{"id":89251,"date":"2024-12-19T13:27:44","date_gmt":"2024-12-19T02:27:44","guid":{"rendered":"https:\/\/www.institutedata.com\/?p=89251"},"modified":"2025-01-22T10:51:28","modified_gmt":"2025-01-21T23:51:28","slug":"transform-your-future-making-the-leap-to-data-science-and-ai-careers-in-2025","status":"publish","type":"post","link":"https:\/\/www.institutedata.com\/us\/blog\/transform-your-future-making-the-leap-to-data-science-and-ai-careers-in-2025\/","title":{"rendered":"Transform Your Future: Making the Leap to Data Science and AI Careers in 2025"},"content":{"rendered":"

The demand for professionals in the data science field and AI is growing fast as we move into 2025. For people in non-tech fields, switching to a future-focused career can be tough, but also very rewarding. Data science and AI offer not only job growth but also stability and good salaries. This guide aims to help non-tech professionals make a successful move into these exciting fields.<\/span><\/p>\n

Why Data Science and AI?<\/b><\/h2>\n

These fields are all about using data to make smart choices and tackle tough problems. With more data than ever, there\u2019s a big need for people who can make sense of it all. Data science and AI jobs are on the rise, offering good pay and a chance to really make a difference.<\/span><\/p>\n

These careers aren\u2019t just about crunching numbers; they\u2019re about helping solve real-world issues. Whether it\u2019s improving healthcare or making businesses run smoother, data science and AI are at the heart of it. As more companies see how valuable data is, they\u2019re looking for skilled folks to join their teams, making data science skills highly sought after. Now\u2019s a great time to think about stepping into this exciting world.<\/span><\/p>\n

Industry Growth and Labor Statistics<\/b><\/h3>\n
    \n
  • Data science and AI are among the fastest-growing fields.<\/span><\/li>\n
  • Job opportunities are expanding rapidly, with significant growth across many sectors including finance, healthcare, e-commerce, and more.<\/span><\/li>\n<\/ul>\n

    Industry Projections<\/b><\/p>\n

    According to the Bureau of Labor Statistics, data science jobs are projected to grow by over 30% in the next decade, making it one of the most promising fields for job seekers.<\/span><\/p>\n

      \n
    • These careers are stable and reliable in an increasingly automated and data-driven world.<\/span><\/li>\n
    • Data analysts play a crucial role in transforming and manipulating large datasets for business analysis, bridging the gap between data scientists and business analysts.<\/span><\/li>\n<\/ul>\n

      \"\"<\/p>\n

      Demand Across Industries<\/b><\/h3>\n
        \n
      • Healthcare<\/b>: Data science is transforming patient care, predictive analytics, and personalized medicine.<\/span><\/li>\n
      • Finance<\/b>: Risk analysis, fraud detection, and algorithmic trading are all powered by data.<\/span><\/li>\n
      • E-commerce<\/b>: Data scientists help optimize customer experience, recommendation engines, and inventory management.<\/span><\/li>\n
      • Manufacturing<\/b>: Data science aids in predictive maintenance, improving production efficiency, and reducing costs.<\/span><\/li>\n
      • Data management systems<\/b>: Data management systems are essential for integrating and maintaining diverse data sources, enabling employee access to information and supporting data-driven decision-making.<\/span><\/li>\n<\/ul>\n

        \"\"<\/p>\n

        Comparing Data Science to Other Careers<\/b><\/h3>\n
          \n
        • Diverse Job Roles<\/b>: Unlike many traditional non-tech roles, data science offers a wide range of opportunities:<\/span><\/li>\n
        • Data Scientist<\/b>: Solve complex problems by interpreting data.<\/span><\/li>\n
        • Data Analyst<\/b>: Focus on analyzing data to answer business questions.<\/span><\/li>\n
        • Data Engineer<\/b>: Build and maintain data architecture.<\/span><\/li>\n
        • Machine Learning Engineer<\/b>: Develop algorithms that allow machines to learn from data, requiring strong programming and statistical skills, as well as a solid educational background, typically at the master’s or Ph.D. level. Machine learning engineers play a crucial role in enabling businesses to understand customer behavior and implement effective marketing strategies.<\/span><\/li>\n
        • Variety of Sectors<\/b>: Data science roles are available across multiple sectors, making it easy to find a niche that aligns with your interests.<\/span><\/li>\n
        • Problem-Solving Opportunities<\/b>: Compared to traditional roles, data science allows for more creative problem-solving and hands-on application of analytical skills.<\/span><\/li>\n<\/ul>\n

