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AutoML (Machine Learning) in 2020

Posted on April 3, 2020April 3, 2020 by Dexlab

AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, is likely to become the most sought after development in computing sciences for more reasons than one.

Data scientists with competent skills who can work on big data, advanced analytics, and predictive models are few and hard to find. However, AutoML programs have made life easier for businesses and organisations by coming to the rescue of lesser skilled professionals.

Bridging the skill gap, AutoML is helping lesser skilled professionals build models using the best diagnostic and predictive analytics tools.

“AutoML packages like auto-sk learn can automatically do the model selection, scoring, and hyperparameter optimisation. Services like Amazon Forecast and Google’s Cloud AutoML also help in determining the algorithm to fit best with the data,” says a report.

With time, the amount of data generated by computer systems will have grown exponentially, and “the world of analytics, AI, machine learning and data science will see a wave of data and training. And, with the increasing amount of data, here’s why AutoML might be the most used technology in 2020.”

Hastening The ML Process

It takes human beings a longer time to build ML models than it takes automatic systems to, and accuracy is not always at par on the part of human beings. It would take less time for AutoML to construct a model and businesses are slowly preferring to use automated machine learning to amplify their predictive power for the need for insights from big data is only growing.

“An ML process typically consists of data pre-processing, feature selection, feature extraction, feature engineering, algorithm selection, and hyperparameter tuning. These take up more time to implement and require considerable expertise; AutoML, on the other hand, removes the trouble of going through some of these tedious processes.”

Addressing The Skills Gap

AutoMLis helping bridge the skills gap, especially in non-tech companies or companies with less data science expertise. “With the launch of Cloud AutoML, based on Neural Architecture Search (NAS) and transfer learning, Google believes that it has the potential to make the existing AI/ML experts more productive along with helping the less skilled engineered to build a powerful AI system.”

AutoML, also, hasmade machine learning a democraticsystem. It has helped “to carry out processes like hyperparameter tuning, selection of algorithms, and finding the appropriate model — as these tasks are tedious and at the same time complex.”

Bettering Scalability

Machine Learning requires massive amounts of data to work on and training a model takes a long time, especially if the model is big. “AutoML, on the other hand, makes it easy to handle data, train model, evaluate, experiment, and even deploy the model for different use cases as it takes on the task to find the best algorithm for the task to be done.”

To enrol in a course on AutoML, do peruse the DexLab Analytics website today. DexLab Analytcis is a premiere Machine Learning training institute in Delhi and NCR.

Why Machine Learning Matters

Posted on March 30, 2020March 30, 2020 by Dexlab

Machine Learning, a subset of artificial intelligence, is a process of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that computing systems can learn from data, identify patterns in them and make intelligent decisions with minimal human intervention.

Importance of Machine Learning

The growth in volumes of data sets, and cheaper and more powerful computational processing and affordable data storage has triggered resurgence in interest in machine learning.

“All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks,” a report says.

Uses of Machine Learning

Machine Learning has been adopted by several key industries working with large amounts of data. Machine Learning helps businesses grow by gleaning actionable insights from these data sets.

Financial services

Machine Learning has revolutionised the banking sector giving financial institutions and banks the opportunity to “identify important insights in data, and prevent fraud.” The business insights can help companies identify investment opportunities or help investors know when to trade. “Data mining can also identify clients with high-risk profiles, or use cyber-surveillance to pinpoint warning signs of fraud.”

Government

Governmentsown an unimaginable amount of data and they can use this to their advantage. With the help of machine learning, they can mine data sets for insights. “Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.”

Health care

Machine Learning has helped the healthcare industry evolve thanks to wearable devices and sensors that can use data to assess a patient’s health in real time and improve diagnosis and treatment.

Retail

Machine Learning helps study and analyse customers’ purchase history and recommends what items a customer is likely to prefer buying. It predicts buying patterns and tastes and choices. It helps retailers offer a personalised experience to shoppers, implement a marketing campaign, optimize prices and plan merchandise supply.

