and then act.
                  7. Optimize performance criteria using past experience.

                  ✔ Image Processing
                  ✔ Voice Recognition
                  ✔ Optical Character Recognition


                  Machine learning can keep an eye or keep track of apartments. For example, there are several machine learning applications in the industry of smart homes and these key allow the house owner to learn when there is movement in a house with push-up messages.

                  As we all aware of how Machine learning has made dramatic improvements in the past few years and we are still very far from reaching human performance and still many times, the machine needs human assistance to complete its task. But as the technologies growing/developing so fast that day is not far when it will completely
                  capture human minds.

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