What are the best Data Science and Business Analytics Courses for working professionals in India?

The term analytics and data science have garnered a lofty prominence in the past decade mostly used interchangeably. As it stands strong today, business analytics is finding applications across functions ranging from marketing, customer relationship management, financial management, supply chain management, pricing and sales, and human resource management among others. It has also made a place for itself across industries, spanning its wings even to the most traditional ones such as Manufacturing and Pharma. 

A bachelor’s degree with a minimum of 2-3 years of work experience is mandatory to enrol for almost all business analytics or data science programs out there. It holds a great career scope for graduates in the field of engineering, business management, marketing, computer science and information technology, finance, economics, and statistics among others. 

All things said and done, there are certain challenges that professionals might face while looking out for the perfect business analytics or data science course to steer their career on the growth path:

– Lack of time and issues with balancing work and course schedules

– Financial Barriers

– Inflexibility in the course structure

– Obsolete curriculum or irrelevant modules

– Inaccessibility to the course

An institution or a course that focuses on combating these challenges and provide a comfortable, valuable, and manageable learning experience is the ideal course for professionals. At Great Learning, we strive to focus on these issues and design courses to suit the needs and resources of the aspirants.

Here is a comparative study of various Data Science and Business analytics program with Great Learning’s PG program in Business Analytics and Business Intelligence:

What are the best Data Science and Business Analytics Courses for working professionals in India?

Great Learning has been changing the lives of professionals across domains for over 5 years now. Having imparted more than 5 million hours of learning, we have touched professionals in 17 different countries, and are working towards reaching more geographies to transform careers of professionals across the globe. 

The post-graduate program in Business Analytics and Business Intelligence was the first program to be launched by Great Learning in the year 2014. Since then, there have been more than 50 batches with 5000+ professionals enrolled and successfully completed the course. The program has been ranked #1 Analytics program in India for 4 years in a row by Analytics India Magazine and has involved 300+ Industry Experts and 25+ India’s Best Data Science Faculty to impart quality skills and practical learning. Having propelled more than 2,500 career transitions, the success of the program can also be gauged by the fact that 90% of our alumni refer the course to other professionals. 

Best business analytics and business intelligence course for professionals

Our alumni have been placed with some of the top Analytics firms and reputed MNCs such as IBM, Accenture, HSBC, KPMG, LatentView, Myntra, Rakuten, RBS, Shell, Tiger Analytics, UST Global, and many more, with an average salary hike of 48%. This alone speaks a lot about the value and industry relevance of the program. Know more about Great Learning’s PG program in Business Analytics and Business Intelligence here. 

Book a call <Lead gen landing page CTA> with us and our learning consultants will guide you through the program details and the specific queries that you might have. 

Here are a few testimonials by our PGP BABI Alumni. Read-Along:

GL puts a lot of effort to make the curriculum up to date matching world-standards Sowmya Vivek, Independent Consultant – Data Science, ML, NLP

The best part of GL is its experienced faculty – Sriram Ramanathan, Associate Director for Data Products at Scientific Games

The best takeaway is the approach with which I now perceive business problems – Pratik Anjay, Data Scientist at Walmart

The course aided my old desire to pursue finance as a career – Sahil Mattoo, Data Scientist, DXC Technologies

The guidance from the GL faculty is an important driver of my success – Priyadarshini, Analyst at LatentView

 

10 Most Common Business Analyst Interview Questions

Preparing for a Business Analyst Job Interview? Here are a few tips and the most useful and common business analyst interview questions that you might face. 

Before attending an interview for a business analyst position, one should be through about their previous experience in the projects handled and results achieved. The types of questions asked generally revolve around situational and behavioural acumen. The interviewer would judge both knowledge and listening skills from the answers one presents. 

The most common business analyst interview questions are:

 

1. How do you categorize a requirement to be a good requirement?

A good requirement is the one that clears the SMART criteria, i.e., 

Specific – A perfect description of the requirement, specific enough to be easily understandable

Measurable – The requirement’s success is measurable using a set of parameters

Attainable – Resources are present to achieve requirement success

Relevant – States the results that are realistic and achievable

Timely – The requirement should be revealed in time 

business analyst interview questions

 

2. List out the documents used by a Business Analyst in a project?

The various documents used by a Business Analyst are:

a. FSD – Functional Specification Document

b. Technical Specification Document

c. Business Requirement Document 

d. Use Case Diagram

e. Requirement Traceability Matrix, etc.

