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A Data Scientist Training Ground<\/h2>\n\n\n\n
Since 2013, NYC Data Science Academy<\/a> has successfully trained thousands of students and worked with over 500 hiring partners. It prides itself as a powerful training ground that provides high-caliber, accelerated training through a number of immersive programs in its area of expertise: data science. By focusing on data science alone, the academy is able to update its curriculum based on trends that are being observed in the field today. NYC Data Science Academy firmly believes in the importance of training their data scientists to be well-rounded. While some bootcamps offer the option of studying either Python or R language, NYC Data Science Academy recognizes the benefits of getting to grips with not just one, but both languages, before heading into the industry. So, who would be a good fit for NYC Data Science Academy? While anyone can enroll in a program, some prior background experience goes a long way. This experience doesn\u2019t have to be in data science; a background in STEM or another relevant field is equally valuable. Without much further ado, let\u2019s take a closer look at which programs are on offer and what they entail. Data Science Bootcamps<\/strong> NYC Data Science Academy\u2019s bootcamps offer a blend of theory and practice to ensure that students are equipped with a wide range of skills that can be put to use in a variety of real-life contexts. As mentioned, the bootcamp program stands out in that it teaches students both Python and R languages. Given that Python and R are the most dominant Data Science languages in the industry, being able to know both certainly puts graduates at an advantage in the job market. Learning the theory that underpins data science is paramount, but it is not much use on its own; in order to fully benefit from theory knowledge, you must be able to put it into practice. This is where NYC Data Science Academy\u2019s four projects come into play. These projects allow students to demonstrate their capabilities in a range of different contexts. The first project involves building a data visualization app from scratch using R. The second, building a web parsing and data analysis project using Python. The third project is an end-to-end machine learning project. Last but not least, there is the capstone project. Students can focus their capstone project on a subject of their choice, giving them more autonomy over their work. Many students choose a subject relating to their own experience or area of interest, and previous projects have covered areas such as healthcare, education, and music. Both full-time and part-time bootcamps are available at NYC Data Science Academy. The first option is the full-time program, which is an immersive 16-week bootcamp. This bootcamp, which was previously carried out in-person, has now shifted to a \u2018remote live\u2019 online platform that enables synchronous learning to take place in real-time with instructors and classmates. This means you\u2019ll be interacting as you would in a live classroom, and in a position to ask questions and clarify any doubts. Encompassing approximately 30-40 hours of study a week, this full-time program is demanding, so it\u2019s a good idea to make sure you have no other commitments if you want to get the absolute most out of the experience. If full-time study isn\u2019t possible, or if it\u2019s not what you\u2019re looking for, then you also have the option of studying the part-time program that lasts 24 weeks. The content of the curriculum remains the same, but the difference is that you will be learning asynchronously, that is, at your own pace via pre-recorded videos. In addition, this format supports those lectures with live learning sessions, hackathons, instructor office hours, and 1:1 mentoring. This is a great option for those with work or family commitments who are looking for more flexibility and the opportunity to organize their own schedule. The Academy recommends 20-30 hours a week for the part-time course, but as always, the more time you can dedicate, the better. As you\u2019ll be working more independently than you would in the full-time group setting, it helps to have some background in the field (either in STEM, analysis, or data analysis). However, don\u2019t panic if that\u2019s not the case, as there are also secondary programs available and Professional Development Courses<\/a> which can act as a stepping stone to prepare you before beginning the bootcamp. These courses tend to last between 4 and 6 weeks, ranging from Introductory Python<\/a> to more advanced Deep Learning, and are a great way to introduce you to the fundamentals before moving into a more in-depth bootcamp program. <\/p>\n\n\n\n <\/p>\n\n\n\n Data Science Bundles <\/strong> NYC Data Science Academy also offers data science bundles courses<\/a> in which you can enhance your skills in Python, R, machine learning, and more. These bundles offer a combination of part-time online courses that you can choose according to your interests and aims, allowing you access to a broad curriculum at a discounted bundle price. Additionally, all bundles offer optional financing plans<\/a> via a Climb Credit loan which can be repaid in smaller monthly installments. <\/p>\n\n\n\n <\/p>\n\n\n\n NYC Data Science Academy is made up of a strong community network of over 2,000 working data scientists, like-minded peers, and data experts across a range of fields. During your studies and beyond graduation, you are part of this community, interacting with fellow students as well as instructors, mentors, career advisors among others. <\/p>\n\n\n\n <\/p>\n\n\n\n Instructors<\/strong> The great thing about the Academy\u2019s instructors is that they not only hold positions as data scientists in the industry but also all come from academic backgrounds with extensive experience teaching at Universities. This industry insight coupled with strong pedagogical knowledge makes for instructors who are highly skilled in their role. One of these instructors, Drace Zhan, highlighted their \u201citerative approach when designing the curriculum, having a quarterly review based on industry and student feedback on what are the most critical skills required to adapt to a changing job market. Our projects\u2019 guidelines are carefully curated to reflect real-world demands from data analytics to data science roles.\u201d<\/p>\n\n\n\n <\/p>\n\n\n\n Mentors<\/strong> In addition to the expertise of instructors, students also have ongoing access to the support of a mentor who is there to help answer any questions or give advice and feedback throughout the course. “The one-on-one mentor support was very helpful with coursework and the projects, but also with career advice. I met with the mentors on a weekly basis and all 9 mentors worked as data scientists in the industry,” said Kesman Valdes, who is now a senior manager at Quality Systems Crown Holdings, Inc.<\/p>\n\n\n\n <\/p>\n\n\n\n Career Advisors<\/strong> Landing a great job can be hard work, especially in the current climate. Fortunately, NYC Data Science Academy provides its students with ample assistance when it comes to the job search, offering services ranging from personalized resume feedback sessions and interview guidance to access to the Academy’s network of hiring partners and events. \u201cAfter completing the program, I have had the opportunity to join several networking events, get interview skills coaching, and work one-to-one on my resume with a career services strategist,\u201d said Kesman. Luke Gray, now a data scientist at Naval Systems, also praised the Academy\u2019s career advisors, stating: \u201cThey devote a lot of time to your resume building, interview preparation, and student networking, and, personally, I found the alumni presentations they prepared were some of the most insightful presentations I’ve had. If you are serious about becoming a data scientist, you go to this school.\u201d Moreover, as a member of the community, you\u2019ll have lifelong access to the Academy\u2019s network and industry connections, whether you\u2019re still on your program, newly graduated, or 5 years into your data science career. The cost of tuition<\/a> at NYC Data Science Academy stands at $17,600, and payments can be made either by cash, credit card, check, or wire-transfer. Although not a minor sum, the cost is much cheaper than that of a college degree in a similar field, and more importantly, it is an investment in a successful career. If you have the option of paying upfront for the course, this investment is well worth it thanks to an increase in salary potential that will allow you to make the cost back in a short period of time once you are in your new role as a data scientist. Another thing to note is that the asynchronous programs are split into two semesters, so there is also the option of splitting the tuition fee into two separate payments. If upfront payment is not viable, don\u2019t worry. The Academy has partnered with Climb Credit <\/a>and Ascent Funding<\/a> to offer financing options should you wish to enroll in one of the programs. Ascent Funding offers 3 or 5-year loans with fixed interest rates regardless of income, employment, or educational background, and also offers the option to borrow up to an extra $7,500 for living costs. Similarly, Climb Credit also offers fixed interest rate loans on different types of credit, including students with no credit, and international students as long as they are able to apply with a suitable co-borrower. These loans are paid at around $400 per month over the course of 60 months. Boasting a robust curriculum encompassing the ins and outs of data science in both languages of Python and R, NYC Data Science Academy<\/a> puts itself forward as a strong option for those looking to kick start their career in the field. The Academy aims to put graduates in the best possible position to land a fulfilling role in data science upon finishing their program by equipping them with the most highly sought-after skills in the industry, a goal that is aided by high quality teaching and strong career services.
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<\/p>\n\n\n\nFundamentals First<\/h3>\n\n\n\n
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<\/p>\n\n\n\nThe \u201cWe\u201d Community <\/h3>\n\n\n\n
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<\/p>\n\n\n\nFinancing Matters<\/h3>\n\n\n\n
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<\/p>\n\n\n\nConclusion<\/h2>\n\n\n\n
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