We caught up with Sandeep, a recent graduate of the Post Graduate program in Analytics, for a quick chat to get his perspective on the program, the curriculum, Imarticus Learning’s placement process and more. Tell us a little bit about yourself.
Sandeep: My name is Sandeep Singh. I recently completed my B.Sc. in Computer Science and was looking for an avenue to enhance my analytics skills and start my career.
I came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.
How has your experience been with Imarticus Learning? Sandeep: My experience with Imarticus Learning was super! The course focused on practical training with hands-on learning of various analytical tools and thorough practice with numerous datasets.
Looking back, I see the importance of actually applying Analytical tools and techniques to the projects I worked on because it gave me a running start when I began working.
What has changed since you joined Imarticus Learning? Sandeep: Since the day I joined Imarticus my confidence has been boosted to a very high level. Through the practice of various analytical tools such as R, Python, SAS, Tableau, etc. I’ve come to believe in myself. My soft skills have also been elevated with the help of business communication workshops, mock interviews, and soft skill sessions throughout the course.
Would you recommend the program to someone else? Sandeep: While researching various institutes, I came across some reviews that say Imarticus Learning is fake. Well, I wanted to see for myself and now that I have, I would definitely recommend Imarticus. If you’re looking for an institute, the first thing that comes to mind is the faculty and the learning material.
The faculty and staff are very cooperative and help you both inside and outside the classroom. The learning material is extensive and covers every aspect of data analytics. The best part is all of the lectures, notes, datasets, and quizzes are stored in an online Learning Management system and is available to students anytime, anywhere.
What do you like most about Imarticus?
The best thing about Imarticus Learning was the course content, the cooperative staff and the informative notes that are easily accessible. The resume building workshops and mock interviews definitely prepared me for the placement drives and I was able to crack the interview and land a job at M Technologies.
Looking to get started on your data science career, Speak with a counselor and get matched with the best course for you.
According to the reports of a joint study by Forbes magazine and Scrum Alliance, Agile-suites have become the buzz word of the latest models for SME businesses and across sizes that include startups, large companies with cross-functional teams, and everything in between. Being so is synonymous with faster marketing times and efforts, improved financial results, rapid innovations, and better workplaces.
Management practices today demand the implementation of effective change management, constant changes in requirements of the backlog, and snapping free of traditional ladders in organizations to scale and transition to a Scrum-Agile environment. The Agile transitioning parameters:
The all-important parameters when training and recruiting for a successful agile organization are: Developing talent:
Upskilling and certifications are the norms today with many large organizations retrenching the employees who do not up-skill. Agile environments require developing Scrum minded talent. An aspirant for agility needs formal training, a can-do attitude, creativity, and innovative capacities to learn rapidly, enthusiasm for the Agile thinking, knowledge of Scrum practice, change management skills and effective CSM’s motivation. Stability and Loyalty in employees:
Crucial to all growth is its dedicated workforce. One of the best means to earn these parameters is to retrain employees interested in change and the benefits including financial ones that such changes can bring in. CSM/ Coach Motivation:
Being transitioned needs a knowledgeable, certified coach or SM to lead by example. Staff motivation and retention sees an upswing when the organization is committed to collaborative work culture, where loyalty and retention may depend on the rewards, biasing Scrum environments effectively, re-skilling and training and many more such factors. Employee Skills:
Retraining employees is possible by doing advanced courses at Imarticus who are reputed globally for their excellent courses. Agile does encompass technological changes incorporation and cross-functional knowledge of fields like Big Data analytics, Deep and neural networking, AI, ML and visualization techniques which will need to be over-hauled and re-trained for effectiveness. Perceptual changes:
The very success of the technology and framework lies on adapting the uniform mindset required to become self-organized. Lip-service is insufficient and involvement from the grassroots levels up-wards will need drastic Agile changes in perception. The benefits that accrue:
1. Scrum tools ease the process of managing the Scrum projects even if they work remotely. These tools are reliable, help the teams to work, aid team collaboration, and facilitate the team’s and organizational productivity: Ex: Jira, Scrum Vivify, Manuscript, Scrum DO, Axosoft, Targetprocess, QuickScrum, Yodiz, ScrumDesk, ScrumWise, Zoho sprints, and ClickUp.