 

3. What is the difference between BRD and SRS?

SRS (Software Requirements Specifications) – is an exhaustive description of a system that needs to be developed and describes the software – user interactions. While a BRD (Business Requirements Document) is a formal agreement for a product between the organization and the client. 

The difference between the two are:

business analyst interview questions

 

4. Name and briefly explain the various diagrams used by a Business Analyst.

Activity Diagram – It is a flow diagram representing the transition from one activity to another. Here activity is referred to the specific operation of the system.

Data Flow Diagram – It is a graphical representation of the data flowing in and out of the system. The diagram depicts how data is shared between organizations

Use Case Diagram – Also known as Behavioural diagram, the use case diagram depicts the set of actions performed by the system with one or more actors (users).

Class Diagram – This diagram depicts the structure of the system by highlighting classes, objects, methods, operations, attributes, etc. It is the building block for detailed modelling used for programming the software.

Entity Relationship Diagram – It is a data modelling technique and a graphical representation of the entities and their relationships. 

Sequence Diagram – It describes the interaction between the objects. 

Collaboration Diagram – It represents the communication flow between objects by displaying the message flow among them.

 

5. Name different actors in a use case diagram?

Broadly, there are two types of actors in a use-case:

a. Primary Actors – Start the process

b. Secondary Actors – assist the primary actor

They can further be categorized as:

i. Human

ii. System

iii. Hardware

iv. Timer

 

6. Describe ‘INVEST’.

The full form of INVEST is Independent, Negotiable, Valuable, Estimable, Sized Appropriately, Testable. With this process, the technical teams and project managers to deliver quality products or services.

 

7. What is Pareto Analysis

Also known as the 80/20 rule, Pareto Analysis is an effective decision-making technique for quality control. As per this analysis, it is inferred that 80% effects in a system are a result of 20% causes, hence the name 80/20 rule.

 

8. Describe the Gap Analysis.

It is utilized to analyze gaps between the existing system and its functionalities against the targeted system. The gap is inferred to the number of changes and tasks that need to be brought in to attain the targeted system. It compares performance between the present and the targeted functionalities.

 

9. Name different types of gaps that could be encountered while Gap Analysis

There are mainly four types of gaps:

a. Performance Gap – Gap between expected and actual performance

b. Product/ Market Gap – Gap between budgeted and actual sales numbers

c. Profit Gap – Variance between targeted and actual profit

d. Manpower Gap – Gap between required and actual strength and quality of the workforce in the organization

 

10. What are the various techniques used in requirement prioritization?

Requirement prioritization, as the name suggests, is a process of assigning priorities to the requirements based on business urgency in different schedules, phases, and cost among others.

The techniques for requirement prioritization are:

a. Requirements Ranking Method

b. Kano Analysis

c. 100 Dollar Method

d. MoSCoW Technique

e. Five Whys

 

Stay tuned to this page for more such information on interview questions and career assistance. If you are not confident enough yet and want to prepare more to grab your dream job as a Business Analyst, upskill with Great Learning’s PG program in Business Analytics and Business Intelligence, and learn all about Business Analytics along with great career support.

How will Cybernetics And Artificial Intelligence build our future?

We live in a world where what was considered science fiction mere decades ago has become a reality. Global, wireless internet coverage, 3D printed technologies, the Internet of Things powered by AI-based assistants, and, of course, cyborgs, are all part of the reality we live in.

Cyborgs? Yes, those are real. Look at Dr Kevin Warwick. The man can operate lights, switches, and even computers with the power of his mind thanks to a handy chip implant. Neil Harbisson has overcome achromatopsia thanks to an implant that allows the artist to process colours in real-time on a level unachievable by anyone else on the planet. 

If you were to do some research, you’d find out that these pioneers are merely the tip of a cybernetically enhanced iceberg bringing up the real question: if we’ve already come so far, what awaits us in the future?

Cybernetics

Some of the most prominent projects diving into the exploration of cybernetics feel like they were taken from a cyberpunk novel. And yet they are real. More on the matter, they mark what is potentially the future of humankind as a species. 

