Management - Finance, Tech & Analytics Career Resources

Which Career Is More Promising: Data Scientist or Software Developer?

Posted on July 11, 2021January 23, 2024 by Imarticus Learning

As per a study, about 2.5 quintillion bytes of data are produced every day. The number is about to increase by many folds in the future. The pace at which data is being generated demands more data science professionals. That’s the reason data science jobs are now regarded as one of the highest-paid jobs across the globe. And, people are joining data science training to explore a career in data.

As, firms are hiring employees who can build technical applications, and collect, analyse and predict data. That’s the reason students are enroling in data science training courses that are best and give job assurance.

Companies are keen to employ data scientists, data analysts, web developers, full-stack developers, and much more technology professionals. According to Imarticus Learning and Analytics India Magazine’s research, there will be a 30.1% increase in data science and analytics jobs in 2022 compared to 2021.

However, more competition in Information Technology requires software career enthusiasts to learn the latest technology for survival. The primary responsibility of data scientists is business analytics. They also work on constructing data and software products using algorithms. The demand for data scientists is high due to the shortage of skilled people for start-ups and corporates. Global companies are searching for professional data scientists to up their game in this competitive world.

Who is a data scientist?

Data scientists collect and interpret large amounts of structured and unstructured information. They analyse, process, and model data before interpreting the findings to develop actionable plans for businesses. The data scientist uses computer science, statistics, and mathematics for data interpretation.

Who is a software developer?

Software developers are the thinkers and doers behind computer programs of all kinds. While some software developers specialise in a single program or application development, others build massive networks or underlying systems that trigger and power other programs. As a result, there are two types of developers: applications software developers and systems software developers.

The approach of data scientists and software developers

To use and organise data in a structured or unstructured form, a data scientist becomes essential and can master the art with the right Data Science Training. Hence, there is a need for a data scientist, with sound knowledge of handling data that demands the best data science training and experience.

Software developers collaborate with computer programmers to test the convenience of using a software program. The component that powers digital systems is software which is the core element of computers. To be precise, software engineering can be termed as the application of engineering principles for designing, structuring, developing, and implementing software. An efficient software developer understands the client’s needs and then develops the software as a solution to the client’s needs.

Important tools used in software development and data science

To be productive, data science career aspirants should be knowledgeable about statistics, software programming, and analytical thinking. In addition, a data scientist should have the urge to learn and be updated about new programming languages. Python, Swift, and Ruby, apart from mastering SQL, Spark, machine learning, and Hadoop are the most important to learn.

A software developer should know the latest programming languages along with a bachelor’s degree in computer science. A good understanding of computer programming by understanding the functionality of new tools is a plus. Along with various skills, a software developer should possess to flourish in the field of analytical skills. Followed by attention to detail, creativity, problem-solving techniques, and interpersonal skills are vital.

The pay scale for data scientists versus software developers

A report from Payscale.com reveals that the median salary for a data scientist is around 6,00,000. It is not much different in the US. According to a survey by Glassdoor, the median salary for a data scientist is $91470, which is great. So, it is evident that data science can offer ample money and growth. But, the salary also depends on the qualifications, skills, experience, and location. Firms also play a vital role in deciding the pay.

Currently, the median salary for software developers is $1,00,80 and the growth in employing software developers is set to increase by 24% in a decade. In addition, an estimate states that there are 3.7 billion mobile users worldwide. It demands newer and better applications for smartphones and tablets. It increases the demand for software developers globally. Factors that affect the salary of a data scientist are similar to those of software developers.

Roles and job titles for a career in data science versus software development

A few important job titles that a data scientist can do are data architect, data scientist, and business analyst. In fact, the roles of data administrator, and business intelligence manager are also in demand. Global companies in the digital world, like Facebook, Twitter, IBM, and Apple, are racing to hire the best data scientists. So, the demand for data scientists is growing like never before.

Some of the interesting career options that connect to software developers are Computer programming. Computer system analysts, web developers, computer network architects, and computer hardware engineers are also on the list. Though data science is a relatively newer concept than software development, it is increasing rapidly.