          \"\"<\/p>\n

          High Earning Potential<\/b><\/h3>\n
            \n
          • Competitive Salaries<\/b>: Data science careers come with high earning potential compared to other industries.<\/span>\n
              \n
            • Data Scientist<\/b>: Average salaries range from $95,000 to $130,000 per year depending on experience and location.<\/span><\/li>\n
            • Machine Learning Engineer<\/b>: Can earn upwards of $140,000 annually, especially in tech hubs.<\/span><\/li>\n<\/ul>\n<\/li>\n
            • Bonuses and Perks<\/b>: Many companies offer additional perks, including bonuses, stock options, and continuing education support.<\/span><\/li>\n
            • Room for Growth<\/b>: With experience, data professionals can move into senior roles or specialize further, which often results in significantly higher pay.<\/span><\/li>\n<\/ul>\n

              Impact of Machine Learning on Society<\/b><\/h3>\n
                \n
              • Real-World Impact<\/b>: Data science is not just about crunching numbers; it\u2019s about making a difference. Data scientists help solve pressing issues like climate change, disease prevention, and urban planning.<\/span><\/li>\n
              • Shaping the Future<\/b>: AI and data science are leading advancements in areas like autonomous vehicles, smart cities, and advanced healthcare diagnostics.<\/span><\/li>\n
              • Ethical Data Use<\/b>: There\u2019s a growing focus on using data ethically to ensure privacy, fairness, and transparency, giving data professionals a crucial role in shaping responsible technology.<\/span><\/li>\n<\/ul>\n

                Interested in a Data Science & AI course?<\/b><\/h3>\n

                Visit the Institute of Data\u2019s Data Science & AI Program page to download a course outline for free. Alternatively, contact our local team for a free consultation if you would like to speak with someone directly about the program.<\/span><\/p>\n

                Key Skills Needed for a Successful Transition<\/b><\/h2>\n

                Soft Skills vs. Technical Skills<\/b><\/h3>\n
                  \n
                • Technical Skills<\/b>: Having a strong foundation in programming languages like Python, R, and SQL is essential for analyzing data and building data management systems. Additionally, proficiency in machine learning techniques and data visualization tools is crucial for interpreting data and deriving valuable insights.<\/span><\/li>\n<\/ul>\n
                    \n
                  • Data Analysis<\/b>: Understanding how to collect, clean, and interpret data to make informed decisions.<\/span><\/li>\n
                  • Machine Learning<\/b>: Familiarity with machine learning techniques and frameworks like scikit-learn, TensorFlow, or PyTorch.<\/span><\/li>\n
                  • Data Visualization<\/b>: Ability to present data insights through tools like Tableau, Power BI, or Matplotlib to tell a compelling story.<\/span><\/li>\n
                  • Programming Languages (Python, R)<\/b>: Proficiency in Python or R is essential, as these are the most commonly used languages in data science. Knowledge of SQL is also important for data manipulation.<\/span><\/li>\n
                  • Analytical Skills<\/b>: Key to interpreting data and deriving insights. Being able to analyze data sets critically and understand patterns is crucial for problem-solving.<\/span><\/li>\n
                  • Communication Skills<\/b>: Important for presenting findings effectively to stakeholders who may not have a technical background. The ability to translate complex data insights into actionable business strategies is a highly valued skill.<\/span><\/li>\n
                  • Problem-Solving Mindset<\/b>: The ability to approach challenges methodically, think critically, and use data to solve real-world problems.<\/span>
                    \n<\/span>Mastering these skills, including both technical and soft skills, is essential for success in this field.<\/span><\/li>\n<\/ul>\n