2. The Scrum success story has spurred recruitments in large, medium and startups including Philips, Honeywell, Dell, Siemens, Accenture, Capgemini, Allstate Insurance (India), Informatica, and Zensar Technologies. Scrum tools are conducive to remote-working and are extremely reliable across platforms, technologies, and types of teams. Life in an Agile-Scrum cross-functional team of experts is an excellent aid in collaboration, communication, and contribution of the true Scrum team members. No one is different in the framework and success depends on the team performance spurred by an able SM.
3. Scrum has the potential the playing ground of organizations and is poised to affect everyone in the environment. Why being Agile has become popular: The changes brought about by the transformation are welcomed by employees and organizational management alike because
The agile work-hierarchy has turned flat and encourages the thinking of all as Scrum team-members who are equal and work collaboratively.
Over half the organizations have transitioned successfully.
The framework has included effective Scrum mentors and Agile coaches to help and aid the teams to perform better.
Re-training is easy by doing a formal course at Imarticus.
Agile framework and Scrum values have very many benefits for the organization including efficiency and productivity with rapid time-boxed frequent viable products releases.
The methodology is actually a philosophy that is equally applicable in personal tasks and life.
Conclusion:
Agile running on a Scrum framework is akin to sharpening the blades of success. Everyone from a flexible director and project manager, the self-organized Scrum team, the crucial strategy of an Agile Coach’s role, and the transforming mindset are set to impact everyone in the chain and across processes. Do a good pro degree with Imarticus in the classroom mode to get your CSM or Agile Scrum certification and contribute positively to your team and organization.
One of the biggest developments in the world of computer science has undoubtedly been Artificial Intelligence. The ability of your machine to learn and understand all about certain processes and then implement methods to improve the same is one of the most in-demand jobs today.
It becomes necessary to evaluate a company’s software and see how they can implement artificial intelligence methods.
There is so much that is possible while applying artificial intelligence in marketing. By 2020, more than 30% of the companies worldwide will use AI to help streamline their sales. This will help them increase efficiency and focus more on converting sales and rates.
Here are a few other places where AI will play a prominent role:
Driverless vehicles:
Automated vehicles aren’t a dream anymore. The likes of Tesla have already started implementing driverless cars on the road. The U.S. Department of Transportation has gone ahead and released certain definitions and rules pertaining to the various levels of automation which can be implemented.
Uber was also acquired by Google in order to help scale their properties and capture the driverless market in time. AI could help save lives lost in accidents and potentially save close to 30,000 people in the United States every decade.
As it is a disruptive technology, it is expected to create some big changes. It can also automate many jobs which affect people. In the near future, it is expected to be used for opportunity more than threats.
2. Process automation:
Robotic process automation refers to the use of machine learning to automate tasks dependent on rules. It will help individuals focus on certain crucial aspects of their work and leave the routine work to machines.
Automated projects will take up a bulk of the automation work in the world of machine learning and artificial intelligence. Companies are always looking to be cost-effective and automated machinery will help them achieve that goal over the long term.
3. Sales and marketing:
Artificial Intelligence is also being employed in so many sales and marketing sectors. AI can be used as a useful tool to make repetitive tasks much easier. This includes tasks such as scheduling, paperwork and even timesheets to make it easier.
Marketing teams will also be able to weed out fake leads from genuine ones to make it easier to choose the right people to market to. They will be able to make the process simpler and allow everyone to get better at their daily tasks.
Overall, artificial intelligence is on the route to make the world a better and easier place to live and work in. It is a disruptive technology which will create dramatic changes. It can also be used to automate a multitude of jobs, especially in the production sector and make it easier for companies to become cost-effective.
Over the long run, where this will head to cannot be predicted but by the looks of it, it seems like a good place to be in. With Imarticus, you will be able to take up an artificial intelligence course that makes it simpler for you to succeed. In the battle between machine learning vs artificial intelligence, you are the real winner!
Deep Learning, ML and AI are all used to support facial recognition and used traditionally the Eigenvalues for vectors and spaces defining the features of the space projected by the face. In 2012 AlexNet tweaking and deep learning technologies like the DeepID, DeepFace, FaceNet, and VGGFace went beyond the human capacity to recognize faces by aligning, using feature extraction, detection, and recognition techniques. Thereby the use of verifying faces in a photograph under various lighting conditions, an aged face, with glasses or without facial hear was made possible by leveraging deep learning of face datasets and model representations.
The recognition software is biometric in nature and can accurately identify, authenticate and verify a face just by comparing the facial features and contours against very large databases. It is widely used for:
The enforcement of the law by the police and detection agencies.
