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Category: Analytics

A Complete Guide For Deep Learning!

Posted on by Imarticus Learning

Deep Learning is known as neurally organized or as learning of various levels. It is one piece of an even more extensive type of group of the techniques used for machine learning in the aspect of learning and retrieving information, instead of undertaking the particular calculations. Also, learning could be directed, or semi-managed or even unsupervised.

Hence, careers in the field of Deep Learning renders organizations with different kinds of arrangements for systems in order to look after the issues of complex explanatory and also drives rapid developments in the counterfeit consciousness.

Complex undertakings such as- picture examination and discourse, can be performed with the help of models prepared by fostering calculations of deep learning amidst an immense amount of information.

These models of Deep learning are generally identified with the data preparing as well as with correspondence designs that are in a system of organic sensory, for example, the neural coding that attempts to characterize one connection between distinct data and the related reactions of neurons inside the brain. Therefore, a career in deep learning looks prospering.

What are the job positions that one can expect in the field of Deep Learning?

Mentioned below are the job positions that a person who specializes in deep learning can look out for:

  1. Research Analyst
  2. Data Scientist
  3. Neuroinformatics
  4. Image Recognition
  5. Research Scientist
  6. Deep Learning Instructor
  7. Full-stack deep learning web developer
  8. Process Engineer for Natural Language
  9. Software Engineer
  10. Data Analyst
  11. Data Engineer
  12. Bioinformatician
  13. Software Developer
  14. Research Fellow
  15. Applied Scientist
  16. A lead manager in Deep Learning

This shows that a career in Deep Learning has lots of options to make a future in.

Career Outlook

The information researcher hunts through enormous measures of unstructured as well as organized information in order to give fractions of knowledge; plus, it also helps to meet the particular business requirements or needs and objectives. Similar work needs to be done if you have pursued the Machine-Learning courses.

From where should you pursue the deep learning course?

Imarticus Learning is one of the best platforms to learn and help yourself make a future in the field of Deep Learning. Here, you will get to learn all the skills that are essential to becoming an expert in the field of Deep Learning. Because there are a number of skills and academic study required, Imarticus offers a ‘Machine Learning & Deep Learning Prodegree’, in association with the edtech partner, IBM.

It is the first-of-its-kind certification course of more than 145+ hours of training. This provides in-depth data science exposure, as well as, big data, machine, and deep learning as well. The meticulous curriculum-aligned as per the industry provides a comprehensive knowledge of Python as well as data science for a flourishing future and career in machine learning and big data as well. This program also stars seven projects of industry, various case studies as well as periodic interaction with industry leaders inside the ecosystem of machine learning.

What Are Some Tips And Tricks For Training Deep Neural Networks?

Posted on by Imarticus Learning

Deep Neural Networks aid AI applications such as image and voice recognition to function at unprecedented accuracy. A Deep Neural network is basically an array of several layers, where each layer sieves raw data into a structured mathematical model. 

The process of making the data flow through the various layers is called Deep Neural Network Training. In humans, we also start recognizing an object once we have seen it several times. If you saw just one “car” in your entire life, you might not be able to recognize a car again if you saw a different model this time. 

In Data Science, this is easier said than done. Therefore, we have some tips and tricks that you can use when you sit down to teach your DNN to distinguish cars from trucks.

Normalization is Effective

Normalization layers help group logical data points into a higher consolidated structure. An apparent increase in performance has been recorded when using Normalization.

You can use it three ways;

  • Instance Normalization – If you’re training the DNN with small batch sizes. 
  • Batch Normalization – If you have a large batch size, supposedly more than 10, a batch normalization layer helps. 
  • Group Normalization – Independent of batch size, it divides the computation into groups to increase accuracy. 

Zero Centering 

Zero Centering is considered as an important process for preparing your data for training. Just like normalization, it helps in providing accurate results later. 

In order to zero center your data, you should move the mean of the data to 0. You can do this by subtracting the non-zeroed mean of the data from all the data inputs. This way, the origin of the data set on a scalar plane will lie on 0, making it Zero Centered.

Choose the Training Model Wisely 

One thing that you’ll come across when you learn Deep Learning, is that the choice of model can have a significant impact on training.   

Commonly, there are pre-trained models and there are models you train from scratch. Finalizing the right one that corresponds to your needs is crucial. 

