Critical skill-sets to make or break a data scientist 

Ever since data took over the corporate world, data scientists have been in demand. What further increases the attractiveness of this job is the shortage of skilled experts. Companies are willing to pour their revenue into the pockets of data scientists who have the right skills to put an organization’s data at work.

However, that does not mean it is easy for candidates to grab a job at renowned organizations. If you’ve been wanting to establish a career in data science, know that it takes the right set of skills to be considered worthy of the position.

What exactly then do you need to become an in-demand data scientist?

Here are a few valuable skills required for data scientist to inculcate before hitting the marketplace looking for your ideal job.

Programming or Software Development Skills

Data scientists need to toy with several programming languages and software packages. They need to use multiple software to extract, clean, analyze, and visualize data. Therefore, an aspiring data scientist needs to be well-versed with:

– Python – Python was not formally designed for data science. But, now that data analytics and processing libraries have been developed for Python, giants such as Facebook and Bank of America are using the language to further their data science journeys. This high-level programming language is powerful, friendly, open-source, easy to learn, and fast.

– R – R was once used exclusively for academic purposes, but a number of financial institutions, social networking services, and media outlets now use this language for statistical analysis, predictive modelling, and data visualization. This is a reason why R is important for aspiring data scientists to get their hands on.

– SQL – Structured Query Language is a special-purpose language that helps manage data in relational database systems. SQL helps you in inserting, querying, updating, deleting, and modifying data held in database systems. 

– Hadoop – This is an open-source framework that allows distributed processing of large sets of data across computer clusters using simple programming models. Hadoop offers fault tolerance, computing power, flexibility, and scalability in processing data.

Problem Solving and Risk Analysis Skills

Data scientists need to maintain exceptional problem-solving skills. Organizations hire data scientists to work on real challenges and attempt to solve them with data and analytics. This needs an appetite to solve real-world problems and cope with complex situations. 

Additionally, aspiring data scientists also need to be a master at the art of calculating the risks associated with specific business models. Since you will be responsible for designing and installing new business models, you will also be in charge of assessing the risks that entail them. 

skills required for data scientist
Summary of critical skills required for data scientists

Process Improvement Skills

Most of the data science jobs in this era of digital transformation have to deal with improving legacy processes. As organizations move closer to transformation, they need data scientists to help them replace traditional with modern.

As a data scientist, it falls upon you to find out the best solution to a business problem and improve relevant processes or optimize them. 

It makes a lot of sense for data scientists to develop a personalized approach to improving processes. If you can show your potential employer that you can enhance their current business processes, you will significantly increase your chances of landing the job.

Mathematical Skills

Unlike many high-paying jobs in computer science, data science jobs need both practical and theoretical understanding of complex mathematical subjects. Here are a few skills you need to master under this set:

– Statistics – No points for guessing this one, but statistics is and will be one of the top data science skills for you to master. This branch of mathematics deals with the collection, analysis, organization, and interpretation of data. Among the vast range of topics you might have to deal with, you’ll need a strong grasp over probability distributions, statistical features, over and undersampling, Bayesian statistics, and dimensionality reduction. 

– Multivariable calculus and linear algebra – Without these technologies, it is hard to curate the modern-day business solutions. Linear algebra happens to be the language of computer algorithms, while multivariable calculus is the same for optimization problems. As a data scientist, you will be tasked with optimizing large-scale data and defining solutions for them in terms of programming languages. Therefore, it is essential for you to have a stronghold over these concepts.

Deep Learning, Machine Learning, Artificial Intelligence Skills

Did you know, as per PayScale, the data scientists equipped with the knowledge of AI/ML get paid up to INR 20,00,000 with an average of INR 7,00,000? Modern-day businesses need their data scientists to have a basic understanding, if not expertise, over these technologies. Since these areas of technology have to do a lot with data, it makes sense for you to have a foundational understanding of these concepts.

Learning the ins and outs of these concepts will highly increase your data science skills and help you stand out from other prospective employees.

Collaborative Skills

It is highly unlikely for a data scientist to work in solitude. Most companies today house a team of data science experts who work on specific classes of problems together. Even if not in a team of data scientists, you will definitely need to collaborate with business leaders and executives, software developers, and sales strategists among others.

