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Category: Analytics

What is Linear regression: What is it? How does it help? When is it used?

Posted on by Imarticus Learning

 

Linear regression is a technique for the analysis of data that is statistical in nature. It is used to determine the nature and extent of the linear dependence between independent variables and a dependent variable.

The two kinds of linear regression are

·         Simple linear regression

·         Multiple linear regression

Both use a single dependent variable. When the dependent variable is predicted from a single independent variable it is called simple linear regression. When the dependent variable is predicted using multiple independent variables it is called multiple linear regression.

Data Considerations in Linear Regression:

There are many requirements of the data to qualify for use in linear regression. Almost always the dependent variable uses a scale of continuous measurement ( Ex: test scores from 1 to 50). The independent variable scale could be continuous or category wise. (Ex: Girls Vs Boys).

Linear Regression and Correlation:

Regression analysis is normally used to make predictions. Correlation and simple linear regression are alike since both establish the extent of the linear relationship between the dependent and independent variables. While linear regression defines the variables as dependent and independent, the correlation makes no such differentiation. Further linear regression always predicts the dependent variable as against the independent variables be it one or many.

Here are some of the uses of linear regression.

1. Defines relationships:

Regression analysis can be used for the following tasks where relationships are very tangled and complex. Like

  • Multiple independent variables modeling.
  • Use for analysis categorical or common variables.
  • Model curvature from polynomials.
  • Analyze the effects of interaction and find the extent of the dependence of the independent variables on other variables.

2. Control the variables:

Regression can control statistically every model variable. To make a regression analysis the variable’s role need to be isolated from the other variables and their roles. This means one must reduce the confounding variables effects on the variable. This is achieved by keeping the values of all other independent variables constant and then evaluating the linear simple regression analysis of the dependent variable against one independent variable only.

Thus the model will only evaluate the relationship between these two variables while effectively isolating the other variables. To control the other confounding variables in the regression analysis one needs to only include them in the model and hold the other variables at a constant value.

Let us look at an example to understand the practicality of linear regression analysis. A regression analysis study on mortality from coffee drinking was recently conducted. The analysis showed that higher the coffee intake higher was the risk of dying for the excessive coffee drinker. The initial model had however not included the fact that a large number of coffee drinkers also smoked.

Once included the regression analysis actually determined that normal coffee drinking did not raise the risk of death but actually decreases the mortality rate. Smoking, on the other hand, did increase mortality rates and the risk of death was higher with increased smoking. This is a good example of the technique of role isolation of the variables holding the other variable in the model constant.

Through this one example, we are able to study the effects of coffee drinking on the mortality rate while holding the variable of smoking constant and also studying the effects of smoking on the mortality rate when holding coffee drinking or the other variable constant.

In addition to the above findings, the regression study demonstrates how the exclusion of just one variable that is relevant to the model can lead to misleading and contradictory results. It is hence crucial that the model includes all relevant variables, isolates the roles of each variable and also controls the role of the variables effectively for linear regression results to be true and accurate.

Omitting variables and uncontrolled variables can cause the model to be biased and unbalanced. To reduce such bias a process of randomization is applied to true-life analysis experiments where the effects of the variables are equally distributed to ensure the biasing by the omitted variables.

Conclusion:

Regression analysis can be very effective in predictive models. If you would like to learn more about this subject you can do a data science course at Imarticus Learning where you will ace this subject and also learn to use the technique to real-life situations.

What Are The Machine Learning Use Case in IT Operations?

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Machine learning and AI can together take your enterprise to better productivity and efficiency which translates to better profitability.ML is used to make the algorithmic programs executable and uses many ML tools. The oldest tool for ML is Shogun. Without ML none of the tasks in an AI device can run.

ML’s iterative capacity is crucial as the ML trained models independently adapt to new data. Their self-learning ability akin to the human brain learns from previous experiences and computations to give repeatable, accurate and reliable decisions and results. With the advent of data analytics, the increased dependence on AI devices and data being generated by the nanosecond Machine Learning Training has gained popularity and impetus in the last decade.

Why training is crucial:

Nearly all employees are short on workplace skills and need training in these weak areas whether it be learning a new language, an advanced technique in your expertise area, or plain communication and change management. A Machine Learning Course program helps assimilate best practices in the skills required at your job. It also progresses your career to the next level with certification. Departmental training programs improve your morale and bring all employees with similar knowledge and skills on a single platform.

ML use cases for IT operations:

For sheer want of space and time, we shall discuss such cases superficially with the intent of defining the areas where ML is beneficial for the IT operations. Some of the complex areas where ML is used are:

RPA-Robotic Process Automation:

One can undertake digitization of processes in a few months instead of many years with legacy systems. The legacy system need not be replaced since ML ensures the bots can operate on such a system based on your ML algorithm. This includes all processes from existing process analysis, RPA bots programming, and humans using the bots. The implementation time is greatly cut down and the cost of replacing legacy systems avoided. Take a look at such times in the below chart.