Full-spectrum vision. Typically, humans believe that the way we “see” the world is the only possible way. Cybernetics engineers would beg to disagree. A simple injection of nanoantenna has proven to give lab mice the superpower of night vision. The experiment taking place in the University of Massachusetts has only recently moved towards practical studies of the effects the antenna have on rodents, but it has already proven itself to be among the first stepping stones towards cybernetically enhanced eyesight. Additional breakthroughs in the field have shown promising results in turning eyes into video cameras, or even development of artificial retinas capable of returning sight to the blind. 

Artificial brain cells. Modern advancements in the niche of cybernetics have already grown neurons – the basic components of a human brain – in laboratory conditions. These cells, artificially raised on an array of electrodes are proving themselves as a superior replacement to the hardware and software controllers we have today. 

More on the matter, scientists are already using brain-computer interfaces in medicine. Most are designed for therapeutic purposes such as the Deep Brain Stimulation designed to aid patients with Parkinson’s disease.

We will be able to use said technology to create connections that operate via the remaining neural signals allowing amputee patients to feel and move their limbs with the power of their mind. In some cases, as it was with Nigel Ackland, some might even go as far as to use the word enhancement when talking about top tier prosthetics.

Enhanced mobility. Stronger, faster, more durable – those are the goals of military-grade exoskeletons for soldiers that are already branching out into the medical niche and serve as prosthetics for amputee victims.  The combination of hardware and AI-based software eliminate the boundaries of human capabilities while minoring the vitals of the wearer in real-time. 

Technopsychics. The University of Minnesota is working on a computer to brain interface capable of remotely piloting drones. The machines can detect the electrical signals transmitted by the brain to control functioning machines in real-time. If you can navigate a quadcopter through an obstacle course using only the power of your mind today, imagine what we’ll be piloting remotely tomorrow. 

Nanorobots. Self-repair, growth, and immunity to diseases will soon be true thanks to a simple infusion of nanobots into your bloodstream. Modern researches explore the idea of developing your blood cell’s little helpers that can be controlled in the cloud from your smartphone!

Artificial Intelligence

As you may have deduced from the examples above, the advancements in the cybernetics niche are directly related to the progress we make with Artificial Intelligence or Machine Learning technologies. 

We need the software capable of driving the hardware to its limits if we are to dive deeper into cyborg technology. Artificial Intelligence is supposed to become the bridge between the man and the machine according to prominent research such as Shimon Whiteson and Yaky Matsuka. These scientists are exploring new ways AI can help amputee patients to operate their robotic prosthetics. 

Furthermore, AI is expected to take control of machines doing sensitive work in hazardous areas. According to BBC, we already have smart bots capable of defusing bombs and mines yet they still require a human controlling them. In the future, these drones (and many more, responsible for such challenging tasks as toxic waste disposal, deep-sea exploration, and volcanic activity studies, etc.) will be powered purely by algorithms. 

Lastly, machines are expected to analyze and understand colossal volumes of data. According to Stuart Russell, The combination of AI-powered algorithms and free access to Big Data can identify new, unexpected patterns we’ll be able to use to mathematically predict future events or solve global challenges like climate change. 

What a time to be alive! 

If you wish to learn more about Artificial Intelligence technologies and applications, and want to pursue a career in the same, upskill with Great Learning’s PG course in Artificial Intelligence and Machine Learning.

 

15 Most Common Data Science Interview Questions

Data Science is a comparatively new concept in the tech world, and it could be overwhelming for professionals to seek career and interview advice while applying for jobs in this domain. Also, there is a need to acquire a vast range of skills before setting out to prepare for data science interview. Interviewers seek practical knowledge on the data science basics and its industry-applications along with a good knowledge of tools and processes. Here is a list of 15 most common data science interview questions that might be asked during a job interview. Read along.

 

1. How is Data Science different from Big Data and Data Analytics?

Ans. Data Science utilizes algorithms and tools to draw meaningful and commercially useful insights from raw data. It involves tasks like data modelling, data cleansing, analysis, pre-processing etc. 

Big Data is the enormous set of structured, semi-structured, and unstructured data in its raw form generated through various channels.

And finally, Data Analytics provides operational insights into complex business scenarios. It also helps in predicting upcoming opportunities and threats for an organization to exploit.

data science vs big data vs data analytics
How is Data Science different from Big Data and Data Analytics?