Conclusion

This article has showcased the digitisation, technology, data rise, and demand of technology professionals. The high demand for data scientists, and software developers have given rise to the skills gaps. If you are eager to learn new tools for efficient data handling, you can grab a chance to flourish as a data scientist in the long run. Enrol for the best data science training course and enter the world of data.
But, selecting the best institute for data science training is not easy.

You should enquire about the faculty, course curriculum, and the job assistance that they provide. It is always good to get interview assistance, career monitoring, and resume-building tips from the experts. If the institute provides all this then it is the best for the learners.

It should be your priority to learn coding and programming if you want to become a software developer. Furthermore, the amount of data being produced every day is high, which signifies long-term growth and attractive salary await a talented data scientist. So, in the end, we can say that both careers in data science and software development are promising. It depends on individual preferences and interests. Both, career options are good and offer career growth.

For more details, you can also search for – Imarticus Learning. You can drop your query by filling up a form from the site. You can also contact us through the Live Chat Support system. To explore more you can visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, and Gurgaon.

Advanced Data Science Skills to Stay Relevant in the Post-Pandemic World!

Posted on July 9, 2021March 26, 2024 by Imarticus Learning

The need to upskill to meet the dynamic demands of a technology-first world has been around for the past few years; it has only become more urgent in the wake of the COVID19 pandemic. The emergence of new technologies such as Artificial Intelligence, machine learning and data science has set the tone for the future.

In the post-COVID19 world, there are a few advanced data science skills that, when added to the toolkit of a data scientist, can make or break their career.

To ensure that your core competencies are strengthened as a data scientist, you can sign up for a comprehensive data science training course that explores the following:

#1: Geospatial Technologies

With more people working on data-driven decision processes, geospatial data has helped better planning and processing of the system. This knowledge has proved invaluable in tracking the COVID19 outbreak all over the world; come the near future, geospatial technology will likely be extended to other research areas as well.

A geospatial data scientist will need to sift through vast geographic and demographic datasets that hide gold nuggets of insight across diverse research topics.

#2: Natural Language Processing (NLP)

NLP gained traction even before the pandemic reared its ugly head. That said, it is only set to increase in importance and reach in a post-pandemic world.

Most organisations often implement self-service systems, such as bots that come with multi-language optimized NLP to help solve customer problems.

Data scientists of the future must understand NLP and master it enough to help companies develop automated solutions for a better post-COVID outcome.

#3: Computer Vision

Computer vision is an artificial intelligence field which trains computers to interpret and comprehend the visual world. It uses digital images from cameras and videos as well as deep learning models to recognise and distinguish objects correctly. With the help of algorithms, computer vision is also integrated to follow up with a programmed response. In current scenarios, computer vision has proved helpful in containing the outbreak and regulating quarantines and social distancing in cities across the world. In the future, where maintaining distance might become the norm, data scientists specializing in computer vision will automatically become more hire able.

#4: Data Storytelling

With data analytics becoming a prime concern for companies across industries, the need for good data storytelling has increased. The benefit of data analysis is not just in the evidence it provides but also in how it is made meaningful and impactful. Gripping storytelling makes it easier for non-data-scientist stakeholders to understand the value of the information and the possibilities it poses.

Data presented as contextual stories, rather than isolated data points, makes individuals more likely to understand the impact, decipher patterns and make more informed decisions.

In turn, as data storytelling would help business leaders with powerful insights, it would help them better prepare for the post-pandemic world’s opportunities.

#5: Explainable AI

Considering that AI has reached into nearly every area of human life, companies must be able to trust computers and their decisions. This is where the need for explainable AI emerges. Until now, companies build and sourced AI models that predicted accurate insights from large data dumps. In a post-pandemic world, they may well shift to models that also provide explanations for predictions. Explainable AI is a step forward in reducing the mistrust in non-human workflows. It makes AI systems more transparent and much fairer and all-inclusive than they were earlier.

Conclusion

Advanced data science skills are crucial to the cause of innovation and growth. Advanced upskilling is an integral step for data scientists looking to become more than relevant in the coming years.

How A Big Data Can Be Used In Retail Banking?

