5 Big Data Analytics Skills That Will Boost Your Salary

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Are you wondering what the buzz about big data is? Should you make a transition to a career in big data analytics? Well, whether you are already in data analytics or in IT, you have come to the right place. As the buzz around big data gathers momentum, more and more professionals are gearing up to improve their employable skills in big data analytics.

Why so, you may wonder.

It is a data-driven world today where more and more businesses are implementing analytics in a big data ecosystem. A Gartner survey shows that more than 75 % of businesses are investing in big data. Whether it is the banking and financial industry, insurance, eCommerce, health sector or travel, all of these are grappling with voluminous data in real-time. Making sense of this data is the domain of the big data analyst.

As the always-connected environment in everyday life gains traction with more and more connected devices, data is generated in huge volumes at great speeds in real-time. Big data analytics has emerged as a leading disruptor in most sectors, powering data-driven insights for transforming customer experiences. Big data is here to stay, and the best way to carve a high-paid salary in analytics is by walking the big data path!

Why is there a need for Big Data Analytics skills?

With the rapid implementation of data analytics in critical business operations and decision making, there has been an increase in the demand for big data experts among other analytics professionals. Individuals with the right set of Big Data analytics skills will have an immense opportunity to pursue a rewarding career in analytics across industries.

According to the latest industry reports, big data analytics salaries are at an all-time high and expected to get better. Entry-level salaries begin at Rs 7.5 lakhs p.a. and go up to Rs 12.10 lakhs p.a. at the senior levels. According to an Edvancer-AIM study on analytics career scope, the median salaries offered are Rs 10.5 lakhs p.a, while 40% of advertised analytics jobs offer salaries of more than Rs 10 lakhs p.a. The percentage by hiring across tools and skills records the highest for R statistical tool at 36 %, followed by Python at 30%; and Hadoop and SAS at 20% each.

At the same time, it is about your data analysis competency and logical reasoning. The more you learn the technical and open source frameworks dedicated to handling big data analytics, the better is your chance to get a salary that you have always dreamed of.

Here are the top five big data analytics skills that will likely boost your salary:

1. Apache Hadoop

The Apache Hadoop is an open source project that allows fast processing and insights into huge volumes of structured and unstructured data. The Hadoop has emerged as the driving force behind the growth of big data analytics, with spin-offs in a BI environment. The powerful big data platform offers the Hadoop expert the ability to leverage the central elements of the Hadoop stack for deep and fast analytics. So learning the Hadoop framework, together with the underpinning programming models, such as the Map Reduce, Hive, Pig, and HBase, is critical to earning analytical brownies in big data analytics.

2. Apache Spark

In-memory computing enables high-performance algorithms for faster processing. The capability of end-to-end insights in real- time with sophisticated ‘what if’ simulations, has seen increased adoption of Apache Spark by big data practitioners. As mastering the Spark is considered a challenge for many analysts, the professional who is equipped with skills in Apache Spark is sure to command a salary on his terms.

3. Machine Learning and Data Mining

Machine learning is an AI technology that powers big data analytics by learning rules iteratively for improved analytics. Getting trained in machine learning algorithms and rule defining for spotting anomalies and patterns is a popular demand in sectors like banking, finance, and trading. The ability to build predictive analytics in a big data engine for personalized customer experience is a highly sought-after skill.

Data mining is a technique that integrates structured and unstructured data from multiple disparate channels for big data analytics. It has the capability to pull any data, including social media, for a 360-degree view of the customer. If you are armed with the skills of data mining, you can bring value to a business. In turn, you can demand the top salary for your data mining skills.

4. NoSQL

Distributed, scaled-out NoSQL powered databases continue to be the rage. NoSQL database skills power big data analytics from huge data, with quick iterations and coding. Its many advantages over the traditional RDBMS have pushed NoSQL to the forefront. The distributed architecture of NoSQL framework allows high-performance big data analytics at massive scale, making it almost indispensable in recent times. So the analytics professional armed with working knowledge of one or more NoSQL databases can certainly boost his salary.

5. Statistical Tools

The power of statistical reasoning cannot be underestimated, especially for analytics presentations for businesses. Statistical tools like R, SAS, Matlab, SPSS, or Stata are long-time favored tools for enterprise big data analytics. Analytical tasks with massive data can be handled with ease, such as coding with social media analytics; data mining, clustering, and regression models. The advantage of R as an open source model lends its ability to be paired with other technologies and big data products. The statistical tools are a great learning curve for those who want to work as quants, or in the field of retail, insurance or market research.


