IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post Top 10 high paying IT certifications in the world in 2022 appeared first on Artificial Intelligence.

Flutter Widgets

Hello World Example

Types of Widget

We can split the Flutter widget into two categories:

• Visible (Output and Input)
• Invisible (Layout and Control)

Visible widget

The visible widgets are related to the user input and output data. Some of the important types of this widget are:

Text

A Text widget holds some text to display on the screen. We can align the text widget by using textAlign property, and style property allow the customization of Text that includes font, font weight, font style, letter spacing, color, and many more. We can use it as like below code snippets.

Button

This widget allows you to perform some action on click. Flutter does not allow you to use the Button widget directly; instead, it uses a type of buttons like a FlatButton and a RaisedButton. We can use it as like below code snippets.

In the above example, the onPressed property allows us to perform an action when you click the button, and elevation property is used to change how much it stands out.

The post Flutter Basic Query appeared first on Artificial Intelligence.

Artificial Intelligence (AI) has been redefining society in ways we have never anticipated. Technology is clinging to us in every walk of our lives, right from unlocking our smartphones to our day-to-day activities, online shopping, intelligent car dashboards, autonomous robots and so on. Though the concept of AI was first talked about in the early 1950s, forming a basis for many computer learning and complex decision-making processes, it is only of late, where processing huge amounts of data is required, that this field of technology is picking up pace.

What is in the AI basket?

AI is not a technology, rather it is a science or field of study. It is a constellation that encompasses a lot of statistical computation methods, pre and post analyses techniques for handling structured and unstructured data. It is an interesting endeavour of replicating and stimulating human intelligence through machine and deep learning platforms, natural language generation, virtual agents, text-voice-image recognition, AI optimised hardware, robotic process automation, cognitive search system and so on. It has a goal of utilising all the technologies to make intelligent machines.

Growth of AI in India

AI is the tool of innovation being experimented with, in almost all Indian domains, including healthcare, education, agriculture, finance, automobiles, energy, retail, manufacturing, scientific research with autonomous discoveries in place. In India, companies like Walmart, Google, Microsoft, Amazon, Samsung are into AI-based research and product offerings. Still, our country has a lot of potential to expand its research in this cutting-edge technology. Most of our educational, government and private institutes cradle and motivate AI researchers, innovations and start-ups.

The Government is pushing the private sector and offers many opportunities through DST, Niti Aayog, IndiaAI and many more, to create innovative technological solutions and fund AI-based start-ups. The start-ups are focussed in the cities like Bengaluru, Hyderabad, Ahmedabad, Mumbai, Delhi for AI-based businesses.

Why AI matters in today’s scenario

AI, which emerged from the research world as a proof-of-concept has been strategically scaling up due to the pace of digitisation. AI is favoured for its large data processing, end-to-end efficiency of decoding complex processes, improved accuracy and help in decision-making, intelligent offerings, smart services – content, task automation and so on. We can see its overwhelming development in healthcare, pharmaceutical, scientific research, and e-commerce.

The interactive applications of Google, DeepMind’s Alpha Fold, BenevolentAI, chatbots such as Clara and Zini; Aryoga Setu, Co-Win, Amazon, Zomato, Swiggy are among the few proving to be our pandemic tech saviours.

Impact of AI in business

Business over the years has evolved from local corner shops to the booming online shopping platforms. These modernised techniques not only make individual lives easier, but also streamline business processes for improving consumer experience, sales forecasting and automated decision making to meet business goals. Businesses work well when humans, machines and technologies integrate for each other’s benefit. Today’s business world is solely dependent on AI, Cloud, Big Data technologies of which e-commerce and m-commerce are the mainstream, having a great business impact globally. In synch with global developments in innovation and automation, India too has brought about a digital transformation over the last two decades. Now, technological developments have gained pace more than what has been predicted; the pandemic played a great role in its quick transformation and adoption.

How to look for jobs in AI?

Today, Artificial Intelligence is a lucrative domain, promising job growth in a competitive IT industry. Four out of five C-suite executives believe that they need to speed up data processing and automation, if they have to survive in their business. So, recruiters look for advanced technical skills, extensive practical experience. AI skills secure the top place among the fastest growing job profiles over the recent years. The prominent job roles include big data engineer, business intelligence developer, data scientist, data analyst, cyber analyst and expert, AI-Deep learning-machine learning engineer, computer vision specialist along with equivalent research jobs.

How can one find a good job in AI? The answer is, there are several avenues and opportunities to be had by connecting with experts via LinkedIn, technical blogs, career fairs and company career sites. The tech talks given by companies in university, conclaves hosting academics-government-industry groups will help you understand the actual employment needs and goals. Always aim to seek opportunities at government and industry-funded research labs during the early years of your higher education. This will help you to nurture your skillset to the best. Work for open-source and stack overflow contributions which will add value to your technical profile. Technical competitions like hackathons/ideathons/makeathons will upskill your innovative ideas along with the required life skills.

Globally, today we are in a challenging situation. All, irrespective of the sectors, are working on the revamp strategy to balance the economy post Covid-19. AI will endeavour to revive the profitability and development of industry . New and advanced opportunities are expected to open up.

AI is and will be driving a promising future in the new normal. It will be the main driver for emerging and new technologies. So, take an interdisciplinary approach to hone your skills in an ever-evolving field. Think big, start small, act fast.

The post The smart role of Artificial Intelligence in today’s world appeared first on Artificial Intelligence.

