Latest news with #datascience
Forbes
13-05-2025
- Business
- Forbes
3 Things You Need To Break Into AI Jobs In 2025
A job in artificial intelligence is one of the most coveted careers in 2025. Google searches for 'AI jobs' have surged from a mere 53 points to 88 points over the past 12 months, according to Google Trends data. And it's pretty easy to figure out why. AI jobs have rapidly surged in popularity, especially since the AI boom in late 2022, when ChatGPT was released. From that time on, almost every startup is AI-focused, venture capital has poured millions into this technology, and most forward-thinking and innovative companies want to implement AI into their workflows and operations. Additionally, leading AI roles such as data scientist and machine learning and engineer have reported eye-watering salaries of as much as up to $1 million, particularly at Silicon Valley key players like OpenAI. Even at other companies, these, as well as enterprise tech sales, have secured salaries of as much as $200,000. But what does it take to secure one of the most coveted jobs in tech? How can you land a role at an exciting startup or within a tech company ready for change? Jobright analyzed AI jobs this year and discovered that the top five AI roles for new grads are: The numbers assigned to each role represent the actual number of job openings available in the U.S., based on Jobright researchers analyzing roles posted by private U.S. companies who are listed on Crunchbase. One might be puzzled as to why the figure for the number of job openings for entry-level AI roles is so low. That's because, according to their report, only about 15% of AI jobs are junior or entry-level; a full 85% of all job postings are senior, mid-level, or require specialist and niche expertise. So while getting an AI-enabled job (like a role in marketing that requires you to have applied AI skills) is far easier and more tangible, landing a role as an ML engineer when just starting your AI career is going to prove quite difficult. Here are a few things you can do to smooth the process: Internships are a great way to build experience and to practically apply what you've learned through your college studies. It helps strengthen your confidence and introduces you to the world of work as well as introducing you to the new industry you'll be spending most of your time in for the rest of your career. More than this, it's a time to flex not only your technical skills but also your human or power skills such as communication, empathy, and problem solving. These prove useful to add to your resume especially when applying for roles at fast-scaling startups who need evidence or proof of your adaptability, eagerness to learn, and ability to get the job done and flex fast. Earn your microcredentials and learn specific niche skills to boost your hireability. Jobright recommends looking beyond the basics and strengthening your skills in ML-annotation, backend, infra, and optimization, as well as PyTorch and TensorFlow frameworks. Additionally, focus on complementary 'soft' or power skills, like problem-solving, critical thinking, creativity, and adaptability, which employers deem as being in even greater demand than AI skills, according to an AWS survey. Even if you don't have any projects you've worked on from a job, you can build your own portfolio through initiating your own apps, projects, and case studies. Hiring managers want to see real-world examples of your skills, so the more you apply your theoretical understanding to solving real-world problems, the better. Link to your portfolio on your website, resume, and LinkedIn, and walk them through your process and tools/tech stack used. Of course, if you work in enterprise sales you won't need such a solid grasp of the technical side. However, it's still important to understand basic terminology, know your customer's pain points, and be able to adequately interpret value and deliver a solution to your business customers. To land a well-paid AI job, you need a strategic approach. It's not sufficient to have graduated from a leading college magna cum laude; it's what you do afterwards that counts. How do you communicate your value and ensure you future-proof your career by remaining relevant with your skills?
