Contact Us
×

Category: Analytics

How are Online Retailers Using Big Data Analytics?

Posted on by Imarticus Learning

Data is being generated at every moment of the day and has grown from retailers using their own data to databases available across industrial verticals. It is so huge that cloud storage is now the buzz word. Data analytics with the Big tag deals with data primarily and the predictions or forecasts from analyzing databases that help with informed decision making in all processes related to business. This could run into volumes of several petabytes of data.
But, why would one need a Big Data Analytics Course? Because smaller databases that are less than a terabyte size-wise can be tackled with traditional tools. However, modern data tends to be unstructured and comes in the form of videos, audio clips, blog posts, reviews, and more which are challenging to clean, organize and include huge volumes of data.
The tools and techniques involved in the capture, storage, and cleaning of data need necessarily to be updated. One also would need faster software that can compare databases across platforms, operating systems, programming languages and such complexities of technology.
The speed and agility of analytics offer big advantages and savings in making informed business decisions. That’s why investing in data analytics and Data Analytics Training is such a popular choice across industrial verticals and sectors.
Let us look at the data analytics improvements of some real-life examples.

Offering marketing insights:

Foresight from analytics has the potential to change marketing strategy, operations and more in all firms. Whether it be effective marketing strategy or promotional campaigns, decision making, purchasing, cost-saving measures, targeting the customers, promoting products or improving efficiency through the predictions, insights, forecasts, etc help make those decisions. Just look at the campaign of Netflix covering over 100 million customers for inspiration.

Boosting retention and Customer-Acquisition:

Coca Cola used their data foresight to draw up their retention and loyalty reward programs and to improve their services, products, and customer stories. Besides boosting sales such improvements trigger loyalty too.

Regulatory compliance and Risk Management insights:

Singapore based UOB did their risk assessment and management for the financial sector and budgeting. Foresight and predictions can also be effectively used as a critical investment in regulatory compliance.

Product innovations:

Take the example of Amazon’s diversification into groceries, food, and fresh-foods segment. Their analytics program was based on the acceptance of customers trends and successfully helped innovate product lines, design models of innovation in saleable products, etc.

Management of logistics and supply-chains:

This essential field can be transformed very effectively as Pepsico did with improved processes, scheduling deliveries, warehouse management, reconciling logistics and shipment needs and more.
Budget and spending predictions:
The loyalty of customers is reflected in spending patterns and data is collected from use of credit cards, effects of promotional programs and customer retention data, web users log-in data, IP addresses, etc to gauge predictions for spending and effective budgeting. Did you know that Amazon analyses accounts that run into astounding figures like 150 Mil customers and their analytics programs increased sales by 29 percent and new customers by 40 percent? That’s huge profits from data analytics!

Bettering customer service:

Improvement in customer experience yields big dividends as in the case of Costco where specific customers who were at risk with listeria contamination in fruits and were warned instead of creating a scare with emails to all customers.

Demand forecasting:

Just look at the Pantene and Walgreens hair-care products sales figures. They promoted the products based on a demand prediction of weather and anticipated higher humidity affecting sales of anti-frizz hair products. Pantene recorded a 10 % increase and Walgreens a 4% sales increase. Smart use of data analytical predictions by retailers!

Research on journeys of customers:

This graph is never a straight line and when in retail marketing analytics with many thousands of customers, one can help understand data like where an individual customer will seek product info, how and where to reach such customers, why the customer loyalty changed, etc. Looking for the needle in the haystack is now easy with data analytics.

Concluding note:

All enterprises, especially in the retail sector, need big data analytics to have reduced operational expenses, a competitive edge, enhanced customer loyalty, better productivity, and retention. The demand for data analysts keeps growing alongside the growth of data and is an ideal choice of careers with scope, payouts, and growth. If you wish for a Data Analytics career, then do a big data analytics course at the reputed Imarticus Learning. Their data analytics training with assured placement, certification, soft skill modules,industry-suited curriculum, and real-time project work offers the best career choices. Enroll today!

How Do You Start Learning Artificial Intelligence? Is it Possible to Get Research Work in The Field of AI?

Posted on by Imarticus Learning

The last decade saw the introduction of Machine Learning Training, Deep-Learning and Neural networks in AI to acquire the capacity to reach computational levels and mimic human intelligence.
The future scope of Machine Learning appears bright with ML enabled AI being irreplaceable and a composite part of evolving technologies in all verticals, industries, production means, robotics, laser uses, self-driven cars and smart mobile devices that have become a part of our lives. It thus makes perfect sense to learn Machine Learning and make a well-paying career in the field. Since the early 50’s a lot of research has gone into making these developments possible, and the trend for continued research into AI has made it the most promising technology of the future.