In the race to secure the best salary in big data analytics jobs, a single skill development is not enough. Each of the above skills has their own merits. So plan a gradual scale-up of big data analytics learning, with all the five skills, to fetch you a good analytics position. The salary package and career graph in the big data analytics landscape have never been better! So go ahead, and arm yourself with the necessary technical skills. Learn big data with big data certification and online training programmes on-the-go, together with your studies or present job. Get trained on the most relevant big data analytics tools that best-fit your career trajectory. Strengthen your analytical skills in a big data environment with suitable weekend programmes, and domain-based management skills for that salary package booster. So if you are a student or analytics newbie, there is no time like the present.

5 Must-Haves On Your Machine Learning Resume

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Companies are today hard-pressed to find good machine learning talent, What they want from the pool of candidates, is one who already comes to the table equipped with the skill-sets, theories and coding ability needed for the task.

The skill requirement is not only restricted to the knowledge of machine learning algorithms and when to apply what, but also how to integrate and interface. The core skills required are technical, with a good understanding of mathematics, analytical thinking and problem-solving. While the specific skill requirement for each profile differ, there are core machine learning skills that are constant for all roles.

Here are the top 5 must-haves on your AI resume:

1. Probability and Statistics

The theories of probability are the mainstays of most machine learning algorithms. If you are familiar with probability, you are equipped to deal with the uncertainty of data. Getting a grasp of the probability theories like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models; is a must if you want to be considered for a machine learning job that centers around model building and evaluation.

Closely linked to probability is statistics. It provides the measures, distribution and analysis methods required for building and validating models. Statistics provides the tools and techniques for creation of models and hypothesis testing.

Together, probability and statistics make the framework of ML model building. So this is the first thing to consider when building your machine learning resume.

2. Computer Science and Data Structures

Machine learning works with huge data sets, so a fundamental knowledge of computer science and the underlying architecture is a compulsory attribute. Expertise in working with big data and analytics, and complex data structures, are a must. Thus a formal course or degree in computer science is compulsory for a machine learning career. Additionally, your resume must display your skills at working with parallel/distributed architecture, data structure like trees and graphs, and complex computations. These are required to apply or implement, at the time of programming. Additional certifications for practising problems and coding will hone your ability with big data and distributed computing. Experience in computer science applications will go a long way in securing you a job in this field.

Read Also: Linear Regression for Beginners – Machine Learning

3. Programming Languages – C/C++, R, Python, Java

To apply for a job in ML, you obviously need to learn some of the commonly used programming languages. Although machine learning is largely bound by concept and theory, it implements any language with the essential components and features. Some programming languages are considered especially suited to complex machine learning projects. So a working knowledge of these programming languages makes add to your resume.

C/C++ are used where memory and speed are critical, as they help to speed up the code. Many machine learning libraries are also developed in C/C++ as they are suited for embedded systems. Java, R & Python work very well with statistics. Despite being a general programming language, Python has several machine learning-specific libraries that find a use for efficient processing. Knowledge of Python helps to train algorithms in various computing architecture. R is an easy-to-learn statistical platform, increasingly used for machine learning and data mining tasks.

Having a degree, certificate or online diploma in these languages, make for a good resume. As an engineer or student of science, you may already be skilled in C++, Java, and Python. You can also learn these languages online in your spare time, and practice on projects for special mentions on your CV. Programming languages like Python and R and their packages make it easy to work with data and models. Therefore, it is reasonable to expect a data scientist or machine learning engineer to attain a high level of programming proficiency and understand the basics of system design.

Read Also: 11 Most Common Machine Learning Interview Questions

4. Machine Learning Algorithms

Applying machine learning libraries and algorithms is part of any ML job. If you have mastered the languages, then you will be able to implement the inbuilt libraries created by other developers for open use. For instance, TensorFlow, CNTK or Apache Spark’s MLib, are good places to work upon. You can also begin with practicing programming algorithms on Kaggle. This can find mention in your resume as well.

However, to get considered for a machine learning job vis-a-vis other competing applicants, you need to have the know how to implement these effectively and in which scenario.

5. Software Engineering and Design

Software Engineering and System Design, are typical requirements of a machine learning job role. A good system design works seamlessly allowing your algorithms to scale up with increasing data. Software engineering best practices are a necessary skill on your resume. As a ML engineer, you create algorithms and software components that interface well with APIs. So technical expertise in software designing is a must while applying for a machine learning job.


An application for machine learning job role requires careful planning and consideration. Machine learning is all about algorithms, which in turn stems from a good knowledge of big data analytics and requisite programming languages. Sound engineering or technical background is a must. However, the applicant who includes as many of the required skills in the resume stands a better chance of getting selected. So, are you all set for a career in machine learning?

Click here to explore a career in Machine Learning.