Today, most DNN developers are using pre-trained models for their projects as they are resourceful in terms of the time and effort required to train a model. It’s also called Transfer Learning. VGG net and ResNet are common examples.  

The key here is the concurrency of your project with the pre-trained model. In case you can’t get a satisfactory model design, you can train a model from scratch too. 

Deal with Overfitting
Overfitting is one of the most popular problems in DNN training. It occurs when the live run of the training model yields exceptionally good results but the same wasn’t observed during the test runs. 

The problem is basically caused when the DNN starts accepting the attenuations as the perfect fit. This can be dealt with, using the technique of Regularization, which adjusts the problem of overfitting using an objective function. 

Conclusion

Wish you’d know more? Take up a deep neural network training course on Imarticus and start your progress today. DNNs are becoming increasingly popular in data science-related careers. Just like everything else, you can use the first-mover advantage with pro-active learning. 

11 Steps to Find the Perfect Remote Job!

Posted on by Imarticus Learning

Amid perhaps the worst healthcare situation in decades, one aspect that has caused serious anxiety in people is the current and future states of work. There is no clarity about what the future holds for several professions in India and elsewhere in the world. Professions have turned into remote jobs that are not bound by a ‘work time’, adding to the stress, low productivity, panic, and even paranoia.

If you feel you are affected by this situation, it is time to explore the world of remote jobs. It can be as a backup to your current job (that may be in peril), helps in your business management functions or to secure your future in case of a layoff. If not anything, you can at least prepare yourself for the future when your current job also transforms permanently into a virtual activity.

Whatever may be your needs, this 11-step guide to finding the perfect remote job will always come in handy. Read on…

Step-By-Step Guide to Finding Remote Jobs

This has been prepared with help from established workers who have flourished in their specific professions while working remotely for decades.

Step 1 – Assess Your Situation

Much like how to finance pundits ask you to invest money with certain goals in mind, looking for remote jobs online should also be based on aspiration, a goal, an aim.

Are you currently unemployed and thus looking for a freelance job where you can work from home? Were you laid off recently and are now looking for a quick alternative? Are you looking to generate a secondary income?

Answers to these questions will help you measure the required intensity in your remote job hunt. Higher the intensity (and desperation), more religiously you should follow the next steps.

According to a report published (pdf) by Outlook India, over 1,000 lakh jobs are going to be at risk because of the Covid-19 situation in India. So, even if it feels like your job is secure today, you should start looking for remote work options.

Step 2 – Assess Yourself and Your Interests

Before you get down to the assessment of your skills, you should take this opportunity to think of things that you have always wanted to do.

You may have worked as a logistics controller for ten years but deep down you have always wanted to be a photographer. Why not hit two birds with one stone and pursue a remote job in something that you are passionate about?

Take a few days to think about such interests and note them down. Then you can move to the next step: your employment assessment.

Step 3 – Jot Down Your Professional Abilities

If you were laid off, the best way to jump back to employment is to convert your day job into a remote one. All by yourself. But how?

Take the example of Beena from Raipur who was one of a dozen employees who were given furlough in March 2020 by their company in the small savings bank sector. The 32-year-old mother of two was devastated to learn that her salary would stop being credited after two months. However, she refused to be bogged down by the situation. After all, she had bills and EMIs to pay. More than that, a growing family to feed and nurture with her partner.

Within a week, she set up her own small finance consultation business. She now works out of her home while following social distancing measures with her two kids and partner.

If you are in the same position as Beena, the first thing you need to do here is to list down your professional capabilities. Is your profession remote work-friendly? If yes, it’s time to assess your skills and experiences and start your own little office at home.

If you feel you were in the line of fire as a direct impact of the novel coronavirus, you should not lose heart. The fact that you are still employable should give you the confidence to tread forward.

But what if you are looking at a remote job as a side hustle?

Step 4 – Collect Bankable Skills

A smart professional will take advantage of the lockdown situation in India to amass new skills that she can later sell to earn money on the side. If you already have a job, you should think from that angle.

Find a remote job that you are comfortable with and then learn the skills required to be eligible to carry out that job. For instance, if you are interested in cooking, you can think of starting online cookery courses. The skills that you will then need are video production, YouTube, and online video publishing basics, digital marketing, and social media marketing.

It may look complex at the start but all you need is to start with the basic concepts of this skill set to at least enter the remote work scene of that field. In case you’re following the step 2 idea, look out for skills required for that specific job.