Therefore, when putting all of the necessary skills in perspective, do not forget to inculcate teamwork and collaborative skills. Define the right ways of bringing issues in front of people and explaining your POV without exerting dominance.

It might also help you to be able to explain data science concepts and terminologies in a simple language to non-experts.

For the year 2019, the total number of analytics and data science job positions available are 97,000, which is more than 45% as compared to the last year. Trends like this act as a magnet to attract fresh graduates towards a career in Data Science. As a data scientist, you need to wear multiple hats and ace them all. Since the field is currently expanding and evolving, it is hard to predict everything that a data scientist needs to know. However, start by working on these preliminary skills required for data scientist and then move your way up.

If you are interested in moving ahead with a career in Data Science, then you should start inculcating the above-mentioned skills to improve your employability. Upskilling with Great Learning’s PG program in Data Science Engineering will do the most of it for you!

Artificial Intelligence Weekly Round-up: July 9, 2019

Here are a few Artificial Intelligence updates from last week to keep you informed.

Indeed’s 2019 Report of Top 10 AI Jobs and Highest Salaries is Finally Out!

Like every year, Indeed published a report analyzing the tech industry’s top artificial intelligence (AI) jobs and highest salaries. There was a considerable increase (29%) in the number of AI jobs as compared to last year’s report.

How AI and Machine Learning Helps in Up Skilling to Better Career Opportunities

AI will create nearly 2.3 million jobs by next year. Nearly all forms of enterprise software, factory automation, transport, and other industries are increasingly using AI-based interfaces in their daily operation. In fact, by 2030, AI may end up offering USD 15.7 trillion to the global economy…. [Read More]

5 Industries that Heavily Rely on Artificial Intelligence and Machine Learning

Machine Learning and Artificial Intelligence are pushing every industry towards precise business analysis and optimizing operations. Here are 5 industries that heavily rely on AL and ML technologies to grow and perform better…. [Read More]

10 Breakthrough Technologies 2019, Curated by Bill Gates

Here is a list of 10 breakthrough technologies, that as per Bill Gates, will rule the year 2019…. [Read More]

Artificial Intelligence, the Future of Work, and Inequality?

With Artificial Intelligence coming into play many jobs will be displaced and employees will relocate to different jobs. For low- and medium skill workers, it is likely that the relocation will occur in the lower rung of jobs, meaning either lower pay or fewer benefits. Workers who possess skills that are complementary to new technologies will benefit in the form of higher wages. Hence, citizens and policymakers concerned with the rise of automation should focus on its effects on inequality, and upskilling could be a solution to this… [Read More]

Happy Reading!





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.

Machine Learning and Its 5 New Applications

In less than 4 years your own personal devices will be so sophisticated that they’ll know more information about you than the closest people around you. This is just one of the many scenarios that are in front of us thanks to the advancements made by artificial intelligence. Machine learning is moving towards these advancements and for better or worse it is going to play a huge role in the world’s most important industries. And more and more IT professionals are shifting their careers in this sector that is only destined to grow.

Can we give a definition to machine learning and why do we have to pay attention to it? On a large scale, we can say that machine learning models are an application of AI in which algorithms independently predict outcomes. To get an idea of what we are talking about, machine learning takes advantage of these models that are capable to process large datasets extract insights and make accurate predictions without the need for much human intervention.

Many of this models implement machine learning with r and it’s no wonder that a lot of programmers choose to take a machine learning course to advance in their career or change their professional ladder altogether.

Going back to what machine learning is, we can state that many value-generating implications result from the accelerated development of this technology, and many are poised to radically streamline the business world.

In this article, you will find 5 new applications that are certain to change our day to day life in the nearest future, at least our business life at the beginning.

Reviews that can make or break businesses

Now more than ever people go online and browse sites such as Yelp to look at the reviews other customers left for a certain restaurant and make up their mind and choose which venue they will spend the evening at. Yelp knows that and in the last couple of years, it has implemented machine learning to better its picture classification technology. Yelp’s machine learning algorithms help the company’s human staff to compile, categorize, and label images more efficiently. It will surely make a big difference when choosing a restaurant and when complaining about it!