2. What is the use of Statistics in Data Science?

Ans. Statistics provides tools and methods to identify patterns and structures in data to provide a deeper insight into it. Serves a great role in data acquisition, exploration, analysis, and validation. It plays a really powerful role in Data Science.

 

3. What is the importance of Data Cleansing?

Ans. As the name suggests, data cleansing is a process of removing or updating the information that is incorrect, incomplete, duplicated, irrelevant, or formatted improperly. It is very important to improve the quality of data and hence the accuracy and productivity of the processes and organization as a whole.

 

4. What is a Linear Regression?

The linear regression equation is a one-degree equation of the form Y = mX + C and is used when the response variable is continuous in nature for example height, weight, and the number of hours. It can be a simple linear regression if it involves continuous dependent variable with one independent variable and a multiple linear regression if it has multiple independent variables. 

 

5. What is logistic regression?

Ans. When it comes to logistic regression, the outcome, also called the dependent variable has a limited number of possible values and is categorical in nature. For example, yes/no or true/false etc. The equation for this method is of the form Y = eX + e – X

 

6. Explain Normal Distribution

Ans. Normal Distribution is also called the Gaussian Distribution. It has the following characteristics:

a. The mean, median, and mode of the distribution coincide

b. The distribution has a bell-shaped curve

c. The total area under the curve is 1

d. Exactly half of the values are to the right of the centre, and the other half to the left of the centre

 

7. Mention some drawbacks of the linear model

Ans. Here a few drawbacks of the linear model:

a. The assumption regarding the linearity of the errors

b. It is not usable for binary outcomes or count outcome

c. It can’t solve certain overfitting problems

 

8. Which one would you choose for text analysis, R or Python?

Ans. Python would be a better choice for text analysis as it has the Pandas library to facilitate easy to use data structures and high-performance data analysis tools. However, depending on the complexity of data one could use either which suits best.

 

9. What steps do you follow while making a decision tree?

Ans. The steps involved in making a decision tree are:

a. Pick up the complete data set as input

b. Identify a split that would maximize the separation of the classes

c. Apply this split to input data

d. Re-apply steps ‘a’ and ‘b’ to the data that has been divided 

e. Stop when a stopping criterion is met

f. Clean up the tree by pruning

Decision tree
Steps involved in making a Decision Tree

10. What is Cross-Validation? 

Ans. It is a model validation technique to asses how the outcomes of a statistical analysis will infer to an independent data set. It is majorly used where prediction is the goal and one needs to estimate the performance accuracy of a predictive model in practice.

The goal here is to define a data-set for testing a model in its training phase and limit overfitting and underfitting issues. The validation and the training set is to be drawn from the same distribution yo avoid making things worse.

 

11. Mention the types of biases that occur during sampling?

Ans. The three types of biases that occur during sampling are:

a. Self-Selection Bias

b. Under coverage bias

c. Survivorship Bias

 

12. Explain the Law of Large Numbers

Ans. The ‘Law of Large Numbers’ states that if an experiment is repeated independently a large number of times, the average of the individual results is close to the expected value. It also states that the sample variance and standard deviation also converge towards the expected value.

 

13. What is the importance of A/B testing

Ans. The goal of A/B testing is to pick the best variant among two hypotheses, the use cases of this kind of testing could be a web page or application responsiveness, landing page redesign, banner testing, marketing campaign performance etc. 

The first step is to confirm a conversion goal, and then statistical analysis is used to understand which alternative performs better for the given conversion goal.

 

14. What are over-fitting and under-fitting?

Ans. In the case of over-fitting, a statistical model fails to depict the underlying relationship and describes the random error and or noise instead. It occurs when the model is extremely complex with too many parameters as compared to the number of observations. An overfit model will have poor predictive performance because it overreacts to minor fluctuations in the training data.

In the case of underfitting, the machine learning algorithm or the statistical model fails to capture the underlying trend in the data. It occurs when trying to fit a linear model to non-linear data. It also has poor predictive performance.

 

15. Explain Eigenvectors and Eigenvalues

Ans. Eigenvectors depict the direction in which a linear transformation moves and acts by compressing, flipping, or stretching. They are used to understand linear transformations and are generally calculated for a correlation or covariance matrix. 

The eigenvalue is the strength of the transformation in the direction of the eigenvector. 

 

Stay tuned to this page for more such information on interview questions and career assistance. If you are not confident enough yet and want to prepare more to grab your dream job in the field of Data Science, upskill with Great Learning’s PG program in Data Science Engineering, and learn all about Data Science along with great career support.