In 2013, Google started a pioneering and ostentatious effort to manufacture robots. With time, its target has become self-effacing but with time the technology is also subtly more advanced. Since 2013, the internet company has depleted tens of millions of dollars in buying six robotics start-ups in Japan and in the United States. The project consisted of two teams that are majoring in machines that moved and looked like humans. Andy Rubin, the Vice President of engineering who was behind the effort of creating such projects named it Replicant in a nod to Google’s great ambition. Also, the name was used in a science-fiction movie called ‘Blade Runner’.

However, with the passing time over the next few years, Google sold off the companies that have been acquired by it or shut them down. The Japanese conglomerate SoftBank bought the best-known project named Boston Dynamics from Google.

In recent years, with the advancement of modern technology, Google accumulated and reassessed its target on the mechanics of complex robots. For the last few years, Google has been remodeling its program focusing on robots that are much more manageable and simpler than human-shaped machines.

Vincent Vanhoucke, who previously helped Google in building Google Brain that researches artificial intelligence is now leading the new robotics at Google. This new effort is called robotics at Google. It includes many of the engineers and researchers who worked under Mr. Rubin. Its new model will be able to learn skills independently using machine learning without human intervention, like traversing in a warehouse that is filled with unexpected objects. The machines may not impressive or attractive as the earlier humanoid robots but this more advanced technology incorporated inside them gives them more perspective in the real world.

In the warehouse and on factory floors, Robots are already in use but they can only operate certain specific tasks only like turning screws or picking up objects. Taking the help of machine learning Google wants the machines with which it is working to learn on their own.

The post GOOGLE MADE A COME BACK IN THE WORLD OF ROBOTICS appeared first on Artificial Intelligence.

Data science professionals, check out these jobs by industry giants

The demand for data science professionals was huge in 2020, and it is only growing in 2021.

While industry giants have embraced the capabilities of data science, small and medium-sized businesses have started to leverage the power of data science to improve their business processes. Owing to digital transformation, data science practices help businesses make intelligent and data-backed smart decisions, formulate strategies to make better products and solutions, manage large amounts of data efficiently to get valuable insights and predict outcomes. To utilize most of these features to increase ROI, organizations are rapidly hiring data science professionals.

If you are a data scientist or analyst, check out these hottest job openings from companies around the globe and look for your next challenge.

1. Data Analyst, Revenue Intelligence

Company: Twitter

Location: San Francisco, USA

Responsibilities

• Develop strong collaborative relationships with key stakeholders in Sales, Finance, Operations, Engineering, IT, and across the company.
• Partner with Product, Engineering, IT, and Operations teams to design, develop & deliver robust and scalable reporting automation.
• Define data systems and tools with product and engineering to produce revenue dashboards and other business intelligence reports.
• Provide daily revenue insights to the CFO and executive team.
• Develop scalable reporting solutions for non-finance stakeholders.
• Simplify complex problems & huge data sets to extract non-obvious insights.
• Be part of a great team, but capable of operating independently – managing relationships, deliverables, and expectations with your business partners.
• Be personable, approachable, and able to connect with each and every person on the team and throughout the business.
• Remain detail-oriented while working across a wide variety of projects.

Qualifications

• Bachelor’s degree required, preferably in Mathematics, Computer Science, Statistics or a Business-related degree.
• At least 4 years of work experience.
• Advanced experience with SQL and pulling data from relational databases.
• Tableau/front end design for Business Intelligence.
• R or Python is a strong plus.
• Superior Excel and proficient PowerPoint skills.
• Web design and development and/or experience in Salesforce is beneficial.
• Demonstrated ability to work collaboratively across different functions and effectively influence senior business partners.
• Possess a demonstrable grasp of Twitter, mobile (i.e., device and carrier markets), web, and media markets.
• Exhibit an enthusiastic, can-do spirit and approach your work with curiosity, resilience, and a growth mindset in a fast-paced, dynamic, and often ambiguous work environment.
• Have a demonstrated commitment to ongoing personal growth and development.
• Passionate about technology and relish the opportunity to learn and apply new technical concepts to your work.
• An expert in SQL
• Demonstrated ability to work collaboratively across different functions and effectively influence senior business partners.
• Strong experience with Database Management systems and coding (Vertica, MySQL, Oracle, Python, Scala, Java) who enjoys solving complex and unstructured problems.
• Deep experience in Demonstrated ability to simplify complex data sets and extract non-obvious insights with frameworks and analyses.
• Strong ability to project manage and to remain detail-oriented while working with numerous stakeholders.
• Knowledge of the digital ad space is a strong plus.

2. Product Insights Analyst

Company: Apple

Location: Cupertino, USA

Job Summary

The Product Insights Analyst is responsible for measuring and reporting on the hardware quality of Apple’s products. This is an exciting opportunity to directly impact Apple’s customers’ experience and support Apple products throughout their life cycle.

Responsibilities

• A key resource to cross-functional teams.
• Analyze, develop and monitor visualizations and alerting mechanisms to detect quality excursions.
• Capability to handle multiple projects simultaneously and set priorities accordingly.
• Participate in feedback loops to continuously improve product quality and the customer experience.
• Articulate findings in the data and work closely with others to identify the root cause and implement corrective actions on products.

Qualifications

• 5 years of experience in manufacturing or customer support analysis.
• High proficiency experience with Tableau visualization, including large-scale dashboard deployment and maintenance.
• High proficiency with SQL and relational databases such as Teradata.
• Knowledge of coding/scripting in Python/Java.
• API data access/pipeline development.
• Data Science / ML experience.
• Knowledge of FileMaker.
• Knowledge of SAP material master, Bill of Materials a plus.
• Solid communication skills
• Expertise in converting business problems into technical solutions.
• Attention to detail.