Once you have a hold on what services you will offer or what skills you will sell, the next step is to start looking for appropriate remote jobs online.

Step 5 – Start Looking for Remote Jobs

Sites like Upwork.com, Freelancer.com, and Fiverr.com are great global platforms to start looking for relevant remote jobs. However, due to the sheer volume of jobs and a sudden spike in competition these days, it is recommended that you scout for remote jobs locally first. That will give you better and faster results.

Having said that, there are certain jobs that will be easier to find during this harrowing time. Some of them are listed below:

  • Freelance writer or editor
  • Social media manager
  • Web or software developer
  • Video development
  • Podcast maker
  • Remote IT jobs (network engineer)
  • YouTube management
  • Tableau expert

Even in these broad remote work-friendly professions, there are niches that you can focus on. For example, healthcare writing is a hugely lucrative skill right now. But then, writers with healthcare qualifications will be preferred (or even hired) by those who need it.

If you feel you have to tweak your designation (for example, from digital marketing manager to website manager), do it.

Step 6 – Search Deeper and Smartly

Caught the point about searching for remote jobs in your local area? It’s the best piece of advice you will ever read on this topic because of the current static condition of the entire country or world. Earlier, online remote jobs were preferred for their convenience and talent quality. Right now, it is more of a mandate. An American startup (like Tuxler) which has always had a remote workforce now has similar requirements as an Indian startup based in Bengaluru.

In that vein, it is not enough to search for remote jobs online. You should go deeper and farther.

Here are some tips:

  • Talk to professionals in and out of your sector and spread the word about your service offerings
  • Put yourself out there on LinkedIn
  • Connect with your past colleagues
  • Follow message boards on Facebook, Reddit, and other India-based forums

Only when you spread your wings wide will you find a handful of leads. And once you find them, hold on to them.

Step 7 – Set Up Your Process

Working from home may sound easy but only for the first few days. When you have to do it for weeks and months, it can be challenging. So, before you even take up assignments, you should build a rough process first.

This includes but is not limited to:

  • Setting up a makeshift office in your house
  • Taking care of the essentials like laptop, power, internet, accessories
  • Any remote job-specific requirements (e.g.: electronic drawing board for designers)

This is important because just laying a blueprint will give you a fair idea about its feasibility. You don’t want to spend loads of money just to set up an online remote job. In an ideal scenario, it should cost you nothing.

Step 8 – Bring in Your First Customer

Even the smallest request for your service should be seen as an opportunity. Your goal here should be to gain your first customer. It can be in the form of a company looking to get some posters designed, a businessman looking to manage his social media, or a businesswoman looking for assistance in online tutoring her kids.

Your focus should be more on converting the incoming user and less on the finances. A lot of beginner remote workers make the mistake of focusing only on money. That’s a bad move, especially in this pandemic situation.

Step 9 – Focus on Retention

Another mistake that beginners make while remote working from home is to focus on new leads rather than servicing their existing customers.

Client retention is important in remote working because gaining a lead is perhaps the most difficult part. It is a lot easier to lose a client than to gain one. Auxiliary activities like contract creation and legal matters can wait.

Step 10 – Start Another Side Hustle

There is a high chance that your first remote job idea does not take off. And that is where the secondary side hustle comes in.

If you are unable to gain any leads on your chosen remote job even after months, it is time to move to another idea. This is where the lists you made in steps 2 to 4 will help you. Go back and see how else you can put your skills to good use.

Step 11 – What If Nothing Works?

Repeat the steps again. If the perfect remote job seems elusive at the first attempt, try a dozen more times. After all, you still have that spark to do something and succeed in life even during a global crisis such as this. Right?

The situation calls for introspection. But what it also calls for is an exploration of the idea of remote working. Experts have been calling it the future of work for years, and now even the naysayers have joined them with affirmation. This truly is a golden opportunity to start a remote job.

Febin George’s journey at Imarticus Learning – From Engineer To Data Analyst!

Posted on by Imarticus Learning

It is a familiar experience for many of us – we study for one particular field, but then decide to pursue a career in another area of expertise. Febin George is accustomed to such a journey, having graduated as an engineer in 2015, only to then dream of becoming a data analyst one day.

Fortunately for him, he chose to join Imarticus Learning to develop his analytical skills and help him achieve his career in Data Analytics.