Is Design Thinking PepsiCo’s Secret to Market Dominance?

Pepsico CEO Indra Nooyi took up the reins of the company when it was facing considerable drop in sales. As a way to address this, she revised her business strategy to make it more inclusive for consumers. She famously went after Mauro Porcini and sought his expert advice to redesign PepsiCo’s user experience. Eventually, her team resolved the problems by relying on an iterative process of understanding users and providing instinctive solutions.

Under the leadership of Indra Nooyi, PepsiCo prioritised user-specific solutions, designed products that were more human-centric and earned the company record-breaking revenues apart from accolades. From designing touch-screen fountain machines (Pepsi Spire) to launching a special line of women’s snacks, PepsiCo reconditioned the way consumers interact with products. Mauro Porcini successfully introduced a more consumer-centric PepsiCo to the world with design thinking being the key driver behind all these changes  

Design_Thinking

Other Companies taking Cue from PepsiCo

Using design thinking to drive business means designing solutions with customers in mind – not only will that lead to more customer satisfaction but also establish businesses as distinguishable brands. What company wouldn’t want that? Global leaders are already using design thinking to align their customer’s goals and step into the future. Let’s take a look at the top companies who have already benefited from this model. 

Apple:  Apple is undeniably a classic example of how reconstructing user experience through innovation can lead to revolutionary success. At its core, Apple remains a company that has always championed innovation and delivered unique customer-driven experiences – all thanks to design thinking. Apple products ranging from iPhone, MacBook to ios not just bring you exquisite usability but also optimised functionality. From providing a holistic user experience to predicting customer needs, Apple has successfully shown the rest of the world how it’s done.

Nike: Nike has been a pioneer in merging sports with fashion. A brand which primarily targeted athletes and helped them enhance performance has now become quite a fashion trailblazer. “Move forward” (their pet phrase) not only dictates their designs but also aptly captures their user imagination. All along, design thinking has been instrumental in shaping their advanced products and services.

Google: Needless to say, Google has been acing the game and how! Whether it is Google map or Google Pixel’s image software, Google products are glaring examples of enhanced designs. Google teams are constantly thinking ahead of time and designing products and services that answer futuristic customer needs. Google’s constant endeavour to design products with a focus on user experience has established the brand as a world leader in design thinking.

Design Thinking has been around for longer than we think and its focus towards building enhanced user experiences has made it a much coveted strategy for brand building today. To put it in Porcini’s own words,

“People don’t buy, actually, products anymore, they buy experiences that are meaningful to them, they buy solutions that are realistic, that transcend the product, that go beyond the product, and mostly they buy stories that need to be authentic.”

PepsiCo’s success has inspired many other companies to rethink their business strategy and hire design thinking experts. If you are an enthusiast, learn more about it here.

Your essential weekly guide to Artificial Intelligence – July 17

Stay updated with the Artificial Intelligence breakthroughs, applications, and advancements across the globe.  

How La Liga is Using AI to Make Business Decisions 

In a very interesting interview, Sergi Torrents of LaLiga explains a calendar tool that the league uses to make decisions on what the kick-off time for matches should be. The calendar selector decides kick-off time with highest match audience. La Liga also uses sunlight planner to optimize the match schedule. Watch the video here!

Skilling Millennials in Disruptive Tech Will Take India to USD 5 Trillion Mark

To make India a $5 trillion economy by 2024-2025, the country urgently requires a renewed focus on skilling the youth in advanced technologies like robotics, Artificial Intelligence (AI), Internet of Things (IoT), 3D printing, data analytics and quantum computing. According to policy think-tank, Broadband India Forum (BIF), IoT and AI-based applications will create over 28 lakh jobs in rural India over 8 to 10 years with an annual value of Rs 60,000 crore.

This New Poker Bot Can Beat Multiple Pros – At Once

The 32-year-old Darren Elias is the only person to have won four World Poker Tour titles and has earned more than $7 million at tournaments. Despite his expertise, he learned something new this spring from an artificial intelligence bot. Poker has long been seen as a grand challenge for AI researchers, with properties similar to many real-world situations. Unlike in chess, poker players must choose actions without knowing what cards their opponents hold and Pluribus is doing a great job at beating elite poker professionals.