3. Data Analyst, Quality Systems and Data Science

Company: Tesla

Location: Fremont, USA

Job Summary

Tesla’s Quality Systems and Analytics team is looking for a key player on the team who can help drive quality data analytics and help cross-functional engineering organizations to provide opportunities in product quality improvements. The candidate should have experience working with large data sets, finding the best ways to engineer the data to help create critical KPI metrics, building innovative visualizations and dashboards all the while keeping in mind what improvements can be driven in underlying data systems.

Responsibilities

• Analyze manufacturing, equipment, and vehicle data and extract useful statistics and insights about failures in order to drive meaningful improvements to product quality and customer experience.
• Work effectively with engineers and conducting end-to-end analyses, from data requirement gathering to data processing and modeling.
• Interpret data, analyze results using statistical techniques and provide ongoing reports
• Identify data sources where the potential value is not fully realized and invent new means with which to interact and gather insights from them.
• Monitoring key product metrics, understanding root causes of changes in metrics
• Identify, analyze, and interpret trends or patterns in complex data sets and depict the story via dashboards and reports.
• Acquire data from primary or secondary data sources and maintain databases/data systems to empower operational and exploratory analysis.
• Maintaining existing data visualizations, data pipelines, and dashboard enhancement requests.
• Automating analyses and authoring pipelines using SQL, Python, Airflow, Kubernetes based ETL framework.
• Develop and maintain real-time data streaming and processing pipelines using Kafka.
• Drive underlying data systems improvement by working with key cross-functional stakeholders.
• Perform data quality validations to ensure data creation is as per the business needs and expectations.
• Work with management to prioritize business and information needs.

Qualifications

• BS/MS in Management Information Systems, Computer Science, Math, Physics, Engineering, Statistics, or other technical fields.
• 3+ years of work experience in data analytics or engineering-related fields.
• Strong knowledge of SQL and experience with multiple data architecture paradigms (MySQL, Microsoft SQL, Vertica, Oracle, Kafka, Spark).
• Proficient at ad-hoc analysis using SQL queries, python data analysis packages (e.g.Pandas, Numpy), report writing, and presenting findings.
• Proficient in data visualization techniques and tools using Tableau, Power BI, Superset, Matplotlib, Plotly, etc.
• Knowledge of various data communication protocols (e.g. REST API, Websockets).
• Strong understanding of various statistical techniques to effectively summarize data findings.
• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Strong knowledge of data warehousing concepts, data mining tools, and techniques
• Knowledge of manufacturing and service operations preferred.
• Able to work under pressure while collaborating and managing competing demands with tight deadlines.
• Excellent communication skills and ability to work with different business stakeholders to understand, identify and translate business challenges into data projects.
• A passion and curiosity for data and data-driven decision-making.

4. Data Scientist

Company: TikTok

Location: Singapore

Responsibilities

• Design and build advanced analytical solutions and products to deliver superior business outcomes using research and performing quantitative and qualitative analysis on markets, products and competitors.
• Prove the business impact of projects using methods like experimental designs, cohort matching, and causal inference.
• Understand underlying data sources and their limitations. Create innovative approaches to answer pressing questions, prepare complex data analyses and models that help solve issues, drive the scaling of automated processes and deliver significant measurable impact.
• Act as an analytics translator, communicating complex data insights through exploratory analysis and research to discover potential bottlenecks to suggest improvement and workflow of internal teams.
• Liaise with team members and management in understanding their data needs and providing data support.

Qualifications

• 2-7 years of hands-on mathematical modeling experience in a business environment.
• Proficiency in Python packages such as pandas, seaborn, scikit-learn, dplyr or nltk
• Advanced knowledge of statistical and data mining techniques (k-means clustering, regression, decision trees, clustering, neural networks, etc.).
• Proven experience in working with large datasets and relational databases (Hive, SQL)
• Ph.D. in quantitative fields like Computer Science, Engineering, Statistics, Mathematics, or related field required.
• Distinctive communications skills and ability to communicate analytical and technical content in an easy-to-understand way to both technical and non-technical audiences.
• Intellectual curiosity, along with excellent problem-solving and quantitative skills, including the ability to disaggregate issues, identify root causes, and recommend solutions.

5. Data Scientist (Deep Learning)

Company: Binance

Location: Singapore

Job Summary

As a data scientist, you must be a quick learner with stellar machine learning skills, rich and solid experience from inception to business impact, strong communication capabilities, and work well in cross-functional teams (e.g. with data users from different marketing and consumer teams). This is a great opportunity to enhance your machine learning /data science skills in markets and familiarize yourself with Blockchain-based technology and etc. while making a real impact on Blockchain and Crypto-Currency technology globally.

Responsibilities

• Conduct applied research and development in AI applications that involve computer vision, natural language processing, speech processing, and other related techniques.
• Work closely with other team members on implementing algorithms that power user-focused products.
• Build and release models, and elevate customer experience and track impact over time.

Qualifications

• MSc or Ph.D. in Machine Learning, Computer Vision, Computer Science, or Applied Mathematics with at least 5 years of relevant industry experience.
• Excellent hands-on experience with popular ML frameworks such as PyTorch or TensorFlow.
• Fluent in English both written and spoken.
• Experience with application development practices at scale.

Preferred Qualifications

• Good experiences in Computer Vision particularly on OCR and text extract with deep learning-based end-to-end solutions.
• Good global team collaboration skills and experiences are good add-ons as well.
• Publication record at top conferences and journals is a plus.