Posted on July 8, 2021January 23, 2024 by Imarticus Learning

Like in all successful business ventures, the field of banking is no exception. Big Data drives decisions. The successful use of such large-volume data-based applications already exists and is hugely popular too. Retail banks are big data-driven with nearly all its processes being already supported by such data to deliver business value to their customers.

Their advantages and competitive value is data fueled and depends on the insights provided by the most effective use of such data. It is surprising that in spite of having had access to such large databases for over a decade now, Retail Banking is yet to exploit the numerous benefits uses of big data in retail-banking can bring in.

A data analyst Retail Bankingintern or freshman makes a handsome payout package and the range of the salary depends on the skill-set, certification, and experience. The skills required can vary depending on the employer and industry. As they climb the ladder the promotions depend on continuous skill up-gradation, managerial and leadership skills. Hence, soft-skills and personality development are also important attributes.

Big Data transformation benefits:

With the move by customers to digital transactions many banks did invest substantial efforts in dedicated teams, advanced analytics, appointing data officers, and upgrading their infrastructure. The early adapters are the survivors and have evolved more competitive as new-age banks offering customer-need based services based on Big Data insights. There are many areas where banks are yet to ramp up their use of big data to reap benefits according to the Boston Consulting Group’s reports.

The three main abilities that are leading transformations are:

Data: Multi-source multi-system huge volumes of data petabytes being available which include high definitions of detail and features.
Models and ML: The models are now more insightful thanks to the evolution of better ML software which enables decisions and predictions that are data-driven.
Software technology: The hardware-software clustering technique in software like Hadoop has proven to be big-data centric and allowing use of complex databases non-structured and structured in a cost-effective manner.

There are at least six areas in Retail Banking which focused and coordinated big-data programs can lead to substantial value for banks in the form of increased revenues and bigger profits.

IMPROVING CURRENT PRACTICES WITH POINT ANALYTICS: Applications of big data analytics for individual needs can be simple and yet powerful with the point analytics method.

TRANSFORMING CORE PROCESSES WITH PLATFORM ANALYTICS: Big data and point analytics can be used to improve customer risk assessment and for effectively tapping the marketing potential measures analyzed.

TRANSFORMING CORE PROCESSES WITH PLATFORM ANALYTICS: Big data applications can transform the collection process with step-by-step optimization to bring in a 40 percent savings in terms of writing off bad debts, with effective use of mining outdated customer information, their predispositions, and newer behavioral models.

BOOSTING IT PERFORMANCE: Big-data IT technologies should have need-based linear scaling to reduce costs. Data-intensive models, mining omnichannel customer experience, balancing data warehouse workloads and effective leveraging of data can help.

CREATING NEW REVENUE STREAMS:

A European bank used new architecture, hybrid data-warehousing combining banking tech and big-data by clustering the Hadoop commodity servers. Budget savings were 30 percent with all functionalities!

GETTING THE MOST FROM BIG DATA:

This involves these basic steps of infra and people management detailed below:

Assess the present situation: Banks needs to bring in newer innovative applications as a differentiator from the competition where all organizational levels collaborate to contribute to the use and needs-based model.

Be Agile: The agile requirements of communication, collaboration, and contribution across all processes will help big data transform them.

Critical capability cultivation: If not implemented the cultivation of critical capabilities can hinder the big data transformation of processes. Limiting the capability to the vision essentials is recommended in all domains of big data capabilities.

The three domains of Big data capabilities that Retail Banking should question itself about are:

The usage of data
The engine driving the data
The ecosystem of the data

Retail banks should necessarily explore and act on these domains effectively by using smaller discrete programs to take their strategy to execution.

Conclusion:

BIG business for all banks comes from effectively exploring Big Data. Such large institutions who cash in early will stay ahead of the other banks by adapting technology into the very fabric of their banks for its many benefits.

The future holds great promise for development in the field of Retail Banking and to make a high-paid scope-filled career even without experience. Start your Big Data Analytics Course at Imarticus Learning and take advantage of their assured placements and certification. All the best with your career in big data and retail banking!

For more details, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Welcome to the Data Science Club of Imarticus Learning!