Before he had enrolled himself in Imarticus Learning’s Data Analytics course, Febin openly admits that he had little to no prior knowledge of what being a professional data analyst would entail. In essence, he was a blank slate when it came to the complexities of data science.

Now though, that is certainly no longer the case. Thanks to the comprehensive training he received at Imarticus Learning, Febin landed a job placement at M-Technologies Pvt. Ltd. as a full-time Data Analyst, something which he did not expect to happen so quickly.

Febin believes that only with the nurturing guidance of the profoundly experienced faculty members involved in Imarticus Learning’s Data Analytics course was he able to come so far in a relatively short period of time. His determination to further his understanding of data science was aided by the hands-on training he received on various modern-day analytical tools, big data concepts, and machine learning practices.

Having gained an intimate knowledge of data analyzing tools such as R, Python, and SAS from ever-helpful Imarticus professors during the Data Analytics course, as well as immensely beneficial industry insights from guest lecturers, Febin was able to rapidly developed his foundational understanding of the subject matter.

Going beyond the exhaustive course material on data analytics alone, Febin was also provided with much-needed soft skill training in order to polish his personality and prepare him to tackle tricky interviews in a distinguished manner. According to him, Imarticus Learning’s career assistance acts as a resume builder and makes a candidate far more appealing to prospective employers.

With the unwavering dedication of the Imarticus staff and the treasure chest of analytical knowledge he received from the Data Analytics course, not to mention his own drive, Febin George finally became the data analyst he dreamt of being. As he puts it, “Going for a data industry-specific interview requires knowledge on current trends, which is where Imarticus Learning really excels. I recommend Imarticus Learning to anyone seriously considering becoming a data analyst or scientist.”

To learn more about Febin George’s journey at Imarticus Learning, please click here.

Imarticus Special: 25% off on Data Analytics Courses!

Posted on by Imarticus Learning

What is data analytics?

In the contemporary landscape, data science is at the core of every industry that leverages progressive technology to target and reaches its audience. Data analytics has multiple aspects and can be explained as the process of cleaning, transforming and analyzing data to obtain valuable insights and draw important conclusions. The significance and vitality of data analytics are such that it can help to decode the world around us using statistical figures.

In the digital era, an enormous amount of data is generated and stored regularly from people all across the globe. Whether you are using your social media account or making a purchase online through your favorite e-commerce store, all the internet powered activities generates data that is stored electronically for future reference. Businesses are using data analytics to extract useful information about the customers that can help them design their products and services as per the customer’s need and demand. Some of the key aspects of using data analytics are listed below.

Identifying secret insights

Data is analyzed by businesses to obtain hidden insights that couldn’t have been normally obtained; these secret insights are used as per the requirements of the corporations. Data analytics helps to identify trends and patterns that help to project prospects.

Conduct market analysis

Conducting market analysis is an important application of data analytics. It helps to understand consumer behavior, the general trends and competitor’s strategies. It can help to find out the strength and weaknesses of other players in the industry.

Why opt for a data analytic course?

As we have already established that data science and analytics are at the core of corporate operations. Data analytics has gained widespread popularity in the last decade given the benefits it has to offer to businesses. This has caused a surge in demand for data analytics professionals.

As per the latest estimates, the demand for data analytics professionals in the industry is much higher than the current supply of professionals who are equipped to handle complex data. The data analytics industry is estimated to grow multiple-folds in the coming decade as more and more companies will adopt data science for smart decision making. A career in the field of data analytics is very rewarding and lucrative for those who are interested in making sense of facts and figures.

Imarticus data analytics courses

Imarticus Learning is a technology-driven educational institute that provides an immersive learning experience to students who want to kick start their career in industries such as analytics, financial services, AIML, business analysis, etc. The primary objective of Imarticus Learning is to reduce the skills gap that persists in the economy. The courses offered by Imarticus takes a comprehensive approach to learn both the theoretical and practical aspects of the subject.

Imarticus postgraduate data analytics course is among the best data analytics program available online. It is a well-designed postgraduate data analytics program that studies duration of 450+ hours. The training course takes a hands-on-learning approach to cover foundational concepts of the best analytical tools such as R, Python, SAS, Hive, Spark, and Tableau.

