What is Visual Studio Code?

Visual Studio Code (VS Code) is a free, open-source code editor developed by Microsoft. It is widely used for programming, supporting multiple programming languages with features such as debugging, syntax highlighting, intelligent code completion, snippets, and code refactoring. It is lightweight, yet powerful, and runs on Windows, macOS, and Linux.

Top Use Cases of Visual Studio Code

1. Web Development: Supports HTML, CSS, JavaScript, and modern frameworks and libraries like React, Angular, and Vue.js.
2. Cloud-Native Development: Integrated with Azure and supports Docker and Kubernetes for developing and deploying microservices.
3. Data Science: Supports Python with extensions for Jupyter Notebooks, data visualization, and machine learning.
4. Application Development: Supports languages like C#, Java, Python, and others, useful for both desktop and mobile application development.
5. Extension Development: Developers can create their own extensions to add new languages, themes, debuggers, and to connect to additional services.

Features of Visual Studio Code

• IntelliSense: Provides smart completions based on variable types, function definitions, and imported modules.
• Debugging: Built-in debugging support for Node.js, JavaScript, and TypeScript, with extensions for other languages like Python and PHP.
• Git Integration: Offers built-in Git commands for committing, pulling, and pushing changes to a repository.
• Extensions: A rich ecosystem of extensions to enhance functionality for different languages and tools.
• Customizable: Highly customizable through JSON settings, allowing users to tweak the editor’s appearance and behavior.

Workflow of Visual Studio Code

1. Setup: Install VS Code and relevant extensions for your development environment.
2. Project Initialization: Open or create a new project and configure workspace settings.
3. Coding: Write code with IntelliSense assistance for more efficient coding.
4. Version Control: Use integrated Git support for version control.
5. Debugging: Use the built-in debugger to set breakpoints, inspect variables, and step through code.
6. Testing and Deployment: Utilize extensions for deploying applications or running tests.

How Visual Studio Code Works & Architecture

Visual Studio Code operates on an architecture that separates the frontend (the editor itself, which runs as a web application) from the backend services. The backend is powered by Node.js, which handles extensions and integrations. This separation ensures that VS Code remains responsive, regardless of what operations are being executed in the background.

How to Install and Configure Visual Studio Code

1. Download and Install: Go to the Visual Studio Code website, download the appropriate version for your OS, and install it.
2. Open and Setup: Open VS Code and install essential extensions (e.g., for Python, install the Python extension).
3. Configure Settings: Customize settings by accessing File -> Preferences -> Settings. Configure user and workspace settings as needed.

Step-by-Step Tutorials for Visual Studio Code: “Hello World” Program

Example with Python:

Install Python Extension: From the extensions tab, search for ‘Python’ and install it.

1. Create a New File:
   • Open VS Code, then go to File > New File and save it with an appropriate name, like hello_world.py if you are using Python.
2. Write the Code:
   • For a Python file, enter: print("Hello, World!")
3. Run the Program:
   • Open the integrated terminal (Terminal > New Terminal) and type python hello_world.py to execute the code.
4. View Output:
   • The message “Hello, World!” will be displayed in the terminal, indicating that your program has run successfully.

Thanks for visiting

The post What is Visual Studio Code | Visual Studio Code Architecture & Hello World Tutorial appeared first on Artificial Intelligence.

The Defense Threat Reduction Agency wants information on companies, universities and other organizations working on artificial intelligence, machine learning and data science technologies that could help counter weapons of mass destruction and other emerging threats.

DTRA intends to use AI, ML and data science tools to improve decision-making and situational awareness for countering WMD and supporting deterrence missions, automate the identification of CWMD and deterrence objects and activities and facilitate information delivery to meet warfighter operational needs, according to a request for information posted Friday.

The technology interest areas outlined in the RFI include AI-enhanced modeling and simulation, natural language processing, computer vision, high performance computing and multiagent systems.

The agency is seeking information on data analytics, cloud platforms for data transfer and harmonization, data storage and accessibility, automated data labeling and other data-related capabilities.

DTRA has asked interested stakeholders to share information on other specific interest areas, including the detection of spectral emissions, sensor data integration, human/computer interface and extraction of actionable information from noisy data.

The post DTRA Seeks Info on AI, Machine Learning, Data Science Tech Capabilities appeared first on Artificial Intelligence.

Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in the industries these days. With its growing popularity, the jobs related to data science are also growing. Here are the latest data science jobs one can apply for this weekend.

Data scientist at Hire Digital

Location: Kolkata, West Bengal

Responsibilities

• Design and implement machine learning models.
• Work closely with data engineers to set up data warehouses and pipelines that will feed into predictive or analytical models.
• Design and implement machine learning pipelines taking into account costs, accuracy, and technical risks.
• Work closely with cross-functional teams to understand business requirements and make appropriate technical recommendations.
• Identify opportunities from in-house big data to improve business performance (data mining).

Requirements

• 5-7 years of experience in applying AI/ML and data science techniques to solve business problems.
• Degree in Computer Science.
• Any software engineering and product shipping experience is a plus.
• Background in the Consumer-Packaged Goods industry is a plus.
• Good communication, analytical and conceptual skills.
• Self-driven, a hunger for learning, and a penchant for teamwork.