Posted on July 8, 2021March 26, 2024 by Imarticus Learning

Imarticus Learning is among the top online education providers in India. After its data science course helped many data science aspirants to build a successful career in the industry, it has now come with a new campaign i.e. the ‘Data Science Club’. This club will aim at addressing the shortage of data scientists in India. It will also help in unifying data science aspirants from all over the country & giving them a chance to interact.

You can bring the data science club to your college/university with just a simple process. They already have registered 30 colleges from locations like Delhi, Tamil Nadu, and Karnataka.

The registration process is open & you can experience a whole new aspect of data science. One can also join the data science community of Imarticus on various social media platforms.

Mission & Vision of the Data Science Club

To promote students across India to build a successful career in data science.
To address the talent gap in the data science industry & shortage of skilled data scientists in India.
To facilitate the exchange of ideas & information relating to data science between club members across PAN India.
To provide industry-oriented learning of data science involving technological advancements & tools used in the industry.

Registration Process

Generally, most of the colleges don’t even have a data science club. You could be the first to introduce a data science club at your college/university. You can visit the Imarticus website and can easily register your college/university for the data science club. You will be required to fill a google form asking for a few details like college name, address, department, designation, email address, etc. The Imarticus panel will get back to you and will inform you about further proceedings.

Benefits of Being a Club Member

This data science club will facilitate its members from various colleges across India in understanding the importance of data science. It aims at motivating aspirants for building a successful career in data science and bridging the talent gap in the current data science industry. The benefits of joining the data science club of Imarticus are as follows:

Students of member colleges can attend any event/competition under the data science club for free.
You will get to attend lectures or webinars from industry experts/professionals.
The members of the club will get to test themselves by participating in the national level hackathon.
You will get to attend data science workshops under this club. You will also get a certification from Imarticus Learning for being a part of the data science club.
The members of the club will also undergo the faculty development programme.
Eligible members/students of the club will also get full placement support from Imarticus.
You will get to know about the industry practices & trends by being a member of this club. You will also get to know about the right career roadmap in the data science industry.

If you want to make a transition from data science aspirant to an expert, you have to grab this wonderful opportunity which will bring you closer to the data science community in India. One can also opt for the data science course provided by Imarticus Learning to know about the data science aspects in detail.

Why Artificial Intelligence is better than Other Technology

Posted on July 8, 2021October 15, 2021 by Imarticus Learning

Living in the 21st century, we have been able to see a lot happening in the field of technology and its advancement. Artificial Intelligence in that regard is the latest and the most developed version of classic technology. However, the term artificial intelligence still perplexes many people as they are not aware of its details. Even though people are using gadgets birthed by artificial intelligence, they may still not know about it.

What Artificial Intelligence Exactly Is?

AI is that field of machined technology that imitates humans and their functions on various levels. These machines or robots are functioned using various codes and algorithms to make them work as humanly as possible. It deals with the behavioural part of the machines which has a connection or a relation with intelligence. This branch of computer science has made it possible for the machines to think and act rationally and humanly to a great extent.

Why is AI Better than Other Technology?

Artificial Intelligence is any day better than human intelligence or works efficiently as compared to other technologies of the world. This can be made clear through the following points:

Mitigating Risks: AI has been working incessantly in reducing the risk factor associated with completing a task as compared to other technologies. To quote it as an example, forest fires can be handled more effectively with AI robotic drones instead of manual machines to put out the fire. AI can minimize the risks associated with human lives in many areas like radiation, electricity, hydropower and fire etc.
Zero Errors: Unlike other forms of technology or human intelligence for that matter, AI works so efficiently that it leaves no chance for errors or mistakes. Owing to a situation, human beings can alter the ways they work but AI does not change frequently according to their surroundings in all situations.
Remarkable Anticipation: The technology preceding AI is not accurate enough to make correct judgements about the events which are about to happen. However, forecasts or predictions made by AI are far more accurate. Likewise, AI sensors to forecast and measure the intensity of an earthquake beat the normal scales to measure the earthquakes. Also, AI functions with far less human involvement as compared to other technology.
Saves Time and Money: The classic technological advancement would just let you type a song name in the search list and it will present you a list of preferred songs. However, Siri and Alexa, a gift of artificial intelligence are voice regulated gadgets which work on your personalized instructions. There is no default set of questions and answers fed in these gadgets. They respond to any question with any suitable answer, isn’t that amazing?
Reduced Human Intervention: When technology functions on its own without human intervention, it can turn out to be the best. To illustrate, a doctor carrying out a surgical procedure makes use of all the modern gear and instruments but can still perform differently with different patients of the same conditions. However, this is not the case if robots carry out the surgery by themselves. This points to the fact that AI outshines human interference while making use of technology.