10 Dos and Dont’s for a Successful Cloud Computing Career

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As more and more enterprises turn to the cloud, the need for experts in the cloud computing industry has never been greater. If you’re willing to make a career in this industry, there’s no better time than now. However, being one of the most lucrative industries of today, it requires bona fide expertise to derive the best results out of the agile cloud-based environments.

The bottom line is – this high-growth field requires a lot of hard work before you can actually come up trumps and forge your career path.

Therefore, before you break into the realm of cloud computing, here’s a bit of information to help you navigate it. Read on to know the meaning of cloud computing and check out some important dos and don’ts of successfully working in the cloud computing industry.

What is Cloud Computing?

Put simply, cloud computing is the delivery of on-demand computing services over the Internet. More than a fad, the switch from traditional software models to the Internet has become a necessity today.

From databases to networking, servers, software, and storage – enterprises get all this and much more from cloud providers, who charge for these services on a pay-as-you-go (PAYG) basis with different features and pricing packages.

This means users of the cloud can get access to a range of services, whenever needed, without having to worry about the number and types of hardware and software required to run the business.

Dos and Don’ts for a Successful Cloud Computing career

Do get your basics right

Just like any other field, to ace the cloud computing world, you must get your basics right. Learn cloud computing concepts to get the knack of the intricate nature that the on-demand cloud environment has. From Infrastructure as Code (IAC) to DevOps, a lot needs your attention. And once you have mastered the basics, then move on to acquire knowledge about all those specialist areas where you want your career’s prime focus to be.

Do gain hands-on experience

Just understanding the concepts or gaining fundamental knowledge about cloud computing will not ensure success in the field – getting practical experience will. Learn the ropes by using some of the platforms offered by the leading cloud providers of today. You can try AWS, Google Cloud Platform or Microsoft Azure for free for a limited time duration and put your knowledge into practice.

Do learn new skills and technologies

No matter whether you want to become an expert cloud architect, engineer, or developer – to excel in the cloud computing field, you must gain a better understanding of myriad technologies and acquire skills that can help you be a pro at what you want to do.

Do get certified

Getting a cloud computing certification is one of the most significant steps to ensure your success in the cloud computing industry. It builds trust amongst potential employers that your cloud skills are at par with the industry benchmarks. So, get enrolled in one of the top cloud computing courses online and become a certified cloud professional while honing your skills.

Do get expertise in analysis

As an expert cloud professional, you will often be required to analyze a project’s performance, scalability, availability, and security. Therefore, besides getting the necessary cloud computing training, it is important to raise your game by becoming good with numbers and analysis too.


Don’t limit your knowledge

Being a fast-evolving technology that it is, cloud computing requires deeper technological insights on various related subjects to help you make well-informed decisions. Thus, stay on top of industry trends by attending seminars, workshops, and by participating in cloud computing conferences and increase your knowledge.

Don’t ignore the importance of project management

Handling multiple vendors and managing teams call for a good understanding of project management. So, do not ignore the importance of acquiring a strong project management skill set to perform different tasks in the business computing landscape.

Don’t choose your role in haste

When it comes to choosing a specific role in the arena of computing, you must take your pick as per your aptitude and interests. Do not act in haste and carefully weigh the different career options available in the cloud computing world.  

Don’t forget to explore job sites

Avoid getting stuck in a rut by career by exploring job sites and figuring out the next step in your cloud computing career. Apart from helping you find new jobs, many such sites also inform about the latest skills cloud computing providers are looking for, interview questions asked from previous candidates, and some (LinkedIn) even help you connect with existing cloud professionals.   

Don’t hesitate to share your knowledge

Investing some of your time to showcase your knowledge and skills on online platforms can be a sure shot step towards climbing that ladder of success in the cloud computing field. So, don’t hesitate to create a personal blog or using platforms like LinkedIn for sharing your industry experiences and the knowledge that you have gained. Move into an enriching growth phase by adding value to the field with your insights.

Having covered some Dos and Don’ts of a successful career, let me remind you that now is the right time to upgrade your skill, learn new things and advance your career in cloud computing. Being part of it is like being a part of the movement that has reinvented IT on a global scale.

5 Mistakes Aspiring Business Analysts Make

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A business analyst plays a crucial role in understanding a company’s assets. They have to make sure that the company deliverable match the end users’ needs. They also act as a bridge between the business team and the IT department, communicating the requirements, changes and goals derived from the testings and analysis. Their main job consists in implementing numerous variants of the same testing, even managing different testings at the same time.

business analyst is also expected to bridge the gap between the traditional and advanced practices of information gathering;  this one single task helps all the project team members save time that would be used otherwise to pull data from a spreadsheet and then feed it into the Excel sheet to make it accessible at different points.