Jordan Times
11-05-2025
- Business
- Jordan Times
From learning to leading: AI's call to action
In today's rapidly evolving world, artificial intelligence is no longer a futuristic concept. It has become a powerful force reshaping how we work, think, and define value. This marks the most significant shift in labor dynamics since the Industrial Revolution, but this time, the change is powered not by machines, but by intelligent algorithms. Unlike past technologies, AI doesn't just assist humans, it increasingly performs tasks independently. As AI takes center stage, governments, educators, and industries must urgently rethink how we train, employ, and adapt the workforce. The numbers are stark. AI could impact up to 300 million traditional jobs worldwide, especially those involving routine or administrative work. At the same time, it is expected to create around 169 million new roles, many in highly specialized areas such as machine learning, data science, and AI governance. The message is clear: AI is not the enemy of employment, it is a catalyst for change. The real threat is not the technology itself, but our readiness to adapt. Jordan's technical education sector shows promise, but challenges remain. A recent report by the Information and Communications Technology Association of Jordan (int@j) revealed 7,247 IT graduates in 2023, with more than 25,000 students currently enrolled in IT-related fields. By 2025, between 8,000 and 8,500 are expected to graduate. Yet a majority of these students still focus on traditional majors like Computer Science (31per cent), Computer Information Systems (19 per cent), and Software Engineering (17per cent). This signals a misalignment between academic training and the evolving demands of the global tech economy. To close this gap, academic institutions must revamp curricula to include emerging fields such as AI, big data, and cloud computing. Forecasts indicate that by 2030, nearly 40 per cent of job skills will change. Lifelong learning is no longer optional—it is a national and economic necessity. New professions like 'AI Ethics Specialist' and 'Machine Learning Engineer' are already shaping industries. Preparing students for these roles must become a strategic priority if Jordan hopes to remain competitive in the global digital economy. AI is no longer limited to supporting human tasks. Today's systems can manage entire workflows, learn from feedback, and make independent decisions. This means many jobs based on repetitive tasks or routine judgment are at risk of automation. However, new opportunities are emerging in AI development, system oversight, ethical regulation, and human-AI collaboration. These roles demand not just technical skills but creative, strategic, and interdisciplinary thinking. With every leap in technology, the skills gap widens. It's essential for both the public and private sectors to provide continuous learning opportunities, targeted training programs, and modernized educational infrastructure. International models can serve as guides. Estonia, for instance, has successfully integrated AI into public services and education. Jordan can benefit from similar initiatives by forging international partnerships and focusing on strategic knowledge transfer. This is not just a moment of transformation, it is a national imperative. The question is not whether AI will shape the future, but whether we will help shape it. Jordan's universities, in collaboration with the government and industry, have a unique opportunity to lead, not by reacting to change, but by driving it. AI is not eliminating humans from the equation; it's inviting them into a new chapter of creativity, innovation, and growth. Navigating this new era will require vision, investment, and courage. The future of AI in Jordan begins in the classroom, the lab, and the policy room. Will we rise to meet it? The answer starts today.
Geeky Gadgets
10-05-2025
- Business
- Geeky Gadgets
Break Into High-Paying Data Careers Without a University Education
What if you could land a six-figure job in the booming data industry without ever setting foot in a university classroom? It's not just a pipe dream—it's a reality for thousands of professionals who've bypassed traditional education paths and carved out lucrative careers in data analytics, machine learning, and beyond. With average salaries for roles like data scientist and machine learning engineer exceeding $100,000—and some even hitting $150,000—the demand for skilled data professionals has never been higher. The best part? You don't need a degree to get in the game. What you do need is a clear strategy, the right tools, and a commitment to learning. That's where this walkthrough comes in. In this guide, Mo Chen will show you how to break into five of the highest-paying data jobs—and do it faster than you might think. You'll uncover the exact skills, certifications, and projects that can set you apart, even if you're starting from scratch. From mastering tools like Python and SQL to building a portfolio that wows recruiters, this step-by-step approach is designed to help you stand out in a competitive market. Whether you're eyeing a career as a data analyst or dreaming of becoming a machine learning engineer, you'll find actionable tips to kickstart your journey. The path may not be traditional, but the opportunities are real—and they're waiting for you to seize them. Top High-Paying Data Careers 1. Data Analyst Data analysts play a critical role in helping organizations make informed, data-driven decisions. With an average salary of approximately $85,000 in the US, this role serves as an excellent entry point into the data field. Key Skills: Proficiency in tools like Excel, SQL, and data visualization platforms such as Tableau or Power BI is essential. Familiarity with programming languages like Python or R can provide a competitive edge. Proficiency in tools like Excel, SQL, and data visualization platforms such as Tableau or Power BI is essential. Familiarity with programming languages like Python or R can provide a competitive edge. How to Prepare: Begin by mastering a data visualization tool and building a portfolio that highlights your ability to analyze and present data effectively. Earning certifications, such as DataCamp's Data Analyst Certification, can validate your expertise and boost your credibility. 