The whole PG program is spread across three semesters. In addition to the theoretical understanding of the data analytics related concepts, students will also gain practical exposure through case studies, live capstone projects, hackathons, and mentorship by industry experts.  The course also entails mock interviews to boost your employability, industry leaders for guidance, placement assurance and much more.

Imarticus Learning is providing a limited period offer on its special data analytics course. Enroll without any delay to avail 25% off on your data analytics course!

AI in the FinTech Industry: What Will 2020 to 2025 Look Like?

Posted on by Imarticus Learning

The financial industry has, for long, been keen followers of technological advancements for their own benefit. Many big names in the industry have been early adopters of disruptive technologies in a bid to streamline processes, reduce manual labor and negate the chances of error.

Artificial intelligence is a paradigm-shifting field that the financial industry has forayed into very recently, sitting still at the tip of the iceberg. Here is a breakdown of the trends, growth and scope of Artificial intelligence Training in the FinTech industry during the years to come.

AI in FinTech: Global Market Share, Size and Investment Analysis

In 2019, the AI in the FinTech market was estimated at USD7.2 billion. By 2025, this figure is expected to reach a staggering USD35.40 billion, according to a Mordor Intelligence report. The Compound Annual Growth Rate (CAGR) has been put at 31.5% for the years between 2020 and 2025.

This double-digit surge is no doubt a result of exponential technological advancements and deeper penetration of the internet. Software tools are expected to receive the largest market share because the need of the hour, and the foundation of all further processes, is the extraction of data.

When it comes to deployment, cloud-based AI developments are expected to rake in the highest CAGR in the following years when compared to on-premise deployments. This goes hand in hand with the shift in data storage and management from on-site servers to remote, centrally-controlled cloud silos to facilitate better access and higher security.

Regionally, AI in FinTech is gaining traction across many geographical splits. The current largest market is North America; however, Asia Pacific is expected to see the fastest growth in the coming years. This comes off the back of massive research and development investments in developed economies in the United States and Canada. Europe, South America, Africa and the Middle East will also see a surge in AI adoption and advancements, though perhaps not at the scale of Asia Pacific as yet.

AI in FinTech: Trends and Growth

Fraud prevention: AI is expected to be deployed the most to ensure fraud detection and prevention. Naturally, this segment will drive most of the IT expenditure in companies of varying sizes. This trend appears in a bid to keep up with the changing face of fraud in the FinTech industry as well as the greater proliferation of digital channels and the need to secure them all.

Transactional bots: As financial entities solidify their online presence, transactional bots and digital assistants will increase to keep up with remote demands. Apart from managing customer relationships, these assistants will also be equipped to deal with term life renewals, cheque or balance notifications, withdrawal limit warnings and more.

Risk profiling: AI will become a massive driving force in evaluating client credit risk and creating profiles. Using historical client data and outliers, logical algorithms can segregate risks by range, allowing advisors and risk managers to make more accurate mitigation decisions.

AI in FinTech: Challenges

Cultural changes: With changing landscapes and evolving customer demands, cultural shifts within the company are inevitable. Employees at all levels must be reoriented so that the introduction of AI becomes helpful rather than disruptive.

Security: Increased exposure to digital forums, ironically, also means being laid bare to cyber-threats. While adopting artificial intelligence in any form, financial entities must strengthen security systems at the same time.

The final word

In light of the changes to come, it is imperative that new-age students enroll in a FinTech online course that encourages deeper thinking. With every shift in the level of computational power, FinTech industry leaders will be seen integrating beyond-human technologies into nearly every critical stage of their operations. The leaders of tomorrow, then, will benefit from a FinTech online course that preps them to make and implement these changes with minimum disruption and maximum confidence.

Four Ways Business Analysts Can Use AI to Work Smarter

Posted on by Imarticus Learning

Artificial Intelligence (AI) is one of the most sought-after technologies espoused by businesses across the world. The organizations that uphold the value of innovation rely on artificial intelligence as the long-term business objective to achieve new or enhanced business strategies or business models. With businesses becoming more technology-driven and depend on cloud computing to store important assets and data, cybersecurity is always a matter of concern. Artificial intelligence and machine learning can help protect the data by identifying suspicious activities. Shortage of skilful professionals has become a back burner for many business organizations. Many organizations are considering it worth to invest in training their employees in AI.