In businesses for biometric logging in and out.
In banking to ensure KYC and restricted access to lockers.
In AR and VR applications for animated film making.
Authentication through facial recognition:
The most useful advantage of facial recognition is that facial contours do not change and can be captured from a distance. It never fails since faces cannot be replicated or imitated successfully. The technology itself is of a non-contact biometric type and has been successful in restricting entry, ensuring attendance, for crime prevention, law enforcement and as a security measure. The technology is also inexpensive and infallible when compared to other methods like fingerprinting, Retinal scans and such biometric methods which are contractual in nature needing the voluntary provision of data for further process.
Many devices need and work on authentication based on face photograph verification either taken from videos or still photos. Human beings are very good at this task and deep learning simulates the same process. Deep Learning and ML use ConvNets for the analysis and identification processes. Such neural networks are highly intelligent, self-taught and have other applications sewn in like the NLP processor, video analyzer, recommender modules and such. The four essential steps involved are: 1. Detection which involves detection and using a boundary box for the image face. It generally falls into two categories namely
Based on features and using hand-work filters based on knowledge of the domain.
Based on images and ML where neural networks work on extraction and location of the image. 2. Alignment tasks normalize the photometry, geometry and such parameters with the database since most photographs contain more than one face and need to be aligned. The alignment output depends on the following task categories.
Binary labels for class and probability.
Similarity parameters.
Category labels.
3. Extraction of facial features is used for the task of recognition. The tasks can be further classified as tasks for
Matchingand finding the best results.
Similarityanalysis for faces.
Feature transformationand generation of new similar face images.
4. Face Recognition itself consists of two main tasks to identify any given image. Namely,
Verificationwhere features of the identified face are mapped to the given image.
Identification where a given image is mapped against the database.
ML has proved to be invaluable to Deep Learning solutions. The present-day technological advancements make facial recognition and such issues easy. One has to choose the algorithm and feed in the given face image or data. The built-in neural network and trained dlib models will then take care of analyzing the face, comparing it against its databases and giving us an accurate match of the face against it. Further, the face recognition software on Github is easy to use, has a great library and is a rapid install. Conclusion:
Deep learning machine algorithms and neural networks can currently manipulate, detect and identify facial contours from very large databases very quickly and this ability is far beyond human capacities.
If you are interested in such specific applications you will need to do courses that are skill-oriented in ML, Neural networks, Deep Learning, handling databases and applications, AR, VR, and such futuristic technology. Most of these courses are offered by Imarticus Learning where learning is practically based and you are job-ready from day one. Who does not like able-mentorship from certified trainers, a widely accepted global certification and assured placements when looking to transition careers? Don’t wait too long. The route and opportunities are just right at the moment.
How Analytics And Data Science is helping OYO To Enhance Customer Experience?
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 travelled 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 scienceprogram. 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 by 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 second 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 as the 2018 unicorn amid the disrupted 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 customers 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 Course 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!
The economic sector is now extremely digitized to the benefit of companies and organizations. The techniques to reach out to customers and have a position in the market are no more in need of a physical entity. Digital transformations along with machine learning applications have cut out on various limitations to reach out to customers. Let’s have a look at the top 5 reasons at how machine learning is the cause for these prospects:
Removal of physical constraints has opened up several opportunities for trading sites and market campaigns. One doesn’t need to go to a bookshop to get a book but can quickly look up on the internet- not only restricted to a hardbound but can also opt for the more economical option of eBook. The physical retail outlets have also been linked to online stores so that customers can reach out to the product even if they don’t have the physical access to the shop. Machine learning gives you the best options for your shopping preferences, and there’s almost always a guarantee to get what you want. A digitized shop doesn’t need a limited space to spread out its resources.
Consumer customized feeds are the latest trend possible exclusively because of machine learning. It’s an unquestionable fact that your organization can expect growth only if it attracts the right consumers. The flow of consumers is subject to competition and convenience. Every person has ample amount of options to go for his specific needs and hence, you have to have something exclusive to provide them such that they return to you if not because of the product, then because of the convenience in getting the product. The present age values individualism and customers should feel prized.
Filtering of advertisements on the World Wide Web can help the inflow of the right customers to a brand with the usage of Machine learning. It’s a fact that billions of data are fed to the internet with every click of users and if all this data is classified with the help of algorithms, it can be determined where to broadcast a particular advertisement to expect a maximum number of clicks. Ads are a costly commodity, and one can either thoughtlessly waste them or smartly utilise for the growth of the brand.