6. Data Scientist (Car Experience)

Company: Spotify

Location: Stockholm, Sweden

Responsibilities

• Co-operate with cross-functional teams of data scientists, user researchers, product managers, designers, and engineers who are passionate about Spotify’s consumer experience.
• Design, implement, and maintain ETL pipelines in Scala and Google BigQuery.
• Perform analysis on large sets of data to extract impactful insights on user behavior that will help drive product, design decisions, and business performance.
• Communicate insights and recommendations to stakeholders across Spotify.
• Be a key partner in its work to build out the company’s product strategy so that it is relevant in the daily lives of consumers.
• You will work out of the Stockholm office.

Qualifications

• At least 3+ years relevant experience and a degree in computer science, engineering, statistics, mathematics, economics, or another quantitative subject area.
• Strong interpersonal skills and are comfortable working with multiple stakeholders
• Start-up mindset, comfortable with juggling multiple tasks at once. Prepared to tackle problems outside of one’s comfort zone.
• Know how to understand and tackle loosely defined problems and come up with relevant answers and impactful insights.
• Strong SQL skills and proficiency with Python, or similar programming languages, even better if experienced in Scala / Spark.
• Extensive experience using various analysis techniques, such as linear and logistic regression, significance testing, and statistical modeling.
• T-Shaped. Your primary area is data science but you are comfortable with the second area such as data engineering.
• Bonus point if experienced with Google Cloud Platform infrastructure.

7. Analyst, Creative Labs – EMEA

Company: Netflix

Location: Amsterdam, Netherlands

Responsibilities

• Leading and attending cross-functional meetings with partners to understand ongoing and upcoming product business objectives and prioritizing creative questions and ideas according to those business objectives (emerging markets, automation-assisted asset creation for scale, etc.).
• Prioritizing business objectives and ideas from key stakeholders to develop creative curiosities into testing hypotheses and strategies.
• Drafting and driving the execution of specific qualitative and quantitative analyses on the various creative assets.
• Collaborating with data science partners on metric, tooling, and dashboard development is crucial to providing feedback to creative design strategies.
• Partnering with product innovation partners to inform and ensure optimal execution of tests and related initiatives.
• Identifying opportunities specific to your market while staying highly aligned with your core Labs team and their objectives.
• Constantly documenting and communicating upcoming tests and changes in strategies and operations as they relate to the creative assets.
• Consistently rethinking and improving the ways by which we collect ideas, aggregate insights, and communicate those insights broadly.
• Addressing questions and issues related to tests and results surrounding creative assets from both engineering and creative producer partners.

Qualifications

• 6+ years of relevant work experience as an analyst, product manager, data scientist or creative program manager.
• Experience with A/B testing, data analysis, and ideation of new product features and processes.
• Ability to effectively distill and communicate complex concepts in a digestible manner.
• The soft skills to recognize when to challenge conventional thinking, the courage to voice suggestions and the maturity to accept feedback on ideas and work.
• Understanding of and passion for algorithms and machine learning.
• Demonstrated data fluency; comfortable with large datasets, constructing queries, designing data visualizations and extracting meaning from your analysis.
• Bachelor’s degree or higher.

8. Data Scientist

Company: Warner Bros. Entertainment

Location: Hamburg, Germany

Job Summary

In this role, you should be highly analytical with a talent for analysis, math, and statistics. Critical thinking and problem-solving skills are of course essential for interpreting and explaining data to normal human beings. You will be responsible for developing and building innovative data science solutions that utilize machine learning algorithms, statistical and quantitative modeling approaches to support content creation, marketing, distribution strategies, and other initiatives.

Responsibilities

• Building a data infrastructure in Amazon Web Services to ensure a holistic view of the data ecosystem.
• Responsible for reporting in media, programmatic & social.
• Develops data clusters for programmatic audience marketing & responsible for data segmentation.
• The Data Scientist will work in a team to get the best out of data for our business, find unseen patterns, and derive meaningful information for business decisions.
• Analyzing, compiling, cleansing, interpreting, joining, and staging data.
• Partner with content analytics and engineering teams to develop tools and foundational data sets for consumption from technical and non-technical stakeholders.
• Delivering business intelligence and data visualization dashboard solutions.
• The candidate should be a SQL expert; must be able to optimize queries and join datasets efficiently.
• Proven track record of performing comprehensive analyses independently.
• Synthesize large volumes of data and complex concepts into clear accessible learnings.
• Prepare and present data science results and analytical insights to stakeholders and leadership.

Qualifications

• Ability to swiftly gain competence with a range of custom management systems and tools (Business Intelligence, Data Dashboards, SAP).
• Experienced in DMP data segmentation.
• Experienced with Ad Words, FB Business Manager, YouTube Insights, Doubleclick, Sizmek Backends, Amazon Web Service infrastructure.
• Has a good understanding of technical web implementation (Pixel Code, HTML)
• Experienced with Python.
• Excellent written and oral German and English communication skills required.
• High tolerance for ambiguity and a fast-changing environment.
• Detail-oriented and cares about the quality of the input data as well as how the processed data is ultimately interpreted and used.
• Strong communicator, for both technical and non-technical audiences
• Excited to work in the entertainment industry.

9. Data Scientist

Company: McKinsey & Company

Location: Lisbon, Portugal

Job Summary

You will be involved in codifying the company’s best analytics expertise into a centralized library and methodology and delivering that library to clients.

In this role, you will deploy your analytics expertise into production, measuring and delivering tangible insights and value for the clients leveraging data science methodologies. You will create innovative approaches to answer McKinsey & Company’s clients’ most relevant questions around commercial topics and ensure that these approaches are codified and can be re-used. You will act as an analytics translator and as an owner of advanced pieces of analytics code. You will have a strong understanding of the design and development process of data analytics tools and experience working with data engineers, product teams, and clients. You will also help build the teams by collaborating with others across the practice and beyond and mentoring younger tenured colleagues.