Business analyst’s job is so significant at every stage of the product development process and the customer’s journey that even a single mistake can compromise the entire business effort. This is why it is so crucial to have a prior knowledge that one can easily acquire through a business analytics certification.

It may happen some time that a business analyst could ask for the approval of documented requirements without a prior understanding with all his supervisors. This is a common mistake which could cause serious damage to business plans. As a business analyst, you may want to get a fast approval to move forward to the next testing phase, but this move would get you in a difficult position in front of the stakeholders who surely want to express their opinions and concerns before moving on an uncertain path.

Analysts could also be tempted to use the template of BRD (Business Requirements Document) to check all the projects when in reality every single phase of the job requires a different point of view and measuring parameters. Using the BRD template may instead hurt the reviewing phase as it could hit a roadblock on a project that requires a totally different parameter to be measured. They could end up consuming more time than expected and getting results that are irrelevant to the task. This is why it is always advisable that you have a background in business analytics training.

Here you will find the most common five mistakes business analyst makes, and we will give you the prompt solutions in order to avoid them.

  1. You decide to review the task you are carrying on to move forward in the testing phase all by yourself. This is a very common mistake made by new business analysts that want to rush to the next stage of their project without taking in consideration what their superiors have to say about the job done so far. Always keep in mind that as a business analyst it is very important to share your findings with the leadership team and the company owners to understand their viewpoint about the task.
  2. You do not employ a technical language when it comes to show a technical job you have  carried on so far. Business analysts are expected to keep the requirements tested and well structured. A skill you can learn thanks to your business analyst training.  Business analysts should take advantage of the SMART approach: Specific – Measurable – Attainable – Realisable – Time-bound. Going a step ahead from this commonly used ‘SMART’ approach, you can follow – SMART-CC that adds Complete & Concise also to ‘SMART’ acronym. The first organism you have to keep accountable of your job is your organisation itself. As your primary job is running numerous tests about the product and its components you have to use a technical language shared by the other counterparts involved. This way, there will be no confusion where you are going to expose your findings.
  3. Getting into the design phase too early. It will often happen during your practice as business analyst that stakeholders and business owners will try to move to the design phase of the product or software without giving a first look to the insights you have gathered during your research and testing phase. You have to step in and reinstate that first of all they have to look at the results gained from the testing stage, and only after the gained data gets a seal of approval they can get to the design and implementation phase.
  4. By now, it will be clear to you that a crucial part of your job is to keep an open and clear line of communication with the company decision makers. And here lies another big mistake: not having a proper conversation with them. You should engage in meetings based on the data you have gathered so far and in meetings that take place in order to spread light on the business owners’ needs and requirements. No further communication should be involved, so that your team doesn’t get confused or distracted.
  5. Do not think that even though you have done your thorough analysis and gathered enough data to move forward on the next step of your job, you can do so without consulting your business superiors. Too many business analysts assume they are good to go as soon as they finish the testing phase; yet, this can be the moment they commit the major number of mistakes.

Finally, keep in mind that good communication is the core of your job as a business analyst. Master this skill and all other phases will be a breeze to carry out!

7 Things Your Boss Wants You to Know About Big Data Analytics

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How many times have you found yourself face to face with your boss, head in the clouds, without knowing what to say about the latest advancements done by your team, or about the latest trends in your field in big data analytics?

Thankfully, you can take the guesswork out of the equation since we have done the heavy lifting for you. This article focuses on the 7 things your boss expects you to know about big data. Finally no more awkward stares at your next meeting!

You already know that AI has been playing a major role in every field for quite some time and it is destined only to grow in the upcoming years, as well as big data analytics, which is about to revolutionise the relationship between machines and humans.

More and more industries are investing big money in Big Data, Hadoop and cloud technologies, new title jobs are rising in the IT field and data management is becoming a crucial role in many enterprises. We don’t see just the role of big data analyst, we see the up and coming data manager profile. 13% of the data related jobs will be done by them and will support the business analysts in a greater way.  Big data analytics will also get sorted in a managed way without any error.

What about those firms that rely on outsourcing their data analysis to SaaS? At the moment 66% of them employ a third party to manage big data, and this number is only destined to grow. Another player that is going to take a huge portion out of big data analytics is academia along with some of the new research labs like the non-profit open AI.

Without further ado, let’s see what you must know in order to wow your boss at your next meeting and further your knowledge in 2018!