The moment an algorithm understands your diagnosis.

Big Data and data science are already making a revolution by leaps and bounds in the healthcare sector. But what if incredibly accurate machine learning algorithms were capable to process large sets of healthcare data without breaching confidentiality contracts? what if a couple of lines of code could give you the results of your analysis or diagnosis, could tell you if you are at risk for particular diseases or genetic issues?

Now, this scenario may become a reality in the near future as Dr. Ed Corbett says in a recent scientific paper: “It’s clear that machine learning puts another arrow in the quiver of clinical decision making.

“Machine learning in medicine has recently made headlines,” said Corbett, the medical officer at Health Catalyst. “Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer.”

The retail store that already knows what you’ll buy tomorrow

 Do you need another incentive to learn machine learning? Just think for a minute about this statistic: the international retail sector has consistently generated over $20 trillion in sales a year for the past few years. This not only testifies that well being of this industry but also tells you that retail and online retail stores are gathering millions of raw and consumer-behavior data that could be split into consumer shopping patterns and tendencies. And yet, many enterprises are not taking advantage of this incredible amount of information, that’s why your role as programmer or engineer with a machine learning course come to play. There is a massive opportunity to implement machine learning models that enable retailers to better understand their customers and provide a more personalized customer experience.

Thanks to this well of data, you could enable any retailer with the ability to provide recommendations for the end user or better impact the whole customer’s journey, from his first visit to purchase, to the subsequent follow-up.

Separating chaff from the wheat in content and news delivery.

The case of fake news invading our social media feed has escalated to a point where Facebook announced that they will hire 3000 employees with the only purpose to monitor the platform’s newsfeed. The curation of content is not only a problem for Facebook and Twitter though. Other Silicon Valley giants like Google are creating teams of experts with the only intent to stop fake news from spreading.

Emerging machine learning and AI platforms, such as Orions Systems, are providing proprietary systems to “grow and adapt the interactions between humans and artificial intelligence” for tasks like moderating content at scale.

Taking a new stand against cybercrime

Recent studies have found out that cybercrime will cost to enterprises and companies more than 6$ trillion per year by 2021. It says that companies will invest over 1$ trillion in cybersecurity within that same year to prevent cyber attacks and data breaches. Researchers are developing clever ways to implement machine learning models to detect fraud, prevent phishing and defend against cyber attacks.

It is clear that machine learning is opening the doors to an exciting and interesting field for programmers and IT professionals. Taking a machine learning online course will benefit your career and give you the possibility to work in an ever-changing field and in different business verticals.

Everything You Need To Know About Machine Learning

If they be two, they are two so
As stiff twin compasses are two;
Thy soul, the fixed foot, makes no show
To move, but doth, if the other do.

 And though it in the center sit,
Yet when the other far doth roam,
It leans and hearkens after it,
And grows erect, as that comes home.

Such wilt thou be to me, who must,
Like th’ other foot, obliquely run;
Thy firmness makes my circle just,
And makes me end where I begun.

I love John Donne’s poetry. I always wondered if I would ever be able to write like that or come up with an analogy as brilliant as the prongs of a compass compared to the two souls in love. While I stroll around doing my usual business, I trust that there will come a day when some brilliant data scientist, hopelessly in love, and inclined to poetry will come up with an algorithm to find another analogy that will do John Donne proud and give me something to rave about. Until then, the good Samaritan that I am will help someone become a data scientist to inch closer to making it a reality.

So, What Exactly is Machine Learning?

Machine Learning is a subset of Artificial Intelligence such that a computer is able to learn on its own without explicit programming code. It uses statistical techniques to enable the system to ‘learn’ better and make future decisions and predictions based on the million data entries it has already evaluated. It grows in sophistication and accuracy and improves with every new experience or exposure (to the dataset.) A machine learning algorithm gains more and more ‘experience’ every time it processes a new data set.