2. Data Scientist Data scientists are among the most sought-after professionals in the data industry, with average salaries exceeding $100,000 in the US. They use statistical methods and machine learning to extract meaningful insights from complex datasets. Key Skills: A strong foundation in statistical analysis, machine learning, and programming languages like Python or R is crucial. The ability to build impactful projects and communicate findings effectively is equally important. A strong foundation in statistical analysis, machine learning, and programming languages like Python or R is crucial. The ability to build impactful projects and communicate findings effectively is equally important. How to Prepare: Participate in Kaggle competitions to gain hands-on experience, develop a diverse portfolio, and pursue certifications such as DataCamp's Data Scientist Certification to demonstrate your skills to potential employers. Top Data Careers That Don't Require a College Education Watch this video on YouTube. Gain further expertise in data analysis by checking out these recommendations. 3. Data Engineer Data engineers are responsible for designing and maintaining the infrastructure that supports data analysis and machine learning. With average salaries exceeding $100,000 in the US, this role requires a strong technical foundation and problem-solving skills. Key Skills: Expertise in Python, SQL, database management, ETL processes, and big data tools like Hadoop and Spark is essential. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud is also highly valuable. Expertise in Python, SQL, database management, ETL processes, and big data tools like Hadoop and Spark is essential. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud is also highly valuable. How to Prepare: Focus on gaining hands-on experience with data pipelines, cloud technologies, and database systems. Certifications like DataCamp's Data Engineer Certification can help you stand out in a competitive job market. 4. Business Intelligence Analyst Business intelligence analysts bridge the gap between data and business strategy by transforming raw data into actionable insights. With an average salary of around $100,000 in the US, this role combines technical expertise with business acumen. Key Skills: Proficiency in SQL, data visualization tools like Tableau or Power BI, and basic statistical analysis is essential. Industry-specific knowledge can further enhance your value in this role. Proficiency in SQL, data visualization tools like Tableau or Power BI, and basic statistical analysis is essential. Industry-specific knowledge can further enhance your value in this role. How to Prepare: Build a portfolio of projects that showcase your ability to analyze data and provide actionable recommendations. Certifications such as Google's Business Intelligence Professional Certificate can strengthen your qualifications and demonstrate your expertise. 5. Machine Learning Engineer Machine learning engineers design, develop, and deploy predictive models, making them integral to organizations using artificial intelligence. With average salaries exceeding $150,000 in the US, this role demands advanced technical skills and a deep understanding of machine learning frameworks. Key Skills: A strong foundation in mathematics, including linear algebra, calculus, and probability, is essential. Proficiency in Python and experience with machine learning frameworks like TensorFlow and PyTorch are also critical. A strong foundation in mathematics, including linear algebra, calculus, and probability, is essential. Proficiency in Python and experience with machine learning frameworks like TensorFlow and PyTorch are also critical. How to Prepare: Focus on building machine learning models and creating diverse projects that demonstrate your expertise. Certifications such as IBM's Machine Learning Professional Certificate or Microsoft's AI and ML Engineering Professional Certificate can validate your skills and enhance your employability. General Tips for Starting a Data Career Without a Degree Entering the data field without a university degree is entirely possible with a strategic approach. By focusing on skill development, practical experience, and certifications, you can position yourself for success. Here are some actionable tips to get started: Start Small: Begin by mastering one foundational skill, such as SQL or Python, before expanding into more advanced areas. Building a strong foundation is key to long-term success. Begin by mastering one foundational skill, such as SQL or Python, before expanding into more advanced areas. Building a strong foundation is key to long-term success. Build a Portfolio: Create projects that showcase your ability to solve real-world problems using data. A well-curated portfolio can often outweigh formal education in the eyes of recruiters. Create projects that showcase your ability to solve real-world problems using data. A well-curated portfolio can often outweigh formal education in the eyes of recruiters. Pursue Certifications: Certifications from reputable platforms can demonstrate your commitment to learning and enhance your credibility in the job market. Certifications from reputable platforms can demonstrate your commitment to learning and enhance your credibility in the job market. Use Online Resources: Take advantage of free and paid online resources, such as tutorials, courses, and forums, to continuously improve your skills and stay updated on industry trends. By focusing on the right skills, gaining hands-on experience, and earning relevant certifications, you can unlock opportunities in high-paying data roles and build a successful career in this dynamic field. Media Credit: Mo Chen Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
