Big Data and analytical skills are becoming an important part of the IT industry worldwide. Hence, there is a growing demand for people who possess the required skills and above all, are competent to work in these core sectors.
However, acquiring big data and analytical skills is not as difficult as one tends to believe. Generally, there are many other online courses that are available for free, while you can take paid, specialized courses to improve your skills.
Here we list nine top free online courses to boost the Big Data and Analytical Skills.
Basics of Big Data
For starters, the Basics of Big Data is a very important course. It helps you understand what Big Data is all about. Free online courses are available from Udemy. It includes online tutorials on Hadoop, Hive, MapReduce, Spark, and Pigs. This course provides you with essential information related to Big Data and provides an in-depth understanding of various technologies that are currently in use. Udemy’s free online course also gives you real-world success stories to help you better understand what Big Data is all about. Take this course as launch-pad for your career in Big Data.
Spark for Starters
Databricks, the parent organization of Spark provides several online courses free. The best among these for Big Data and Analytical Skills is the introduction to Spark. You can also learn Apache Spark, which is vital for you to build a career in companies that look at complex analysis from real-time data acquired through multiple sources, Databricks’ other free online courses help you develop more skills.
R-Basics
Also available free from Udemy, R-Basics allows you to learn about open statistical program language- R. The course covers everything from downloading and installing R as well as its support packages, simple steps on utilizing the program and various code lines. This free course includes videos and self-assessment tests.
Python for Starters
Offered by Python Software Foundation this free online course is vital for Big Data and Analytics. “Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high-level dynamic data types, and classes. Python combines remarkable power with very clear syntax. Python is portable: it runs on many Unix variants, on the Mac, and on Windows 2000 and later,” says it’s providers. You can read the free beginner’s guide and take online tutorials for using this software.
Basics of Machine Learning
Available from Udacity, this course for beginners is free. However, you need to know basics of Python and use of statistics. Other than Udacity, there are various other online free tutorials that help you get insights into machine learning. These basic and free courses enable you to choose a machine learning course that can help make a career in this field.
Level Probability and Statistics Guide
You can avail a free, self-learning course on Level Probability and Statistics from the Stanford University. However, this is only an introduction to the subject. You will need to further develop your skills in Level Probability and Statistics use by hands-on training or through a paid course.
Free Artificial Intelligence Basics for Beginners
Udacity also offers a free course on Artificial Intelligence Basics for beginners. This self-paced and self-learning course is aimed at creating a better understanding about AI and its uses, future projections and practical uses in Big Data and Analysis.
Data Management with R
Data Management with R course to boost your Big Data and Analytical Skills is available free from a variety of sources. It teaches you how to use R for data analysis, statistical computing, and reading graphics. The course is available from Microsoft for free but you have to pay for the certification.
Hadoop for Starters
For those with Linux and Java skills, Hadoop Starter Kit is an excellent free online course for beginners. “The objective of this course is to walk you through step by step of all the core components in Hadoop but more importantly make Hadoop learning experience easy and fun. By enrolling in this course you can also get free access to our multi-node Hadoop training cluster so you can try out what you learn right away in a real multi-node distributed environment,” states website of Udemy, its provider.
In Conclusion
It is vital to remember, these free courses usually serve as an introduction to the complex field of Big Data and Analysis. You may wish to opt for paid courses that are available from various reputed providers, to acquire additional skills for making a career.
In general, there are online courses that are free; others charge a fee for the exams and certification- which is worth the money. Learning about Big Data and Analytics from any of the above mentioned courses is advisable. Considering the high salaries, a profession in Big Data and Analytics fetches. Exam fees and certifications usually cost between US$ 25 and US$ 100, depending upon the free course. Charges vary according to your country of residence.
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There is little doubt in anyone’s mind that big data is the key to business growth and success these days. This is exactly why over half of businesses have incorporated the futuristic technology into their daily practices.
However, to SMBs, big data analytics still seems like a game for the big time players with huge budgets. Limited budgets and an overall misunderstanding of its practicality is what’s holding many business owners back from embracing the technology, of which could make all the difference in their success.
Big data integration does not have to be an enormous undertaking, nor does it have to be used in every single aspect of the business. For SMBs that are just testing the waters, there are three key areas that stand to benefit greatly from big data support. Let’s discuss.
Understanding Customer Intentions
Providing your customers with the best CX possible is a huge challenge for big and small enterprises alike. But the key to unlocking a great experience for each consumer is understanding what they want and what they like. Sadly, just over 20% of companies design their CX around analytical data, meaning that 80% of businesses are essentially guessing or basing their strategies on limited experiences.