Data scientist at PayPal

Location: Chennai, Tamil Nadu

Primary responsibilities:

• Conceive, design, and monitor fraud risk management strategies to manage fraud losses and improve business profitability for consumer lending products
• Identify opportunities and gaps within the current portfolio of PayPal’s Fraud Risk controls, including continuously evolving fraud trends
• Formulate & propose solutions to ensure optimal balance between user experience, business enablement, operational expense, and loss exposure
• Communicate concise and actionable business strategies and present new strategy recommendations to senior management for approval
• Monitor performance of existing & new solutions and optimize to ensure desired results
• Work closely with partners in Risk Platform, Data Sciences, Operations, Product Management, Legal & Compliance, and other teams to formulate and execute fraud risk solutions
• Collaborate with external partners, including external credit/banking partners and data vendors

Education & Required Skills:

• Bachelor’s degree in Mathematics, Statistics, Operations Research, Finance, Economics or related quantitative discipline
• 2-5 years proven credit or fraud risk analytics experience or equivalent
• Must be an intuitive, organized analytical thinker, with the ability to perform detailed analysis
• Proficiency in SQL and Excel. Proficiency in at least one statistical analysis tool: SAS / R / Python
• Strong written, oral, and interpersonal skills a must including the ability to explain and/or present analysis
• Ability to contribute to strategic discussions and represent Risk in cross-functional meetings
• Ability to manage a large, diverse set of to-dos – prioritize, stay on top of multiple workstreams, monitor progress
• Ability to work with leadership & stakeholders to define project scope and direction, driving large pieces of the work independently
• Experience working with cross-functional, geographically distributed teams, managing by influence is a plus.

Data Analyst at Uber

Location: Hyderabad, Telangana

The company is looking for a skilled Data Analyst to join their FinTech – Data Analytics team at Uber, Hyderabad to support their downstream financial systems. In this role, the employee will get an incredible opportunity to leverage analytics and science to get insights from financial data, help develop their financial reporting systems, detect anomalies in data, and streamline reporting of various financial metrics. The company will work closely with senior leaders in Finance, Product, Data Science, Engineering, and other stakeholders on fast-moving, high-stakes problems. A deep analytical passion and the ability to execute on key business priorities is a must for this role (financial knowledge is not expected to apply for this role but is a plus). Their performance is measured by the insights the employee gives, the effectiveness of communication, and the initiative to drive ideas and implement them into action.

Data Scientist at Volumetree

Location: Chandigarh

Responsibilities

• Identify valuable data sources and automate collection processes
• Undertake to preprocess of structured and unstructured data
• Analyze large amounts of information to discover trends and patterns
• Build predictive models and machine-learning algorithms
• Present information using data visualization techniques
• Propose solutions and strategies to business challenges
• Collaborate with engineering and product development teams

Requirements

• Can work independently
• Proven experience as a Data Scientist or Data Analyst
• Knowledge of SQL Python and ML
• Experience in data mining
• Understanding of machine-learning and operations research
• Knowledge of R, SQL, and Python; familiarity with Scala, Java, or C++ is an asset
• Experience using business intelligence tools (e.g., Tableau) and data frameworks (e.g., Hadoop)
• Analytical mind and business acumen
• Strong math skills (e.g., statistics, algebra)
• Problem-solving aptitude
• Excellent communication and presentation skills
• BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or another quantitative field is preferred
• Understand business requirement
• KPI identification

Data Engineer at Fortunefootprints.com

Location: Bengaluru, Karnataka

Responsibilities:

• Design and implement the data processing pipelines for different kinds of data sources, formats, and content for the Near Platform. Working with huge Data Lakes, Data Warehouse and Data Marts are part of this challenging role.
• Design and develop solutions that are scalable, generic, and reusable.
• Responsible for collecting, storing, processing, and analyzing huge sets of data that is coming from different sources.
• Develop techniques to analyze and enhance both structured/unstructured data and work with big data tools and frameworks.
• Collaborate closely with Data Scientists and Business Analysts to understand data and functional requirements.
• Design, build and support existing data pipelines to standardize, clean, and ingest data.
• Participate in product design and development activities supporting Near’s suite of products.
• Liaise with various stakeholders across teams to understand business requirements.

The post GET RECRUITED: TOP DATA SCIENCE JOBS TO APPLY THIS WEEKEND appeared first on Artificial Intelligence.

But, still, there are a lot of high-paying certification programs that professionals can opt for to compete in the competitive world.

If you are planning for a next move in USA, then completing these IT certification programs will boost your skills and abilities, and it will surely help you to stand out in the job market of united states.

With the changing business landscape, the demand for skilled individuals has increased immensely and organizations are focusing more on candidates who have completed certificate courses in these specific fields.  The demand for below-mentioned skills is sky-high, and the supply of professionals is meager.

Master in DevOps Engineering (MDE):- DevOps positions are consistently ranked among the highest paying salaries in the IT industry. DevOps is here to stay. And with that, the need to obtain a DevOps certification is no longer trendy, it’s quickly becoming a non-negotiable. Having DevOps certification makes you ready to work in a team of cross-functional members, including QA, developers, operation engineers, and business analysts.

DevSecOps Certified Professional (DSOCP):- Security in IT’s is a significant issue in today’s digital era, and Cyber attacks are on the rise and the Cyber threats won’t go away overnight. With this harsh reality, it’s inconceivable that any organization today would neglect the security aspect of the DevOps methodology. With this in mind, all businesses are driven digitally and IT management have moved to prioritize security and compliance at all levels. As more organizations see the benefit of end-to-end security implementation, DevOps will either fade away or get absorbed into DevSecOps.

SRE Certified Professional (SRECP):- SRE certification proves that the professional have understanding of set principles and practices designed to help organizations reliably and economically scale services. A certified professional can make an organization’s infrastructures far more stable, predictable, and scalable – all essential elements of software engineering, development, and operations.