Qualifications

• Bachelor’s degree in a quantitative field like computer science, engineering, statistics, mathematics, or related field; advanced degree is a strong plus.
• 2+ years of hands-on data scientist mathematical modeling experience in a business environment or equivalent.
• Proficiency in Python, R.
• Advanced knowledge of statistical and data mining techniques (regression, decision trees, clustering, neural networks, etc.)
• Proven experience in working with large datasets and relational databases (SQL)
• Knowledge of additional programming languages is a plus (C++, Java).
• Intellectual curiosity along with excellent problem-solving and quantitative skills including the ability to disaggregate issues, identify root causes, and recommend solutions.
• Strong leadership skills; ability to inspire others, build strong relationships and create true followership.
• Result-driven, demonstrating a professional attitude.
• Strong people/team orientation skills.
• Distinctive communications skills; ability to communicate analytical and technical content in a comprehensible way.

10. Data and Insights Analyst

Company: Procter & Gamble

Location: Sydner, Australia

Responsibilities

• Lead analytical projects and be accountable for delivering business value.
• Lead creation and execution of analytic action plans that address top-priority business challenges and create valuable insights.
• Develop in-depth business and analytical knowledge for building relevant solutions and services – spanning retail analytics, eCommerce, media analytics, and others.
• Consult and coach business teams on appropriate uses of data, analytical solutions, and tools.
• Work in multi-functional teams to evaluate business activities.
• Work with a diverse group of people within the P&G organization and the global analytics community as well as with external business partners.

Qualifications

• Undergraduate or Masters’s Degree level qualifications. Preferred (but not a requirement) disciplines include Business Analytics, Business Studies/MBA, Economics, Statistics, Engineering.
• Min 2 years work experience in a business-facing role, preferably within FMCG or similar industries.
• Track record of solving tough business problems, all the way from understanding a business question to communicating and actioning the solution, leveraging multiple, complex data sources in the process.
• Work experience mining vast amounts of data using statistical methods or machine learning models to provide actionable insights.
• Advanced knowledge of analytical tools. Work experience or certification in SQL and Python or R.
• Excellent written and verbal communication skills to influence others to take action
• Comfortable handling multiple priorities, strong problem-solving skills, and a “can-do” attitude.
• Business background and interest and technical/analytical skills are preferred.

All the above information is taken from credible sources. For more information about privileges and perks, head to the company’s official websites to read and apply.

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With technology new and old, instructors try to recreate the interactivity of their pre-Covid classroom.

Deep learning is advancing at lightning speed, and Alexander Amini ’17 and Ava Soleimany ’16 want to make sure they have your attention as they dive deep on the math behind the algorithms and the ways that deep learning is transforming daily life.

Last year, their blockbuster course, 6.S191 (Introduction to Deep Learning) opened with a fake video welcome from former President Barack Obama. This year, the pair delivered their lectures “live” from Stata Center — after taping them weeks in advance from their kitchen, outfitted for the occasion with studio lights, a podium, and a green screen for projecting the blackboard in Kirsch Auditorium on their Zoom backgrounds.

“It’s hard for students to stay engaged when they’re looking at a static image of an instructor,” says Amini. “We wanted to recreate the dynamic of a real classroom.”

Amini is a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS), and Soleimany a graduate student at MIT and Harvard University. They co-developed 6.S191’s curriculum and have taught it during MIT’s Independent Activities Period (IAP) for four of the last five years. Their lectures and software labs are updated each year, but this year’s pandemic edition posed a special challenge. They responded with a mix of low- and high-tech solutions, from filming the lectures in advance to holding help sessions on a Minecraft-like platform that mimics the feel of socializing in person.

Some students realized the lectures weren’t live after noticing clues like the abrupt wardrobe change as the instructors shifted from lecture mode to the help session immediately after class. Those who caught on congratulated the pair in their course evaluations. Those who didn’t reacted with amazement. “You mean they weren’t livestreamed?” asked PhD student Nada Tarkhan, after a long pause. “It absolutely felt like one instructor was giving the lecture, while the other was answering questions in the chat box.”

The growing popularity of 6.S191 — both as a for-credit class at MIT, and a self-paced course online — mirrors the rise of deep neural networks for everything from language translation to facial recognition. In a series of clear and engaging lectures, Amini and Soleimany cover the technical foundations of deep nets, and how the algorithms pick out patterns in reams of data to make predictions. They also explore deep learning’s myriad applications, and how students can evaluate a model’s predictions for accuracy and bias.

Responding to student feedback, Amini and Soleimany this year extended the course from one week to two, giving students more time to absorb the material and put together final projects. They also added two new lectures: one on uncertainty estimation, the other on algorithmic bias and fairness. By moving the class online, they were also able to admit an extra 200 students who would have been turned away by Kirsch Auditorium’s 350-seat limit.

To make it easier for students to connect with teaching assistants and each other, Amini and Soleimany introduced Gather.Town, a platform they discovered at a machine learning conference this past fall. Students moved their avatars about in the virtual 6.S191auditorium to ask homework questions, or find collaborators and troubleshoot problems tied to their final projects.

Students gave the course high marks for its breadth and organization. “I knew the buzzwords like reinforcement learning and RNNs, but I never really grasped the details, like creating parameters in TensorFlow and setting activation functions,” says sophomore Claire Dong. “I came out of the class clearer and more energized about the field.”

This year, 50 teams presented final projects, twice as many as the year before, and they covered an even broader range of applications, say Amini and Soleimany, from trading cryptocurrencies to predicting forest fires to simulating protein folding in a cell.