Now Artificial Intelligence Algorithm to Help Predict Storms, Cyclones

Using Artificial Intelligence (AI), researchers have developed an algorithm to detect cloud formations that lead to storms, hurricanes and cyclones. The study, published in the journal IEEE Transactions on Geoscience and Remote Sensing, shows a model that can help forecasters recognise potential severe storms more quickly and accurately.

Alberta Researchers Use AI to Detect Depression in Voices

PhD student Mashrura Tasnim and professor Eleni Stroulia developed a methodology that combines several machine-learning algorithms to more accurately detect depression using acoustic cues. A realistic scenario is to have people use an app that will collect voice samples as they speak naturally. The app, running on the user’s phone, will recognize and track indicators of mood, such as depression, over time.

A Chinese AI Startup is Tracking Lost Dogs Using Their Nose Prints

Megvii, a Chinese AI startup that supplies facial recognition software for the Chinese government’s surveillance program, is expanding its technology beyond humans to recognize different faces of pets. Megvii says it has an accuracy rate of 95 per cent and has reunited 15,000 pets with their owners through the app. Read how they do it!

Happy Reading!

 

 

 

What are the career prospects for a DevOps Engineer?

The DevOps domain is getting attention for its role in building better communication, collaboration, and agility between software development and operations teams. The role of a DevOps engineer is hard to understand because it is the product of a dynamic workforce which has not yet stopped evolving.

DevOps is a software development strategy that bridges the gap between developers and their IT counterparts. It is a practice that aims to merge software development, quality assurance, and deployment and integration operations into a consolidated and continuous set of processes.

DevOps is a natural extension for Agile and other continuous delivery approaches. With DevOps, organizations can release tiny features quickly and incorporate the feedback they receive from stakeholders rapidly.

It’s good to note that DevOps is not merely a set of actions, but more a philosophy that facilitates cross-functional team communication.

What is DevOps Engineer?

DevOps engineers work with the software developers and IT professionals to track code releases. They are the people who wear multiple hats – software development, deployment, network operations, and system admins. Teamwork stands at the core of a DevOps practice and the overall success of a process depends on the same.

As such, DevOps engineers are expected to have a thorough understanding of various concepts such as version control, serverless computing, integration, testing, and deployment. 

The role of a DevOps engineer is formed out of the need of businesses to get hold of their cloud infrastructure in a hybrid environment. Organizations who work with DevOps spend relatively less time on managing configurations, deploying applications, and making tweaks and updates.

The Skills Needed for a Successful DevOps Engineering Career

According to Puppet, the most critical skills for a DevOps engineer are:

– Coding and scripting

– Process re-engineering

– Communication and collaboration

*Out of these, process re-engineering is the most selling skill.

 

Other skills that can enhance a DevOps engineering career are:

– Software development, system administration, and an understanding of all basic IT operations.

Experience and expertise with tools such as GitHub, Puppet, Jenkins, Chef, Nagios, Ansible, and Docker.

– Besides knowing off-the-shelf tools a DevOps engineer should also be well-versed with the basic coding and scripting languages such as Bash, PowerShell, C#, C++, Python, PHP, Ruby, Java, and so on.

– An understanding of database systems such as SQL and NoSQL database models.

– Communication and interpersonal skills are critical for a DevOps engineer since they have to ensure that the entire team behind a software works effectively and share and appreciate feedback to support continuous delivery.

The Roles and Responsibilities of a DevOps Engineer

In DevOps, there are frequent changes made to any software system which automatically entail testing and deployment. A DevOps Engineer is responsible to handle the IT infrastructure according to the business needs of the code deployed in a hybrid multi-tenant environment, needing continuous performance monitoring. 

Therefore, a DevOps engineer must be aware of the various development tools which are used by software developers to write new code or enhance the existing code.

A DevOps engineer needs to collaborate with the team to handle challenges that spring up in the coding or scripting part including libraries and SDKs. A DevOps engineer handles code that needs to fit across multi-tenant environments, including the cloud.

Here are the roles and responsibilities of a DevOps engineer, in a nutshell:

– Apply cloud computing skills to deploy upgrades and bug fixes across AWS, Azure, or GCP.

– Design, develop, and implement software integrations on the basis of user feedback and reviews.

– Troubleshoot and resolve production issues and coordinate with the development team to simplify and streamline code deployment.