Why study AI:

AI rules and has become a reality in our lives in so many different ways. From our smartphones and assistants like Siri, Google, Alexa etc, the video games and Google searches we do, self-driven cars, smart traffic lights, automatic parking, robotic production arms, medical aids and devices like the CAT scans and MRI, G-mail and so many more are all AI-enabled data-driven applications, that one sees across verticals and without which our lives would not be so comfortable. Fields like self-learning, ML algorithm creation, data storage in clouds, smart neural networking, and predictive analysis from data analytics are symbiotic. Let us look at how one can get AI skills.
Getting started with AI and ML learning:
To start AI learning the web offers DIY tutorials and resources for beginners and those who wish to do free courses. However, there is a limit to technical knowledge learned in such ‘learn machine learning’ modules, as most of these need hours of practice to get adept and fluent in. So, the best route appears to be in doing a paid classroom Machine Learning Course.

Here’s a simple tutorial to study ML and AI.

1. Select a research topic that interests you:

Do brush through the online tutorials on the topic on the internet. Apply this to small solutions as you practice your learning. If you do not understand the topic well enough use Kaggle the community forum to post your issues and continue learning from the community too. Just stay motivated, focused and dedicated while learning.
2. Look for similar algorithm solutions:
The process of your solution would essentially be to find a fast solution and it helps when you have a similar algorithm. You will need to tweak its performance, make the data trainable for the ML algorithm selected, train the model, check the outcomes, retest and retrain where and when required by evaluating the performance of the solution. Then test and research its capabilities to be true, accurate and produce the best results or outcomes.

3. Use all resources to better the solution:

Use all resources like data cleaning, simple algorithms, testing practices, and creative data analytics to enhance your solution. Often data cleaning and formatting will produce better results than self-taught algorithms for deep learning in a self-taught solution. The idea is to keep it simple and increase ROI.

4. Share and tweak your unique solution:

Feedback and testing in real-time in a community can help you further enhance the solution while offering you some advice on what is wrong and the mentorship to get it right.

5. Continue the process with different issues and solutions:

Make every task step problem you encounter an issue for a unique solution. Keep adding such small solutions to your portfolio and sharing it on Kaggle. You need to study how to translate outcomes and abstract concepts into tiny segmented problems with solutions to get ahead and find ML solutions in AI.

6. Participate in hackathons and Kaggle events:

Such exercises are not for winning but testing your solution-skills using different cross-functional approaches and will also hone your team-performance skills. Practice your collaborative, communicative and contributory skills.

7. Practice and make use of ML in your profession:

Identify your career aims and never miss an opportunity to enroll for classroom sessions, webinars, internships, community learning, etc.
Concluding notes:
AI is a combination of topics and research opportunities abound when you learn to use your knowledge professionally. Thus the future scope of Machine Learning which underlies AI contains newer adaptations which will emerge. With more data and emerging technological changes, the field of AI offers tremendous developmental scope and employability in research and application fields to millions of career aspirants.
Do a machine learning training at Imarticus Learning to help with improving your ML practical skills, enhance your resume and portfolio and get a highly-paid career with assured placements. Why wait?

How Can You Learn About Healthcare Data Analytics and Get Training and Certification Online?

Posted on by Imarticus Learning

The healthcare field has seen many improvements with the application of data analytics. From record-keeping, medical device calibrations, research on disease management, predictions of epidemic outbreaks, and suggestions of personalized health and treatment measures, data-analytics, ML, AI, and big data all play crucial and ever-increasing roles. Online courses are excellent as they address the pressing personnel shortage for certified data analysts and scientists. They do not make specialists of you. However, they do equip you with a generalist’s overview of the healthcare sector, update and refurbish the required skills, and offer certifications in a short period.
A paid Data Analytics courses, on the other hand, will help you hone your skills by practical learning application, effective mentoring and makes you a job-ready contributor to healthcare data analytics projects. It also serves to boost the first-timer’s confidence. During the interview rounds for your dream career and job, you will, of course, be tested on how you propose to use your skills to tackle problems that will arise and a good grasp of modeling and your industry-relevant measurable certification will go a long way.
Requisite Educational Qualifications:
Being an introductory and fundamental course, there is no necessary qualification specified. Data analysts can learn Data Analytics online and sometimes might need a basic degree with an understanding of subjects like mathematics, computer science, statistics, engineering, economics, etc. Most of these courses improve foundations and strengthen your skills. Hence, many pursue online courses at reputed institutes to give themselves the knowledge of how to apply their learning across various verticals. And truth be told, today it is all about data and no field including the healthcare sector, is free from using the same for furthering growth, efficiency, and technology.
Classroom learning during your Data Analytics Training will be needed to acquire crucial role skills including the comprehensive capture, cleaning, and organization of databases, the applications of data to business strategy, and effective communication of the analysis reports. Familiarity with excel techniques and statistics will be a plus point.
What the course teaches:
Let’s explore what most courses cover or do not cover and are moot requirements for a data analytics job-role.