Big data can help to demystify the question of what your customers want by analyzing their past actions. For example, data collected from POS systems can be used to track purchase frequency and common product bundles for better shopping suggestions and personalized reminders for past customers.
Additionally, intense research and data analyzation from customer searches and trending keywords can make it crystal clear what exactly your audience is looking for. Keyword tracking systems like SEMrush record and analyze web traffic data, which you can use to form strategies that align with the goals of your customers. By identifying the pressing questions through this type of research, your brand can provide the exact answer that your audience wants.
Small businesses need to know that understanding your customers is much more than just creating profiles that breakdown the age, sex, and location of your audience.
Hire Better Talent
Bringing in the best talent is essential for any business to grow. But, making one bad hiring decision can set small companies way back. 75% of companies report that they have made a poor choice when hiring someone, which resulted in a $17,000 loss in profits due to lower quality work, or the time lost looking for a replacement.
People analytics can help HR departments make their entire recruiting process more precise and data-driven from start to finish. Systems like Predictive Index use people analytics to gauge each applicant’s aptitude and qualifications to predict their likelihood of success with the company.
Every candidate completes personalized assessments that measure their potential, giving recruiters the exact data that they need to make a more informed final decision. Furthermore, Predictive Index uses AI technology to record applicant behavioral patterns and create reference profiles for better recruiting strategies in the future.
By using “people analytics” throughout the recruiting process, SMBs can discover better talent and understand the qualities that signal better output for the job. Many companies and HR departments have turned to data-backed recruiting tools to automate much of the hiring process, such as qualifying resumes and scheduling initial interviews. This can save HR teams all kinds of precious time that can be used to focus on more important matters.
Plan for a Big Future
Finally, one of the most common – and possibly most effective – uses for big data is predictive analytics for business planning. This is an application of data mining technology that breaks down the numbers to make accurate business forecasts for all sorts of industries.
Walmart has been using big data for predicting sales trends and executing smarter inventory management for years; proving that retail companies can improve their demand and supply cycles with this kind of technology, even if it is on a smaller scale.
Many SMBs have several concerns when it comes to using this kind of technology, especially in terms of cost and complexity. However, the cost of integrating predictive analytics may be insignificant when compared to the potential losses from failing to foresee shifts in the market. Businesses that use predictive analytic technology have reported that it has made significant improvements in their lead generations and sales numbers by providing the insight they need to stay one step ahead.
Software tools like InsightSquared make it easy for small companies to analyze data and plan accordingly. InsightSquared collects previous data from CRM systems to create reports and accurate forecasts for more targeted marketing and sales strategies. These reports also answer key questions like the current status of conversion rates or whether teams are on track to hit their sales quotas for the quarter.
While some changes are impossible to foresee, using predictive analytics is a smart way for smaller businesses to make sure that they are ready and able to handle whatever comes their way.
In Conclusion
Big data is not just for big businesses these days. Every company can stand to benefit from the power of number-driven insights, and thanks to modern software systems, integrating these valuable datasets is simpler and more accessible than ever for SMBs.
There is no point to resisting this massive shift in the business world. The longer you wait to adopt big data, the further behind you are falling.
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Chatbots have become a significant part of our everyday lives. Judging by the market situation today, it seems like chatbots are here to stay. Although chatbots aren’t being used to their full potential, the new millennium has brought a change for the better. Chatbots are finding their place in marketplaces worldwide, and recent results stand behind scientific assumptions that the artificial intelligence is slowly ‘conquering the world’.
Information technology is moving a lot faster than other industries nowadays. As a result, budget-retailers like Nordstrom Rack rely on chatbots to provide assistance to their customers, and so does TJ Maxx. The number of online stores embracing chatbots goes on and on, and their influence is becoming larger by the day while also extending to various industries. Consequently, it seems that soon enough, chatbots will become the most effective marketing tool of the millennium.
No matter how much times change, direct communication will always be the best way to sell products. Be that as it may, technological advancement has brought a new way of communication – the interaction between robots and humans.
One thing is indisputable – the future belongs to chatbots. However, it’s still unclear how much and in which direction will this technology continue to grow. Namely, futurist Ray Kurzweil claims that chatbots will become indistinguishable from humans by 2029. Moreover, we already know that the beginnings of chatbots date back to 1950. That being said, if the chatbot can grow to be indistinguishable from humans in nearly 8 decades, it’s hard to imagine the distances it will reach by the end of this century. Check out the infographic below which outlines more predictions of the sort as well as more mind-blowing data.