  1. This year natural language will play a major role in big data analytics. Among those companies that are going to up their game in the big data field, 25% of them will adopt the point-and-click analytics with colloquial interfaces. Big data analysis through Hadoop will be supported by this new feature, in which data can be interrogated with the help of natural language. The results can also be obtained in real-time visualisation.
  2. Your enterprise will rely heavily on the decision-making skills provided by big data. As much as 20% of the companies employing big data analytics will ask for the help of machine learning and AI to formulate new actions and get real-time instructions. Big data analytics will sift through the millions of data provided to the company to enable the customers with the best solutions, to get the best deal from suppliers, and to hand over the instructions to the employees. The companies will save time and money thanks to this smart move. Another point to highlight to your boss!
  3. Thanks to big data analytics and Hadoop, your company will lower the unstructured data in order to see a more efficient way to deal with structured data. In 2016 a global survey offered the view on the state of unstructured data. It found out that more than 100 terabytes of the unstructured data doubled that year. The sources of the data weren’t analysed either by most companies. Thanks to data & analytics, now the gap between structured and unstructured data is filling up quickly and the analysing of data is becoming more and more precise and scalable.
  4. Know where to put your money when it comes to data lakes. At the moment many companies are investing a lot of money in data lakes without even knowing if they will get a return of any sort on their investment. Within this year 33% of those company will move away from data lakes to see if they can make smarter decisions with big data analytics.
  5. 50% of companies working with cloud computing will opt for public based clouds for their operations. These companies expect to see a better investment in public clouds than in-house facilities and expect to get more benefits from this move.
  6. Enterprises will shift from an operation-centric approach to a customer-centric approach. For this purpose, big data analytics will play a crucial part in how to run a business, since more intelligence will be necessary to capture and advance the customers’ needs and requests. The companies will face more pressure to deliver an excellent customer experience and results.
  7. Chief Data Officers will work closely with CEOs to use big data in a more active way, instead of a defensive plan. Big data will be thoroughly analysed and tested to discover new ways to consistently deal with the customer and put in front of him the best results and answers to his needs. This will be a major shift in the way big data are used. No more just for internal analysis, but it will be employed for a more aggressive interaction with the end user.

There you have it, the 7 crucial points you need to know about big data analytics. This year will see a major shift in how to deal with big data analysis, and you will be on your way to be in the front seat when it happens!

Top 8 Interview Questions for Cloud Computing Professionals

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You own a company that is rapidly growing and you are looking forward to expanding your IT department. Maybe you are building your in-house IT facility and it’s time to hire some cloud computing experts that will take charge of the new duties. This could turn out as a difficult task for non-technical managers who have no knowledge about cloud computing and have to make highly technical questions.

Here are the top 8 interview questions for cloud computing experts.

1. What types of data are used in cloud computing?

This may sound as a simple question, but in order to answer it, an IT professional has to show he or she is up to date with the latest trend in cloud computing. In fact, there are three types of data necessary to work with the cloud to save and store various data files. First of all, you want the candidate to talk about unstructured data. Unstructured data is data that has an unknown or unclassified structure. You can classify as unstructured data all those file types such as images, video, text and search engine results. After unstructured data, a cloud computing expert should talk about structured data. This type data is processed, accessed, and stored in a fixed format. An example of structured data is contained in database management systems. Finally, we find semi-structured data, which is a mix of structured and unstructured data. A good example of this type of data is XML format.

2. Can name some of the most important cloud platform databases? 

Cloud computing is a technology that is rapidly evolving in terms of speed, scalability, and efficiency. This is happening especially thanks to open source software that are becoming an integral part of the cloud.Just to name a few, your candidate should be familiar with MongoDB, CouchDB, and LucidDB. The first one, MongoDB, is written in C++, it offers high storage capabilities. Also, this database system is schema free and document-oriented. CouchDB, on the other hand, is based on Apache server and is very efficient and reliable at its job (which is storing data).Finally, we find LucidDB, employed for data warehousing, and it is written in Java/C++.

3. What is our advantage as a company adopting cloud computing facilities?

This can be a crucial question, as it implies the core question whether or not your company will get any advantage from building an in-house cloud computing department. Also, it gives you a clear view of how much your candidate understands the importance and benefits found in cloud computing. The reasons to build a cloud computing division within your company are many, and your prospect should be able to name those reported below. – Thanks to cloud computing, your data backup and data storage will be safe- Your IT department will be able to boost your server capabilities without the need to invest on hardware.- Your software will be able to work on any operating system- You will be able to use your cloud computing as a software as a service (SaaS) without the need to include a third party company- As you opt for building a new IT capability within your company you will increase productivity and cut costs.- You will be able to grow and scale your business.