That having been said, it is also necessary to be aware that process complexity determines analysis and programming times, and the automation levels and time-taken for human interaction with the bots can vary. Other factors that affect the time are the training through a Machine Learning Course for the bot-interacting employee and error testing.

Predictive maintenance:

Predictive maintenance to minimize operational disruptions is another crucial area where ML helps.  The uptime and maintenance costs are the factors directly impacted by the regular maintenance of robots, bots, and connected machinery. In business parlance, this translates into cost savings of millions of dollars. A Nielsen study shows that some industries suffer downtime costs of 22,000 USD per minute.

Manufacturing/Industrial analytics:

Many industrial assets like Chillers, Boilers, Batteries, Turbines, Transformers, Valves, Circuit Breakers, Generators, Meters, and Sensors are all connected to the ML through IoT platforms. Popularly referred to as industrial-analytics ML helps reduce maintenance costs, manufacturing effectiveness and reduces downtime throughout the system from production to logistics.

Supply chain and inventory optimization:

ML leverages the optimization of such processes to the next level greatly reducing supply chain costs while increasing the organization’s efficiency and productivity.

Robotics:

ML helps automate physical logistics and manufacturing process by introducing automated advanced robotics. The resultant effects are improved effectiveness and time-saving.

Collaborative Robot:

Cobots use ML to achieve automation with a flexible process. The Cobot’s ML process, engineers the automated response of flexible robots to learn from past experience and mimicking.

Qualitative benefits:

Some of the benefits of using ML-enabled use cases are: 

  1. Better performance.
  2. Improved production continuity and rhythm.
  3. Increased worker productivity.
  4. Increased availability of time for repair and maintenance work.
  5. Better team preparation and interventions.
  6. Effective management of inventories and spare parts.
  7. Reduced costs on energy.

A study conducted by McKinsey reports that ML has the potential to use cases and provide the following benefits.

  1. Downtimes reduced by 50 percent.
  2. MTBF increased by 30 percent.
  3. The useful life of machines upped to 3-5 percent.
  4. Inventories in spares cut to 30 per cent.
  5. Maintenance costs declined by 10-40 percent.
  6. Injuries to the workforce declined by 10-25per cent.
  7. Waste reduces by 10-20 percent.
  8. Advanced analytics reduces environmental impact, improves employee morale and customer satisfaction.
  9. Betters product quality and improves performance.

In parting, if you want to do a Machine Learning Course to effectively learn ML applications try Imarticus Learning. Their machine learning training fast-tracks your career with widely-accepted ML certification. For more details in brief and further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

How Criminals Are Using AI and Exploiting It To Further Crime?

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AI can use the swarm technology of clusters of malware taking down multiple devices and victims. AI applications have been used in robotic devices and drone technology too. Even Google’s reCAPTCHA according to the reports of “I am Robot” can be successfully hacked 98% of the time.

It is everyone’s fear that the AI tutorials, sources, and tools which are freely available in the public domain will be more prevalent in creating hack ware than for any gainful purpose.

Here are the broad areas where hackers operate which are briefly discussed.

1. Affecting the data sources of the AI System:

ML poisoning uses studying the ML process and exploiting the spotted vulnerabilities by poisoning the data pool used for MLS algorithmic learning by. Former Deputy CIO for the White House and Xerox’s CISO Dr. Alissa Johnson talking to SecurityWeek commented that the AI output is only as good as its data source.

Autonomous vehicles and image recognition using CNNs and the working of these require resources to train them through third-parties or on cloud platforms where cyberattacks evade validation testing and are hard to detect. Another technique called “perturbation” uses a misplaced pattern of white pixel noises that can lead the bot to identify objects wrongly.

2. Chatbot Cybercrimes:

Kaspersky reports on Twitter confirm that 65 percent of the people prefer to text rather than use the phone.  The bots used for nearly every app serve as perfect conduits for hackers and cyber attacks. Ex: The 2016 attack on Facebook tricked 10,000 users where a bot presented as a friend get them to install malware. Chatbots used commercially do not support the https protocol or TLA. Assistants from Amazon and Google are in constant listen-mode endangering private conversations. These are just the tip of the iceberg of malpractices on the IoT.

3. Ransomware:

AI-based chatbots can be used through ML tweaking to automate ransomware. They communicate with the targets for paying ransom easily and use the encrypted data to ensure the ransom amount is based on the bills generated.

4. Malware:

The very process of creating malware is simplified from manual to automatic by AI. Now the Cybercriminals can use rootkits, write Trojan codes, use password scrapers, etc with ease.