Docker & Kubernetes Certification:- The container management tool Docker and Kubernetes are used in DevOps process to manage software parts as isolated, self-sufficient containers that can be deployed and run in any environment. In today’s market, professionals with Docker and Kubernetes skills are highly sought.

Master in Microservices:- Designing your product or application architecture can be tough as much a business decision as a technological one. Microservices is a particular way of developing software, where applications are structured as a collection of autonomous services OR we can say its a way where a large complex application are broken down into individual small-apps that are responsible for one specific product function. This skill allows large companies to gain agility and new tech capabilities to meet the growing customer demands.

Master in Big Data:- Big data professionals helps organizations to work with their data efficiently and use that data to identify new opportunities. Different techniques and algorithms can be applied to predict from data. Multiple business strategies can be applied for future opportunities and success of the organization and that leads to smarter business strategies, more efficient operations, and higher profits. With huge opportunities and investment in the Big Data technologies, certified professionals carrying the skills of big data are in huge demand.

Master in Artificial Intelligence:- This is one of the best certifications you can own if you want to lead the AI-driven technological revolution. It is not just about replacing the human component of the industry. It’s also about making it easier to make decisions based on observable patterns, use logic and reasoning to form conclusions, and build pathways to boost efficiency and production. It is not an easy discipline, but this is the reason why salaries in the AI industry are much higher than average.

Master in Machine Learning:- Machine Learning is one of the fast-emerging technology with high demand in the industry. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about Machine Learning and becoming a Certified Machine Learning Professional is a great idea and may even be a very wise career move! Naturally, you will be a hot asset for potential employers if you possess domain knowledge and skills in this field.

Master in Data Science OR Analytics:- Data Science is the latest tech trend that has taken the industry by storm. Companies and organizations, irrespective of their trade, are adopting Data Science tools, technologies, and solutions to promote innovation, increase productivity, boost sales, and maximize customer satisfaction. Once you receive the certification, you can apply for promising roles like Data Science, Data Analyst, and many more DATA-driven roles.

Dynatrace

Quantum Computing

AIOps

DataOps

OpenShift/Tanzu/Rancher/Linkerd/Envoy/Traefik/istio/consul

These certifications program will require a few months of hard work and an investment of time and money but once you’ll successfully complete the course you will be ready to achieve your goals no matter what they are.

Wrapping Up

Hope you would found our list of In-Demand IT Certifications For A Great Career in USA useful. Whether you’re a new working professional or an experienced professional, you won’t have trouble following these courses. At DevOpsSchool.com All of these certification courses are delivered by best-in-class trainers and mentors who will guide you every step of the way. Before selecting any certifications program, one only needs to be clear about their goal, know which path to take, and preserve in order to make the best of that opportunity.

Talk to our certification advisor if you need more information and guidance. (contact@devopsschool.com or +91 700 483 5930 (Call/WhatsApp))

The post The 13 In-Demand IT Certifications For A Great Career in USA appeared first on Artificial Intelligence.

The behaviour of the new and unknown disease is too complicated and unpredictable for data science to handle

People today aspire to use Big Data in elections, sports, healthcare, business, national planning, and where not. Michael Lewis’ 2003 book Moneyball, depicted how the manager of Oakland Athletics built up a successful baseball team by using data and computer analytics to recruit new players. ‘Moneyball’ culture soon began to dominate every bit of our life. And Silicon Valley entered into it with a new set of professionals — data scientists — who, according to Harvard Business Review, is the most attractive job of the 21st century.

People today expect data science to devise profit-making business strategy, come up with winnable election tactics, or generate world cup-triumphing playing mechanisms. Often, data scientists also aspire to do so by using the heart-beat of data. But, can they succeed? Big Data Analytics maybe like ‘churning the ocean’ in search of ‘nectar’ hidden deep in it, as depicted in the great epic Mahabharata. That’s a gigantic project, for sure. One needs a lot of efforts and expertise to obtain the nectar, but there’s every chance to get deceived by other substances — including deadly poison — obtained in the process of churning.

The ongoing pandemic, however, provided a golden opportunity for data science to exhibit its strength. It was its litmus test as well. As early as April 2020, a Harvard Business Review article perceived: “In many ways, this is our most meaningful Big Data and analytics challenge so far. With will and innovation, we could rapidly forecast the spread of the virus not only at a population level but also, and necessarily, at a hyper-local, neighbourhood level.”

Misleading predictions

As Covid-19 yielded loads of freely available data, various data scientists came up with lots of predictions and strategies — that of the eventual number of infected, eventual number of deaths, duration of lockdown needed to control the pandemic, etc.

In fact, forecasting the trajectory of the disease over time became almost a fashionable exercise to many. No wonder, in many cases, these were even contradictory in nature, and eventually most of these predictions proved to be utterly wrong, misleading and useless.

Predicting the future course of events by using the techniques of data science reminds one of the Tom Cruise starrer 2002 Spielberg movie Minority Report, where the PreCrime police force of Washington DC in 2054 even predicts future murders using data mining and predictive analyses!

In practice, data science often use statistical models and techniques, which are based on various underlying assumptions. Often, the real data doesn’t satisfy the assumptions of these models.

For example, for analysing the data of the pandemic, models such as SIR, SEIR or some of their variants were widely used. But, the dynamics of a new and unknown disease maybe far more complicated and unpredictable, and it’s most likely that they would fail to satisfy the assumptions of those classical models or their tweaks. Thus, serious error is bound to occur, which would get compounded with loads of data. Then, running routine software packages for analysing big data is never adequate, and is often incorrect.

With the ever-expanding horizon of ‘Internet of Things’, data is growing exponentially. The size of the digital universe was predicted to double every two years beyond 2020. The ongoing pandemic might have induced a higher rate of increase!