The advanced applications of machine learning are known to mimic the human brain. As a baby, you start to learn different things. Your mother tells you that “this is a ball” hundreds of times before you can register it implicitly. Then comes an orange and as a child you think it is a ball (it is small, green, and round) until you grow up to add other variables like edible/non-edible, living/thing, squishy / firm etc. to decide if it is a ball or an orange. Machine learning as a branch of computer science enables a system to learn the exact same way.

Apple’s Siri, Google’s Personal Assistant, and Amazon’s Alexa are all examples of Machine Learning. Driverless cars, sentiment analysis, robotics are more advanced applications where machine learning is the prime catalyst. Corporate giants like Uber and Amazon are using machine learning at an unprecedented level of sophistication. While Uber schedules its rides, develops effective geo-mapping techniques, identifies hotspots for demand, and determines surge pricing; Amazon uses machine learning in several of its products like Alexa, Polly, Prime Air, recommendation engines on its e-commerce site, etc.

Types of Machine Learning

Machine learning is of various types and anyone interested in pursuing an artificial intelligence course or a machine learning certification must know what they are dealing with. A career in artificial intelligence and machine learning will always be super exciting but seldom a walk in the park. Let’s get started:

Supervised Learning

Supervised Learning infers a function based on the labelled training data fed to the system which is then used for mapping newer data. Supervised learning generalizes the learning algorithm from training data to unseen data to draw conclusions. The input data is X, and the output is Y, so Y = F(X). This exact rule is then applied to unseen data. There are several techniques of supervised machine learning algorithms such as linear regression, logistic regression, decision trees, and multi-class classifications, and support vector machines. All of this may sound like Greek in the beginning, but here are a couple of examples explaining supervised learning:

  • – One of the classic examples of supervised learning is the spam folder created with your Gmail or icloud account. A machine learning algorithm classifies whether an email is spam or not given a particular set of attributes. These attributes/variables could be whether you have moved similar emails (emails from the same user, same subject, etc.) into the spam folder or Google’s own algorithm of finding cues in the subject and mailer content that classifies as spam. This is an example of classification.
  • – Another one is as simple as choosing your office wardrobe. Supervised learning can classify if a dress or outfit falls into the “office-wear” category or not by determining the color, type, fabric, hemline, etc. Isn’t this how we buy clothes? Most of our choices and ‘fashion-sense’ can be defined by a set of variables and rarely do we ever experiment outside the box. You will be more surprised at how random it may seem, and yet how patterns are easy to identify if we get down to it.
  • – If these are too generic for you, let us consider a real business problem of credit score. A common scenario is a bank wanting to know if a particular account holder is a good case for a loan or a credit card. They evaluate so by giving a credit score to the customer. Millions of data records from past transactions and customer history are fed to the system for it to calculate if a new candidate is eligible for a loan.

Unsupervised Learning

In the absence of a training data set and direct input (X), no corresponding output is known beforehand. This is unsupervised learning. It is based on finding hidden trends and patterns that may not have a set output. Think of any classic business problem. Not all cases will work on supervised learning. The need for unsupervised learning is more a consequence of symptomatic data that needs to be evaluated in order to figure out a potent solution. Several techniques are used for unsupervised learning such as k-means clustering, association rules, principal and independent component analysis, and apriori algorithms. Unsupervised Learning has an alchemical feel to it!

Let’s look at a couple of examples:

  • – Suppose that it is your first day at a new office and you know no one. Since you have no prior information about any of them, you would start by classifying them based on gender, age, experience, demeanor, appearance, gait, behavior, etc. This is a classic example of unsupervised learning where you didn’t know the people or the attributes that define them but at the end of the day, you have some idea about who you are likely going to be friends with, or work well together. You would be able to easily spot clusters of people who have lunch together, who hang out together, take breaks together, etc. If you need more information about a person (Person A), you will identify with ease the person who would most likely know Person A the best.
  • – Another example is sentiment analysis of informal textual or non-textual communications on the internet. Communication can be textual like a tweet or an online forum and it can be non-textual such as video, audio, and images. Unsupervised learning makes it possible to understand the sentiment behind an image or a tweet by finding structures or relationships between the different inputs here. Various studies have found the patterns drawn from unsupervised learning can be more accurate than other machine learning methods outperforming the myths around sentiment analysis and providing reliable solutions.