5. Identity Theft and Fraud:

The generation of synthetic text, images, audio, etc of AI can easily be exploited by the hackers. Ex: “Deepfake” pornographic videos that have surfaced online.

6. Intelligence garnering vulnerabilities:

Revealing new developments in AI causes the hackers to scale up the time and efforts involved in hacking by providing them almost simultaneously to cyber malware that can easily identify targets, vulnerability intelligence, and spear such attacks through phishing.

7. Whaling and Phishing:

ML and AI together can increase the bulk phishing attacks as also the targeted whaling attacks on individuals within a company specifically. McAfee Labs’ 2017 predictions state ML can be used to harness stolen records to create specific phishing emails. ZeroFOX in 2016 established that when compared to the manual process if one uses AI a 30 to 60 percent increase can be got in phishing tweets.

8. Repeated Attacks:

The ‘noise floor’ levels are used by malware to force the targeted ML to recalibrate due to repeated false positives. Then the malware in it attacks the system using the AI of the ML algorithm with the new calibrations.

9. The exploitation of Cyberspace:

Automated AI tools can lie incubating inside the software and weaken the immunity systems keeping the cyberspace environment ready for attacks at will.

10. Distributed Denial-of-Service (DDoS) Attacks

Successful strains of malware like the Mirai malware are copycat versions of successful software using AI that can affect the ARC-based processors used by IoT devices. Ex: The Dyn Systems DNS servers were hacked into on 21st October 2016, and the DDoS attack affected several big websites like Spotify, Reddit, Twitter, Netflix, etc.

CEO and founder of Space X and Tesla Elon Musk commented that AI was susceptible to finding complex optimal solutions like the Mirai DDoS malware. Read with the Deloitte’s warning that DDoS attacks are expected to reach one Tbit/sec and Fortinet predictions that “hivenets” capable of acting and self-learning without the botnet herder’s instructions would peak in 2018 means that AI’s capabilities have an urgent need for being restricted to gainful applications and not for attacks by cyberhackers.

Concluding notes:

AI has the potential to be used by hackers and cybercriminals using evolved AI techniques. The field of Cybersecurity is dynamic and uses the very same AI developments providing the ill-intentioned knowledge on how to hack into it. Is AI defense the best solution then for defense against the AIs growth and popularity?

To learn all about AI, ML and cybersecurity try the courses at Imarticus Learning where they enable you to be career-ready in these fields.

Business Analyst Interview Questions

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If you’re searching for business analyst interview questions then you could be an interviewer or interviewee. But no matter which side of the table you are the interview itself is your chance to evaluate the answers of each other. Do not be nervous and try to stay calm, positive and genial throughout.

Business Analyst General Interview Questions

One should expect the interviewer will test your knowledge based on your resume and facts stated in them, your solutions in hypothetical situations and Agile related questions where the answers are likely to be based on your applications of Agile to BA situations like macroeconomics, fiscal policies, your ability to work on the team and your ability to creatively resolve tricky issues. General questions may have no right answer. These business analyst interview questions assess your soft skills and interpersonal communication skills.
Some of these are

  • Lead me through your resume.
  • What is your best positive feature or worst negative feature?
  • How would you measure BA success?
  • Would you like to ask us any questions?

In all your answers be brief, keep your answers simple, answer with spontaneity and honesty. Facts are easily verifiable and deceptively simple to answer wrongly.

