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How Will Artificial Intelligence Affect Our Lives in the Future?

6 minute read | October 28, 2021
Sakshi Gupta

Written by:
Sakshi Gupta

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As a curious tech enthusiast, a few days back, I was reading about the buzzwords of this century, Artificial Intelligence and Machine Learning, it was completely fascinating how these technologies are changing and are likely to change the coming future of human life on earth. That is the reason, I have decided to dedicate this article on Artificial Intelligence Future so together we can explore the possibilities of a new era with machine learning and artificial intelligence.

Artificial Intelligence Future – Less Artificial, More Intelligent

“[AI] is going to change the world more than anything in the history of mankind. More than electricity.”— AI oracle and venture capitalist Dr Kai-Fu Lee, 2018

From driverless cars to voice automation in homes, artificial intelligence has progressed rapidly and is no longer just a concept from sci-fi movies and books. Artificial Intelligence Future is arriving faster than the predictions shown in the long hailed film “Minority Report” set in 2054. According to a research by scientists at the University of Oxford, Artificial Intelligence will be better than humans at translating languages by 2024, writing school essays by 2026, selling goods by 2031, write a bestselling book by 2049, and conducting surgeries by 2053. In the next few years, AI will become an integral part of our lives reaching the level of super-intelligent machines to exceed human intellectual competencies.

  • Imagine opening the door of your hotel room without a key, but rather through facial recognition tools. Your face will become your identity making everyday transactions easier and efficient
  • Get ready to have your products delivered by smaller drones straight to your doorstep within minutes of placing an order.
  • AI-based virtual assistants will place human-like calls to book an appointment at, say, your neighbourhood salon understanding the nuance and the context of the conversation.
  • Prepare yourself to be operated by a Robot Surgeon. In the next few years, a physical surgeon will only be a spectator as a robot actually performs the surgery and helps patients better understand their care options.

These are just a few examples of how artificial intelligence will change the future. Artificial Intelligence Future advancements seem like a long way off, but they will be here sooner than we can even think off. Top technology companies are in a race to implement artificial intelligence in our day to day lives -which will lead us to a really fantastic and exciting artificial intelligence future.

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How Will AI Change the Future?

Based on the recent technological advancements in AI, here’s how artificial intelligence will change the future –

1. Artificial Intelligence Future in Healthcare

Nearly 86% of the mistakes can be prevented in the healthcare industry and AI will play a vital role in this. The future of AI in healthcare is a step towards democratizing healthcare for the benefit of patients and healthcare professionals alike, while at the same time making it less costly and more accurate through AI-powered predictive care. Predictive analytics coupled with artificial intelligence can help understand various factors (place of birth, eating habits, local air pollution levels, etc) that influence a person’s health. In future, we can expect AI-powered healthcare systems to anticipate when a person is most likely to develop a chronic disease and suggest preventative medication to cure it before it worsens.

With several types of research underway on developing AI-powered applications to help doctors diagnose and treat patients, AI will definitely be a game-changer in imparting better medical care to patients. You can expect a totally different future for healthcare as robots interact with patients, check on their health condition, and evaluate the further need for an appointment with a doctor. We will still need doctors, nurses, scientists, and the list goes on. However, AI will simplify our lives by making the clinical and healthcare data we generate more actionable.

Artificial Intelligence Future in Retail

  • The global market for Artificial Intelligence in Retail is expected to grow over $5 million by 2022.
  • Accenture reports that AI investments in retail will boost revenues by 38% by the end of 2002.

These statistics are a clear proof of evidence that AI promises a great future for retailers with diverse usage possibilities for better business decision making. In future, you can expect delivery of up to 5-pound packages in less than 30 minutes, all thanks to AI-powered drones. Amazon is already working on it to determine proper safety and reliability of operations for delivering packages but there is no tentative date yet on the commercial use of these drones. However, in the next decade, you can anticipate the autonomous delivery of goods and food immediately with drones.

Not just autonomous delivery but the future of AI in Retail is more autonomous and individualised with realistic scenarios that include virtual racks customized according to data-defined persona, connected dressing rooms with screens, and a lot more personalization based on previous history and trends making consumer choices less stressful and chaotic.

2. Artificial Intelligence Future in Banking

According to IHS Markit’s AI in Banking report, the global business value of AI in Banking anticipated reaching $300 billion by the end of 2030. Artificial Intelligence is all set to take centre stage in the next decade in verticals like business intelligence and security with reduced cost, increased productivity and enhanced customer experiences. Robo Advisors in wealth management will become a common sight and game-changers in the banking space-saving significant amounts of time for wealth managers and customers. The banks of the future will not just personalise their services and products but will use AI to personalise customer experiences. A great example of such personalisation would be removing the need to produce an ID card when you walk into the bank branch and still continue to be greeted with your name and complete knowledge of your entire bank account history.

3. AI to Open Up Millions of New Job Opportunities

“Artificial Intelligence will take our jobs!” is the most common fear surrounding artificial intelligence in the future. With artificial intelligence automating all kinds of work, we can think of a more comfortable future for ourselves that will create new jobs and not displace them. According to a report on the Future of Jobs by World Economic Forum, AI will create 58 million new artificial intelligence jobs by 2022.  There is an excellent chance that by 2030 AI will outperform humans in most of the mental tasks but that does not mean it will take away jobs. 

In fact, the Indian AI industry has doubled in size in 2019 compared to the previous year. In just one year, 3 times more companies are working on AI-based projects and this momentum is likely to continue. India almost doubled its artificial intelligence engineers in 2019 (from 40K in 2018 to 72K in 2019) but still faces a talent shortage. The growth in the Indian AI industry is powered by professionals transitioning into artificial intelligence engineer job roles by upskilling themselves through various mentored artificial intelligence courses.

Artificial Intelligence Future is coming – coming soon!  Now is the time to prepare for the age of AI by investing in education and training. The choice remains with you – will you upgrade your skills to stay ahead of the curve or will you remain still and stagnant in the industry. Aren’t you eager to be a part of this fourth industrial revolution? Reach out to our career coaches to find out how Springboard’s Artificial Intelligence Course can help you upskill and land a top AI gig at one of the top tech companies.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.