Oil and gas

“Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.”

Transportation

“Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.”

For more on Machine Learning algorithms and artificial intelligence, do checkout the DexLab Analytics blog section. DexLab Analytics is a premiere institute of Machine Learning training in Delhi which trains professionals and students in all aspects of the technological science through both online classes and classes conducted in the National Capital Region.

Machine Learning Algorithms in Self-Driving Cars

Posted on March 27, 2020March 27, 2020 by Dexlab

Machine Learning algorithms have revolutionized sectors like automation in ways one could have hardly imagined a few years ago. For instance, take the self-driving car. According to a report, with“the integration of sensor data processing in a centralized electronic control unit (ECU) in a car, it is imperative to increase the use of machine learning to perform new tasks. Potential applications include driving scenario classification or driver condition evaluation via data fusion from different internal and external sensors – such as cameras, radars, LIDAR or the Internet of Things.”

An expert explains how machine learning algorithms are used in autonomous cars. Supervised and unsupervised algorithms are used to perceive information through the car’s infotainment system. For instance, the system can relay information about the driver’s health status and direct the vehicle to a nearby hospital if something is found to be wrong. “This machine learning-based application can also incorporate the driver’s gesture and speech recognition, and language translation.”

The algorithms can be classified into two major categories on the basis of their learning ability- supervised algorithm and an unsupervised algorithm.

Supervised algorithms “learn using a training dataset, and keep on learning until they reach the desired level of confidence (minimization of probability error).” They can be sub-classified into classification, regression and dimension reduction or anomaly detection.

Unsupervised algorithms “try to make sense of the available data. That means an algorithm develops a relationship within the available data set to identify patterns, or divides the data set into subgroups based on the level of similarity between them.” Unsupervised algorithms can be largely sub-classified into clustering and association rule learning.

The third set of machine learning algorithms falls somewhere between supervised and unsupervised learning. Reinforcement learning has sparse and time-delayed labels – the future rewards. “Based only on those rewards, the agent has to learn to behave in the environment.”

One of the main tasks of any machine learning algorithm in the self-driving car is continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. These tasks are mainly divided into four sub-tasks:

Object detection
Object Identification or recognition
Object classification
Object localization and prediction of movement

Machine learning algorithms can be loosely divided into four categories: regression algorithms, pattern recognition, cluster algorithms and decision matrix algorithms. One category of machine learning algorithms can be used to execute two or more different subtasks. For example, regression algorithms can be used for object detection as well as for object localization or prediction of movement.

Regression Algorithms

This type of algorithm is used to predict events. “Regression analysis estimates the relationship between two or more variables, compare the effects of variables measured on different scales and are mostly driven by three metrics, namely:

The number of independent variables
The type of dependent variables
The shape of the regression line.”

Pattern Recognition Algorithms (Classification)

“In ADAS, the images obtained through sensors possess all types of environmental data; filtering of the images is required to recognize instances of an object category by ruling out the irrelevant data points. Pattern recognition algorithms are good at ruling out these unusual data points. Recognition of patterns in a data set is an important step before classifying the objects. These types of algorithms can also be defined as data reduction algorithms.”

Clustering

Sometimes the images gathered by the system are unclear and it is difficult to detect and locate objects in them. It is also possible that the classification algorithms may miss the object and fail to classify and report it to the system because the images are low-resolution, with very few data points or discontinuous data. “This type of algorithm is good at discovering structure from data points. Like regression, it describes the class of problem and the class of methods.” The most commonly used type of algorithm is K-means, Multi-class Neural Network.”

Decision Matrix Algorithms

“This type of algorithm is good at systematically identifying, analyzing, and rating the performance of relationships between sets of values and information. These algorithms are mainly used for decision-making. Whether a car needs to take a left turn or it needs to brake depends on the level of confidence the algorithms have on the classification, recognition and prediction of the next movement of objects.”