Business Analyst Technical Interview Questions

  1. Explain differences between the Business Requirements, Functional Requirements and Technical Requirements documents.
    A BRD explains the business requirements. An FRD explains how business requirements can be achieved. A TRD explains how the Technical Designer requirements will be met. This trick question is not just about the answer but tests your Agile knowledge and application of the acronym to the Backlog list and other concepts in Agile from traditional and Agile perspectives.
  2. Where is PESTLE used?
    The PESTLE BA tool acronym expansion is Political, Economic, Sociological, Technological, Environmental, Legal environments assessed for opportunities, pressures and constraints.
  3. What are “Porter’s 5 Forces”?
    The framework is used to analyze competition levels in an industry, product substitutes, rivalry, new entrant’s threat and such factors for assessing competitiveness to influence organizational strategy. It was built and named after Harvard’s Prof. Porter.
  4. Explain Use Case Model.
    The Use Case model uses case descriptions and a step-by-step use case diagram to define the processes, role of actors and areas of the story.
  5. What does “Swim Lane” diagram mean to you?
    Using swim lanes is a widely used best practice in modelling techniques of processes showing the trigger for a specific event and the task sequences performed by a particular actor in the Use Case model.
  6. Tell us your definition of User Story.
    A User Story is used by Agile teams and is a format requirement from the user point of view got from Extreme Programming with the format:
    As a <user role>, I want
    <requirement> so that
    <business value>
  7. What is INVEST?
    The acronym expansion is Independent, Negotiable, Valuable, Estimable, Sized Appropriately and Testable. It checks the User Story effectiveness and uses it as a criterion for building them.
  8. Explain “Usability”.
    Usability is about the qualities that are end-user suitable. It is about the right system functionality and the user interface. The UX-user experience is the ultimate measure focused on.
  9. Explain non-functional and functional requirements:
    Functional Requirements are those that the solution will do or allow users to do. Non-functional requirements are “Quality Characteristics” like compatibility, security, performance etc, that measure how well the system behaves against the standards set for it and are essential to the system.
  10. Tell us about Kano Analysis:
    The Kano model explains customer satisfaction applied to the theory of product development by Professor Noriaki Kano in the 1980s. Customer preferences are put into five categories namely exciters, satisfiers, dissatisfiers or the wow, will and want factors. The model focuses on customer needs and tries to cash-in on perceived product features differentiated for product and marketing analysis.
  11. Explain MoSCoW prioritization briefly:
    MoSCoW stands for the acronym used in Agile approaches for requirements prioritized on a time-dependent scale in the DSDM Agile Project Framework.
    • Must Have
    • Should Have
    • Could Have
    • Won’t Have this time

Use these business analyst interview questions as a reference and work on your own questions if you were in the interviewer’s shoes.
Interviews need one to be thoroughly knowledgeable on topics related to business analysis, Agile framework and market conditions. One should use creativity in answering business analyst interview questions. Most of the questions tend to test knowledge, attributes of soft-skills, interpersonal communication skills and technical subject knowledge.
Did you know that the Agile and business analyst courses at Imarticus Learning offer mock interviews, assured placements, and soft-skill development too as part of their course learning? Why wait then? Join today!

Also Read: Top 25 Agile Interview Questions

How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

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How Important Is An Application Domain In Regards To Post-Graduate In Machine Learning?

If you wish to do ML research, either academic or in the industry, then you need to be a great coder and get to working with the elite in the ML domain. But, for the following reasons, you would still have the advantage.

  1. ML research is the right path since there is an acute shortage of qualified practical doctorates in ML. Spending a few years under the best in the domain of ML can actually help improve your knowledge and practical skills through effective mentorship. There are ML mentors like Geoffrey Hinton, Nando Freitas, Yann LeCun, Andrew Zisserman, Andrew Ng, etc who are well known for their work and contribution to research.
  2. Attaining proficiency in Machine Learning Training needs proficiency in data, mathematics, statistics, linear algebra, calculus, differentiation, integration, and a host of other subjects to do research in the ML domain. If you have these it still takes 3-5 years before you get to writing effective algorithms.

Most software engineering jobs in industries do not provide you time for reading or research. Further, you will lose out on practicing your development skills. Since the ML programs on the market today are more or less ready to use, it makes perfect sense to learn Machine Learning Course.

To answer which way you should proceed read on. One can opt for any of the two ways of applying ML. To research and applications. Let us explore these choices.

ML research:

Learning about the science of machine learning is actual research. An ML researcher is constantly exploring ways to push the scientific boundaries of the science of ML and its applications to the Artificial Intelligence field. Such aspirants do have a post-graduation or even a Ph.D. in CS with frequent and periodical publications of their research presented at the top ML conferences and seminars. They are visible and popular in these research circles. The ML researcher is looking for something to improve upon and thanks to their efforts technology are always cutting edge and progressing in pace with developments.

When you need to tweak your applications and seem to go nowhere with it, it is these ML researchers who can get you up from 95 to 98 percent accuracies or more by offering you a personalized and customized solution. The ML researcher really knows his wares well. The only drawback is that he may never get the opportunity to actually deploy his solutions in applications. He knows the theory and is devoid of practice in SaaS delivery, deploying to production or translating the research finding into a practical app.

Machine Learning application:

In comparison to the researcher, the ML application is about the engineering of ML. An ML engineer will take off from where the researcher left. He is adept at using the research and turning it into a valuable practical application or service. They are adept at services of cloud computing and services like the GCP of Google or AWS from Amazon. They are fluent in Agile practices and can diagnose and troubleshoot anywhere in the SDLC of the product.

These ML engineers are often not as recognized as the ML researcher for want of a decorated Ph.D. and referral citations. But they are the people you must go to if you want your customers to be happy with ML-driven products. These application engineers have years of experience and deployments of thousands of products to their credit.

Consult an ML application engineer before you deploy products or services in the market. Your decisions should be based on your business domain, the product or services on offer and the methods of delivering it to the targeted market.