Reinforcement Learning

Reinforcement learning has an input, a hypothesis of an outcome, and the grade for output. Inspired by behavioral psychology, it uses observations from the environment to take actions maximizing rewards and minimizing risk. A reinforcement learning algorithm (agent) learns from the environment iteratively until it has exhausted all possible states. ‘Ideal behavior’ is determined in a specific context by measuring performance based on reward feedback. The end goal is to maximize the reward feedback in each case. Q-Learning, Temporal Difference (TD), Deep Adversarial Networks are some of the common reinforcement learning algorithms. Self-driving cars, robotics, and games such as chess, etc. are some of its popular applications.

  • – It mostly applies to gaming where it rates the moves made by a player and learns accordingly. Imagine yourself playing chess online. The computer doesn’t know your next move. But once you move a piece, reinforcement algorithm will help it react with the environment to find its most rewarding move as a response to yours. The system will make a move according to the initial state and the action performed to change it and determine positive or negative reward to decide.
  • – Self-driving cars is work in progress by companies like Uber, GM, Ford, etc. “By 2021, Ford hopes to have a self-driving vehicle with ‘no gas pedal’ and ‘no steering wheel,’ with no need for the passenger to take control “in a predefined area,” Ford Motor CEO, Mark Fields told CNBC at the Detroit auto show. He further added, “In our industry, the word autonomous is being used very, very liberally. There’s different levels of autonomy. The question that should be asked when a company says they’re going to have an autonomous vehicle … is at what level.” (SAE International determines the capability of a self-driving vehicle on automation from zero to five – level five meaning complete automation).

Applications of Machine Learning

Some of the most common applications of Machine Learning are:

  1. Natural Language Processing – NLP is critical for bridging the gap between machines and humans. Its complexity arises from the fact that it takes years for a human being to build command over a language and yet it is so difficult for them to stick to rules and semantics. Human Language is ambiguous wrapped in layers of context, tone, and subtleties making it excruciatingly painful to define with an algorithm. But as with all matters, homo sapiens are nowhere near giving up. Constant work on NLP is in progress where scientists are trying to capture the true meaning, context, and emotion behind a sentence by collecting metadata.
  2. Medical Outcomes Analysis – Medical practitioners will be able to predict the lifespan of those suffering from diseases with more and more accuracy. Whether it is drug discovery or management, medication or best course of treatment, disease prevention, or reducing fraud and abuse in the healthcare sector, machine learning is already making waves. McKinsey Global Institute estimates that applying machine learning techniques to better inform decision making could generate up to $100 billion in value based on optimized innovation, enhanced efficiency of clinical trials and the creation of various novel tools for physicians, insurers, and consumers.
  3. Banking and Fraud Detection – One of the key areas where the finance sector is benefitting from machine learning happens to be fraud detection. Machine learning is enabling corporates to identify fraudulent transactions thanks to the cashless transactions and trail data available for every account holder. It won’t be long before robots also start recommending investment schemes based on an account holder’s financial history. Citibank has collaborated with Portugal based fraud detection company Feedzai that works in real-time to identify and eliminate fraud in online and in-person banking by alerting the customer.
  4. Marketing – According to Forbes, “84% of marketing organizations are implementing or expanding AI and machine learning in 2018 and 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.” Sales and Marketing are experiencing the power of machine learning firsthand by accurately measuring the impact of their campaigns in real time, engaging the right audience, bringing in quality leads, and overall reducing costs to maximize profits.
  5. Robotics – More as robot learning, machine learning is enabling robots to increasingly behave like humans but process information faster like supercomputers. From drones to self-driving cars, performing surgeries to manufacturing products at lightning fast speed, machine learning is being used extensively to process physical data in dynamic environments, learn by imitation, and take data-driven decisions accurately. According to Harvard Business Review, “The Age of Smart, Safe, Cheap Robots Is Already Here: As technology has advanced and robot production has scaled up, costs have fallen by about 50% since 1990 — while U.S. labor costs have risen 80%. In China, manufacturing wages have risen five-fold just since 2008 as employers have chased workers eager to switch jobs for better pay.”