Conclusion
Embracing a positive approach to make use of Artificial Intelligence can always turn out to be in the best interest of every human being. Although, AI has eased the lifestyle and the way people used to work but still, exploring it outside the boundaries of positive development can cause harm to humanity at once.

If you wish to explore the field of AI and aspire to learn artificial intelligence, you must check out Imarticus learning for the same.

3 Tips on Building a Successful Online Course in Data Science!

Posted on July 8, 2021March 28, 2024 by Imarticus Learning

3 Tips on Building a Successful Online Course in Data Science!

The coronavirus pandemic is undoubtedly one of the biggest disruptors of lives and livelihoods this year. Thousands of businesses, shops and universities have been forced to shut down to curb the spread of the virus; as a result, massive numbers have turned to their home desks to work from and to tide over the crisis.

The pandemic has also influenced the surge of a new wave of interest in online courses. Over the past few months, many small and large-scale ed-tech companies have sprouted up, bombarding the masses with a wider range of choices than ever before. Many institutions have chosen to give out their courses at a minimal price and yet others for free. The format of these classes is different– hands-on, theoretical, philosophical, or interactive– but the ultimate goal is to take learning online and democratize it.

Naturally, it’s an opportune time to explore the idea of creating an online course– a data science online course, in particular, seeing as futuristic technologies will see a profound surge in attention come the next few years.

Here are a few tips to get the ball rolling on your first-ever online course in data science:

Create a Curriculum

Data science is a nuanced and complex field, so it won’t do to use the term in its entirety. It is important to think up what the scope of your course will be. You will need to identify what topics you will cover, what industry you want to target (if any), what tools you might need to talk about, and how best to deliver your course content to engage students.

General courses are ideal for beginners who don’t know the first thing about data science. This type, of course, could cover the scope of the term, the industries it’s used in as well as job opportunities and must-have skills for aspirants.

Technical courses can take one software and break it down– this is also a great space to encourage experiments and hands-on projects. Niche courses can deal with the use and advantage of data science within a particular industry, such as finance or healthcare.

Choose a Delivery Method

There are a plethora of ed-tech platforms to choose from, so make a list of what is most important to you, so you don’t get overwhelmed. Consider how interactive you can make it, through the use of:

Live videos
Video-on-demand
Webinars
Panels
Expert speakers
Flipped classroom
Peer reviews
Private mentorship
Assessments
Hackathons

The primary draw of online classrooms is also how flexible they are. Consider opting for a course style that allows students to learn at their own pace and time. Simultaneously, make use of the course styles listed above to foster a healthily competitive learning environment.

Seek Industry Partnerships

An excellent way to up the ante on your course and set it apart from regular platforms is to partner with an industry leader in your selected niche. This has many advantages– it lends credibility to your course, brings in a much-needed insider perspective and allows students to interact outside of strict course setups. Additionally, the branding of an industry leader on your certification is a testament to the value of your course; students are more likely to choose a course like yours if this certification is pivotal in their career.

Other ways by which you can introduce an industry partnership include inviting company speakers, organising crash courses on industry software and even setting up placement interviews at these companies. The more you can help a student get their foot in the door, the higher the chances of them enrolling and recommending.

Conclusion
Building an online course in data science is no mean feat. However, it’s a great time to jump into the ed-tech and online learning industry, so get ready to impart your knowledge!

Data Literacy Is Very Much a Life Skill– Here Are 4 Reasons Why?

Posted on July 8, 2021March 22, 2024 by Imarticus Learning

The world is no stranger to data; in fact, in recent times, the world has found itself being bombarded by more facts and statistics than ever before. At quite the same speed, people have also been faced with fake facts, viral social media forwards with little to no truth.