“The extra week really helped them craft their idea, create some of it, code it up, and put together the pieces into a presentation,” says Amini.

“They were just brilliant,” adds Soleimany. “The quality and organization of their ideas, the talks.”

Four projects were picked for prizes.

The first was a proposal for classifying brain signals to differentiate right hand movements from the left. Before transferring to MIT from Miami-Dade Community College, Nelson Hidalgo had worked on brain-computer interfaces to help people with paralysis regain control of their limbs. For his final project, Hidalgo, a sophomore in EECS, used EEG brain wave recordings to build a model for sorting the signals of someone attempting to move their right hand and left.

His neural network architecture featured a combined convolutional and recurrent neural net working in parallel to extract sequential and spatial patterns in the data. The result was a model that improved on other methods for predicting the brain’s intention to move either hand, he says. “A more accurate classifier could really make this technology accessible to patients on a daily basis.”

A second project explored the potential of AI-based forestry.Tree planting has become a popular way for companies to offset their carbon emissions, but tracking how much carbon dioxide those trees actually absorb is still an inexact science. Peter McHale, a master’s student at MIT Sloan School of Management, proposed that his recently launched startup, Gaia AI, could fly drones over forests to take detailed images of the canopy above and below.

Those high-resolution pictures could help forest managers better estimate tree growth, he says, and calculate how much carbon they’ve soaked up from the air. The footage could also provide clues about what kinds of trees grow best in certain climates and conditions. “Drones can take measurements more cheaply and accurately than humans can,” he says.

Under Gaia AI’s first phase of development, McHale says he plans to focus on selling high-quality, drone-gathered sensor data to timber companies in need of cheaper, more accurate surveying methods, as well as companies providing third-party validation for carbon offsets. In phase two, McHale envisions turning those data, and the profits they generate, to attack climate change through drone-based tree-planting.

A third project explored the state of the art for encoding intelligent behavior into robots. As a SuperUROP in Professor Sangbae Kim’s lab, Savva Morozov works with the mini cheetah and is interested in figuring out ways that robots like it might learn how to learn.

For his project, Morozov, a junior in the Department of Aeronautics and Astronautics, presented a scenario: a mini cheetah-like robot is struggling to scale a pile of rubble. It spots a wooden plank that could be picked up with its robotic arm and turned into a ramp. But it has neither the imagination nor repertoire of skills to build a tool to reach the summit. Morozov explained how different learning-to-learn methods could help to solve the problem.

A fourth project proposed the use of deep learning to make it easier to analyze street-view images of buildings to model an entire city’s energy consumption. An algorithm developed by MIT’s Sustainable Design Lab and PhD student Jakub Szczesniak estimates the window-to-wall ratio for a building based on details captured in the photo, but processing the image requires a lot of tedious work at the front end.

Nada Tarkhan, a PhD student in the School of Architecture and Planning, proposed adding an image-processing convolutional neural net to the workflow to make the analysis faster and more reliable. “We hope it can help us gather more accurate data to understand building features in our cities — the façade characteristics, materials, and wall-to-window ratios,” she says. “The ultimate goal is to improve our understanding of how buildings perform citywide.”

Based on student feedback, Amini and Soleimany say they plan to keep the added focus on uncertainty and bias while pushing the course into new areas. “We love hearing that students were inspired to take further AI/ML classes after taking 6.S191,” says Soleimany. “We hope to continue innovating to keep the course relevant.”

Funding for the class was provided by Ernst & Young, Google, the MIT-IBM Watson AI Lab, and NVIDIA.

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Today, in this modern era, there is absolutely no shortage in the implementation of data science to address real-world issues. Industries like healthcare, education, banking and finance, e-commerce to name a few make use of data science extensively. The fact that data is enormous and is increasing exponentially calls for a better implementation of data science.

What are the applications of data science in the real world?

Not one, not two but hundreds and thousands of fields make use of data science in ways beyond imagination. Had it not been for data science, the technological world wouldn’t have enjoyed the ease and comfort that we get to see today. Here are a few of the many applications that have changed everything for the better and made life easier than ever.

Medical industry/healthcare sector

This is surely one of those sectors that one cannot do without. This sector has seen probably the best applications of data science. Developing a drug is not as easy as it sounds. It not only involves deep knowledge but also a lot of patience and time in bringing the drug to the market. Sometimes, a single drug might require over 10 years to be developed. But data science in combination with Machine learning can aid in shortening the entire process. Not just this, with Data Science applications, one can also enable an advanced level of treatment through research in genetics and genomics. Integration of different kinds of data with genomic data in disease research is possible using data science. This further implies deeper understanding of genetic issues in reactions to particular drugs and diseases.

These are just two areas that make use of data science on a regular basis. But, what it is to be noted is that the use of data science in the medical industry is endless and can address a lot of issues with great precision.

Search engines

All the search engines like Google, Yahoo, Bing, etc. make use of data science algorithms to deliver the best result for the searched query and that too in a fraction of seconds.

Recommendations

E-commerce industry has been ruling the world for quite some time now. Also, while looking for a particular product, the list of suggestions, recommendations that follow cannot be unnoticed. This is exactly where data science comes into play. This happens in case of OTT platforms as well. Suggestions and recommendations based on the users history is a common scenario these days. Giants like Amazon, Twitter, Google Play, Netflix, Linkedin, etc. all implement data science.

Autocorrect and autocomplete

The number of people using smartphones needs no special mention. While typing anything, it’s quite a common scenario to witness the word getting corrected automatically. Also, while typing something, the rest of the word or sentence is completed automatically. These predictive search techniques employ the usage of data science that thereby help in saving the user’s time.