Automation of the process makes it instantaneous and efficient. On digital websites, there’s no reason for the delay in your order since every user command works independently and simultaneously along with multiple others. Hence, you don’t have to wait in a line depending on large mechanical machines and waste several minutes and even hours on something that can be done within seconds with AI.
The benefit of the digital interface reaches both the retailer and the customer simultaneously. If you place an order, while the money gets directly transferred into the bank account of the seller, the customer also receives the assurance of getting the product hassle-free to her doorstep. There’s no scope of mistrust, and it’s a transaction of mutual satisfaction. Several other units of the common benefit from this transaction. One may also notice how the operation has acquired several dimensions of linkages benefiting not just the giving and gaining parties but also other units involved in it.
Finally, one can assume that Machine learning through its ability of implicit learning has given greater power to the people involved in a specific transaction. On the one hand, one can never forget that it all follows the policy of pleasing the consumer. As market competition rises, machine learning is just another necessary addition to your organization. Related Article:
The business scenario today has evolved and kept pace with technological developments. And AI has been at the helm of the change experience impacting literally every area that affects growth and development. The changing economic, geopolitical and social environments are in a state of constant flux and need businesses to adapt very quickly to tide over the changes in organizational dynamics, critical business glitches like employee retention and hiring or landscape requirements like being scalable and Agile.
Artificial intelligence can help bridge over troubled waters in many areas where human intelligence and limitations fail. Let us explore some of these critical areas where AI has and still has the potential to improve the business scenario. The successful customer and user experience:
The experience of the customer is what tells brands apart and this differentiator is best exploited through successfully harvesting of the data and changes brought about by AI. Research and use of Walker data suggest that large multinationals like Adobe, Intuit, and EMC have benefitted greatly by entwining the customer experience into their operational daily routines of marketing, sales, and operational routines. And AI makes it possible to offer those great user-experiences crafted from forecasts and gleanings of data on why the customer buys, when and for how much, how the competition fares and their latest parleys, or what the customer wants from you.
The arsenal of data forecasts and insights can personalize an individual’s experience to match his needs, budget, etc, through a more seamless integrated process that offers high satisfaction and customer loyalty. The results are most helpful in rapidly predicting markets, changing products, forecasting customer- behavior, and staying up to date with the latest offers of technology. Thus AI is the one tool that has immense potential in accumulating, understanding and changing the fortunes of business enterprises by forecasting touch-points, trends, brand preferences, pricing strategies and more. Bettering the hiring process:
The acquisition of skilled talent is critical to all businesses. However, most processes like recruitments, interviews, talent hunting, employee-referrals, and assessments are subject to very many biases, nepotism, controls, and flaws.
For bettering the hiring process certain tasks are all important. Firstly, one has to cast the net wide. Secondly, the talents need to be matched to the job requirements and the process of pivoting in on the right candidate needs to be free of human errors and bias. Lastly, the holistic use of data using the latest developments needs to be deployed. Not surprisingly, AI aided assistants today can make short work of the recruitment process while ensuring a great supply database for recruitments and keeping in mind the specifics of talent growing into higher roles and reducing the pitfalls of employee migration and retention issues. Retaining and engaging the employees:
Skill and talent lie at the core of the hiring process. With increased demand comes the problem of retention and employee engagement turning into a competitive minefield. Poor management practices, lack of growth on the job and employee engagement have turned into major contributors for lack of retention of employees as is evident from surveys conducted by SalesForce and Gallup.
AI has enabled cutting-edge technologies like analysis of employee sentiments, biometric trackers, and such AI-empowered techniques can aid in effective retention through timely motivation, employee empowerment, continued learning opportunities and ensuring deserving rewards, career growth, skill up-gradation and more. More engaged employees mean better retention, employee loyalty, and engagement. Conclusion:
In parting, it is valid to note that AI helps the new operations in business which in turn can change the dynamics of a beyond satisfying customer-experience, growing engagement with employees, hiring and retention. People are assets to the company and the twist that AI and technology have brought in can easily transform companies through efficient dynamics, change and people management.
To learn all about futuristic technologies like adaptations of artificial intelligence, powering AI through effective Machine Learning, scouring the growing volumes of data through Deep Learning and beyond to futuristic technology like blockchains for fintech industries try the Imarticus Learning experience.