Being data literate has moved from being a niche requirement to being a life skill that allows people to distinguish between fact and fiction. Data literacy is a way of exploring and understanding statistics in a manner that provides meaning and insight.

This meaning isn’t relegated only a data science career or to businesses looking for an edge over competitors. It applies to society and its interconnected systems as a whole.

To drive the point home, here are a few advantages that data literacy offers when looked at as a life skill:

Recognising the Sources of Data

Data is everywhere, especially in a world where nearly everything is digital and produces and consumes more data. There are many different ways in which data exists, including graphs, images, text, speech, video, audio and more. Recognising the different sources of data is the first step towards working with data. The sources, formats and types all have a role to play in determining the use (and potential misuse) of data, which in turn drives data literacy.

Acknowledging the Self as a Consumer and Producer of Data

The messages you send, images you post and likes you leave on social media are examples of data. So are the transactions you make and the searches you conduct on search engines such as Google and Bing. Today, nearly every single person in the world is a data producer; those sources of data are vital to value-generating processes across industries and markets.

Similarly, people are daily consumers of data even if they don’t perceive it as that. The COVID19 pandemic has brought this into the light even further– front page statistics are at the back of everyone’s mind, as are the names of containment zones and the best practices for sanitisation.

Recognize Biases and Fallacies

Data literacy gives the people more agency to call out those producing statistical data that is biased, twisted or outright incorrect. As citizens, consumers and valued members of a society, it is imperative that every individual is able to identify false promises or glossed-over issues that allow wrong-doers to continue as they were.

Data literacy gives people the power and the evidential backing to call out those intentionally or unintentionally propagating mistruths and fallacies through awry statistics. This way, data literacy plays a pivotal part in politics, economics and ethics of a society, indeed of the world.

Improves Data Storytelling

Instead of data points presented on their own, data that is presented as descriptive stories make individuals more likely to understand the effect, decipher trends and make more educated decisions. While data storytelling is imperative to learn for those taking a data science course, it is just as important for members of all other fields to better present their arguments such that they catch eyes.

Data has never been a strictly academic factor; however, it has often been painted as complicated, invasive or unnecessary to penetrate everyday lives. Data storytelling ensures that data is taken even further out of that box and presented as actionable insights to even the average Joe Bloggs.

Conclusion

The focus on data science and literacy shouldn’t just be restricted to mathematics and algorithms but everyday applications of data in daily lives. Data understanding allows people all over the world to take more control of what they’re producing and consuming. Data fluency and literacy is achievable by all.

How Business Management Training Can Help To Enhance The Quality of The Product?

Posted on July 7, 2021March 21, 2024 by Imarticus Learning

Business management training allows the trainees to formulate a broad mindset about the functioning of a business and various elements associated with it. One can get a subject-specific understanding through business management training.

Some specific fields that you can master after business management training are:

Marketing
Product
Markets
Customers
Communication
Finance
Operations
Strategizing
Policy Formulation
Information Technology

Apart from these fields, there are other subjects as well which can help you become a successful business manager and contribute largely towards the enhancement of the quality of a product.

How can the quality of a product improve through business management training?

Understanding the customers

In business management training, the trainees get to know a lot about consumer behavior and various theories like price sensitivity. In this wake, business management training can help to understand the perspective and requirements of the customers.

Changes or modifications in a product can be made based on the customer-oriented research of the market. Accordingly, enhancements in the quality of the product can be made to satisfy the customers.

Easing out the communication process

To enhance the quality of a product, all the employees of an organization must have a common mindset and are well coordinated. In case there are some disputes or lack of communication in the organization, it is hard to bring positive changes in any aspect of business functioning.

In business management training, the trainees are guided well about maintaining healthy communication within an organization. This can help in easing out the process of communication leading to a positive work environment that can facilitate enhancement in the product and its quality.

Understanding the importance of demand

Whenever the demand for a specific product is created in the market, it needs to be fulfilled quickly. Imagine a situation; where customers are demanding a product but there is no supply in the market, gradually the demand is subject to fall.