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The world has become increasingly saturated with millions of options available to consumers. So, it has become extremely important for enterprises to limit the choices they offer their customers. The best way to do this is to provide customized and catered offers which are specific to individuals. Personalization engines make that process easier for enterprises. Organizations in this niche provide technological solutions that help create customized digital experiences for individual consumers.
Moreover, by collecting and analyzing customer information, personalization engines also help in communicating with individual consumers. Thus, any form of communication, be it messages, emails, or newsletters, can all be personalized to an extent for specific customers. Thereby provoking an open dialogue and subsequently ensuring customer loyalty.
Organizations today are successfully expanding this niche field. With evolving innovation and expertise in the field of Big Data, AI and Machin Learning, they have made it easier for enterprises to provide customized services for to their consumers. And while most them are from North American or European soil, the personalization bug has spread to the other side of the world too. In fact, study reports have revealed that APAC marketers and consumers are more inclined to personalized marketing than their Western counterparts.
Adobe and Econsultancy released a joint report earlier this year, the “Digital Intelligence Briefing: 2018 Digital Trends.” It mentions that APAC marketers are more open to digital personalization and are willing to invest in the skills and technology required for it. About 34% of marketers from APAC are ready to utilize personalization engines for better customer engagement. On the other hand, only about 16% of North American marketers feel the same way.
Quite obviously, there is a lot of potential for personalization engine platforms to grow in the APAC region. With many MNCs and newer start-ups investing in the power of digital personalization, it no longer seems like a niche field.
Below are five key-players in the APAC region, who are changing the way enterprises interact with their consumers. By the looks of how things are going, the playing field will only get bigger.
(The organizations are listed in alphabetical order.)
1. Crayon Data: Based in Singapore, Crayon Data is a big data and AI startup, with an ambitious vision to ‘simplify the world’s choices’. Crayon’s flagship product MayaTM is the world’s only choice engine, delivering digital personal experiences centred around taste. Maya enables enterprises to link their behaviour data to Crayon’s Tastegraph to create massive Personalised Taste FingerPrints for millions of their customers. Crayon currently works with leading banks, card issuers, Airlines and Hotels in APAC, India, ME and the US.
2. Dynamic Yield: Dynamic Yield is an American MNC, whose omnichannel personalization technology stack helps marketers increase revenue by automatically personalizing each consumer interaction across digital devices. It uses an advanced machine learning engine to build actionable customer segments in real-time, enabling marketers to take instant action via personalization, recommendations, automatic optimization & real-time messaging – in a single platform. The company has more than 100 customers, primarily in ecommerce and media.
3. Flytxt: Flytxt is an independent market leader in intelligent customer engagement technology. Their flagship product NEON-dX allows enterprises to drive personalized and contextual customer engagement across digital touch points using analytics and artificial intelligence. Headquartered in Amsterdam, the company has since expanded to West Asia, with offices in Singapore and India.
4. IgnitionOne: IgnitionOne is a global leader in cloud-based digital marketing technology. The IgnitionOne Marketing Platform is built on a data-driven foundation that delivers the tools marketers need to build audiences, score individuals within those audiences while optimizing results across paid and owned channels and different devices. IgnitionOne currently scores over 300 million users monthly in 75 countries and powers more than $60 billion in revenue each year for leading brands and advertising agencies. They have offices around the world, including India and Sri Lanka
5. Infosys AssistEdge: One of India’s biggest MNCs, Infosys launched AssistEdge a couple of years back. It is an “Enterprise Ready” Robotic Process Automation (RPA) product. Using the power of RPA, AssistEdge now has over 50,000 live bots in 50+ countries. Being a customer service platform, it enhances customer experience and boosts contact center agent productivity by providing an intuitive dashboard.
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As the Internet of Things (IoT) continues to steer operations in the 21st century, numerous challenges are coming to light.
While the IoT still has the potential to transform business for owners, employees and customers alike, those who already embrace this next-gen network still have some work to do.
Not only are they trying to make the most of IoT integration to benefit their own company, but they’re also treading new ground and serving as role models for those who have yet to take the plunge.
1. Meeting Customer Expectations
In the 1990s, the widespread availability of internet access forever changed the way consumers shop. It also switched the customer’s focus from standardized, mass-produced goods to customized products and services.
With the year 2020 on the horizon, customers have higher expectations than ever before. According to a recent report by Salesforce, 57 percent of consumers are more interested in doing business with an innovative or forward-thinking company — and 50 percent won’t hesitate to switch brands if their needs go unmet.