The Agile Scrum Tutorial are succinct with due emphasis on the practical applications of knowledge and concepts coupled with invaluable modules of self-development and soft-skill training. Besides, one gets the mentorship of certified and industry-drawn mentors and instructors. Go ahead and make the most of opportunities and jobs on offer in their placement program too. Why wait then?
Most of us have heard the term “business analyst”, but seldom people know what their expertise or roles are. According to the BABOK Guide by the International Institute of Business Analysis, business analysis is “knowing how organizations function to accomplish their objectives and defining the capabilities an organization needs to produce products and services to external stakeholders”. In short, they bring about the change stakeholders want to see in their organization.
Explaining all the things a business analyst do might not be helpful as this varies from industry to industry. However, there are a few, critical aspects of their role that span across industries and are specific to their key skills. They comprise of:
Understanding the Business A business analyst training courses are primarily required to understand the organization and the way it works in the current scenario. In the second stage, they plan a road-map that details the ideal future of the company, in-line with the inputs of project team members, leaders, stakeholders, and subject matter experts.
One important thing they can do is that they can ask dumb questions without actually looking stupid. These questions often help to devise easier ways to take the organization where it needs to be.
The next thing a business analyst has to do is documentation of the current process to help solve the problem they are trying to refine. These documents involve modelling of the current system using diagrams. These models help to figure out the difference between actual and established protocols.
Devising the Solution After proper understanding of the business and its requirements, brainstorming sessions are led by the business analyst to devise a solution for the problem. Researches are conducted both inside and outside of the organisation. Data analysts participate in the requirements gathering process to ensure his/her knowledge has the depth and context demanded by the problem.
Zeroing in on the best solution for the problem statement given to them, is the responsibility of a business analyst. A business analyst should also translate the organization-specific lingo into the terms that engineers can keep in their mind. To do this, rudimentary understanding of technical systems and their working is recommended. Various tools such as Gap Analysis, Root Cause Analysis, and Business Process Modelling can be used to compare the different solutions.
Blend Business with Technology The job of a business analyst does not end with implementing the solution. They have to make sure that the technical operations meet the business needs of the organization. For that, involvement in system testing and creation of user manual are common practices followed by the analysts.
Business analysts offer choices to the organization without being afraid of ideas being shot down. They collect every data about each process and pick at every facet to see how things work. These people work as a catalyst for the change in the organization. With their documentation and analysis skills, they give valuable suggestions to the organization.
First, they understand the organization, its processes and goals and then implement the changes they need. This quote especially pertinent to the role that business analysts play when Stephen Covey’s fifth habit of highly successful people: “Try first to understand, then to be understood.”
We all commit mistakes, and there is nothing shameful about it. Learning from those mistakes is what makes your life a step ahead. But, while working as a data scientist, you have to be cautious as one number here, and there will end up giving completely different results.
Here are some of the unintended mistakes you might make while working on piles of data. As a data scientist, you must try to dodge off these mistakes.
Being too bookish: No doubt, knowledge comes from books but while working as a data scientist, you have to be practical in approach. Not every problem is solved similarly. There is so much to read- Algorithms, derivatives, statistical functions and again books. But, it is of no use, if you are unable to recall it when the time to apply it arrives. Therefore, try to supplement your bookish knowledge with a bit of practicality.
Start from the basics: Don’t jump to machine learning without even knowing to mean, median, mode. Start with the basics of data science. Once you have a good hold over mathematical operations, fundamentals of mathematics, command over a programming language and an aptitude to apply all these skills for executing real-life problems, start with machine learning and AI.
Choice of wrong visualization tools: Concentrating on a limited number of technical aspects of data science inhibits your learning process. You should try to diversify your selection of visualization tools to look at the assigned data with different elements. Because a particular trick may apply for a few data problems but not for every question, you will encounter as a data scientist. Similarly, not every data problem comes with a pre-assigned mechanism to solve it.
Analyzing data without a decided objective: A data should be analysed keeping in mind what you wish to achieve as a result or else you will end up digging diversified results which might be of no use.
Do not forget ethical issues concerning data: A data can have sensual entries. While working on the data, try to unearth the results which can be helpful for your organisation. As a data scientist, protection of data is also one of your responsibilities.
Never be too proud of your certificates and degrees: Having a degree from a renowned institution is right for your academic health but never be too overwhelmed with those certificates in your hand. After all, the experience is an excellent teacher which you will eventually gain through the passage of time.