In that regard, the managers need to be assured about the proper management of the demand of a product as the quality of any product does not only mean its tangible quality but it also means the services being offered before and after buying it.

Business management training teaches about the demand for a product in the market and some effective ways to deal with it.

After-sales services

Lately, the quality of a product has no longer remained its tangible and physical quality but involves associated services as well. Whenever a customer buys a product, after-sales services like fitting the product, exchanging or replacing are necessary.

Business management training plays a pivotal role in enhancing product quality as areas like information technology and consumer behavior are widely covered which can act as the guiding light to improve performance.

Market forecasting

A manager must have a knack to foresee the trends of the market soon. This can, however, be attained through business management training. The trainees get a piece of proper knowledge about the product market and the change in trends depending upon various dynamic features.

Anticipating the market trends can help the company to be prepared for the challenges that may arise in the future by making necessary enhancements in product quality.

Considering the current scenario where businesses have begun to flourish, a business management career can turn out to be a great option.

In case you require detailed knowledge about the courses and subjects covered under business management training, you must look out Imarticus learning. Imarticus offers you professional courses in the field of business management along with assistance in placements in certified companies.

Why Artificial Intelligence is Invaluable for Weather Forecasting and Disaster Prediction

Posted on July 7, 2021September 6, 2021 by Imarticus Learning

For most people, weather forecasts are simply indicators of whether they need to carry an umbrella or throw on a coat when they go outside. However, for many industries and types of individuals, weather changes and patterns have a direct impact on their lives and livelihoods.

Agriculture, for example, benefits from accurate weather forecasting because farmers can make better planting and harvesting decisions. For governments, weather forecasts factor in their budget plans and disaster relief fund allotments. Businesses that rely on clear weather (or rough weather) depend on weather forecasts to drive several of their operational processes.

From all this, it is easy to gather that accurate weather and disaster forecasting carries much more weight than we think. Artificial intelligence augments the accuracy and reliability of weather forecasting, especially given that so many details fluctuate every day and with every geographical location. It is a great fit, given the volume of data is nigh impossible to sift through with manual labor alone.

In short, the future of artificial intelligence will also see its increasing use in the weather and natural disaster forecasting domains. Here are a few more reasons why:

Managing several sources of weather data

There are currently more than one thousand weather satellites orbiting the Earth, each sending back weather data dumps to various collection points. These data dumps are a mix of information about temperatures, cloud patterns, winds, and pollution levels. Then there are thousands of government and private weather stations around the world, each conducting their own real-time research on weather and climate. It is nearly impossible to sift through all this data manually, but AI algorithms can do it in a matter of hours.

Sifting through multiple data categories

Suffice to say that the amount of data generated from satellites and personal weather stations is too much to fathom, and impossible for humans to sift through. However, Artificial Intelligence training can be applied to segregate and classify data from dumps, as well as to pull out key insights for analysis. This is a preliminary process in the weather prediction model, wherein AI segregates data based on indicators, flags significant shifts or patterns, and keeps data classified such that predictions are made as accurately and as scientifically as possible.

Preparing for potential disasters

Beyond real-time predictions, AI is also used to identify patterns and prepare for natural disasters in advance, off the back of previous circumstances. It may also split this data between geographies, allowing disaster management teams to evaluate which areas will be hit the hardest and prepare for that. This data is also invaluable for civil engineering teams, architectural firms, and city planning teams who need to take weather into account when mapping out residential and commercial areas.

Sending out warnings

Apart from predicting natural disasters, AI can also be leveraged to send out warnings to potential danger zones. This is invaluable when it comes to saving human and animal lives and generally preparing areas for the worse. Warnings can be sent out through media alerts, push notifications, and citizen broadcasts; whatever the method of delivery, AI is vital to sending such notices out in time and to the right people to curb panic and facilitate seamless planning.

Artificial Intelligence Training for Weather Forecasting

Weather forecasting teams and companies need skilled AI scientists and engineers to apply theory to practice in real-time. They need AI professionals who can create automated setups to free human minds for higher-order thinking; they also need pros who are fast on their feet and adept at creative problem-solving.

Using AI for weather forecasting is a whole new ball game – one on which many lives depend.

Category: Management