– Implement automation frameworks and tools – CI/CD pipelines.

– Manage the IT infrastructure, which comprises of the network, software, hardware, storage, virtual and remote assets, and control over data cloud storage.

– Continuously monitor software environments for any loopholes.

– Analyze code continuously and communicate detailed feedback to software development teams to ensure improvement in software and timely completion of projects.

– Collaborate with team members to improve engineering tools, systems, procedures, and security arrangements.

– Optimize and enhance the business’ computing architecture.

– Conduct system checks for security, availability, and performance.

– Develop and maintain troubleshooting documentation to keep up with past and future fixes.

 

Apart from these explicit set of actions, DevOps engineers are also expected to follow the essential DevOps principles:

– Culture inherent in the need for communication, collaboration, and technical processes and tools.

– Automation of processes

– Measurement of the Key Performance Indicators

– Sharing feedback, knowledge, and best practices.

How Much Does a DevOps Engineer Earn?

The job of a DevOps engineer ranks #2 on Glassdoor’s Top 50 Jobs in America. Also, the role of a DevOps engineer has witnessed a jump of 225 per cent in postings on Indeed. An important question that occurs among the DevOps aspirants is What is DevOps Engineer Salary? 

Glassdoor mentions that the average salary of a DevOps engineer in India starts from INR 5.65 lacs per annum for an average of two years experience. For the same set, PayScale suggests that the average salary of a DevOps engineer is around INR 6.6 lacs per annum. PayScale also mentions that pay is also a function of the skill sets acquired by a DevOps Engineer. Also, most of the professionals in DevOps move to other related roles in a span of 10 years.

It is safe to say that a DevOps engineer’s job is quite in demand as businesses try to become more agile and take on continuous delivery approaches over long development cycles.

If you are considering a career as a DevOps engineer, upskill yourself with Great Learning’s DevOps Engineer Certificate Program

This program has elevated my role – Rajesh Kumar, Engagement Lead at Cognizant, UK

Cloud Computing is swiftly becoming one of the top skills considered by tech-professionals to switch their career in. Read what Rajesh Kumar has to say about Great Learning’s PG program in cloud computing and how he was able to complete AWS architect certification.

I have recently completed the Post Graduate Program in Cloud Computing with Great Learning and would like to share my gratitude & experience.

First Things First, Kudos to

– Great Lakes Content Team for preparing & delivering the curriculum aligned for Managers to renew & scale on cutting edge technologies like Cloud, Containers, Microservices, Big Data, Business Transformations etc.

– Experienced mentors & Enter-trainers like Nirmallaya & Shiva, who delivered the content & their experience to students in an exceptional way rather than being monotonous & mediocre

– Emphatic Program Manager – Ekta Singh, Her timely support has kept me on the progress track for successful completion. She played a vital role in pushing to complete labs and projects that instilled confidence to pursue the technology ladder again (though I was core L4 techie a few years back). I truly appreciate her commitment to the overall success of the program.

This program has elevated my role to learn, practice & apply knowledge on all technology & business transformation in the digital world. My role is elevated from the Delivery Manager to Engagement lead focused on Business & Technology.  

I would definitely give credit to PGP-CC for the Training Program, Mentoring Sessions, Sharing abreast of all technology advancements under “Industry Focus” sections & Challenging with Labs, Projects & Capstone projects, that made me achieve AWS cloud practitioner certification and preparation for AWS Architect certification.

Upskill with Great Learning’s PG program in Cloud Computing and unlock your dream career.

Basics of building an Artificial Intelligence Chatbot

Chatbots are not a recent development. The first chatbot was created by Joseph Wiesenbaum in 1966, named Eliza. It all started when Alan Turing published an article named “Computer Machinery and Intelligence”, and raised an intriguing question, “Can Machines think?”, and ever since, we have seen multiple chatbots surpassing their predecessors to be more naturally conversant and technologically advanced. These advancements have led us to an era where conversations with chatbots have become as normal and natural as with another human.

  

Today, almost all companies have chatbots to engage their users and serve customers by catering to their queries. As per a report by Gartner, Chatbots will be handling 85% of the customer service interactions by the year 2020. Also, 80% of businesses are expected to have some sort of chatbot automation by 2020 (Outgrow, 2018). We practically will have chatbots everywhere, but this doesn’t necessarily mean that all will be well-functioning. The challenge here is not to develop a chatbot, but to develop a well functioning one. 