Check out the course structure at DexLab Analytics, a premiere artificial intelligence institute and machine learning institute in Delhi for more on the subject.

The AI Revolution in The Education Sector

Posted on March 23, 2020March 24, 2020 by Dexlab

Artificial Intelligence (AI) is revolutionizing innumerable aspects of our lives, education being one of them. AI has transformed the way we learn, the relationship between the student and the teacher and the very manner in which our curriculum is perceived. This article, the third part of a series on the applications of artificial intelligence, delineates how AI has come to transform the education sector, as we know it.

The biggest contribution of AI to the education sector has been towards enhancing and streamlining the system of teaching students with varying needs across the spectrum, from elementary schools to adult learning centers. Students can be mentally developed in the left side of the brain with more analytical skills or they can be mentally developed in the right side of the brain with more creative and literary skills. Likewise, there may be students with different interests and passions. A strictly uniform curriculum does not suit all students of the same class because people differ in their learning ability and interests.

AI-Enabled Hyper-Personalization

AI is thus being used to customise curricula according to specific needs of each student of a single class. This is being done through the power of machine learning via a method called hyper-personalization. The AI powered system studies and examines the profile of a student and prescribes suitable curricula for her/him. According to a report, it is expected that by the year 2024 onwards, almost 50 percent of learning management tools will be powered by AI capabilities. These AI-enabled e-Learning tools will touch over $6 Billion in market size by 2024.

Smart Learning Tools

Machine Learning and AI are also defining the way hyperper sonalized and on-demand digital content is created to digitise the learning environment. Now students do not have to rote-learn chapter after chapter from textbooks. They are absorbing learning material in the form of condensed bits of information in the form of smaller study guides, chapter summaries, flashcards, as well as short smart notes designed for better reading and comprehension. Learning is therefore becoming gradually paperless. AI systems also have an online interactive interface that helps in putting in place a system of feedback from students to professors regarding areas they are facing trouble understanding.

Digital Conversations

AI systems are also being used to develop the system of tutoring with personalized conversational education assistants. These autonomous conversational agents are capable of answering questions, providing assistance with learning or assignments, and strengthening concepts by throwing up additional information and learning material to reinforce the curriculum. “These intelligent assistants are also enhancing adaptive learning features so that each of the students can learn at their own pace or time frames”.

Adoption of Voice Assistants

In addition, educators are relying heavily on using voice assistants in the classroom environment. Voice assistants such as Amazon Alexa, Google Home, Apple Siri, and Microsoft Cortana have transformed the way students interact with their study material. In the higher education environment, universities and colleges are distributing voice assistants to students in place of traditionally printed handbooks or hard-to-navigate websites.

Assisting Educators

AI powered systems are not only helping students with course work, they are also empowering teachers with teaching material and new innovative ways to educationally express themselves. It is easier to explain a theory with the help of picture cues and graphical representation than mere definitions. The Internet has become a treasure trove of teaching material for teachers to borrow from. Also, teachers are burdened with responsibilities “such as essay evaluation, grading of exams…ordering and managing classroom materials, booking and managing field trips, responding to parents, assisting with conversation and second-language related issues…Educators often spend up to 50% of their time on non-teaching tasks.”AI powered systems can help streamline these tasks and handle repetitive and routine work, digitise interaction with parents and guardians and leave educators with more time to teach students.

When it comes to higher learning, in India at least, more and more artificial intelligence and machine learning institutes are opening up. DexLab Analytics is a premiere artificial intelligence course in Delhi that trains professionals in both AI and machine learning.

The link between AI, ML and Data Science

Posted on March 19, 2020May 23, 2020 by Dexlab

The link between AI, ML and Data Science

The fields of Artificial Intelligence, Machine Learning and Data Science cover a vast area of study and they should not be confused with each other. They are distinct branches of computational sciences and technologies.