Expected payouts:

The Gartner report states that by 2020 the domains of ML and AI will generate 2.3 million jobs. Digital Vidya claims the ML career is great since the inexperienced freshmen land jobs that pay 699,807- 891,326 Rs. If your domain expertise is in data analysis and algorithms your salary could be 9 lakh to Rs 1.8 crore Rs pa.

Conclusion:

For most teams/businesses and teams, ML has many apps that are applicable to its specific needs. You do not need to reinvent it but must know how to use it better. Its an awesome tool for the enterprise and customer’s too! Learn ML at Imarticus Learning. Besides learning how to tweak the ML algorithm through hands-on assignments, project work, and workshops you get assured placements, soft-skill, and personality development modules with a resume writing exercise. Hurry and start today!

Difference Between Data Analyst and Business Analyst

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Data Science is crucial in today’s modern world where AI, ML, VR, AR and CS rule. These sectors are where most career aspirants are seeking to make their careers because of the ever-increasing demand for professionals and the fact that with an increase in data and development of these core sectors, there are plentiful opportunities to land the well-paid jobs.
In the earlier days, data scientists were obscure and restricted in the IT server rooms and department. Today they are the blue-eyed boys in the business world. According to Indeed.com analysis reports, a 4,000 % was reported in this profession. This then justifies why the demand for a trained Data Analyst with domain expertise, mathematical and data engineering skills (who are considered invaluable organizational assets), has been inordinately high. Supply positions are never catching up and their pay packages have seen many a career aspirant’s dreams fulfilled.
An analyst is a specialist in data analysis processing both facts and figures to gauge trends, get gainful insights and make forecasts using predictive analysis. Most people tend to use the two terms business analyst and data analyst interchangeably. Though this can be applied in terms of smaller businesses the “business analyst” in larger enterprises actually covers both systems and data analysis. The scope of the BA is not limited to being only a data analyst but appropriates roles of a data scientist too. What both the analyst and BA do with the data is entirely different and their job skills, the environment of operation and technical skills will definitely differ.

The Role Differences:

The two roles are at NEVER interchangeable in job-roles, and they definitely aren’t the same in terms of career progression, job-scope, payouts, and skills required for the job among other differentiators. The business analyst is definitely better paid since his role demands more and his skills are relatively wider than that of a Data Analyst. To get a better understanding of the job differentiators one needs to look at the job roles of the scientist, analyst and BA.

Data Analyst Role

To manage such large volumes of data and extract information from such data sourced from multiple origins the data analyst is a necessity and a good analyst is a prized corporate asset. Their role in the enterprise is to sift through the data and provide the information, forecasts, predictions and such to the decision makers. The evolution of business strategy and informed decisions is thus dependent on data and the data analyst.

Business Analyst Role

The BA and data analyst roles focus on the use of data in focused roles. The BA assesses data and system infrastructure requirements from a business-perspective. The data analyst, on the other hand, takes interest in the databases and is more focused on placing his insights in the hands of decision makers.
Data analysts are generalists who score over the BA and can tackle more data analysis problems since they have the multi-disciplinary technical skills that include engineering skills of a database engineer, deal with algorithms using the skills of a statistician and have expertise in the data domain/subject matter proficiencies of the data analyst. They focus on insights for business decision making. The BA in addition to being a data analyst also includes focused analysis related decision making on data, systems and infrastructure in decision making.

Skills Required

It is true that a Data Analyst collects databases, manipulates them for foresight and analyzes data for predictions. His presentations, reports, and insights often comprise the latest trends, visualization of the data, and foresight in the form of charts, tables, graphs, histograms and more.
All data jobs need strong business acumen and domain expertise. The technical skills and the level of influence on the organization’s performance mean a good analyst/BA will find the right solution with the most value to the business problems presented.
Mere technical skills and degrees are not enough. Both streams aspirants need to be excellent communicators with the data scientist and analyst who have a problem-solving attitude and can lead from the front. Soft-skills are very important in all teams.
Both data, as well as BAs roles, calls for problem-solving attitude and technical expertise in SAP, PeopleSoft applications and Microsoft Excel suite. The formal educational credential is graduation or business-related degree. An MBA is a plus point.
Becoming a specialized Data Analyst at the reputed Imarticus Learning helps start careers such as business analyst, data scientists, and data analysts. The certification issued at Imarticus is globally accepted as an index of your knowledge and practical skills. So, don’t toss a coin to decide. Explore your career with an Imarticus Business Analyst Course. All the best!

Also Read: Difference Between Business Analyst & Business System Analyst

How Analytics And Data Science is helping OYO To Enhance Customer Experience?