A. Technical Skills:

Computer programming and CS Fundamentals including

  • Dealing with unstructured non-clinical and clinical data including blog posts, videos, reviews, social media posts, audio clips, medical images and videos that don’t fit into tables and are complex to handle.
  • SQL Coding and Databases score in operations like delete, add, query or extract functions used for transforming structures and in analytical functions when working with relational databases like patient records and insurance claims.
  • The platform of NoSQL/Hadoop is preferred with knowledge of Pig, Hive, cloud tools and so on for situations involving the transfer of data, storage, sampling, summarization, filtration, and exploration of data. Apache Spark and Scala frameworks are similar to Hadoop but much faster in handling very big-data volumes.
  • AI, MLand Neural Network knowledge and techniques are essential if you wish to score in the emerging uses of data-analytics to healthcare.
  • Data Visualization techniques that include formatting, editing, graphs, charts, etc. are easy with tools like ggplot, Matplottlib, and d3.js Tableau to make effective data forecasts, presentations and case studies.

·   Language proficiency in 

  1. R Programming.
  2. Coding in Python is recommended for versatility in its applications. Python can be used for all medical and healthcare processes and comes with a variety of libraries for nearly all verticals, browsers, etc.

B. Non-transferable Skills:
These are essentially not taught and depend on practice –

  • Quantitative and problem-solving aptitude skills
  • Grasp of inferential logic, an innovative approach, and great communicative skills
  • Above average skills in attention to detail, reporting and programming skills
  • Business acumen, team-skills, dedication, flexibility, and continued learning form a confident learner

Conclusion:
In parting, do acquire Data Analytics Training certifications online or in a paid course. Attend boot-camps, hackathons, MOOCs, etc. all of which give you support, exposure and mentorship in ML, ConvNet, and data analytics practical techniques. The demand-supply gap for data analysts ensures great payouts and undying scope over the next decade, according to the 2011 reports from Mckinsey and the survey by Accenture.

Attend and learn data analytics from a reputed institute like Imarticus Learning to emerge job-ready and with certification from day one. They stress on the non-transferable skills and personality development as well. Hurry and be an early bird!

We offer data analytics courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.

How Can You Prepare for The Data Science Interview?

Posted on by Imarticus Learning

How Can You Prepare for The Data Science Interview?

Do you have the jitters before every interview? Everyone does! Besides trying to run through the probable questions mentally, you need to stand well-placed with three fundamental attributes. They are aptitude, mathematical knowledge, and proficiency in technical skills. To explain and convince the other person does call for excellent communicative skills and a presence of mind! Commonly, data science courses will include learning of techniques in Big Data, Machine Learning, and programming languages like R and Python.
Before you try and prepare for a data science interview, you need to be honest with yourself and identify your key strengths and weaknesses.
What do you think the questions asked to you will be? Let’s have a look at the best techniques to conquer those butterflies in your stomach advocated by Imarticus Learning to get ahead of the crowd and ensure you emerge successful with a Data science Course.
Task 1: Understand your skill set, job profile, and application:
The essentials for any post in data sciences though, are the practical implementation-skills of your domain knowledge, the tools, and techniques you have competency in, great aptitude and comprehension attributes in quantitative, analytical analysis, programming languages and your confidence in answering questions on them.
Task-2: Crack the technical round:
Cover conceptual understanding of important topics needing the application of programming languages like Tableau, TensorFlow, Scala, Python, SQL, and R. You can expect most interviews to have a skill-test round where questions will be a case-study or assignment based on your skill-sets and implementation values of your learning. This is probably where all your tasks, test cases, project work, and case studies will be the litmus tested.
Task-3: Revise your basic topics well:
Since time and explanations need to be concise and succinct, you would do well to revisit supportive topics of data science like –

  • Concepts in Probability, Bayes Theorem, the distribution probability, etc.

·  Modelling techniques, Linear and non-linear Regression, Statistical Models, Time series,  Models for Non-Parametric data, popular algorithms, data tools, and libraries, etc.