4. Can you talk about platforms that are used for large-scale cloud computing?

With this question, your candidate will have to show his deep knowledge about platforms for data storage and processing, as well as scalability. Two main platforms are Apache Hadoop and MapReduce. Apache Hadoop is an open software platform built specifically for distributed storage and distributed processing of very large datasets. Hadoop is aimed to provide for data storage, data processing, data access, data governance, security, and operations. Introduced by Google, MapReduce marks the new frontier for analysis of large-scale data with this platform. MapReduce provides the user with the ability to process a huge number of datasets using cloud sources and commodity hardware. It provides fault tolerance and transparent scalability at the software level.

5. Name the different service models provided by cloud computing.

Again, this question will test the cloud computing expert’s knowledge of the different types of service offered by cloud computing. There are three different types of service: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The first one, Infrastructure as a Service, is a hardware facility provided by a third party, which also manages it. Platform as a Service consists of the operating system layer and it is operated by another company as well.Software as a Service regards all applications and programs hosted in the cloud.

6. How can cloud computing help our company?

This is an important question especially for mid-size to big companies that are more interesting in building a private cloud than a small business that may opt for the public cloud. Besides private cloud and public cloud, there are also community cloud and hybrid cloud infrastructures. A company may build a private cloud to be used exclusively for its internal needs, it can be supervised by the company itself or a by another enterprise, or both. Its physical position may be where the company is located or may be positioned in a remote data center.A community cloud infrastructure is aimed to provide its services to a limited community of users that have a shared interest in their businesses. What about the public cloud? As the name says, the public cloud is thought to be used by the public. It can be operated by a third organization and its location is situated where the data center of the cloud provider is.When merging two or more cloud infrastructure you create a hybrid cloud, even though the different clouds keep their distinct attributes. They are called hybrid because they share the proprietary tech that enables data and app portability.

7. Why should a company opt for utility computing?

This is a complex question that requires a complex answer: thanks to utility computing the end user will only have to pay for the service on a per use basis. The user will have the ability to increase the number of services he’s using to satisfy his needs. This approach to cloud computing can be very remunerative for those businesses that plan to scale and grow their product.

8. When transferring data to the cloud, what is the best way to make this operation safe?

By adopting a secure key, you can make sure there will not be any data leaks, whether they’re malicious or not, from the cloud storage. This action will make intercepting your data impossible when it moves into the cloud.


How to Start Learning Something New and Keep at It

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February is about to end just like the last bit of motivation with which you made New Year Resolutions for 2018. But before you give up the resolve for a transformed body or a healthy lifestyle it is important that you re-think about another super important goal that might have slipped your mind. One that can make or break your career in the upcoming years, ‘Learning a New Skill’.

If you didn’t include Upskilling or Learning new things to your to-do list of the year it’s high time you gave it another thought. We discussed in our last blog why Upskilling is quintessential for every professional who is eyeing a promotion or career transition in 2018. The agenda of this post is to help you create a roadmap which will help you learn a new skill and practice its application in your personal or professional life.

The easiest thing in our noisy lives today is to get distracted. It could be an urgent report that you have to send by end of the day or a Facebook notification that leads you to waste 30 mins, surfing your timeline watching a cat sing Ed Sheeran’s lyrics. Finding the time to do something new and stay on track with it is next to impossible but with a right plan and great motivation you can ace this uphill task.

So here are a few simple steps you can follow to get your learning started.

  1. Start Prioritising

    Let’s face it there will never be a time when you will be less busy, or ‘relatively free’ and will have the liberty to sit down and learn a new skill. We are all juggling with multiple to-do lists, backlog work, and personal commitments to be fulfilled. The only way you can focus on anything is by giving it more importance compared to the other things you need to do at that moment. 

Prioritisation should be your mantra. Prioritise the work you are doing, basis its urgency or the deadlines and free up a time-slot for learning. It could be an hour while you are traveling to work, the extra 20 mins you can save from your lunch-break or a quiet half hour late at night. Whatever it is that can guarantee you a distraction/noise free slot within your day works. Make sure you do not line up any other task at this time and give undivided attention to learning.

2. Make a Plan

Once you have identified the time you are going to dedicate to learning a new skill it’s important to make a plan. Create goals and target for each stage and reward or penalize yourself as per the outcome. Make sure the learning time that you allocated at the beginning and the intensity of the skill you are planning to learn increase at every stage. This is known an progressive overloading. For example, if you allocated 30 mins as the learning time in week 1, it should increase to 45 mins in the next week and 60 mins at the subsequent one. Progressive overload improves the engagement and interest levels for any task.

3. Break it Down

Congratulations, if you followed the 2 steps mentioned above, for you have discovered the time to learn something new and also have a plan of action ready. The next thing you need to work-out are a few details to make this plan more exhaustive and avoid any monotony that can lead to you giving it up mid-way. One trick could be to break this plan into different activities that will ensure you don’t get bored and follow it through.