Artificial Intelligence

Artificial intelligence is an area of computer science wherein the computer systems are built such that they can perform tasks with the same agility as that done through human intelligence. These tasks range from speech recognition to image recognition and decision making systems among others.

This intelligence in computer systems is developed by human beings using technologies like Natural Processing Language (NLP) or computer vision among others. Data forms an important part of AI systems. Big Data, vast stashes of data generated for computer systems to analyze and study to find patterns in is imperative to Artificial Intelligence.

Machine learning

Machine learning is a subset of artificial intelligence. Machine learning is used to predict future courses of action based on historical data. It is the computer system’s ability to learn from its environment and improve on its findings.

For instance, if you have marked an email as spam once, the computer system will automatically learn to mark as spam all future emails from that particular address. To construct these algorithms developers need large amounts of data. The larger the data sets, the better the predictions. A subset of Machine Learning is Deep Learning, modeled after the neural networks of the human brain.

Data Science:

Data science is a field wherein data scientists derive valuable and actionable insights from large volumes of data. The science is based on tools developed with the knowledge of various subjects like mathematics, computer programming, statistical modeling and machine learning.

The insights derived by data scientists help companies and business organizations grow their business. Data science involves analysis of data and modelling of data among other techniques like data extraction, data exploration, data preparation and data visualization. As data volumes grow more and more vast, the scope of data science is also growing each passing day, data that needs to be analyzed to grow business.

Data Science, Machine Learning and Artificial Intelligence

Data Science, Artificial Intelligence and Machine Learning are all related in that they all rely on data. To process data for Machine Learning and Artificial Intelligence, you need a data scientist to cull out relevant information and process it before feeding it to predictive models used for Machine Learning. Machine Learning is the subset of Artificial Intelligence – which relies on computers understanding data, learning from it and making decisions based on their findings of patterns (virtually impossible for the human eye to detect manually) in data sets. Machine Learning is the link between Data Science and Artificial Intelligence. Artificial Intelligence uses Machine Learning to help Data Science get solutions to specific problems.

The three technological fields are thus, closely linked to each other. For more on this, do not forget to check-out the artificial intelligence certification in Delhi NCR from DexLab Analytics.

The Four Important Machine Learning Algorithms in Use

Posted on March 18, 2020March 18, 2020 by Dexlab

The Four Important Machine Learning Algorithms in Use

Machine Learning, a subset of Artificial Intelligence, has revolutionized the business environment the world over. It has brought actionable insights to business operations and helped increase profits acting as a reliable tool of business operations. In fact, its role in the business environment has become almost indispensable, so much so that machine learning algorithms are needed to maintain competitiveness in the market. Here is a list of machine learning algorithms crucial to businesses.

Supervised Machine Learning Algorithms

Supervised Learning involves those algorithms which involve direct supervision of the operation. In this case, the developer labels sample data corpus and sets strict boundaries upon which the algorithm operates, says a report.

Here human experts act as the tutor or teacher feeding the computer system with input and output data so the computer can learn the patterns.

“Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets,” says another report.

The most widely used supervised algorithms are Linear Regression; Logistical Regression; Random Forest; Gradient Boosted Trees; Support Vector Machines (SVM); Neural Networks; Decision Trees; Naive Bayes; Nearest Neighbor. Supervised algorithms are used in price prediction and trend forecasting in sales, retail commerce, and stock trading.

Unsupervised Machine Learning Algorithms

Unsupervised Learning is the algorithm which does not involve direct control of the developer or teacher. Unlike in supervised machine learning where the results are known, in the case of unsupervised machine learning algorithms, the desired results are unknown and not yet defined. Another big difference between the two is that supervised learning uses labelled data exclusively, while unsupervised learning feeds on unlabeled data.

The unsupervised machine learning algorithm is used for exploring the structure of the information; extracting valuable insights; detecting patterns; implementing this into its operation to increase efficiency.