Business Analysts’ Role

Many organisations identify the importance of hiring a business analyst to analyse and interpret the collected data and to use it to expand the scope of the business such as discovering a new market or to expand their services. Thus, business analysts play a key role in business organizations using machine learning tools techniques and AI tools to leverage diverse and rich data available. Four important contributions business analysts can make are:

  1. Help reduce customer churn
  2. Influence customer behaviour
  3. Remodel quality assurance
  4. Building Brand Reputation

Helping Reduce Customer Churn

Customer churn is a huge problem for businesses. Even big corporates like Accenture lost about 52 per cent of their consumers due to poor customer service. While it is impossible to prevent the churn, businesses can use AI to minimize it and achieve breakthrough results. Leveraging machine learning algorithms helps predict customer churn and analyse the cause. Finding customer pain points is the key step in reducing churn.

Influencing Consumer Behaviour

Consumer behaviour sways due to big and small factors. The entry of new players could also influence customer preferences. Collecting data such as income, gender, age, buying preferences and shopping patterns could help in extending personalised offers and thereby increase sales, customer retention and brand recall. Predictive analysis is especially important in industries such as retail and banking.

Remodelling Quality Assurance

Quality assurance is especially crucial in evolving markets, where those who deliver better quality and services than their competitors seize the market. AI plays an important role in gaining this competitive advantage. Artificial Intelligence helps increase productivity, reduce the cost of production, detecting high-risk areas, predict market trends, promote customer satisfaction, and increase profitability.

Consider the example of a construction company. It initially depended on people’s feedback to identify the issues in construction. Later, it started using drones to shoot the exteriors and interiors, but it still required heavy manpower to watch the video and to identify problems. However, things got better as it adapted AI for this task. Using an object detecting model, it could easily detect damages. Additionally, the company could inspect the energy usage across its buildings and detect anomalies. Thus, it could save manpower, manhours, money and time and increase efficiency.

Building Brand Reputation

AI helps in better targeting and thus, helps develop relevant campaigns and create better touchpoints for prospect customers, existing customers, and partners. This helps in building brand reputation and improve brand recall. AI helps understand the preferences of the individual customer and the technology he/she is interested in, thus helps decide the best channel to push the product/service.

Conclusion

This is the best time to take up a business analyst course as today’s day-to-day business needs are increasingly becoming data-driven and professionals who can analyse data and do predictive analysis will be of high demand. AI is going to find application throughout the business world and hence the demand for business analysts will see an increase soon.

Four Prospects of Business Analyst Certification

Posted on by Imarticus Learning

The role of business analysts is increasingly becoming important. Every company is looking to hire analysts who are capable of processing data and interpret the information to help the management to make important business decisions and to design competitive market strategies. Thus, the business analyst plays a key role in current business models. Though the role of a business analyst is not to directly bring revenue to the company, this job is essentially designed to help the company reduce market spends and increase revenue. Thus, business analysts do play a role in the financial prospects of the company. However, you need to validate that you possess relevant skills and ace in the newest technology. Business analyst certifications are the best way to convey this to your future employers.

What is the Role of a Business Analyst?

A business analyst is responsible for collecting, analyzing, and authenticating the data and making inferences to suggest changes to the business processes, system, and business policies. Thus, business analysts shoulder the responsibility of driving the growth of the organization, improve efficiency, profitability, and productivity. A business analyst should be capable of:
• Understanding the business concept
• Suggesting ways to improve the existing processes
• Identifying and designing new features
• Guiding implementation
• Measuring the impact of the newly introduced changes and the success rate

Why is Certification Important?

Certification Increases Your Credibility

Stating data analytics skills in a resume would grab the employers’ attention, but how will you validate them? If you have impressive skills and a good work experience to showcase in your resume, certification serves as good objective source to substantiate those skills and experience.

Helps Increase Salary

Investing in a good certification is worth because it helps increase your salary bracket. Pursuing a certification can result in an average of 11% increase in salary. The ROI of certification is high, that you should consider getting good certifications if you are aiming to get into top jobs. However, you should be judicious while selecting the course. Having a certification merely to show in resume will not help. Make sure you choose a reputed certification program that gets you brownie points.

Helps Build a Strong Peer Network

As per the data available from the professional network site LinkedIn, about 85 per cent openings are filled through networking and referrals. So, it is important to have a network of professionals to get into the role you aspire for. Joining certifications will help you to develop a good professional network.