Inconsistent learning process: Never pile up your work. Make sure you practice daily whatever you have learnt.
Paying no heed towards the development of communication skill: Your knowledge is incomplete if you cannot dissipate among others. Once you are done with your graphics, charts and accomplishment of the daily task, try explaining it to others. Even a person from a non-technical background should be able to comprehend what you are trying to say.
Avoid manipulating the data: Don’t make unnecessary additions to the data to achieve desired goals. Be honest about your job as a data scientist.
Biased sampling: All the sections of the population must be covered. Otherwise, the results would be considered discriminatory.
Doing anything to get your work published: This is a cardinal sin which a data scientist would ever commit.
Machine learning has become quite the trend, you must be noticing a lot of people opting for this particular course. So today we will tell you what the fuss is all about. To put it in simple terms, machine learning is basically learning from data. It involves tweaking of parameters and adjusting data, to get the best possible inference. It takes a little bit of practice to master machine learning, but it is not rocket science, you will get there sooner all later, just make data and algorithms your very best friends. What is machine learning?
To start off, machine learning is all about feeding data into a generic algorithm and help it build its own logic, based on the data fed to it. This way, you don’t have to write codes. The subject can be divided into two main categories; supervised learning and unsupervised learning.
If you are tired of nodding at conversations about machine learning without understanding a thing, it is time you change that by getting hold of a machine learning courses. Believe it or not, it is an amazing skill to have, which will hold a very strong place in your resume or C.V. In fact, in today’s tech-savvy era, not knowing about machine learning is going to have a negative impact on your job. If you have no idea about what is machine learning then be a sport and start from scratch, there is plenty of study material available online and offline. Try to go through the theories, understand the basics and when you are ready, do opt for a machine learning certification course. What is the hype all about?
Truth be told, the hype around machine learning is not going to fizzle out any time soon. It is a very important subject in a number of domains, as the subject has yielded some amazing results and there you can expect even better things in the future. At its core, the subject is really simple, and it involves lots and lots of data. It is very important to have access to as much data as you can possibly derive, and having documentation of the same. The progress made in the field of machine learning within the past decade has been absolutely phenomenal. This is a brand of artificial intelligence which is heavily based on data. The algorithms, as well as the data, helps the model to make accurate decisions, with the least human intervention.
Machine learning is one subject with the help of which we can easily, also very quickly analyze and understand, complex, big data and yield accurate results from it. This can be done on a very large scale, which increases the chances of identifying profitable opportunities. The trend of machine learning
If machine learning facts and trends are anything to go by, then some major breakthroughs are on their way. Organizations can make better decisions without relying on human intervention. By using an algorithm to build models with the help of machine learning. Any industry working with a large amount of data, can make the most of progress and work more efficiently to gain an edge over their competitors. Many people are buying the machine learning trends and are more than willing to imbibe it in their organization whilst making the best use of it. Why is everyone going gaga over machine learning?
With a machine learning certification, you can make yourself useful in the following fields:
Financial services: Banks make use of machine learning to understand investment opportunities, trading trends and identify the clients with high-risk profiles. In fact, acts of fraudulence can be pinpointed with the help of machine learning surveillance. With such cut-throat completion in the finance sector, having a machine learning certification will most certainly prove to be an asset.
Transportation: Transportation is one field, where analyzing data helps in making some of the key decisions. The data analysis of machines learning can help both public and private sector transportation in many different ways.
Healthcare: All thanks to sensors and wearable devices which can assess a patient’s health, a lot of data can be gathered. With the use of machine learning, medical experts will be able to look at the various health trends, point out hazards and even stop epidemics from spreading. This will lead to better diagnosis, treatments, and prevention as well.
Government: The government deals with various different kinds of data, especially in areas such as public safety and utilities. Machine learning can really help in analyzing different kinds of data and find solutions to the impending problems with regards to the civilians. It can also minimize identity theft, online frauds and much more.
Marketing and sale: If you wish to build your career in this field, then you must opt for a machine learning certification course. Capturing data and analyzing upcoming marketing trends, alongside planning new campaigns based on them will become easy.
A course in machine learning opens many vistas of opportunities for candidates in the various fields. It is perhaps because of this reason, people are growing crazy about this particular area of computer studies. It is not the most difficult to master and people with the non-technical background can get a hang of it as well. The bottom line is, machine learning trends are on the high, so you might as well, think of opting for a course and strengthen your position in your organization, as it is a very important skill set in today’s times.
I came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.