Digital marketing and ad tech are the two fields where Unsupervised Learning is used to effectively. Also, this algorithm is often applied to explore customer information and mould the service accordingly.

Semi-supervised Machine Learning Algorithms

Semi-supervised learning algorithms represent features of both supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a unique aspect of itself. Semi-supervised machine learning algorithm uses a limited set of labelled sample data to train itself. The limitation results in a partially trained model that later gets the task to label the unlabeled data. Due to the limitations of the sample data set, the results are considered pseudo-labelled data, says a report. Lastly, labelled and pseudo-labelled data sets are combined with each other to create a distinct algorithm that combines descriptive and predictive aspects of supervised and unsupervised learning.

Semi-supervised learning uses the classification process to identify data assets and clustering process to group it into distinct parts.

Legal and Healthcare industries, among others, manage web content classification, image and speech analysis with the help of semi-supervised learning.

Reinforcement Machine Learning Algorithms

Reinforcement learning represents what is commonly understood as machine learning artificial intelligence.

In essence, reinforcement learning is all about developing a self-sustained system that, throughout contiguous sequences of trials and errors, improves itself based on the combination of labelled data and interactions with the incoming data. The method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk.

Most common reinforcement learning algorithms include: Q-Learning; Temporal Difference (TD); Monte-Carlo Tree Search (MCTS); Asynchronous Actor-Critic Agents (A3C).

Modern NPCs and other video games use this type of machine learning model a lot. Reinforcement Learning provides flexibility to the AI reactions to the player’s action thus providing viable challenges. Self-driving cars also rely on reinforced learning algorithms.

For more on Machine Learning courses in Delhi, check out the DexLab Analytics course structure today.

How AI and Machine Learning are Helping Fight Coronavirus

Posted on March 12, 2020May 23, 2020 by Dexlab

How AI and Machine Learning are Helping Fight Coronavirus

A Toronto based AI-startup detected the outbreak of coronavirus, a large family of viruses which infect the respiratory tract of human beings and animals, hours after the first few cases were diagnosed in Wuhan in December 2019.

More than 100,000 people the world over have been infected by the novel coronavirus since then and more than 4000 people have died, most in China.

The start-up team confirmed their findings and informed their clients about an “unusual pneumonia” in a market place in Wuhan a week before Chinese authorities and international health bodies made formal announcements about the virus and the epidemic. The key to the company’s ability to detect and warn of a possible outbreak of an epidemic is AI and big data.

NLP and Machine Learning

The company uses natural language processing or NLP and machine learning to, says a report, “cull data from hundreds of thousands of sources, including statements from official public health organizations, digital media, global airline ticketing data, livestock health reports and population demographics. It’s able to rapidly process tons of information every 15 minutes, 24 hours a day.”

This information becomes the basis of reports compiled by computer programmers and physicians. Also, they do not just detect the outbreak of a disease but also track its spread and the consequences.

In the case of COVID-19, the company besides sending out an alert, correctly identified the cities that were highly connected to Wuhan using data on global airline ticketing “to help anticipate where the infected might be travelling.”

GDP

“Already, the COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets,” a report says.

The vast amount of X-rays and scans people across the world are undergoing in this outbreak of coronavirus has strained medical resources and systems across the world. That is why AI and machine learning models are being trained to read accurately vast amounts of data tirelessly, and efficiently.

Thermal Scanners

China has already deployed AI-powered thermal scanners at railway stations in major cities to read and record, from a distance through infrared, body temperatures of persons passing to detect a fever. This technology has to a large extant reduced stress on institutions across the country.

But it must be noted that AI is set to become a huge firewall against infectious diseases and pandemics not only by powering diagnostic techniques but by identifying potential vaccines and lines of treatment against the next coronavirus and COVID-19 itself within days.