  • Deep learning, database best practices, ML, ConvNets, LSTM, and other neural networks

You will need to make effective presentations of an industrially-relevant scenario through discussions or case-studies. It is a challenge to present the problem, cite research undertaken by you or others, suggest a valid solution and discuss business outcomes. Ensure you use and showcase your capability to solve problems, reinforce your learning, display solution finding, presentation, and team skills in this round.
Task-4: It’s perfectly valid to not have all the right answers in the personal round:
Data science is a vast field, and innovations happen every day through newer and more optimized models and statistical techniques. There are ten ways to do one thing, and at the end of the day, nobody has all the correct answers. So it’s fine if you do not know anything. However, the flexibility to adapt to teams and accept other’s views, the vision to add value to the employing organization, and learn-on-the-job are non-negotiable in this round.
Task-5: Your resume is the basis of measuring you:
Most times, it is best to mention what matters most in resume writing. Questions asked during interviews will silently explore your admissions. Be prepared to link your learning to your job experiences and prepare for justification of career decisions and choices made and stated in your resume.
Task-6: Continued Learning and practice counts:
An excellent Data science course certification, webinars, community learning, MOOCs and internships are good validations and endorse your desire for continued learning, focus on applications and job-suitability as well. Practice and repeat the reinforcement of your learning curve.
Conclusion:
Especially for first-timer career aspirants, the interview can prove to be very stressful. It is okay to stumble and fail, but the ability to get back up on your feet and justify your strengths is crucial. A Data Science career is a juggling of multiple domains and soft skills, a strong persona, dedication, and intent.
At Imarticus Learning, the methodology is to practically train you as a generalist on all the above tasks and includes resume-writing, personality-development and interview-training modules leading to assured placements. Their certification is widely accepted in industry circles as a skill-endorsement and being job-ready. So, why wait? Enroll today.

Ships Of The Future -Will Run on AI Instead of A Crew?

Posted on by Imarticus Learning

Technology has taken a high route since Artificial Intelligence has gained immense impetus over the years. Alexa and Siri have become household names as millions of their users, start the day, and close the same with them.

Artificial Intelligence is also seen to be transforming a number of industries including the shipping industry.

This means that your cruise ships are about to you take you into the future. They will be driven by artificial intelligence instead of a crew member. In the year 2017, two friends Ugo Vollmar and clement Renault were all set to work on a self-driving car project until they stumbled upon an article that talked about autonomous shipping which made them sail in a different direction.

Human resources and autonomy 

Autonomy would operate in a different manner when it comes to water than it does for roads. In the case of waterways, it will not completely eliminate the human resources on board. This is because when it comes to cars, there is only one person that takes over the entire control to operate it while for ships, there is a bare minimum of at least 20 crew members on board, all of them being assigned crucial duties.

Thus, in the case of roads, that one person can be completely replaced by autonomy, but not all the crew members can be replaced by autonomy in its entirety.

“Diesel engines require replacement of filters in oil systems—the fuel system has a separator that can get clogged. There are a lot of these things the crew is doing all the time” quoted Oskar Levander, the head of Rolls Royce’s autonomous system efforts.

This is why it can be said that the helm is most likely to be operated with autonomy using a robot or remote control while a part of the crew can help in taking care of the vessel. In addition to this, these automated journeys will have special rules created by the International Maritime Organisation which is most likely to happen in the coming years.

Key examples

One of the examples of companies that have employed artificial intelligence in order to robotize ships is Shone. They visualize employing artificial intelligence by planting sensors like radar and cameras that can help simulate a number of hazards around the ship and to navigate amidst them. Autonomous shipping helps in cutting costs of consumer goods as well as provides a safer environment for passenger ferries and cruise liners. Tugboats and ferries are likely to operate autonomously for at least a part of the time, the ones that only operate for shorter distances and time duration.

Finland and Norway have staked out testing areas for pioneering the commercial applications of autonomous systems that are likely to happen on the small coastal waters of Scandinavia. Rolls Royce orchestrated the first-ever public demonstration of an autonomous voyage by a passenger’s vessel. It was a state-run vessel that happened to avoid obstacles for 1 mile and also docked automatically.

Rolls Royce also revealed that on the day of the demonstration and the trails before that, the vessel was able to perform well even in rough waters, handling snow and strong winds which indicates that we are moving towards a world that will have machines employed everywhere to augment our experiences and make life easier.

Transportation made easy

At ports like Scandinavia where small ferries play a crucial part in the transportation network, in order to carry cars across fjords and connecting them to islands, autonomous systems will have it made it a lot easier. This is because the remote-control systems could allow for an expansion of service at the routes that are not very long, especially during the late hours and help reduce staffing, thus cutting costs, increasing efficiency and saving time. You can save big bucks by employing autonomous systems as the crew costs are really high and you can eliminate a big part of the same with artificial intelligence.