Let’s have a look at the basics of creating an Artificial Intelligence chatbot:

Identifying opportunity for an Artificial Intelligence chatbot

The first step is to identify the opportunity or the challenge to decide on the purpose and utility of the chatbot. To understand the best application of Bot to the company framework, you will have to think about the tasks that can be automated and augmented through Artificial Intelligence Solutions. For each type of activity, the respective artificial intelligence solution broadly falls under two categories: “Data Complexity” or “Work Complexity”. These two categories can be further broken down to 4 analytics models namely, Efficiency, Expert, Effectiveness, and Innovation.

Understanding Customer Goals

There needs to be a good understanding of why the client wants to have a chatbot, and what the users and customers want their chatbot to do. Though it sounds very obvious and basic, this is a step that tends to get overlooked frequently. One way is to ask probing questions so that you gain a holistic understanding of the client’s problem statement. This might be a stage where you discover that a chatbot is not required, and just an email auto-responder would do.. In cases where client itself is not clear regarding the requirement, ask questions to understand specific pain points and suggest most relevant solutions. Having this clarity helps the developer to create genuine and meaningful conversations to ensure meeting end goals.

Designing a chatbot conversation

There is no common way forward for all different types of purposes that chatbots solve. Designing a bot conversation should depend on the purpose the bot will be solving. Chatbot interactions are categorized to be structured and unstructured conversations. The structured interactions include menus, forms, options to lead the chat forward, and a logical flow. On the other hand, the unstructured interactions follow freestyle plain text. This unstructured type is more suited to informal conversations with friends, families, colleagues and other acquaintances. 

Selecting conversation topics is also critical. It is imperative to choose topics that are related to and are close to the purpose served by the chatbot. Interpreting user answers, and attending to both open-ended and close-ended conversations are other important aspects of developing the conversation script. 

Building a chatbot using code-based frameworks or chatbot platforms

There is no better way among the two to create a chatbot. While the code-based frameworks provide flexibility to store-data, incorporate AI, and produce analytics, the chatbot platforms save time and effort and provide highly functional bots that fit the bill.

Some of the efficient chatbot platforms are:

Chatfuel — the standout feature is broadcasting updates and the content modules to automatically to the followers. Users can request information and converse with the bot through predefined buttons, or information could be gathered inside messenger through ‘Typeform’ style inputs.

Botsify — User-friendly drag and drop templates to create bots. Easy integration to external plugins and various AI and ML features help improve the conversation quality and analytics. 

Flow XO —  This platform has more than 100+ integrations and the easiest to use the visual editor. But, it is quite limited when it comes to AI functionality.

Beep Boop — Easiest and best platform to create slack bots. Provides an end to end developer experience. 

Bottr — There is an option to add data from Medium, Wikipedia, or WordPress for better coverage. This platform gives an option to embed a bot on the website.

For the ones who are more tech-savvy, there are code-based frameworks that would integrate the chatbot into a broader tech stack. The benefits are flexibility to store data, provide analytics, and incorporate Artificial Intelligence in the form of open source libraries and NLP tools.

Microsoft Bot Framework — Developers can kick off with various templates such as basic, language understanding, Q&As, forms, and more proactive bots. It is the Azure bot service which and provides an integrated environment with connectors to other SDKs. 

Wit.AI (Facebook Bot Engine) — This framework provides an open natural language platform to build devices or applications that one can talk or text. It learns human language from the interactions and shares this learning to leverage the community. 

API.AI (Google Dialogflow) — This framework also provides AI-powered text and voice-based interaction interfaces. It can connect with users on Google Assistant, Amazon Alexa, Facebook Messenger, etc.

Testing your chatbot

The final and most crucial step is to test the chatbot for its intended purpose. Even though it’s not important to pass the Turing Test first time around, it still must be fit for the purpose.

Test the bot with a set of 10 beta testers. The conversations generated will help in identifying gaps or dead-ends in the communication flow. 

With each new question asked, the bot is being trained to create new modules and linkages to cover 80% of the questions in a domain or a given scenario. By leveraging the AI features in the framework the bot will get better each time.

If you wish to learn more about Artificial Intelligence technologies and applications, and want to pursue a career in the same, upskill with Great Learning’s PG course in Artificial Intelligence and Machine Learning.