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According to the CEO and Founder of OYO Rooms Ritesh Aggarwal, the use of analytics and data science helps identify not only the right demand but also the right action for each customer to enhance their experience. Its pan-India 223 city presence boasts of over 2 million check-ins and a total worth of 260 million dollars currently. OYO has used data science technology and analytics successfully in the hotel booking and servicing of accommodation renting segment tapping the mobile users who use the internet and advancements in technological apps to get the best deals and prices.

The OYO story:

In May 2013 OYO started with one hotel booking and had grown to over 8500 hotels and 75K rooms spread over well-targeted metros, commercial hubs, small cities, pilgrimage towns and foreign leisure destinations like Nepal, Malaysia, etc. Their analytics and data science efforts helped provide unmatched prices for well-stacked and standard hotel services while setting the bar for in-room customer experience and budget-accommodation availability in India. OYO’s inspirational story is the result of its CEO’s entrepreneurial debut, and his success is truly inspirational.

Offering standardized stay experiences OYO is spread across 223 cities in all We have revolutionized the legacy-driven hospitality space in India by standardizing the in-room experience and delivering predictable, affordable and available budget-room accommodation to millions of travelers in India,” says Ritesh Agarwal, founder, and CEO, OYO Rooms.

Ritesh hails from Orissa and traveled from the young age of 17 to many hundreds of B and Bs, hotels, resorts, guest houses, etc. to make a curated list of them and help discover such locations that were obscure till date. The introduction of price affordability, standardization of services and customer behavior predictability were the contributive factors to overhauling the way and use of booking with OYO and its analytics and data science program. The importance of training and experience in predictive analysis, data analytics, handling of big data of several petabytes, creating smart self-learning algorithms, and using the latest techniques of neural networking of the ML with AI cannot be undermined according to Aggarwal.

OYO and technology:

The services provided with OYO bookings are standardized with customers getting ac rooms, flat-screen TV, 24×7 customer support, WiFi, complimentary breakfast, quick availability searches, and app-based booking. Of course, the comfortable customer experience brought loyalty and increased its app-reach and revenues with leaps and bounds. The app saw 5 million downloads in the first few weeks and OYO cashed in on data of room searches, availability, fair pricing, standardized services, etc. through its analytics supported app.

Additionally, cab bookings, room-service requests for beverages, laundry, food, etc. were linked in through smart neural networking to provide a seamless 5 seconds 3 tap experience. Thus sales, technology, intelligent data analytics, satisfied, loyal customers and owner engagement driven by the analytical ability of the app helped OYO emerge the 2018-unicorn amid the disrupt industries and stiff competition from CoHo, NestAway, ZiffyHomes, Homigo, WudStay, and SquarePlums.

The analytics and statistics:

According to an HVS report cited by Ritesh Aggarwal, unbranded hotels numbering 2 million are available as against the 112k branded ones. That is a huge, potentially untapped customer market that OYO plans to utilize in its growth to make OYO services a household name and brand to reckon with. Even the funding of OYO was strategically planned to raise 260 million dollars from Sequoia Capital, SoftBank Group, Lightspeed, and GreenOaks Capital. It hopes to raise its capital to over 500 million dollars with SoftBank’s help putting it in the unicorn league.

Parting notes:

Whether it be a bus booking, a train reservation, a connecting flight, the last-mile cab availability, intra and intercity travel, long or short stay vacations, quick food, and laundry services, or undiscovered destinations, OYO has plans to keep its customer numbers growing by catering to their needs reflected in the smart analytics app and media. Their inclusion of shared vacation stays, resort accommodation, and service apartments like Chennai based Novascotia Boutique Homes to their hotel bookings was strategic inclusion planned for the internet savvy mobile user and a trend reflected in the search use of customers in its analytics-based strategic market expansion plans.

Data science analytics is best learned in classrooms with plenty of hands-on and industry-relevant experience. Certification, able mentorship of certified trainers and an assured placement program gives such training courses the leading edge in launching your career. If the OYO story inspires you, then do a Big Data Analytics Courses at the reputed Imarticus Learning. Perhaps you will also take to utilizing the opportunity provided to get entrepreneurial ideas and mentorship assistance to start a successful venture. All the best!

How Big Data Is Changing Disruptive Innovation?

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How big data is changing disruptive innovation?

It is for sure that big data has grabbed the attention of many as the businesses have understood its prominence in this technology-driven world. The meaning of big data goes by its name it refers to a large sum of data to be handled efficiently for a productive business. Online giants who rule the world of technology like Amazon, Facebook, Microsoft, and Google together store about 1.2 Million terabytes of data.

Which is really a staggering amount of data and now we know how influential are they to the society. A new update from DOMO states that approximately 2.5 quintillion bytes of data is being generated every day and the number is going to increase exponentially. So, this has ideally remarked an era where talented data scientists are on a pinnacle and young aspirants choose data analytics training to land on their dream career.