A good break-down of learning activities should include:

a) Research- make sure you do an intense research on the topic you are learning at least once a week to stay on top of the trends and the latest news of the respective industry.

b) Read read and read- Find the thought leaders, industry best-practices, books, white-papers or any source material which comprises the core-theoretical reading material for the skill you are learning. If you can’t find one, identify the right books and subscribe to relevant blogs that can give you this insight.

c) Focus on the basics- The most important step in this journey is to build a solid foundation of the skill, your best bet here would be taking up a part-time course, if that’s not feasible look up at the short term online courses and if possible find one that gives you access to an industry-mentor who can clear your doubts and give you feedback.

d) Practice as much as you can – as important as it is to learn a new skill you need to ensure that you put your learning into practice to make sure that you don’t forget the key takeaways. Find a way to take regular quizzes, assignments that can test your skills and let you know where you stand. You can also look for websites that provide free data-sets, practice assignments etc. that are industry-relevant and cater to different difficulty levels.

A good learning plan will allocate learning time for each of these steps and give due focus to all these activities.

4. Apply the Lessons

One crucial step that most of us tend to forget after we have finished learning something is applying the concepts/takeaways in our personal or professional life. Let’s say you took a class on Negotiation, try to apply the fundamentals of a good negotiation tactic while you are negotiating rates with a company vendor or shopping in a market. Also, make it a point that you attend webinars, workshops, and conferences on the subject matter. This will allow you to stay on top of latest industry trends and also help you build a network of valuable industry professionals.

And with that my friend you are all set to conquer the path of Learning New Skills. Bon Voyage!


What is Upskilling and why you need to pay more attention to it than ever?

Reading Time: 3 minutes

As per the latest NASSCOM report, up to 40 percent of the estimated 4 million workforce in India would undergo re-skilling over the next 5 years. 

You must have come across articles blaring data points like the one above in the past few months and would’ve brushed them aside conveniently, but if you are planning to make 2018 a milestone year in terms of Career Growth it’s time you took the latest corporate buzzword seriously. ‘Upskilling’ has arrived and it is here to stay.

In light of the layoffs witnessed by the IT industry last year Reskilling/Upskilling has become the new mantra to success, and for the all the right reasons. But before we get into why it is super important to upskill yourself more than ever now let’s understand what upskilling really entails.

What is Upskilling?

Upskilling refers to improving the skillset of professionals, usually through training to enable them to perform better in their jobs and help them progress through the company into various job roles and opportunities. Upskilling opportunities can be provided using a number of formats, ranging from short courses to getting higher qualifications or gaining certifications through part-time or full-time programs.

Here are the top reasons Why Upskilling is important for you now more than ever:

  1. It helps you keep up with latest Industry Demand & trends– Learning new skills or improving existing skillsets ensures that you are up to pace with the shift in trends your domain is witnessing. As an example take The Union Budget 2018 and its clear focus on AI, Big Data and Robotics and their application in the Digital Economy. We can clearly see career opportunities are set to rise exponentially in these domains within the next 2-3 years. So if you were to acquire a new skill or develop expertise in a new domain, Analytics, Big Data, AI or IOT could be a great option.
  2. You can increase your pay-check in no-time- According to a staffing solutions company, TeamLease Services, India will face a demand-supply gap of 2 lakh data analytics professionals by 2020. This lag between demand and supply exists not only in India but is a global phenomenon. As per the estimates of McKinsey, the gap between supply and requisite demand for analytics skills in the US will reach 50-60% by 2018. With the industry all set to witness such a huge demand gap you can utilize the opportunity to get a lucrative pay package in the respective domain. A sure shot way to develop a new competency in it would be to take up an industry-oriented program focusing on a new-age skill. You might have to invest a year or 6 months with such a course but the gains in terms of better employment opportunities and rise in pay scale will all be worth it.
  3. You can future-proof your career– More technological advancements sometimes translate to more and more people being replaced at their jobs. A recent threat to millions of jobs is posed by Automation and Artifical Intelligence, although the number of jobs that will be automated due to AI is debatable it is always better to future-proof your career by developing new competencies. It is hence beneficial to become an irreplaceable asset to your organization by constantly proving that you have the grit and enthusiasm to learn new things and stay updated.
  4. Enable a successful career transition– In case you are planning to transition into a new role or enter a new domain altogether, Upskilling will help you facilitate the shift with much more ease. A program or course that enables you to build a body of work through industry-based projects will surely be helpful in tackling interviews with prospective employers with confidence and will display domain knowledge. Look for a program which has a good mix of industry participation which will allow you to network with industry experts and thought leaders and give you opportunities for referrals and endorsements.