                    \"\"<\/p>\n

                    Top Programming Languages to Learn<\/b><\/h3>\n
                      \n
                    • Python<\/b>: Widely used in data science because of its versatility and extensive libraries like Pandas, NumPy, and scikit-learn. Python is ideal for data manipulation, automation, and machine learning.<\/span><\/li>\n
                    • R<\/b>: Popular for statistical analysis, especially in academia and research. R is powerful for data exploration, visualization, and statistical modeling.<\/span><\/li>\n
                    • SQL<\/b>: Essential for working with databases, extracting, and manipulating data. SQL is a foundational skill for all data professionals.<\/span><\/li>\n
                    • Julia<\/b>: An emerging language known for its speed, particularly useful in numerical analysis and computational science.<\/span><\/li>\n<\/ul>\n

                      Key Data Tools, Technologies, and Data Visualization Tools<\/b><\/h3>\n
                        \n
                      • Data Analysis Tools<\/b>:<\/span>\n
                          \n
                        • Excel<\/b>: Still highly relevant for data analysis, particularly for smaller datasets or quick data manipulation.<\/span><\/li>\n
                        • Pandas<\/b>: A Python library that simplifies data manipulation and analysis.<\/span><\/li>\n<\/ul>\n<\/li>\n
                        • Data Visualization Tools<\/b>:<\/span>\n
                            \n
                          • Tableau<\/b>: User-friendly and powerful for creating interactive visualizations.<\/span><\/li>\n
                          • Power BI<\/b>: A Microsoft product that integrates well with other MS tools for business intelligence needs.<\/span><\/li>\n<\/ul>\n<\/li>\n
                          • Big Data Tools<\/b>:<\/span>\n
                              \n
                            • Apache Hadoop<\/b>: Useful for storing and processing large data sets.<\/span><\/li>\n
                            • Spark<\/b>: Ideal for real-time data processing and big data applications.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                              Soft Skills for Effective Collaboration<\/b><\/h3>\n
                                \n
                              • Teamwork<\/b>: Data projects often involve working in cross-functional teams. Being able to collaborate with others, including business analysts, software developers, and marketing professionals, is essential.<\/span><\/li>\n
                              • Adaptability<\/b>: Data science is an evolving field. Willingness to learn new skills and adapt to new tools and methodologies is key to staying relevant.<\/span><\/li>\n
                              • Storytelling with Data<\/b>: More than just analyzing numbers, data scientists need to craft a narrative that conveys insights in a compelling and actionable manner.<\/span><\/li>\n
                              • Project Management<\/b>: Understanding how to manage time, set goals, and deliver data projects effectively.<\/span><\/li>\n<\/ul>\n

                                Steps to Make the Career Change<\/b><\/h2>\n

                                Finding the Right Learning Path<\/b><\/h3>\n

                                Identify your learning style. Understand whether you learn best through structured courses, hands-on projects, or a mix of both. This will help you choose between online courses, bootcamps, or self-study.<\/span><\/p>\n

                                Building a Portfolio<\/b><\/h3>\n
                                  \n
                                • Showcase Diverse Projects<\/b>: Your portfolio should include a mix of data cleaning, exploratory analysis, visualization, and machine learning projects to demonstrate your versatility.<\/span>\n
                                    \n
                                  • Personal Projects<\/b>: Work on projects that interest you personally\u2014this shows passion. For example, analyzing sports data, creating recommendation systems for movies, or working with publicly available datasets like those from Kaggle.<\/span><\/li>\n
                                  • Capstone Projects<\/b>: If you\u2019re taking an online course or attending a bootcamp, treat capstone projects seriously as they simulate real-world scenarios and show that you can handle end-to-end problem-solving.<\/span><\/li>\n
                                  • Collaborative Projects<\/b>: Engage in group projects. They not only demonstrate teamwork skills but also help you tackle larger, more complex datasets that are closer to real-life use cases.<\/span><\/li>\n<\/ul>\n<\/li>\n
                                  • Platform Presence<\/b>: Use platforms like GitHub to host your projects. Create a well-documented GitHub repository with clean code, detailed readme files, and explanations of your process.<\/span><\/li>\n
                                  • Kaggle Competitions<\/b>: Participate in Kaggle competitions to gain practical experience and demonstrate your skills to potential employers. You can showcase your rankings or even your code and findings as part of your portfolio.<\/span><\/li>\n<\/ul>\n