Virtual assistants

No wonder, the extent to which people are using virtual assistants like Siri, Alexa, etc. is way beyond what was expected. These assistants make use of speech recognition techniques to perform tasks like sending messages, browsing web, playing music, making calls, etc. There cannot be a better way to proceed with this than by employing data science techniques.

Manufacturing industries

The main objective of any business is to cater to the needs of the customers and also make profits. Similar is the case of manufacturing industries. But, what has served to be a game-changer for them is the advent of data science. Firms are making use of data science to optimize their production, reducing costs and boosting profits. It also allows them to predict potential problems, monitor systems and also analyze data.

Finance

The fact that the risks which banks and financial institutions have to deal with is alarming. Using data science, it is possible to identify, monitor as well as prioritize the risks.

In addition to the above applications, there are so many areas that can be dealt with easily using data science. Simply put, the list is endless when it comes to catering areas using data science. The potential that this technology has is immense and is known to address a lot of real-life problems as well.

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Artificial intelligence can drive not only India’s economy but also the world economy

India is not only the world’s most populated nation, but also the youngest country in the world. The country is currently in the developing phase. But it has garnered global interest in terms of the fastest developing nation and digital adoption. This is largely because India has the 3rd largest startup ecosystem in the world, according to Startup India. And every year the number of startups in the country is snowballing. Like other countries, India is also making aggressive efforts to achieve the AI-led digital economy, with its “AIforAll” strategy.

The potential of artificial intelligence is not clandestine for today’s enterprises. It frees up organizations from expensive resources for higher-level tasks. AI reduces the time taken to perform a task, enabling multitasking and easing the workload for existing resources. It facilitates decision-making by making the process faster and smarter. It has mass-market potential and can be deployed in any business scenario, for any work, and in every industry.

With keeping such capabilities in mind, countries across the world have built their AI strategies. “AIforAll” aims at enhancing and empowering human capabilities to address the challenges of accessibility, affordability, scarcity and inconsistency of skilled talent. It is designed for the effective implementation of artificial intelligence initiatives to develop scalable solutions for emerging economies.

As artificial intelligence refers to a machine that mimics human behaviour and ability, it is being used in manufacturing, healthcare, agriculture, education and skilling.

AI Startup Ecosystem in Indian Economy

The IT and ITeS services segment is critical to India’s economy. In FY2020, the domestic revenue of the IT industry was estimated at US$44 billion and export revenue was at US$147 billion. The IT-BPM industry’s revenue was estimated at around US$191 billion, growing at 7.7% year over year. It is expected to reach US$350 billion by 2025. On the other side, revenue from the digital segment is expected to grow by 38% of the total industry revenue by 2025, to reach US$1 trillion (INR 69,89,000 crore) by 2025.

In the adoption of artificial intelligence technology, India is making good progress. A report from Accenture suggests that this technology can likely add US$957 billion, or 15% of India’s current gross value in 2035. This progress seems achievable as the country’s AI strategy emphasizes harnessing collaborations and partnerships, and aspires to ensure prosperity for all.

While India has been ranked second on the Stanford AI Vibrancy Index primarily for its large AI-trained workforce, leading technology institutes like the IITs, IIITs and NITs have the potential to be the cradle of AI researchers and startups. With India’s push for digitization and AI initiatives, private firms now are racing to win big contracts by spending huge capital to develop new technologies and spinning out new AI and data science-based startups.

Today, the country is on the way to be a major contributor to AI-powered solutions that address issues not only in India but around the world. For instance, HyperVerge, a B2B SaaS company, builds AI models for real-time image and video analysis. Its deep learning networks power applications for large businesses in financial services, telecom, energy, security and defense. Similarly, India’s eCommerce giant Flipkart is powering its decisions in ordering, distribution and product pricing on its platform using AI.

For big techies and firms, artificial intelligence can foster growth and profitability while transforming businesses. It can help drive innovation for entrepreneurs and young companies while improving public safety and advancing the quality of lives in society as a whole.

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Before we talk about how big data is changing the world, it’s important to understand exactly what it means. In simple words, ‘Big Data’ refers to huge volumes of data. It can pertain to business activities, financial information, customer data, or anything. 

By MIKE KHOREV

The data can be unstructured as well as structured. On its own, it may not mean much. It’s what you do with the data that matters. Successful analyses of such data can offer great insights that can help with strategic and smart business moves.

Traditional data software cannot process big data. However, with emerging lightning-fast computing technologies, it’s possible to collect, process, and analyze tons of data in no time. In this post by SPOPLI Web Development & Services, we look at how Big Data has impacted the world in huge ways.

The Impact of Big Data Across Different Sectors

Government Planning & Census

Governments take all their important decisions based on the data collected from population census. The data helps them understand what people need, their preferences, and their wants. 

When the general public fills out a census form, it helps the government understand income levels, how much, on an average, each family member earns, and how many people need financial support from the government. 

Entertainment

The entertainment industry is big on using Big Data analytics. For example, it can collect data of the previous song or movie release. Based on the conclusions derived, it can predict/determine whether or not the upcoming release will be a hit. 

The data can further help understand entertainment companies the number of downloads a song has received and how much they can earn from CD/DVD sales based on historical data.

Medicine

Medicine is another crucial field where big data holds immense importance as there is always a need for drug improvement. To make regular developments possible, the medical industry collects data of previous medicine. It then looks at parameters such as reaction time on a person and its effect on a certain virus. 

Medicines need meticulous research and detailing of huge data. Based on the mining of large sets of data and research, experts can develop better medicines and find cures for diseases.

Sports

In sports, Big Data helps to understand the strengths and weaknesses of each player. It helps to better strategize the gameplay. In big leagues, data helps decision-makers pick the best players during team selections.