However, unless some event like Cambridge Analytica breaks, we can’t usually understand that our every footstep is added to the ocean of data. The world has become data-addicted. But, with so much data, the needle is bound to come in an increasingly larger haystack.

In 2008, Google launched the web service ‘Google Flu Trends’ project, with an objective to make accurate predictions about outbreaks of flu by aggregating Google Search queries. The project, however, failed — people often search for disease symptoms that are similar to flu, but are not actually flu. And when the much-hyped ‘Google Flu Trends’ project turned to a disastrous failure, people came to understand that big data might not be the holy grail.

Also, current computational equipment are certainly inadequate to handle millions of variables and billions of data points. The number of pairs of variables showing significant ‘spurious’ or ‘nonsense’ correlation would increase in the order of the ‘square of the number of variables’, which are almost impossible to identify.

Thus, churning the ocean of big data may yield both nectar and poison. Separating them out is a daunting task. Statistics is still in its infancy in this context, and is not equipped yet to handle these kinds of problems. Let’s be honest to admit that.

Overall, data science, being reliant on ‘statistics’ for its models and analyses, may not be ready yet for complex predictions such as the complicated yet verifiable trajectory of Covid-19. For the time being, data science’s best bet maybe to get engaged into open-ended unverifiable problems.

The writer is Professor of Statistics, Indian Statistical Institute, Kolkata

The post Data science can’t predict Covid trajectory, yet appeared first on Artificial Intelligence.

Along with the increasing demand for decision-making, the data science jobs to apply also grows

Data scientists are one of the most sought-after roles in the corporate world. As organizations with enormous data and the right data science talents seek more meaningful outputs, executives believe that it can drive more value and enrich their company’s stance. This has drastically surged the need for experts who can gather, study, and understand data to make business decisions. As a result, the demand for data scientists continues to grow even as the overall job market is in jeopardy. Many top companies are opening the door for data science jobs, and are willing to pay high salaries, with other benefits included. But along with the demand, the expectations for data science jobs to apply also grow. The title ‘data scientists’ may soon become obsolete as new technologies change the responsibilities of the profession. Recent data science jobs to apply also demands the candidates be technological mature and bring profit to the company. In this article, Analytics Insight has listed the top data science jobs to apply for this week, especially for aspirants.

• DATA SCIENCE ROUND-UP: BEST ARTICLES ON ANALYTICS INSIGHT
• JOB ALERT: TOP DATA SCIENCE JOBS TO APPLY FOR THIS WEEKEND
• TOP GOVERNMENT DATA SCIENCE JOBS IN INDIA: GET RECRUITED TODAY

Top Data Science Jobs to Apply for This Week

Senior Data Scientists at Flipkart

Location: Bengaluru, India

About the company: Flipkart Private Limited, is an Indian e-commerce company established in 2007. Flipkart started with a primary focus on online book sales and soon expanded to lifestyle products, electronics, home essentials, and even groceries.

Roles and responsibilities: As a Senior Data Scientist at Flipkart, the candidate is expected to drive research and development of various machine learning and statistical models, keeping business goals and product planning at the front. He/she should be able to communicate and collaborate with multiple stakeholders representing various teams. They should understand business and product needs and use advanced modelling techniques to provide fashion to the trends. The candidate should develop research and hypothesis on problem statements around ongoing work to provide additional value to the organization.

Qualifications

• The candidate should hold a B.Tech degree in Computer Science or Statistics.
• He/she is mandated to have over 8 years of professional experience through publications or deployed solutions or projects.
• They should have a deep understanding of applied statistical analysis and predictive modelling is desired.
• The candidate should have standard proficiency in Python or R.

Senior Data Scientist – Adv ML at Optum Global Solutions

Location: Noida, India

About the company: Optum Global Solutions, is a healthcare solution unit of UnitedHealth Group. The company’s deep expertise, advanced analytics, and innovative technology help its customers drive sustainable health economics and more personalized care.

Roles and responsibilities: As a data scientist at Optum, the candidate is expected to conduct and manage outcomes of various studies that include analyzing, reviewing, forecasting, trending, and presenting information for operational and business planning. He/she should support short and long term operational strategies and business activities by developing, enhancing, and maintaining operational information and models. Their primary responsibilities include owning the delivery of projects by working closely with cross-functional data science, data engineering, and project management teams in the role of a lead.

Qualifications

• The candidate should have at least six years of professional experience in the data science field.
• He/she should also have extraordinary skills to solve real-world problems using advanced machine learning techniques.
• They should hold strong theoretical and practical knowledge of some of the most machine learning techniques like Random Forest, Support Vector, Machine, Gradient Boosting Machine, XGBoost, etc.
• The candidate should have a strong theoretical and practical knowledge of deep learning techniques.

Data Scientist at HCL

Location: Noida, Chennai- India

About the company: HCL Technologies, is a leading global IT services company that helps global enterprises re-imagine and transform their businesses through digital technology transformation. The company is primarily engaged in providing a range of software services, business process outsourcing, and infrastructure service.

Roles and responsibilities: The Senior Data Scientists, should perform advanced analysis and create machine learning models for various industries. He/she should work closely with the customer, subject matter experts, and business developers. For each project, the candidate should thoroughly go through the domain and the customers to identify the customer’s data and analytic needs and then visualize an end-to-end analytic solution.

Qualifications

• The candidate should have a Master’s or PhD degree in Math or computer science, or any quantitative field with additional experience.
• He/she should be an expert in applying machine learning algorithms for anomaly detections, regressions, classifications, time-series forecasts, etc.
• They should have good scripting skills in Python or R.
• Experience in image analysis, optimizations, and logistics are also required.