                                    Practical Experience through Internships and Projects<\/b><\/h3>\n

                                    Networking<\/b>: Build connections in the data science community to gain practical exposure.<\/span><\/p>\n

                                      \n
                                    • Attend Meetups and Conferences<\/b>: Platforms like Meetup.com and Eventbrite often have local data science events, workshops, and conferences.<\/span><\/li>\n
                                    • LinkedIn Networking<\/b>: Reach out to industry professionals, join groups focused on data science, and participate in discussions to build visibility.<\/span><\/li>\n
                                    • Mentorship Programs<\/b>: Where you can receive guidance from experienced professionals.<\/span><\/li>\n<\/ul>\n

                                      Freelancing and Volunteer Projects<\/b>: Gain hands-on experience by working on smaller freelance projects.<\/span><\/p>\n

                                        \n
                                      • Freelancing Websites<\/b>: Websites like Upwork, Fiverr, or Toptal are great places to find freelance work that helps build your skills while earning a bit of income.<\/span><\/li>\n
                                      • Volunteer with Nonprofits<\/b>: Many nonprofits have data needs but lack the resources. Volunteering with them can provide real-world experience and help build your portfolio.<\/span><\/li>\n<\/ul>\n

                                        Apply for Entry-Level Roles<\/b><\/h3>\n
                                          \n
                                        • Data Analyst<\/b>: Often an ideal starting point for those transitioning into data science, focusing on cleaning, analyzing, and visualizing data to answer business questions. Data analysts play a crucial role in transforming and manipulating large datasets for business analysis, bridging the gap between data scientists and business analysts.<\/span><\/li>\n
                                        • Junior Data Scientist<\/b>: Entry-level data science positions where you can work on more advanced data projects under supervision.<\/span><\/li>\n
                                        • Internships<\/b>: Look for internships in data-related roles, even if they are unpaid. Internships are valuable for getting hands-on experience and often lead to full-time positions.<\/span><\/li>\n<\/ul>\n

                                          Upskilling Continuously<\/b><\/h3>\n
                                            \n
                                          • Stay Current with Trends<\/b>: Data science is a constantly evolving field. Follow blogs like Towards Data Science, KDnuggets, and Analytics Vidhya to stay up-to-date.<\/span><\/li>\n
                                          • Join Online Communities<\/b>: Engage with communities like Reddit\u2019s r\/datascience, Data Science Central, or specialized Slack groups.<\/span><\/li>\n
                                          • Practice on Real Data<\/b>: Websites like Kaggle, Data.gov, or UCI Machine Learning Repository offer datasets that you can use to practice your skills and work on projects.<\/span><\/li>\n<\/ul>\n

                                            Job Application and Interview Preparation<\/b><\/h3>\n
                                              \n
                                            • Tailor Your Resume<\/b>: Highlight relevant skills, certifications, and projects. Use keywords from job postings to increase the chances of passing through applicant tracking systems.<\/span><\/li>\n
                                            • Leverage LinkedIn<\/b>: Keep your LinkedIn profile updated. Write a summary that clearly outlines your journey into data science, showcase your projects, and get recommendations from peers or mentors.<\/span><\/li>\n
                                            • Prepare for Interviews<\/b>:<\/span>\n
                                                \n
                                              • Technical Interviews<\/b>: Practice common data science interview questions, including those on statistics, machine learning concepts, and coding exercises. Websites like LeetCode and HackerRank are great for practice.<\/span><\/li>\n
                                              • Behavioral Interviews<\/b>: Be ready to discuss your transition journey\u2014why you made the change, what challenges you faced, and how you overcame them. Highlight your passion for data and problem-solving.<\/span><\/li>\n
                                              • Portfolio Presentation<\/b>: Prepare to discuss one or two key projects from your portfolio in depth. Be ready to walk through your process, challenges faced, tools used, and results achieved.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                                                Ready to start your journey into data science? Visit the Institute of Data\u2019s Data Science & AI Program page to learn more about the courses we offer and how they can help you achieve your goals.<\/span><\/p>\n

                                                Challenges and How to Overcome Them<\/b><\/h2>\n

                                                If you’re finding the transition challenging, consider enrolling in a structured program that provides mentorship and guided learning. Visit the Institute of Data\u2019s Data Science & AI Program page to see how our courses can support you.<\/span><\/p>\n