In a nutshell, we can say that we are moving towards living in a world that will be much easy to live in. Machine learning Training and Artificial Intelligence are taking over various industries eliminating its glitches and making operations better and more efficient.

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 form from the website 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, Hyderabad, Delhi and Gurgaon.

Retail Analytics – How Does It Help Boost The Sales?

Posted on by Imarticus Learning

 

SMB retailers benefit in three main ways from retail data analytics. 

1. Knowing Customers:

Singapore’s Dish-the-Fish fish-stall uses inventory and sales analytics on Vend’s retail management platform and cloud-based POS. Owner Jeffrey Tan prior to switching to the platform, bought what he thought to be the fastest selling fish the ikan kuning. On tracking data by the hour on different fish sales frequencies on Vend’s POS system, he found the leatherjacket fish was fast-selling though pricier. Monitoring in real-time also gave Tan the data-analytics ability to track and cater to the preferences and tastes of his clients. According to data from Accenture, 65% of clients buy from brands that know their brand preferences and buying history.

2. Analyzing Trends:

To use data analytics effectively one must know when and what the customers want even before they produce it. Just look at Dash a fashion store! The store’s Retail Director, Dakota DiSanto, admitted that before switching to LightSpeed’s POS system, her staff spent as many as 8 man-hours per week on studying and tracking manually the sales, inventory, re-order items and so on. According to her the real-time inventory view, sales trends and stock levels across their operational stores in Miami and Los Angeles provided them crucial information on the best sellers, re-ordered units, inventory scheduling, etc well ahead of the demand.

3. The True Costs:

Marquis Gardens’ Ostap Bosak, the General manager, used ACCEO’s POS system. Being Toronto based pond-supply retailers he made use of the transport insights from data analysis on their retail operations. Here’s his story.

On evaluating his data he dropped several suppliers as he found they were earning too little and working too much on them. Though they formed a major portion of the revenue generations the sidelined products were more profitable. He then focused on the main two generators of revenue namely the small pond kit and the pond-less waterfall kit. Bosak stated that he was able to better monitor the ROI from his data analysis as it enabled him to watch over the metric of profit with respect to time spent and efforts spent on it. Bosak reasserts that most businesses do not account for the actual man-hours taken while calculating profitability. In his opinion retail data-analytics helps drill into data in greater minute details to help sustain your operations in a fiercely competitive market.  

Which metrics should one analyze?

Analysis of KPIs like foot-fall traffic, margins, sales growth, and walk-in rates speak the numbers-story of any enterprise with accuracy and transparency to enable your making profitable decisions with those data analytics insights. Here are the metrics in retail business analytics every store must and should monitor.

  1. The square foot rate of sales 
  2. Rate of Retail Conversion 
  3. Net Margins on profit

1. Sales/SqFt:

This index helps. Because, when you know exactly how many sales you earn per sqft of space you can assess and gauge the store’s performance to

  • Refurbish your retail layout: Express rearranged the layout of its store bringing its merchandise selling at full-price to the front and taking the other discounted apparel to the rear-end, based on its analytics and trends in sales. The results showed in a spurt of sales in the more profitable full-price range.
  • Pile up effectively: The fashion boutique Covet’s owner Adrienne Wiley cautions retailers to carefully monitor sales data when they decide on inventories and range of products to sell. She benefited by stocking up the necklaces and tweaking the sales/hour figures in her data analytics analysis.

2. Rate of Retail Conversion:

Browsers are common and this metric gauges how many of them you convert into sales or buyers of your merchandise. So, why study and analyze data when you cannot use it. Right? No, wrong! Here’s what to do with it. 

  • Figure out why customers buy and what keeps them from buying: Your low sales could result from poor displays, long billing times, lack of sales reps or customers not finding what they want. If you take the time to speak to and observe customers respond you can find ways to make their journey more pleasurable and this would result in repeat sales and loyal customers.
  • Set goals and train your employees: Employees are an organizational asset. Train employees to make the customer experience good using goal-setting, loyalty-rewards and incentives. The Friedman Group Founder Harry Friedman claims training helps retail organizations push sales 15-25%.

Wrapping up, there can be no doubt that data analytics enables boosting sales. So, do a course in data analysis with Imarticus Learning. They get you career-ready from day one in a variety of interesting subjects.

Why do Corporates Need Big Data Analytics Training?