Data Scientist: Advanced Analytics at IBM

Location: Mumbai, India

About the company: IBM, also known as International Business Machines Corporation, is an American global consulting and technology conglomerate. It builds and advertises computer hardware and software, and provides hosting, infrastructure, and consulting facilities in fields that range from nanotechnology to mainframe computers.

Roles and responsibilities: As a Data Scientist: Advanced Analytics at IBM, the candidate is expected to transform the company’s client data into tangible business value by analyzing information, communicating outcomes, and collaborating on product development. He/she should work with the IBM Q Start team on active exploratory research engagement to prepare for future use case commercialization within a specific industry. They should engage and educate client data science teams to define promising areas of quantum explosion.

Qualifications

• The candidate should hold a PhD or Masters degree in STEM-related fields with knowledge in quantum computing.
• He/she should have five years of data engineering or data science experience.
• They should have proficiency in the classical approach to machines and linear algebra, including support vector machine (SVM) for linear categorization and singular value decomposition (SVD) to reduce the dimensionality of data.

Data Scientists and Engineers at Pfizer Limited

Location: Chennai, India

About the company: Pfizer, is a research-based global biopharmaceutical company that is engaged in the discovery, deployment, manufacturing, marketing, sales, and distribution of biopharmaceutical products. Its global portfolio includes medicines and vaccines.

Roles and responsibilities: The data scientists or engineers working at Pfizer is expected to provide expert advanced modelling and data analytics support to all teams in the manufacturing intelligence organizations and support of project execution at PGS manufacturing sites. He/she should work on supporting the development, testing, deployment, and qualification of process soft sensor models. They should stay abreast of industry 4.0 trends and development in artificial intelligence, machine learning, and work with other MI teams to pilot new advances to drive value for PGS.

Qualifications

• The candidate should have a relevant degree and background in Engineering, Mathematics, Statistics, Physics, Chemistry, Computer Science, or a related technical field with a strong focus on advanced analytics.
• He/she should have data analytics or modelling experience with domain expertise and industrial exposure.
• They should also have data science, ML, and AI experience including Scikit Learn, TensorFlow, and PyTorch libraries.

The post NOW HIRING: TOP DATA SCIENCE JOBS TO APPLY FOR THIS WEEK appeared first on Artificial Intelligence.

These newly launched data science programs in India are worth checking.

In this digitally-powered economy, data is extremely important and the way into the smooth functioning of each enterprise as it helps make faster and better decisions. Similarly, the demand for data scientists in India is also growing. Organizations in India are on a hiring spree to make a data-driven culture, in which data scientists play a key role. Consequently, educational foundations are launching cutting-edge and latest data science programs in India to help data professionals make excellent careers.

Getting a new job and planning a profession both require a strong educational foundation. Newly launched data science programs in India will help data experts to learn the best and latest of data science. Let’s check them out.

MTech programs by IIT Roorkee

Indian Institute of Technology (IIT) Roorkee has introduced two new MTech programs – Artificial Intelligence (AI) and Data Science (DS) under the newly created Centre for Artificial Intelligence and Data Science (CAIDS). Both the programs will start from the 2021-22 academic year. The Centre for Artificial Intelligence and Data Science will play a significant part in forming the AI and DS landscape of the nation by advancing manpower development, research and development, and accelerating opportunities for business.

Online Data Science Programs by NIIT Ltd

NIIT Ltd., a leading in Skills and Development Corporation has introduced a series of online data science programs for a skill-makeover of the digital workforce. The projects lay a fair accentuation on the learning prerequisites of all of those applying from non-IT, non-analytic backgrounds.

The feature of this top-notch online certificate setup is the Advanced Post Graduate Program in Data Science and Machine Learning. This program solidifies the essential fundamentals of data science and envelopes a range of skills relevant to beginner as well as intermediate-level learners or professionals.

NIIT likewise makes some part-time value-adding options such as the Advanced PGP in Data Science and Machine Learning. Moreover, the training firm is likewise offering the Data Science Foundation Program in part-time as well full-time format.

Bachelor of Science (BS) by IIT Kanpur

Indian Institute of Technology, Kanpur, (IIT-K) has introduced another four-year Bachelor of Science (BS) and a five-year Bachelor of Science-Master of Science (BS-MS) degree programs in “Statistics and Data Science” to be offered from 2021-22 academic year by its division of mathematics and statistics.

The program will focus on spaces of crucial statistical and numerical, computational, and data science application courses with students having the freedom to do elective courses from the branch of computer science and engineering and electrical engineering.

The projects will train and arm students with skills applicable to the quickly developing interdisciplinary field of big data analytics. The graduates of the program will be fostered with the skills and information to build flourishing careers in the data science industry and furthermore pursue higher studies in classical and modern statistics, and data science.

Advanced Program in Applied Data Science and Machine Intelligence by IIT Madras

The Indian Institute of Technology (IIT) Madras’ Robert Bosch Centre for Data Science and AI (RBCDSAI) partnered with TalentSprint to introduce a PG Level Advanced Program in Applied Data Science and Machine Intelligence. Applications for the program are currently open. The course will begin in August 2021.

The program is intended for early-career professionals who are keen to start their careers in data science and AI. The college is setting up a devoted career accelerator to work intimately with industry looking for talent and program participants to create win-win outcomes.

The post NEWLY LAUNCHED DATA SCIENCE PROGRAMS IN INDIA appeared first on Artificial Intelligence.