Opens Scope for Self-Improvement

Any job requires constant improvement of the skills to stay relevant in the job market. Certifications help you update on the newest tools and trends in the industry. In a field like big data, which is constantly evolving, staying updated is important. Including a certification in the profile shows your enthusiasm and interest in developing new skills and constantly update. This gives you a competitive edge in the job market.

Conclusion

The scope and future growth prospects are ever-expanding in the domestic and global job market. Today, most business organizations espouse big data analysis to grow the business by processing valuable data. Hence, big data experts can see recruitment spree in the market. However, to make sure you ace the skills, it is important to have a relevant certification. It is natural that after having considerable experience one might overlook the importance of certifications. But you need to know that organizations prefer hiring the best talents available in the market and certifications are the best way to validate that you possess those skills and it is worth hiring you.

Also Read: Benefits of Business Analyst Certification

Big Data And Hadoop – The Career Options! Worthwhile To Explore More!

Posted on by Imarticus Learning

The importance of Data Analytics was realized when data became the focus of major consulting and research firms across the globe. Some of the major decisions involve data on a daily basis. A career in data analytics is just what these organizations look for in a potential employee.

To be successful in data analytics, there are certainly effective ways:

How do you start a career in data analytics?

Understand the tools of the trade: These tools can be SAS, SQL, R, etc. Knowledge of tools is essentially just a start. You can pick the one that is easily accessible to you at the moment.

Learn to use tricks: You can do this by expanding your professional network with experienced professionals within the organizations you plan to join in the future or reach out to institutions that offer a course in data analytics.

Find your way into data analytics: Grab opportunities within your professional network. Take a course in data analytics – there are several renowned institutions that offer it. At Imarticus Learning, the data analytics course exposes you to real-life business examples through case studies and provides you with reading and learning material along with hands-on learning experience which is just the perfect fit to make a career in data analytics.

How can you learn business analytics?

Many organizations across the globe have witnessed a change in the way data influences their business. Analytics is a powerful tool for decision-making, resolving operational issues and boosting performance, allowing companies to reach their full potential and objectives.

There is an increased demand for data science professionals, and by developing analytical skills, even someone who does not already have a business background, can still learn it.

If you are a graduate, getting an MBA can help you gain relevant skills and solve complex business challenges at the organizations you may work in the future.

Here are some of the most effective ways how you can learn business analytics:

Take up an Online Business Analytics Course

At Imarticus, we offer an ocean of opportunities for learners. Our business analytics online course provides a great way to be job-ready and also become a data-driven decision maker. Our courses can be paused, rewound, and visited whenever you want.

Learn Things Hands-On

With our courses, you can add more to your skill-set by gaining a hands-on experience, all while managing other important demands in your life at your own convenience.

Expand Your Network

Your contacts on your professional network, both offline and online, can help you succeed, gain important skills, and help you perform better. You can contact them and learn new experiences through examples and guidance.

How do you learn big data analytics?

Big Data means collecting huge and complex data which cannot be stored easily and process. Big data can be in the form of interactions from the users’ social media, businesses, and mobile phones or transport systems.

Big Data Analytics is the process of managing these huge data sets from various sources using database management tools or data processing applications to be used by data analysts, and deliver the data products to organizations that run on data for their business.

The biggest challenges are capturing, curating, searching, storing, transfer, sharing, analysing and visualization of the huge data collected from random sources.

For being skilled and knowledgeable in using big data for the right purposes, in the right way, and at the right time, one needs to learn big data analytics. Learners can opt for big data analytics tutorial provided by renowned institutions such as Imarticus Learning, that offer a courses in big data analytics.

Learn Big Data Analytics and HadoopA deep-dive into the world of big data offered at Imarticus Learning teaches you the most fundamental concepts and methods and tools used to effectively manage data in general and on-the-job at organizations which deal with big data every day.

How can you learn Hadoop and Big Data?

The most important challenges that come with Big Data are data quality, discovery, analytics, storage, security, and skill shortage. That is exactly why it is necessary to understand Hadoop and Big Data, and to learn to work with them. Hadoop can handle large data volumes, structured or unstructured. Many organizations that deal with data on a daily basis use Hadoop to run applications which have thousands of terabytes of data.  Hadoop can run on a personal computer too. That’s what makes it a really adaptable and useful tool.

So, how does one attain the skills required to use this amazing tool?

The answer is obvious – Big Data Hadoop Training!