2. Easing Security Concerns
The IoT was initially touted as a hyper-secure network that was suitable for storing and transmitting confidential datasets. Although it’s true that the IoT is more secure than the average internet or LAN connection, it’s not exactly the bulletproof shell some users expected.
Some of the most significant security concerns involve both the IoT and the cloud. A recent analysis predicts a loss of up to $120 billion in economic fallout in the takedown of just one cloud datacenter.
Reports also state an annual economic cost of cybercrime at upward of $1 trillion — which is quite a leap for 2017′s record-setting figure of roughly $300 billion.
3. Keeping IoT Hardware Updated
Regardless of how a company uses the IoT or the cloud, data integrity is a common challenge. With so much data coming in from multiple sources, it’s tough to separate useful, actionable information from irrelevant chatter.
It’s critical to calibrate your IoT sensors on a regular basis, just as you would any other kind of electrical sensor. Next-gen sensors are embedded in many different devices, including panel meters, chart recorders, current clamps, power monitors and more, and it’s difficult to synchronize the dataflow between all this hardware without the help of a professional team.
4. Overcoming Connectivity Issues
In its current form, the IoT utilizes a centralized, server-client model to provide connectivity to the various servers, workstations and systems. This is quite efficient for now, since the IoT is still in its infancy, but what happens when hundreds of billions of devices are all using the network simultaneously?
According to updated reports from Gartner, more than 20 billion individual units will connect to the IoT by 2020. It’s just a matter of time before users start to experience significant bottlenecks in IoT connectivity, efficiency and overall performance.
5. Waiting for Governmental Regulation
While some businesses immediately embraced the IoT, others are hesitant. In many cases, these businesses are waiting for government officials to intervene with new standards and regulations.
However, since the IoT, the cloud and even the common Internet aren’t tied to one specific city, state or region, who is responsible for setting these regulations?
Complicating matters even further is the sheer amount of IoT-connected devices. Since these devices originate from many different sources, including international partners and vendors, how does a localized regulatory agency control the quality of incoming shipments?
Although most experts agree that IoT regulation is a necessity, they have yet to formulate any standards or guidelines for the public to follow.
Making the Most of the IoT in its Current State
Despite the challenges and bottlenecks of the IoT in its current state, it still has many benefits in today’s business world.
It’s useful enough that some are willing to throw caution to the wind and make the transition to the IoT — despite all the challenges it provides — to get a jumpstart on their competition before it becomes the next big thing.
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While there remains no cure for Parkinson’s Disease as of yet, Big Data is making major strides in grossly expanding what we know about the disease—and thus making a cure more likely in years to come.
Parkinson’s doesn’t just affect old people, either, as many people once suspected. More recent cases, such as those of Muhammed Ali and Michael J Fox, have helped prove the fallacy of that once commonly-held assumption.
Still, far less is known about the disease than is known. For instance, Parkinson’s is usually diagnosed through a series of 15-minute appointments. Given that Parkinson’s regularly varies in severity, however, those 15-minute appointments may not give doctors or a treatment team a very thorough picture of a patient’s disease.
Andy Grove, a former CEO of Intel, however, is helping researchers making strides—with the use of Big Data. In the last few years, Intel has teamed with the Michael J Fox Foundation to gather data from Parkinson’s patients, with the aim of using that data to better understand the disease.
The project uses a Cloudera-based platform on an Amazon server, gathering 9.7 terabytes of unique data every day, based on the following:
The data stored on the Amazon server, then, is automatically made part of a central database which has been made freely available to researchers and data scientists.
This vast data set, in turn, allows researchers the ability to sort through it with dedicated algorithms, searching for patterns, correlations, and other relationships that help them better understand Parkinson’s disease. This is the sort of work that would clearly have not been possible even 5 or 10 years ago, but the advancements of wearable tech have made it far less scary for Parkinson’s patients.
Most amazing, though, is the sheer quantity of data, and what it allows researchers to sift through. The development of ever-increasing computing power allows researchers more and more capacity to sift through enormous quantities of data, better allowing them to see patterns and relationships as advanced algorithms can work through that data—and that, in turn, helps create meaningful and actionable reports.
While this has been occurring for years in sports and athletics, only recently are we beginning to understand how much Big Data can do for medicine and medical research. Parkinson’s disease research is only one of those frontiers, and as more of that data is analyzed, we may well find ourselves well on our way to a cure.
Click here to read more about Parkinson’s disease. Published with permission.
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