Hope it helped thou understand machine learning, its applications, and its types. Its applications may seem humdrum but take a closer look and all the stuff from sci-fi movies will come to life. As for me, I am off to reading more metaphysical poetry now!

5 Must-Haves On Your Machine Learning Resume

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.

At the same time, these 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. Take additional certification for practicing problems and coding, and hone your ability with big data and distributed computing.

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. Python, R & Java 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.

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?

To explore a career in Machine Learning, click here.

7 Domains Benefiting from Big Data and Machine Learning

The wonders of Big Data and Machine Learning are already dazzling the world. Whether it is driverless cars, quick-wit from robots, or Facebook chatbots that had to be shut down, it is safe to say that no aspect of our life stays untouched from AI and Machine Learning. Having said that, there are several fields that are making key progress in leveraging Big Data to revolutionize the way they function, conduct business, and more importantly the way they envision the future. Here is a glimpse of each:

Manufacturing For a long time, the manufacturing industry was associated with a slew of problems – health risks, worker unions, poor optimization methods, and what not! However, technology and Big Data Analytics (BDA) to be precise emerged as a game changer and is now taking the factories and production units to the next level. Big Data Analytics and Machine Learning are making factories smarter with computerization of manufacturing processes, optimizing quality checks, improving accuracy and quantity of production, and promoting 3D printer factories and MaaS (Manufacturing-as-a-Service) with better collaboration. Read more

Healthcare Apart from helping hospitals and companies to cut down on costs and increasing profits, analytics has been instrumental in improving the quality of life by helping to diagnose diseases, determining the most effective course of treatments, and decreasing the overall mortality rate. As Charles Doarn, director of the Telemedicine and e-Health Program at the University of Cincinnati puts it, “Our healthcare system is in desperate need of reform, and technology is one of the tools that can help. It can be a paradigm shift in how we practice medicine.” Read more

Government Initiatives Thanks to the Union budget 2018, the NITI Aayog will initiate a national program to direct efforts in Artificial Intelligence, and the Department of Science and Technology will launch a Mission on Cyber-Physical Systems to support the establishment of centers of excellence for research, training, and skilling in robotics, artificial intelligence, digital manufacturing, big data analysis, quantum communication, internet of things, etc. Find out how government initiatives will help the government revamp manufacturing and commerce, banking, healthcare, cybersecurity, and even town planning. Read more

Finance and Banking How do machine learning and artificial intelligence impact the financial industry? The financial industry in India or the Banking, Financial Services and Insurance (BFSI) sector in India is a fast-evolving one. How then, do banks and associated organizations save time, costs and yet add value to their operations for smooth functioning? In India, Artificial Intelligence (AI) has begun to play a major role in solving some of the most vital problems faced by both companies as well as customers. Not just banks, but nearly every company whether public or private in BFSI has started using AI for Robo-advisory, risk management and fraud detection, sophisticated high-end trading, and providing superlative customer experiences, etc. Read More

Delivery of Public Services The nation is on a massive-path digitalization. It is, currently, being realized through the Digital India mission. Today, more than 980 million Indians have AADHAR cards, 700 million own mobile phones, and more than 300 million have access to broadband internet connection. This transforms into a massive data set that has the potential to actively transform the public services delivery system. Find out the top 3 to-dos for the Indian government to transform its public delivery system.

Supply Chain Management Supply chain is a natural choice when it comes to Big Data finding its applications. From improving delivery times by synchronizing shipments to identifying better ways to reduce the communication gap between manufacturers and suppliers, today, Big Data Analytics is working as an evolutionary catalyst for the supply chain management to analyze consumer behaviors and habits, improve customer experience by personalizing it, streamlining e-commerce, and managing and distributing inventories exceptionally. Read more

E-Commerce While many industries are still at the nascent stages of figuring out what to do with the huge amount of data at their disposal, e-Commerce is one industry that is already reaping the rewards of their Big Data initiatives. Major players in this industry rely heavily on their team of data scientists to compete in this fiercely dynamic space. The key ways e-Commerce companies are deriving value from Big Data analytics are by personalizing offers, running promotions or big discount days, inventory management, and optimizing pricing by introducing real-time pricing. Read more