Business Analytics coursesImarticus Learning offers courses in Big Data Analytics, which includes learning several tools used in the process. Through a Big Data Hadoop Training, you can understand the complexities of Hadoop, its components, and learn to use it quickly.

The course covers everything you need if you are planning to start a career in Data Analytics. You will also get a deep insight into how big the Big Data market is, the different employment options, the latest trends, the history of Hadoop, Hive, and Pig.

Can the Hadoop course be completed via online training mode?

Due to the major challenges that arise while using Big Data, like data quality, discovery, analytics, storage, security, and skill shortage, it is necessary to understand how to use Hadoop, and to learn to work with it. Hadoop can help handle large data volumes, whether structured or unstructured. Many organizations that deal with data on a daily basis depend on tools Hadoop to run applications with thousands of terabytes of data.  Hadoop is so adaptable that it can even be used on a personal computer.

So, can one learn Hadoop via online training?

Big Data Online TrainingThe answer is – Big Yes!

In Hyderabad, and multiple locations throughout India, Imarticus Learning offers courses in Big Data Analytics, which includes learning how to use several tools. Imarticus Learning offers Hadoop training in Hyderabad. The course offers a deep-dive into Hadoop. It helps the learners understand the complexities of Hadoop, its components, and learn to use it quickly and effectively.

The course covers everything you need if you are planning to start a career in Data Analytics, specifically concerning Big Data. You will also get a deep insight into how big the Big Data market is, the different employment options, the latest trends, the history of Hadoop, Hive, and Pig.

Why Business Intelligence Has A Role in The World Of AI?

Posted on by Imarticus Learning

Have you ever wondered why the sight of black cobra doesn’t scare an infant or a two-year-old baby, but it does scare a child of age 10 or an adult? The answer lies in the adrenalin hormone, secreted by a human body.

We human beings are trained for certain stimuli but if we encounter something new, we may not be able to respond well to the situation. In technology, AI is meant to think like humans by giving it the required amount of training. This training can be given by exposing the AI to a certain amount of data.

To learn business intelligence in detail and understand the role of BI technology in AI, it is very important to understand the exactness of BI and AI and the difference between the two.

Business Intelligence Role in AI

Underlining difference between Business Intelligence and Artificial intelligence

The base function of AI is to think and act like humans whereas the base function of BI is data analysis. The role of AI lies in decision making, whereas the role of BI lies in preparing the reports. The key role of BI is to collect the information, which the organizations can use to make optimized decisions. AI on the other hand uses the organization’s own information along with the information of the industry as a whole, to help the organizations to make the best possible moves.

For example, a simple tool that can convert handwritten text into typed text first uses the general information provided to it. This tool works using AI. Initially, this AI is exposed to a certain amount of handwritten text and it converts it into the typed text. Once the output is there, it requires some manual corrections to train the AI. Next time with a similar input, the output comes out in a better way. For the purpose of decision making, BI provides firsthand information to the AI to work, and later AI is trained to perform in a better way.

To understand the role of BI in AI, let us look at some examples of AI solutions

DOMO is a business management software company. This company has created a cloud-based dashboard that receives data from different apps using approximately 400 software connectors. This data is used for business intelligence.

This way, companies using DOMO can extract the data from different apps such as Salesforce, Shopify, etc. to gain insight into the customer’s sales or product inventory. The extracted data can also be used to spot the trends in the performance of the product. Later, a new feature known as Mr. Robot was added to the dashboard of Domo. The purpose of Mr. Robot is to provide insights to make decisions. This way BI is used to fuel AI.

Complementary nature of BI and AI

 There are certain patterns in the data that humans and BI cannot detect easily, the much-detailed algorithms with a consistent focus on certain parameters can help in the detection of such patterns are helping the decision-makers in a much holistic way.

Considering that the AI is superior to BI in all ways will not be correct. If all the possible constraints are not introduced to the AI, then relying on AI could result in business loss. Similarly, BI can also be extremely useful for decision-makers if the problem is unique, because of the generic nature of AI, which means it is trained to solve general problems.

Despite all the plus points, there always remains a threat to privacy breaches and data security with AI. However, with BI two major issues are organization and its people and data with technology. By considering all the positives and negatives of two systems, it is quite clear that BI in AI helps the organizations to review the data of the past and helps with new features. This helps in saving a lot of time in which organizations spend on data analysis and decision making.