The bottom line

With newer employment avenues opening up in the IT industry, the emphasis is slowly shifting from Scale to Skill. Technical competencies aligned with the Digital Economy are therefore in high demand. The time to explore new waters and ride the new wave is now. Re-skill or perish is the only way forward.

3 Machine Learning Projects for Beginners

Reading Time: 4 minutes

Machine Learning (ML) is a pivotal application of Artificial Intelligence technology and has an enormous potential in a variety of areas including healthcare, business, education, and more.

The fact that ML is still in a nascent stage and has several imperfections/flaws can make it difficult to wrap your head around its fundamentals. However, studying and working on a few basic projects on the same can be of great help. So here are a few to get you started:

1. Stock Prices Predictor

A system that can learn about a company’s performance and predict future stock prices is not only a great application of Machine Learning (ML) but also has value and purpose in the real world. Before you proceed, be sure to acquaint yourself with the following:

  • Statistical Modeling: Constructing a mathematical description of a real-world process that accounts for the uncertainty and/or randomness involved in that system.
  • Predictive Analysis: It uses several techniques such as data mining, artificial intelligence, etc. to predict the behavior of certain outcomes.
  • Regression Analysis: It’s a predictive modeling technique which learns about the relationship between a dependent i.e. the target and independent variable (s) i.e. the predictor. For example understanding the impact of yearly experience on salary.
  • Action Analysis: Analyzing the actions performed by the above-mentioned techniques and incorporating the feedback into the machine learning memory.

The first thing you need to get started is select the data types that are to be used such as current prices, EPS ratio, volatility indicators, etc. Once this has been taken care of, you can select the data sources. For instance, Quandl offers organized financial and economic data. You can download the stock data of several thousand companies in multiple formats such as xml, csv, etc. from here. Similarly, Quantopian offers an excellent trading algorithm development support that you can check out. Now, you can finally plan on how to backtest and build a trading model. Note that you need to structure the program in a way that it’s able to validate the predictions quickly as financial markets are usually quite volatile and the stock prices can change several times a day.

What you want to do is connect your database to your machine learning system that is fed new data on a regular basis. A running cycle can compare the stock prices of all the companies in the database over the past 15 years or so and predict the same for the near future i.e. 3 days, 7 days, etc, and report on the display.

2. Sentiment Analyzer

A sentiment analyzer learns about the “sentiment” behind a text (think emails, IMs, social media posts, etc.) through machine learning and predicts the same using Artificial Intelligence (AI). The technology is being increasingly used on social media platforms such as Facebook and Twitter for learning user behavior, and also by businesses that want to automate lead generation by determining how likely a prospect is to do business with them by reading into their emails.

One innovation that you will need to learn about in this project is classifiers. You can, however, choose any particular model that you are comfortable with, such as Maximum Entropy Classifier or Naïve Bayes Classifier.

You can go about the project your way. However, you would ideally need to classify the texts into three categories- positive, neutral, and negative. You can extract the different texts for a particular keyword and run the classifier on each to obtain the labels. For features, you can use diagrams or even dictionaries for higher accuracy.

3. Sports Matches Predictor

Using the basic working model of machine learning you can also create a system that can predict the results of sports matches such as cricket, football, etc.

The first thing you need is to create a database for whichever sports you are considering. Irrespective of what you choose, you will most likely need to find the records of the scores, performance details, etc. on your own, manually. Using Json for this, however, could be a good idea as it can easily capture the advanced parameters that are involved in a game and help in making more accurate predictions.

If you are well-versed in Python, then Scikit-Learn is your best bet to create the system. It offers a variety of tools for data mining, regression analysis, classifications, etc. You can use human analysis such as Vegas lines along with some advanced parameters such as Dean Oliver’s four factors to get best prediction results.

There are many beginner-level Machine Learning projects like the ones above that you can study. However, it will help if you make yourself familiar with the following first:

Machine Learning Tools: An environment that offers ML tools for data preparation, a variety of ML algorithms, and is capable of presenting the results of the programs, can be a good starting point when you want to get to the core of ML and understand how different modules work together. For instance, Weka, waffles, etc. are some of the excellent environments to start with.

Machine Learning Datasets: AI and ML use a variety of datasets. However, you can just pick one and choose an algorithm that works the best for it. Then you can use an ML environment to observe it closely. You can also change the algorithms to see how they affect the datasets. Machine Learning can only be mastered with a lot of experimentation and practice. While delving into the theory can surely help, it’s the application that will facilitate your progress the most.