Top data science jobs one should apply this weekend

Data science deals with the structuration, of scattered data and analyses them by using several types of tools and make data analysis models. Almost every organization is appointing data scientists at present to get better business value. The following are the latest data science jobs one can apply for this weekend.

Data Scientist at Cognizant

Location: Chennai, Tamil Nadu

Key Responsibilities

• Discover the information hidden in structured & unstructured data by applying data mining techniques, doing statistical analysis, and building high-quality prediction systems
• Evaluating business problems for suitability & feasibility of a predictive solution
• Evaluating & identifying the right machine learning and data science techniques & toolsets to address a variety of predictive analytics problems
• An individual contributor as well as a guide to the fellow team members in the discovery, design, and development of analytical models
• Working with functional & technical teams in the development, pilot & optimization of tools and models using machine learning & Natural Language Processing

Qualifications & Skills

• Advanced degree in statistics, computer science, machine learning, or related fields such as econometrics
• Professional experience in software engineering, with a good understanding of the Information Technology industry
• Experience in data mining (structured/unstructured data) & machine learning experience, with a proven ability to design and develop solutions across ML landscape (supervised, unsupervised, semi-supervised, etc.)

Data Science ML Consultant at Accenture

Location: Bengaluru, Karnataka

Responsibilities

• Data Manipulation & Hardcore passion for -Programming – Python & R
• ETL fundamentals
• Visual Story Telling with Data Visualization – R Shiny / Tableau / Power BI / Spotfire / BO
• Advanced Excel including VBA and PowerPoint skills

Education

• Master’s Degree (Statistics / Econometrics / Economics / Mathematics / Actuarials) or other relevant streams
• E / B. Tech. / M. Tech – proven track of passion for Data Science
• Premier Research Focused Institutes – IIT’s / REC’s / TISS / DSE / IISC etc.

Soft Skills

• Relevant Certifications in Programming & data science
• Excellent written and oral communication skills with the ability to clearly communicate ideas and results to non-technical business people
• You are laser-focused on delivering outcomes with speed and agility
• You are self-driven and thrive in ambiguity

Data Analyst at Amazon

Location: Bengaluru, Karnataka

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, Operations Research, Math, or related discipline.
• Minimum 2+ years of experience as an Analyst role preferred.
• Highly proficient in Microsoft Office and Windows-based applications.
• SQL Knowledge and Hands-on experience are a must.
• Demonstrated Analytical ability, results-oriented environment with external customer interaction.
• Excellent written and verbal communication and presentation skills and the ability to express thoughts logically and succinctly.
• Entrepreneurial drive and demonstrated ability to achieve stretch goals in an innovative and fast-paced environment.

Preferred Qualifications

• Experience with E-Commerce, Retail, and Business Analytics would be an advantage.
• Understanding of data warehousing, data modeling concept, and building new DW tables
• Advanced SQL skills, fluent in R and/or Python, advanced Microsoft Office skills, particularly Excel and analytical platforms

Executive-Data Scientist at NielsenIQ

Location: Greater Vadodara Area

GDDC is responsible for all Design related activities in Data science. GDDC is responsible for setting up the base of any product like retail index, E-comm, Cash & Carry, Scan track service (new/existing). They work closely with all the stakeholders to understand the client requirements & provide recommendations/suggestions to best build up the product which balances expectations from both the client and methodological requirements. The GDDC team is responsible for processes starting from scope definition to panel sample design which would include processes like Sample design, extrapolation…

Data Scientist at PayPal

Location: Chennai, Tamil Nadu

Responsibilities

• Partner with stakeholders (Product, Engineering, Design) on SMB products to build world-class products and experiences
• Identify key metrics, conduct rigorous explorative data analysis, and build business cases that drive decision-making and product prioritization.
• Synthesizing large volumes of data with attention to granular details and present findings and recommendations to senior-level stakeholders
• Recommend A/B testing framework and measure the impact of product initiatives in an unbiased way.
• Drive automation of the repetitive processes, and build self-serve tools (eg: Tableau Dashboards) to empower product stakeholders

Requirements:

• The data-driven mindset with a degree in any quantitative discipline such as Engineering, Computer Science, Economics, Statistics or Mathematics
• 5+ of experience analyzing large, multi-dimensional data sets and synthesizing insights into actionable solutions.
• Fluent in SQL, Excel, and visualization tools such as Tableau or Qlikview; experience with a statistical programming language like R or Python preferred
• Strong communication skills, both written and verbal, to influence cross-functional teams
• Prior work experience in a product analytics space would be highly valued.

The post JOB ALERT: TOP DATA SCIENCE JOBS TO APPLY FOR THIS WEEKEND appeared first on Artificial Intelligence.

Here are the Movies on Data Science One Must Watch

Data science and machine learning are strong technologies that are changing the world in manners that sometimes appear to be right out of a science fiction picture. Today’s robots are capable of not just performing mundane jobs, but also of performing difficult calculations to determine how to design a course for a rocket to follow or generating weather predictions based on past data.

What better medium to investigate the enigmas of data science and machine learning than movies?

Ex Machina

The title is derived from the Latin phrase “deus ex machina,” which means “god from a machine.” Caleb, a developer for one of the global highest internet firms, is chosen to take part in the century’s greatest experiment in the film. What part does he play? To create an artificial intelligence system from the ground up utilising personal information obtained from social networking sites. This film explores humanity’s privacy rights while also demonstrating the potential of machine learning and natural language analysis.

Minority Report

In most cases, current data is utilised to extrapolate additional knowledge, but this is not the case in “Minority Report.” Instead, PreCogs, a group of people with psychic powers, supply the police with information on who, where, and when crimes occur, as well as their perpetrators. The PreCrime police unit will use training trends to pin down the finer elements of a crime in an attempt to prevent it, using graphical data and information supplied by these PreCogs. The value of data science is emphasised as a result of this.