                                                Technical Gaps and How to Address Them<\/b><\/h3>\n
                                                  \n
                                                • Challenge<\/b>: Learning programming and technical tools can be intimidating for many who come from non-technical backgrounds.<\/span><\/li>\n
                                                • Solution<\/b>: Start small. Take introductory courses in programming languages like Python or R and gradually build up your skills. There are many beginner-friendly resources available, such as YouTube tutorials, online courses, and coding practice websites like Codecademy or freeCodeCamp.<\/span><\/li>\n
                                                • Hands-On Practice<\/b>: Practice is key. Dedicate time each day to code, experiment, and build small projects. Start with simple exercises and progress to more complex tasks.<\/span><\/li>\n
                                                • Mentorship and Guidance<\/b>: Seek out a mentor who can provide guidance, answer questions, and offer support as you learn. Many data science communities offer mentorship programs for beginners.<\/span><\/li>\n<\/ul>\n

                                                  Time Management Strategies<\/b><\/h3>\n
                                                    \n
                                                  • Challenge<\/b>: Balancing skill-building with existing commitments like a full-time job or family responsibilities can be difficult.<\/span><\/li>\n
                                                  • Solution<\/b>: Break down your goals into smaller, manageable tasks. Set aside specific hours each week dedicated to learning, and stick to the schedule. Use productivity tools like Trello or Notion to plan and track your progress.<\/span><\/li>\n
                                                  • Prioritization<\/b>: Focus on high-impact learning first. For instance, prioritize learning Python and SQL before diving into advanced machine learning topics. This will allow you to gain confidence in foundational skills before moving on to more challenging concepts.<\/span><\/li>\n
                                                  • Micro-Learning<\/b>: Use small pockets of time to watch tutorials, read articles, or review notes. This can be particularly helpful if you are juggling many responsibilities.<\/span><\/li>\n<\/ul>\n

                                                    Peer Support and Community Learning<\/b><\/h3>\n
                                                      \n
                                                    • Challenge<\/b>: Learning in isolation can be demotivating and lead to giving up when challenges arise.<\/span><\/li>\n
                                                    • Solution<\/b>: Join online forums, Slack groups, or local meetups where you can connect with others on a similar journey. Platforms like Reddit (e.g., r\/datascience), LinkedIn groups, and Kaggle offer opportunities to engage with others, share knowledge, and get feedback.<\/span><\/li>\n
                                                    • Study Groups<\/b>: Form or join a study group. Learning with peers can help hold you accountable and make the learning process more enjoyable. Study groups also provide opportunities for collaborative projects, where you can learn from each other’s strengths.<\/span><\/li>\n
                                                    • Competitions and Challenges<\/b>: Participate in data challenges on Kaggle or other competition platforms. These challenges provide practical experience, and the community aspect allows you to see how others approach the same problem.<\/span><\/li>\n<\/ul>\n

                                                      Imposter Syndrome and Confidence Building<\/b><\/h3>\n
                                                        \n
                                                      • Challenge<\/b>: Many career changers experience imposter syndrome, doubting whether they are truly capable of succeeding in a new, technical field.<\/span><\/li>\n
                                                      • Solution<\/b>: Acknowledge that feeling unsure is normal, especially when stepping into a new field. Start with small wins\u2014completing a project, getting good feedback from peers, or understanding a complex topic. Over time, these achievements build confidence.<\/span><\/li>\n
                                                      • Celebrate Milestones<\/b>: Track and celebrate your progress, no matter how small. Completing a course, building a portfolio project, or even solving a particularly challenging problem are all reasons to celebrate.<\/span><\/li>\n
                                                      • Positive Reinforcement<\/b>: Surround yourself with supportive individuals who encourage your journey. Avoid comparing yourself to experts with years of experience; instead, focus on your own growth trajectory.<\/span><\/li>\n<\/ul>\n