Understanding disruptive innovation

Well, you must read somewhere that “90% of the world’s data is being created in the last couple of years” this is credited for the growth of online. Disruptive innovation can be termed as a new concept or model which alters the function of the monotonous market and influential in creating a new flow in the market. This disruptive innovation creates a disorder in the market place with its bang.

Impactful big data on the limelight

We often notice new innovations in technology and the way it affects business. Off-late Big data technologies like Hadoop and NoSQL has created quite a stir in the minds of businesses about convincing the customers.  Wings of big data like big data analytics course are vital for data science aspirants to identify hidden patterns to understand customer preferences. Big data has jeopardized the conventional flow of the market by inducing advanced computing and eliminating the traditional way of computing for better business.

Let us look into four ways by which big data is changing disruptive innovation:

Big data proved trust-worthy

You may notice that when new technology is introduced in the market, the market takes a while to completely adopt it. But this was not the case with big data due to its flexibility and impressive tools used to leverage profitability in business. Companies invest in employees who learn these tools to keep them par with the trending market scenario.

Ease of access and flexibility

The smooth transition in the software we use in mobile and computer systems induced by big data is quite enjoyable and user-friendly. Users have become more dependant on big data for it has altered the prospects of users on new technologies. Thanks to Hadoop and NoSQL technologies of big data which has created a revolutionary impact on businesses.

Spending for a positive impact

The need for big data and data analytics is different for different companies. Hence, the money spent on big data also varies. But, the companies are spending a huge sum on big data confidently as it proves to be worthy enough. Businesses pay for big data but proper use of tools and techniques in big data may eventually lead to saving a lot of money. It is a kind of predictive science which monitors previous trends and forecasts future trends.

Action and accomplishments

Big data has been successful in creating a satisfied customer base for business by identifying and understanding the needs of the customer beforehand and diverting the business to concentrate on the right track. Target marketing strategy in big data has aided businesses in achieving long-term growth and stability. When new technology enters the market, it is obvious from the customer end to expect certain changes in its attributes, whereas big data leaves the customers surprised with its functionality which other disruptive inventions failed to do so.

To sum up

Convenience and usefulness of big data have made people realize the way a disruptive innovation should work like. When innovations meet the expectation of the customers it is accredited as a technology that helps a business in gaining a competitive edge. Unlike other disruptive innovation, big data is assisting businesses in identifying market trends and disruptions to grab the opportunities, upon failing to take the first step the competitors will surely do so.

For more details, you can also visit – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Evolution Of Data Science In India

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Evolution of data science in India
In today’s bustling world an unprecedented amount of data is being generated by businesses and firms. The pinnacle of data proliferation pressurized on the businesses to employ specialized professionals apt for the tasks. The amount of digital data that exists today is at a staggering rate and is bound to increase by a large extent in the future. Data science is considered a young profession or a relatively new concept which became popular after a decade in 2010.
Initially, statistics and statistical tools were implanted in data science which later clubbed with the growing technology and newer and sophisticated concepts like Artificial Intelligence, Internet of Things and Machine learning for better results. Data science is basically the study of data which is in structured or unstructured form. It is a process of collecting, storing, analyzing and processing the data using various statistical tools and machine learning for producing meaning insights.
Revolutionary data science in India
Before the arrival of data analytics, the blue-chip companies and consulting businesses ruled the analytics market. By removing the barriers in the industry cloud technology has made it possible for start-ups to emerge in this field fearlessly. Big businesses to start-ups all rely largely on data science for solving their simple to complex problems.
Hence, it is appropriate to say data is the new oil for businesses. Many developed countries like USA, UK, Singapore, and Australia are a favorite destination for Indian analytic experts next to software professionals. A study by Analytics India Magazine reveals that the US pays $11b to India for data analytics annually.
The evolution of data science in India is tremendous and hence India is amidst top 10 countries for analytics in the world with over 600 analytic firms out of which half of them are start-ups and the number is expected to increase with the increased efficiency in its solutions and products.
Banking and finance are the prominent industries that use data analytics and revenue generated from these industries is more than 30% in India. Apart from finance and banking, marketing, pharma, advertising, and healthcare are other sectors which rely on data analytics by a large extent. Thus, data analytics, data science, and big data industry are set to double in India by 2020.
Digitalization banking data science to a great extent
In the present business, data is considered the most valuable thing even more vital than money, since data analysts use them for figuring more opportunities. Data science is a multi-disciplinary subject which effectively produces smarter business moves convincing the customers generating more revenue. Digital business is able to build a larger customer base by utilizing smarter data analytic techniques to identify the tastes and preferences of individual customers in India and brings about a satisfying solution.
Once online retailing has established a positive outcome from using big data to understand its customers, the same big data technique is applied in different sectors like engineering, medical, academic research, and social science to name a few. The availability of the huge amount of data has led to its widespread application for arriving at effective and efficient solutions or products.
Cloud and its impact on millennials favorite data science 
Cloud computing has created a revolution in data sciences which has made data centers more accessible at moderate prices thus creating a boom in the Indian market. The trending fact is that earlier only Bangalore and Delhi were major contributors in this sector in India but Pune, Chennai, and Hyderabad are not far from joining the racing market.
Data science is a vast sector with the widespread application of its techniques business identify opportunities, frame better goals and create productive solutions. This has created more demand for professionals who can analyze and scrutinize the data by understanding better insights within the data.
To flourish in this exciting field full of challenges one needs Data Science Training for gaining a comprehensive knowledge of the subject. The millennials like data science as it does not pose and restrict their functionality. Data science and millennials are interconnected as highly responsive and engaging marketing based on their preferences using data analytics drive today’s marketing with the use of advanced cloud technology.
To sum up
As you can see the evolution of data science over the last ten years has been tremendous and will continue to do so with the splurging demand for data analytics across many sectors. Data science is a promising sector which got prominent attention with the advancement in technology. As consumers are embracing digitalization, more scope of data science in India is inevitable.