21

The fact is that science is not by nature an “evil” instrument; rather, it may be moulded to the desires of its users. The film “21” delves into the arithmetic and physics of card counting as well as Las Vegas casinos. The film recounts the actual tale of how six MIT students were capable of taking Nevada casinos for millions of dollars using cryptic talk, hand gestures, and a sharp eye.

The Imitation Game

Alan Turing, often regarded as the “Father of Modern Computer Science,” is credited with “cracking the conundrum” as a mathematician. The Enigma was a major element of the Nazi tactic to safely hide essential information in plain sight, and it has been used by the Germans to encrypt their transmissions. Turing devised a rudimentary computer that could examine all possible permutations at a quicker rate than any person. This aided the Allied troops in securing victory. In reality, this film not only tells the tale of Turing’s thinking, but also recounts the process of constructing the first computer of its sort, giving birth to not just programming and machine creation, but also encryption and cybersecurity.

Moneyball

As “Moneyball” demonstrates, predictive analysis is extremely powerful. Billy Beane, the general manager of the Oakland Athletics, must find a method to recruit fresh players on a limited budget in order to construct a successful club. He accomplishes this through the use of computer-generated past data analysis and predictive modelling, resulting in the creation of a champion baseball club with little to no further financial help.

Margin Call

There is no such thing as a technological bubble. Thousands of diverse elements influence and are influenced by it, which may be harmful, especially when technological flaws and problems emerge. In “Margin Call,” the degree of technology’s effect on our lives is examined in further depth. Junior risk analysts discover that a flaw in the modelling techniques they employ has led to their investment business investing in the wrong items and ending up on the verge of the financial calamity in the initial stages of the financial crisis of 2008. What appears to be a little blunder can have far-reaching consequences for millions of people and this isn’t limited to financial experts. An economic catastrophe like this, which is created by the wrongdoings of both human and machine employees, can have far-reaching consequences for ordinary people.

A Beautiful Mind

The Oscar-winning film “A Beautiful Mind” is well known for its portrayal of John Nash, an economist Nobel laureate. His ground-breaking work on game theory transformed the field of economics by introducing a novel technique of utilising mathematics to analyse complex interplay between zero-sum games. Despite the fact that Nash himself struggled from Schizophrenia before his historic exploration of odd hallucinations during his life, up until his death in 2015, his work has been widely utilised in the computer science field, among others, radically altering the game.

I, Robot

A cop is dispatched to investigate a crime suspected to have been committed by a robot in the year 2035, a circumstance that might pose an even greater threat to mankind. This computing system, known as VIKI, or Virtual Interactive Kinetic Intelligence, utilises data collected from all over the world as well as its own terrifying computational powers to consider taking over the world’s robots only to be stopped by a technophobic police officer and an accommodating robot he meets along the way. The scary potential of computers taking control gets dangerously close to becoming a reality, offering the viewer a look into this terrifying scenario.

Her

Some people’s fantasy, or perhaps dread, is to fall in love with a robot. In the film “Her,” a solitary writer has a genuine bond with his new operating system, which is tailored to his every need. This sophisticated operating system is able to learn and change via experiences by incorporating the characteristics of the programmers that contributed to its development. Smart Operating System Samantha, employs predictive analytics and programming, and it is up to the audience to choose if “her” goals and intents are for the good or for the worst.

WarGames

People have long been captivated by strategy games, especially when they are set in a time of upheaval and conflict. In the film “WarGames,” a young guy enters a top-secret military system through the side door. He plays it seriously, mistaking it for a warfare strategy game, only to nearly ignite World War III. Furthermore, this supercomputer is set up to collect data from all around the world and utilise predictive modelling and simulations to build a “game-like” environment for the young guy to “play.”

Conclusion

Humanity’s use of seemingly cold and uncaring robots to carry out our commands continues to amaze us. Apparently, their films have a sense of mystery and intrigue because of their science-fiction-like attention to today’s ambitions and tomorrow’s reality. In any event, the application of these technologies is a hot issue, and many elements of data science and machine learning are and will continue to be revealed through these films.

The post TOP MOVIES ON DATA SCIENCE appeared first on Artificial Intelligence.

We gauged the perspectives of experts in data science, asking them about the biggest emerging trends in data science

As one of the fastest evolving areas of tech, data science has seen a rise up the corporate agenda as less and less leaders base business decisions on guess work. With added capabilities such as artificial intelligence (AI) and the edge complementing the work of data scientists, the field is becoming more accessible to employees, but this still requires training of data skills, on the most part. In this article, we explore some key emerging trends in data science, as believed by experts in the field.

Increased involvement of AI and ML

Firstly, it’s believed that the involvement of AI and machine learning (ML) will increase further, and enable more industries to become truly data-centric.

“As businesses start to see the benefits of artificial intelligence and machine learning enabled platforms, they will invest in these technologies further,” said Douggie Melville-Clarke, head of data science at Duco.

“In fact, the Duco State of Reconciliation report – which surveyed 300 heads of global reconciliation utilities, including chief operating officers, heads of financial control and heads of finance transformation – found that 42% of those surveyed will investigate the use of more machine learning in 2021 for the purposes of intelligent data automation.”

Data science in insurance

Melville-Clarke went on to cite the insurance industry, often perceived as a sector that’s had difficulty innovating due to high levels of regulation, as an example for future success when it comes to data science.