Technology

Technology is always in need of data mining. It is used for myriad purposes such as advancing devices and understanding old algorithms to refine them. Big Data holds immense value for IoT that relies on the collection of real-time data. 

AI (artificial intelligence) that uses machine learning also processes big data gathered from users’ mobile devices. Every time you look for a product or perform a Google search, that data is gathered by companies. 

Those companies then process the data to better understand customer usage patterns and their buying habits. it enables them to serve customers better by recommending relevant search data and results.

Impact of Big Data on the Financial Sector

Customer Service – One thing that the financial sector lacks is an understanding of its customers better. Knowing what the customers want and their pain-points is the key to devising better advertising strategies. 

Big Data Analytics has made it possible for the finance sector to understand evolving customer expectations better. With this understanding, they can serve their customer better by enhancing the overall customer experience.

Fraud Detection and Risk Management – This is one of the most useful implications of big data. It helps financial companies identify the potential for fraud so they can implement security measures in advance. With the help of a data-driven setup, companies can predict and prevent fraud. 

Better employee engagement – By deploying data-driven analytics, companies better enhance their work performance and create better employee engagement programs. HR managers can identify the best work performances and create strategies that improve the success ratio of employees across the organization.

How Big Data is a Game Changer for Marketers?

Big Data has become a game-changer for businesses as it’s emerging as a tool of great importance across all industries. From a small firm to MNCs, companies are using big data to beat the competition and secure an edge over others.

Below are a few ways in which big data helps marketers and companies:

Confident decision making – Companies thrive on the ability to make quick decisions to act them out quickly. Responding to operational changes and trends is essential for businesses to grow. But, making quick and healthy decisions requires analytics. That’s where Big Data supports companies in a big way. By offering insight based on a huge collection of data, companies can make informed decisions.

Cost Reduction – Businesses are always looking for ways to cut down unnecessary costs. By evaluating things like the effectiveness of staff, their working patterns, and energy usage, companies can identify areas where they can make cost-savings without negatively impacting the other business operations.

Better customer engagement – Every time a customer makes an online search query or looks at a product catalog, all that information is stored with companies. By analyzing that data, companies can serve their customers better. 

Taking their buying preferences, habits, and tendencies into account, companies can create a more custom experience. They can better serve customers by showing them exactly what they come to expect. It also helps with better targeting.

Helps to recognize new streams of revenue – By taking customer trends and expectations into account, Big Data Analytics can tell companies which business avenues are most likely to yield revenue. In this way, businesses can expand and make smart decisions that can pave the way for a profitable future.

Bottom Line

As you can see, Big Data analytics has changed the face of the business world, finance, and many other aspects of the world in lots of ways. And, it’s going to continue doing that.

The post Why Big Data Has Become A Big Game Charger In Today’s World appeared first on Artificial Intelligence.

“We want to expand what Vodafone stands for in consumers; minds,” says Pamela Brown, chief marketing officer, Vodafone Smart Tech. “It’s a significant step because we’re traditionally a mobile phone company.”

Despite years spent dabbling in the Internet of Things (IoT) sector, she admits there is a lot of ground to make up in brand perception. “I’d love this to have higher awareness than it does today. Analysts talk about these massive numbers like there will be 41bn smart devices by 2027. This is significant growth, and I believe Vodafone has a role to play in it.”

Last week, Vodafone introduced the first of its ‘Designed & Connected by Vodafone’ range of smart tech products – Curve, a multi-tracking device that uses GPS, Wi-Fi, Cellular and Bluetooth to help its users to track inanimate valuables such as keys, laptops, bags, as well as keeping connected to loved ones or pets.

It’s the kind of brand extension Vodafone has been gravitating towards for some time.

The company first hit the telecoms scene in the early 90s, as a mobile network provider, and with the proliferation of the internet, it soon got into broadband. Like its mobile competitor EE, Vodafone recognises ‘connectivity’ as its selling point. However, while EE was busy with 5G deals at the Baftas or its sponsorship of Wembley stadium, Vodafone was diving into IoT.

“From the B2B perspective, we’ve been offering smart IoT connections for over 10 years. We have 15 million connections on that side,” shares Brown. And then in 2017, it entered the consumer market with the launch of ‘V by Vodafone’ – billed as a simple system for consumers to connect and manage IoT devices, alongside a product range that included a car dongle, a 4G security camera and a pet location and activity tracker.

The problem is that the term IoT belongs more in a B2B book of buzzwords than in the consumer vocabulary. “Consumers don’t know what IoT is,” Brown says. “But they do know what smart tech is. We learnt a lot with the V by Vodafone range. The category is more embedded in consumers’ mindsets, especially with the explosion of smart speakers.”

Brought on more than two years ago from the team that built Hive, British Gas’ smart home offering, Brown knows a thing or two about changing consumer habits and how to convince people to get on board. She has since pulled the brand out of the ‘IoT space’, as such, and hired a creative director, created an in-house studio and given the brand a full refurb that both aligns with Vodafone’s distinctive red, but also distinguishes itself apart from that side of the business.

“When you think of Vodafone, you probably think of a very red brand. Whereas the new visual identity is a more darker colour palette. We’re trying to bring to life more of the desirability credentials of the device in the imagery and the marketing assets that we create. So we’re using red in a more subtle way.”

The first to be introduced this year, Curve will be followed by two more products which will utilise the same functionalities.

Beyond that, where does Brown expect Vodafone to be in five years’ time? “I hope these products will become every day to people. So whether that’s in-home devices or out of home, we want to use connectivity to help protect people and things that they love.”

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