What Are The Best Online Courses in Data Science Using Python?

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Truth be told, today it is all about data and using it to further business growth and productivity using cutting-edge technology to sift through huge volumes of data. The data and its analysis provide gainful insights that are used in strategic decision making, various business-related predictions, and for gaining foresight into market conditions.

Online Vs Classroom courses:

Online courses are trending and are the latest in a series of measures to equip yourself with new skill sets. You may be either looking to make a career or change professions. Such courses fill the gap in addressing the chronic shortage of trained personnel in the data science field. While the online courses do not make experts of you, they serve the purpose of giving you an overview of the subjects involved and making a generalist of you. You may also find a few free online data science certificate courses.

In contrast to the online data science certificate, the paid classroom certification courses help acquire hands-on practical-learning applications,  an improved skill set, an effective framework for learning and mentorship,  and boosts your confidence. Many also help you acquire certifications that come in handy as measurable proof of your being able to practically apply your skill sets to work issues and industry-relevant applications. However, in terms of being useful to get your dream career and job, remember that many aspects of learning about data sciences and programming languages are best learned through the hands-on approach.

Why Python?

Python is a free open-source general-purpose programming language that is very useful in data sciences. It allows you to create CSV files that help read data in spreadsheets. It also permits other complicated outputs using computational ML clusters. It has a wide range of available libraries like Pandas for tasks like data import from Excel sheets, processing time-series analysis and everything in between. Its ML libraries like PyBrain and Scilkit-Learn provide ready-made components and modules for data processing and neural networks. It is a bit slow but makes up for its excellent compatibility with other languages, simple syntax, and applicability to varied verticals.

Requisite Educational Qualifications:

Most online data science courses are introductory and fundamental in nature and do not require any formal qualification or degree. Data analysts can definitely use the resources online to learn data analytics when they have a good understanding of subjects like Computer Science, Mathematics, Statistics, Economics, Engineering, etc. Allied subjects like Machine Learning, Neural Networks, Deep Learning, and such courses are also available in the mode of online courses.

Online courses do improve your foundation in theory. Many career aspirants pursue such online data science certificate courses at reputed training institutes to equip themselves on how to apply their learning to different situations and verticals. Classroom learning, however, becomes essential to pick up crucial skills like comprehensive data capture, organization of databases, cleaning of data, applying insights to business decisions and strategy and the effective presentation of the insights gained. Proficiency in Microsoft Excel techniques, having good mathematical abilities and the knowledge of statistics are huge advantages.

  • The criteria for selecting these courses are that the online data science certificate
  • Cover all relevant data science process topics.
  • They use free open-source libraries and tools.
  • Basic machine learning algorithms are covered.
  • It covers basic applications and the theory behind them.
  • Projects, assignments and hands-on supervised practical sessions are provided.
  • Lead instructors are certified, engaging and presentable.
  • Courses are rated at least 4.5 on a scale of 5.
  • Frequency of courses can be on-request or monthly.

Conclusion:

Learning reinforcement and hands-on practice scores! With so many resources and a learning data science in Python for free and on one’s own is never easy. It emerges that the paid-courses are better than the online courses in Data Science for their widely accepted certification, mentorship by certified trainers, personalized personality-development modules, a skill-oriented approach with tons of practice and assured placements.

The Imarticus Learning courses deliver skilled well-rounded personnel in a variety of latest technology courses which are based on industry demand. If you want to be job-ready with data science certification from day one, then don’t wait.

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon. Hurry and enroll.