He explained: “The insurance industry, for example, has already embraced automation for processes such as underwriting and quote generation. But the more valuable use of artificial intelligence and machine learning is to increase your service and market share through uses like constrained customisation.

“Personalisation is one of the key ways that banks and insurance companies can differentiate themselves, but without machine learning this can be a lengthy and expensive process.

“Machine learning can help these industries tailor their products to meet the individual consumers’ needs in a much more cost-effective way, bettering the customer experience and increasing customisation.”

The evolution of hyperautomation

Along with rising use of AI and ML models, organisations have been combining AI with robotic process automation (RPA), to reduce operational costs through automating decision making. This trend, known as hyperautomation, is predicted to help companies to continue innovating fast in a post-COVID environment in the next few years.

“In many ways, this isn’t a new concept — the key goal of enterprise investment in data science for the past decade has been to automate decision-making processes based on AI and ML,” explained Rich Pugh, co-founder and chief data scientist at Mango Solutions, an Ascent company.

“What is new here is that hyperautomation is underpinned by an ‘RPA-first’ approach that can turbocharge process automation and drive increased collaboration across analytic and IT functions.

“Business leaders need to focus on how to harness enterprise automation and continuous intelligence to elevate the customer experience. Whether that is embedding intelligent thinking into the processes that will drive more informed decision making, such as deploying automation around pricing decisions to deliver a more efficient and personalised service, or leveraging richer real-time customer insights in conjunction with automation to execute highly relevant offers and new services at speed.

“Embarking on the hyperautomation journey begins with achieving some realistic and measurable future outcomes. Specifically, this should include aiming for high-value processes, focusing on automation and change, and initiating a structure to gather the data that will enable future success.”

SaaS and self-service

Dan Sommer, senior director at Qlik, identified software-as-a-service (SaaS) and a self-service approach among users, along with a shift in advanced analytics, as a notable emerging trend in data science.

“To those in the industry, it’s clear that SaaS will be everyone’s new best friend – with a greater migration of databases and applications from on premise to cloud environments,” said Sommer.

“Cloud computing has helped many businesses, organisations, and schools to keep the lights on in virtual environments – and we’re now going to see an enhanced focus on SaaS as hybrid operations look set to remain.

“In addition, we’ll see self-service evolving to self-sufficiency when it comes to effectively using data and analytics. Empowering users to access data, insights and business logic earlier and more intuitively will enable the move from visualisation self-service to data self-sufficiency in the near future.

“Finally, advanced analytics need to look different. In uncertain times, we can no longer count on backward-looking data to build a comprehensive model of the future. Instead, we need to give particular focus to, rather than exclude outliers – and this will define how we tackle threats going forward too.”

Data fabric

With employees gradually becoming more comfortable with using data science tools to make decisions, while aided by automation and machine intelligence, a concept that’s materialised as a hot topic for the next stage of development is the concept of ‘data fabric’.

Trevor Morgan, product manager at comforte AG, explained: “A data fabric is more of an architectural overlay on top of massive enterprise data ecosystems. The data fabric unifies disparate data sources and streams across many different topologies (both on-premise and in the cloud), and provides multiple ways of accessing and working with that data for organisational personnel, and with the larger fabric as a contextual backdrop.

“For large enterprises that are moving with hyper-agility while working with multiple or many Big Data environments, data fabric technology will provide the means to harness all this information and make it workable throughout the enterprise.”

New career paths and roles

Another important trend to consider regarding the future of data science is the new career paths and jobs that are set to emerge in the coming years.

“According to the World Economic Forum (WEF)’s Future of Job’s Report 2020, 94% of UK employers plan to hire new permanent staff with skills relevant to new technologies and expect existing employees to pick up new skills on the job,” said Anthony Tattersall, vice-president, enterprise, EMEA at Coursera.

“What’s more, WEF’s top emerging jobs in the UK — data scientists, AI and machine learning specialists, big data and Internet of Things — all call for skills of this nature.

“We therefore envision access to a variety of job-relevant credentials, including a path to entry-level digital jobs, will be key to reskilling at scale and accelerating economic recovery in the years ahead.”

The ‘Industrial Data Scientist’

In regards to new roles to emerge in data science, Adi Pendyala, senior director at Aspen Technology, predicts the emergence of the ‘Industrial Data Scientist’: “These scientists will be a new breed of tech-driven, data-empowered domain experts with access to more industrial data than ever before, as well as the accessible AI/ML and analytics tools needed to translate that information into actionable intelligence across the enterprise.

“Industrial data scientists will represent a new kind of crossroads between our traditional understanding of citizen data scientists and industrial domain experts: workers who possess the domain expertise of the latter but are increasingly shifting over to the data realm occupied by the former.”

New tools

Many organisations are being impacted by a shortage of data scientists in proportion to demand, but Julien Alteirac, regional vice-president, UK&I at Snowflake, believes that new tools, powered by ML, could help to mitigate this skills gap in the near future.

“When it comes to analysing data, most organisations employ an abundance of data analysts and a limited number of data scientists, due in large part to the limited supply and high costs associated with data scientists,” said Alteirac.

“Since analysts lack the data science expertise required to build ML models, data scientists have become a potential bottleneck for broadening the use of ML. However, new and improved ML tools which are more user-friendly are helping organisations realise the power of data science.

“Data analysts are empowered with access to powerful models without needing to manually build them. Specifically, automated machine learning (AutoML) and AI services via APIs are removing the need to manually prepare data and then build and train models. AutoML tools and AI services lower the barrier to entry for ML, so almost anyone will now be able to access and use data science without requiring an academic background.”

The post Hot topics and emerging trends in data science appeared first on Artificial Intelligence.