                                                        Access to Real-World Projects<\/b><\/h3>\n
                                                          \n
                                                        • Challenge<\/b>: Many learners struggle to gain practical, real-world experience, which is crucial for landing a job in data science.<\/span><\/li>\n
                                                        • Solution<\/b>: Start by taking part in open-source projects. Platforms like GitHub have many ongoing projects that welcome new contributors. This can provide practical experience while also enhancing your collaboration skills. Participating in projects that involve data management systems can provide practical experience in integrating and maintaining diverse data sources.<\/span><\/li>\n
                                                        • Freelancing and Internships<\/b>: Consider freelance projects or internships, even unpaid ones, to get hands-on experience. Websites like Upwork or Fiverr can provide smaller projects that help build your portfolio.<\/span><\/li>\n
                                                        • Capstone Projects<\/b>: Many online courses and bootcamps include capstone projects, which simulate real-world scenarios. Treat these as if they were projects for an employer, and use them to showcase your abilities in your portfolio.<\/span><\/li>\n<\/ul>\n

                                                          \"\"<\/p>\n

                                                          Benefits of a Career in Data Science and AI<\/b><\/h2>\n

                                                          Take advantage of these incredible benefits by getting started today. Visit the Institute of Data\u2019s Data Science & AI Program page to explore how you can begin your career in this exciting field.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
                                                          Benefit<\/b><\/td>\nDescription<\/b><\/td>\n<\/tr>\n
                                                          Meaningful Work<\/b><\/td>\nData science allows you to solve real-world business problems and create a direct impact, whether it\u2019s improving healthcare or optimizing resources.<\/span><\/td>\n<\/tr>\n
                                                          Flexibility<\/b><\/td>\nMany data science roles offer flexible working arrangements, including remote work, freelancing, and the ability to work across different industries.<\/span><\/td>\n<\/tr>\n
                                                          Job Security<\/b><\/td>\nData professionals are in high demand across multiple industries. As data becomes a critical asset for decision-making, the need for skilled data experts continues to rise.<\/span><\/td>\n<\/tr>\n
                                                          Career Growth<\/b><\/td>\nThe field offers plenty of opportunities for growth. From data analyst roles to senior data scientist positions and even managerial roles, data professionals can steadily advance their careers.<\/span><\/td>\n<\/tr>\n
                                                          High Salary<\/b><\/td>\nData scientists are among the best-paid professionals in the tech industry, with salaries that increase significantly with experience and specialization.<\/span><\/td>\n<\/tr>\n
                                                          Diverse Opportunities<\/b><\/td>\nData science is applied in a variety of sectors such as healthcare, finance, sports, entertainment, and more, providing diverse job opportunities to suit your interests.<\/span><\/td>\n<\/tr>\n
                                                          Problem-Solving<\/b><\/td>\nWork on challenging projects that require innovative and analytical thinking. This makes every day different and intellectually stimulating.<\/span><\/td>\n<\/tr>\n
                                                          Learning & Development<\/b><\/td>\nThe dynamic nature of the data field means you are constantly learning. Companies often provide learning opportunities, courses, and workshops to keep data professionals up-to-date.<\/span><\/td>\n<\/tr>\n
                                                          Impact on Society<\/b><\/td>\nContributing to advancements in AI and data science can help shape the future, from developing smart cities to tackling climate change and creating solutions that benefit society as a whole.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                                                          Conclusion<\/b><\/h2>\n

                                                          A career in data science or AI offers many benefits, from high salaries to job stability and the satisfaction of solving real-world problems. If you\u2019re considering a career change, take the first step today\u2014enroll in a course that can help you gain the skills you need. Visit the Institute of Data\u2019s Data Science & AI Program page to get started or contact our local team for a free consultation.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                                                          The demand for professionals in the data science field and AI is growing fast as we move into 2025. For people in non-tech fields, switching to a future-focused career can be tough, but also very rewarding. Data science and AI offer not only job growth but also stability and good salaries. This guide aims to…<\/p>\n","protected":false},"author":35,"featured_media":89252,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[532],"tags":[],"class_list":["post-89251","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-datascience"],"_links":{"self":[{"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/posts\/89251","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/comments?post=89251"}],"version-history":[{"count":3,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/posts\/89251\/revisions"}],"predecessor-version":[{"id":89307,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/posts\/89251\/revisions\/89307"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/media\/89252"}],"wp:attachment":[{"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/media?parent=89251"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/categories?post=89251"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.institutedata.com\/us\/wp-json\/wp\/v2\/tags?post=89251"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}