Posted on by Imarticus Learning

Why do Corporates Need Big Data Analytics Training?

Data is an invaluable organizational asset in modern times since big-data is being exploited for its value in providing business foresight and predictions based on analytics.

ML, AI, and deep learning algorithms are applied to massive data volumes to provide corporations with the opportunity to use their data to tweak their measurable efficiency, and the decision-making process, and also reach out to a larger number of employees at the same time.

And, data analytics training on big data is no single program. It is a technology combination that helps the corporations extract the best value from data and analytics tasks. 

Corporate advantages:
The important plus factors of data analytics for corporations are explained briefly below.

Database Management: Data can be from different sources and in various formats. Its quality and organizations are of prime importance before going in for big data analytics. 

Since in most large corporations the data travels from department to department, and various subsets may be added or deleted as the case may be, it becomes necessary to have an established repeatable process and a master management program format to maintain the quality of the organizational data. This ensures that the management of data is in synchronization across the organizations.

Mining and modeling of financial data: The technology for this task allows the examination of several petabytes of data produced by the moment. This task will enable you to sift through the data for relevance, and then use the subset for predictions and forecasts which hasten the decision process and in turn impact informed decisions being taken for critical and strategic decisions by the management.

Pricing and modeling using ML: ML trains the machines and AI to help it learn to recognize the patterns involved. This hastens its self-learning process and allows the algorithm to automatically move through more complex data models and still deliver accurate and desired outcomes.

This Big Data Analytics Training capacity is invaluable, especially where unknown tasks and risks are involved or where models need to be continuously auto-generated.

Storage and engineering of Big-data: Hadoop is a commonly-used, free and open-source framework which uses clustering of information on hardware to store larger amounts of data. Since data continually increases in volumes, types, and sources, the Hadoop models used for computation handles very big-data volumes and needs no license. It thus allows for using demographics, sensor data, driver data and market information all on the same platform. The best example here is of the 2000 crisis in Ford and how it overtook the competition in Asian and European markets.

Product development and analytics in-memory: Instead of using the hard disk, such a facility allows the access of data from the system memory instead. This allows quick decisions, analytics and predictable outcomes from the organization’s data. One of the most significant advantages of the system memory is that it is iterative, agile, removes latencies in processing and data preparation while providing for quicker and better decisions and analysis that is interactive especially in product development and modeling.

The task of Predictive Analysis: Here, the technology used consists of algorithms based on statistical modeling and ML techniques. Large corporates use their data for gainful business outcomes based on Big Data Analytics to make the best decision in any given scenario.

Marketing, risk assessment, and fraud detection are just some of the areas that benefit from such analysis. Were you aware that Singapore based OCBC used such insights to achieve a new customer increase rate of 40 percent?

HR capabilities and mining texts: The latest improvements are used to analyze data from text messages drawn from the surveys, comments, Twitter, web posts, emails, blogs, books, and such text-using sources. Such analytics is beneficial in strategizing for competitive leadership, the introduction of new products, newer areas for development, and establishing loyal customer relationships both within and outside the organizations.

Parting notes:
Training and cleaning of data are very important to organizations to take quick and effective decisions at the right time, especially when it comes to strategic and critical business decisions. Since data analytics comprises of a series of technological programs executed in systematic models, it is essential to do a data-analytics course before one makes a career in this field. The scope for such jobs is indeed never-ending because of the sheer volumes of data being generated and available for analysis.

Doing your course with the reputed Imarticus Learning ensures you are job-ready, proficient in data analytics and get a chance to hone your presentation skills too through the soft-skills modules. Top this with certification, and you are all set to start a great lucrative career. Do you have any more doubts? Get in touch with Imarticus today.

What Exactly is The Field and Type of Work That a Data Scientist has to Perform?

Posted on by Imarticus Learning

The field of data science gained prominence when technology enabled Google to introduce the ranking systems for searches. Recommendations were looked at in LinkedIn and data suddenly began to influence all types of newsfeeds.

Currently, the fine art of data science has permeated through every known vertical including Human Resources. Can you even imagine a world devoid of mobile phones, digital payments, or self-driven cars? Yet, just over a decade ago the scenario was very different.

The evolving segment of Data Science Training behind the huge volumes of data generated every second today implies a large number of tasks being powered by insights generated by powerful statistical models. In spite of all this hype, it’s still unclear what this field entails. What exactly does it take to become a data scientist with firms like Google and Apple?

 

What the data analyst and scientist does:

If we look at the most trending and lucrative Data Science Career, the one difference that sets these two categories apart is perhaps the areas of data science they operate in. It is a very huge and diverse field and demands the individual to have a strong understanding of advanced statistics and programming. The scientist’s role is more to clean, organize and make the data available in the desired format first.