Robots

Also, AI and big data are helping revolutionize the medical management system in China. With the outbreak of the pandemic, China hospitals are using robots to reduce the stresses piled on medical staff. Ambulances in the city of Hangzhou are assisted by AI in navigation to help them reach patients and people suspecting an infection faster.

“Robots have even been dispatched to a public plaza in Guangzhou in order to warn passersby who aren’t wearing face-masks…China is also allegedly using drones to ensure residents are staying at home and reducing the risk of the coronavirus spreading further.”

In India, though the virus has been detected in some states, it has not spread as alarmingly as it has in other countries. It is now more than ever important to concentrate on building more robust and competent Artificial Intelligence courses in Delhi and Machine Learning courses in India.

Why Python is Preferred in AI and Machine Learning?

Posted on March 11, 2020March 12, 2020 by Dexlab

Why Python is Preferred in AI and Machine Learning?

Python has become one of the leading coding languages across the globe and for more reasons than one. In this article, we evaluate why Python is beneficial in the use of Machine Learning and Artificial Intelligence applications.

Artificial intelligence and Machine Learning are profoundly shaping the world we live in, with new applications mushrooming by the day. Competent designers are choosing Python as their go-to programming language for designing AI and ML programs.

Artificial Intelligence enables music platforms like Spotify to prescribe melodies to users and streaming platforms like Netflix to understand what shows viewers would like to watch based on their tastes and preferences. The science is widely being used to power organizations with worker efficiency and self-administration.

Machine-driven intelligence ventures are different from traditional programming languages in that they have innovation stack and the ability to accommodate an AI-based experiment. Python has these features and more. It is a steady programming language, it is adaptable and has accessible instruments.

Here are some features of Python that enable AI engineers to build gainful products.

An exemplary library environment

“An extraordinary selection of libraries is one of the primary reasons Python is the most mainstream programming language utilized for AI”, a report says. Python libraries are very extensive in nature and enable designers to perform useful activities without the need to code them from scratch.

Machine Learning demands incessant information preparation, and Python’s libraries allows you to access, deal with and change information. These are libraries can be used for ML and AI: Pandas, Keras, TensorFlow, Matplotlib, NLTK, Scikit-picture, PyBrain, Caffe, Stats models and in the PyPI storehouse, you can find and look at more Python libraries.

Basic and predictable

Python has on offer short and decipherable code. Python’s effortless built allows engineers to make and design robust frameworks. Designers can straightway concentrate on tackling an ML issue rather concentrating on the subtleties of the programming language.

Moreover, Python is easy to learn and therefore being adopted by more and more designers who can easily construct models for AI. Also, many software engineers feel Python is more intuitive than other programming languages.

A low entry barrier

Working in the ML and AI industry means an engineer will have to manage tons of information in a prodigious way. The low section hindrance or low entry barrier allows more information researchers to rapidly understand Python and begin using it for AI advancement without wasting time or energy learning the language.

Moreover, Python programming language is in simple English with a straightforward syntax which makes it very readable and easy to understand.

Conclusion

Thus, we have seen how advantageous Python is as a programming language which can be used to build AI models with ease and agility. It has a broad choice of AI explicit libraries and its basic grammar and readability make the language accessible to non-developers.

It is being widely adopted by developers across institutions working in the field of AI. It is no surprise then that artificial intelligence courses in Delhi and Machine Learning institutes in Gurgaon are enrolling more and more developers who want to be trained in the science of Python.

Skills Data Scientists Must Master in 2020

Posted on March 10, 2020May 23, 2020 by Dexlab

Skills Data Scientists Must Master in 2020

Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.

Technical Skills

Python and R

Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.

GitHub

Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”

Preparing for Production

Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”

Cloud

Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.

Deep Learning

Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.

Math and Statistics

Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.

SQL

Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.

AutoML

Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.

Data Visualization

Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.

Non-Technical Skills

Ability to solve business problems

It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.

Effective business communication

A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.

Agility

Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.

Experimentation

The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.

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