Such data is then leveraged to train algorithms which are specifically used to execute the task on hand with maximum accuracy. The models are optimized, tested and re-engineered to provide desirable output in the form of products like forecasting engines.

In a way, the data scientist ensures the sustainable development and growth of the entire system and could also be called the architects behind a decision. Some firms like Mu Sigma Inc based out of Bangalore and Chicago have been the pioneers in this field in India.

The poorer cousin per se to the data scientist is the data analyst, who uses the created system so engineered to do the final live data-analysis and produce those forecasts and predictions to further particular goals and business outcomes.

So, whether the need is for a product-framework model being developed, a self-taught solution for optimization in a production process, or using the ML algorithm to provide and make decisions on a flight or hotel bookings, one definitely needs the services of both a data scientist and analyst.

Data science today in plain tech-speak is all about the latest technological infrastructure, analysis and repeated testing of pipelines, ML, AI, deep-learning algorithms, neural networks, modeling, decision-making ML, and innovative personalized data end-products.

The field is evolving rapidly:

Companies like Amazon, Airbnb, Etsy, Twitter, Facebook, Google, Apple and many more have greatly contributed to making data science a high-paying career. And, the sheer volumes of data being produced, is so large, that it seems unlikely that the Data Science Career aspirants will face a shortage of jobs, for the next decade or so.

Today data science is contributing to cancer cure and treatment needs, powering investigative tools for the law enforcement, high-tech medical imagery, and technologically advanced MRIs and CAT scans, as also the numerous uses of data in self-drive cars, making recommendations to leverage possible business markets and outcomes, in AI and ML-driven production technology, and providing the latest fintech digital solutions and multi-vertical end-products.

The requisite skills needed:

Since data analysts and scientists make a living off the collection, cleaning and modeling of data, testing and creating dashboards, visualizing petabytes of data, making statistical forecasts and inferences, and providing key verticals and stakeholders with the required decision-making prowess, the skills required of them are also multi-dimensional.

A course in data science training should educate the scientist in very short time-periods with practical skills, latest technology, and an ethical code besides non-transferable soft-skills and managerial experience. Though interest in data-analytics is the prime requisite, a sound degree in finance, economics, CE and engineering is a definite plus.

It also pays to specialize in performing areas like ML, Business Intelligence, and Decision Science which form the how, and whys of data science. Besides the practical aspects of techniques and best practices, you will need to be good at business modeling, data analysis, and communicative skills.

Parting notes:

Making a career in data sciences is definitely a good career choice. To succeed at it you will need a supportive learning partner like Imarticus Learning where the short-term courses are succinct, highly skilled and offer measurable certification. Besides who can refuse the offer of certified-trainers mentorship, assured placements, personality development and being job-ready from the first day?

What are The Top 10 Algorithms in Machine Learning?

Posted on by Imarticus Learning

Machine learning is the essential part of the developing technology of Artificial Intelligence. It analyses enormous amounts of data and comes at customized predictions which can help the user to deal logically with an overload of information.

A student of Machine Learning course must be aware of the need of making algorithms since these are what enhance the self-teaching capacities of the system. There are three primary techniques to design an algorithm- supervised, unsupervised and reinforced.


Also Read: What is The Easiest Way To Learn Machine Learning?

Here is a list of the top 10 algorithms which every Machine Learning student must know about –

  1. Decision Tree is one of the most comfortable supervised structures that is very useful to form deep connections and is based on questions in Boolean format. The fabric is systematic and easy to understand, and it is beneficial to determine model decisions and outcomes of chance-events.
  2. Naive Bayes is a simple and robust algorithm for classification. The “naive” term implies that it assumes every variable to be independent which can turn out as impractical sometimes. However, it is a great tool that is successfully used in spam detection, face recognition, article classification, and other such operations.
  3. Linear Discrimination Analysis or LDA is another simple classification algorithm. It takes the mean and variance values across classes and makes predictions based on the discriminated value assuming that the data has a Gaussian curve.
  4. Logistic Regression is a fast and effective statistical model best used for binary model classifications. Some real-world applications of this algorithm are scoring credit points, understanding rates of success in market investments and earthquake detection.
  5. Support Vector Machines or SVMs are a well-known set of algorithms which is binary based. The principle is to find the best separation of variables in a hyperplane. The support vectors are the points which define the hyperplane and construct the classifier. Some successful sites to try this algorithm is image classification and display advertising.
  6. Clustering algorithm follows the unsupervised technique, and it works on the principle of determining the more similar characteristics of nearby parameters to patch themselves up in a set cluster or group. There are different types of clustering algorithms such as centroid-based algorithms, dimensionality reduction and neural networks.
  7. Linear regression is a very well understood form of the algorithm which works on quite the same mathematical formula of a linear equation in two dimensions. It is a well-practised algorithm to determine the relationship between two variables and can be used to remove unnecessary variables from your target function.
  8. Ensemble methods are a group of learning algorithms working on the principle of predictive analysis. They construct a chain of classifiers such that the final structure is established to be a superior one. They are very efficient regarding averaging away with biases in poll decisions, and the algorithms are entirely immune to the problem of over-fitting.
  9. Principal component analysis or PCA employs an orthogonal transformation to convert relatable variables into a set of uncorrelated variables called principal components. Some essential uses of the method are compression and data simplification
  10. Independent Component analysis or ICA is a statistical method to determine underlying data which come obscured in data signals and variables. Relative to PCA, this is a more powerful method and works well with applications like digital images, documented databases and psychometric detections.

While no algorithm in itself can be guaranteed for a specific result, it’s always ideal to test multiple algorithms cumulatively. The ultimate task of an algorithm is to create a target function which can process a set of input into detailed output data.

Related Article :

AI is Now Being Used in Beer Brewing!

Posted on by Imarticus Learning

AI is now being used in beer brewing -from creating unique beer recipes to adapting recipes as per customer feedback. AI is doing it all…

With the advent of the digital revolution, Artificial Intelligence (AI) has gained immense impetus in recent years. Today, everyone is connected to everything because of the growing importance of the Internet of Things. Right from the time, you wake up until the time you close your day, technology plays a key role in taking you forward.

Alexa and Siri have now become household names and no doubt, why “Her” was a blockbuster in the cinemas. AI and Machine Learning are here to make your work easier, and your life smoother. It is also brilliant to know how even breweries today are using AI to enhance their beer production.

Brewed with AI
As discussed earlier, digitization and technology have significantly impacted our lives across spectrums, and there are several examples of various companies that have started employing AI in their processes to serve their customers better. Breweries are nowhere behind in this race of digitization, so let us discuss a few examples of how they are using AI in order to enhance the experience of the consumers.

Intelligent X
Intelligent X is one of the best examples of how a platform employed AI to enhance their beer. It came up with the world’s first beer, which is brewed with Artificial Intelligence Course and advances itself progressively based on customer feedback. They use AI algorithms and machine learning to augment the recipe and adjust it in accordance with the preferences of the customers. The brewery offers four types of beer for the customers to choose from:

  • Black AI
  • Golden AI
  • Pale AI
  • Amber AI

In order to brew the perfect beer that pleases all your senses, all you need to do is sign up with IntelligentX, train their algorithm according to what appeals to your palate, and you are good to go. In addition to this, you can follow the URL link on your beer can and give your feedback so that they can create a beer you would like. These beers come in classy and minimally designed black cans that reflect their origin and give a feeling that what you are experiencing is the beer from the future.

Champion Brewing
Another example of a very intelligent deployment of AI in brewing beer is that of Champion Brewing. They used machine learning in the process of developing the perfect IPA. They took the big step by initially getting information regarding the best and the worst IPA selling companies to get an insight into how to go about the entire project. Based on the same, did they determine the algorithm of brewing the best IPA with their AI?

RoboBEER
An Australian research team found out that the form of a freshly poured beer affects how people enjoy it. Building on to this, they created RoboBEER, which is a robot that can pour a beer with such precision that can produce consistent foam, pour after pour. These researchers also made a video of how the RoboBEER poured the beer tracked the beer color, consistency, bubble size, and all the other attributes. They then showed the same videos to everyone who participated in the research in order to get seek their feedback and thoughts with regard to the beer’s quality along with its clarity.
Conclusively, this shows how AI has become the nascent yet a very preferred trend, which is even being followed by the breweries around the world. It has added an unusual turn to the way the perfectly brewed well-crafted beer makes its way to your glass. With the help of this ever-evolving technology, we can anticipate our favorite drinks to be made precisely in accordance with our preference only with the help of your smartphone.

By deriving minutest of the insights right from the foam of the beer till the yeast used in the same, companies these days are striving to deliver their best with the help of immense research and execution from the ideation derived from their research amalgamating it with AI and Machine Learning. Looking at the various examples, we can surely say that we are living in the future in the present.

For more information you can also visit – Imarticus Learning contact us through the Live Chat Support or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi and Gurgaon.