If you\u2019re reading this article, chances are you\u2019re interested in the field of data science. There are many ways data science might have piqued your interest\u2014perhaps the potential to explore various industries or the dynamic collaborations and projects you can join. For some, it is the leadership, growth, and development opportunities that appeal to them. Others are intrigued by the challenges in building solutions to real-world problems.<\/p>\n\n\n\n
No matter what part of data science reeled you in, now that you\u2019ve found this interest, you\u2019re likely wondering what opportunities are out there for data scientists and how to jumpstart the kind of career you want. We are here to show you what the data science job field looks like and what opportunities await you.<\/p>\n\n\n\n
NYC Data Science Academy<\/a> (NYC DSA) is one of the leading bootcamps in data science education. To know more about what it can offer, we highlight the story of Joseph Lee<\/a>, a graduate of NYC Data Science Academy and role model to many students.<\/p>\n\n\n\n A 2015 graduate, Joseph is currently the Chief Data Scientist and Co-Founder of reAlpha, a real estate investment company that utilizes algorithms and deep learning. To get there, Joseph had to take several transitions.<\/p>\n\n\n\n From Northwestern University to Berkeley California, from tech startups to finance firms, Joseph Lee\u2019s career trajectory exemplifies the breadth and depth that a career in data science offers. And his time at NYC Data Science Academy played an integral part in ushering him along his journey.<\/p>\n\n\n\n NYC Data Science Academy\u2019s flagship bootcamp covers in-demand tools like R and Python to help you build a strong technical foundation in data science.<\/p>\nApply to NYC Data Science Academy today.<\/a>\n<\/div>\n Joseph first discovered his love for data science when he was pursuing an undergraduate degree at Northwestern University, studying computer science. He was instantly intrigued by machine learning, enjoyed the material he was learning, and wanted to see how he could incorporate it into his career after graduation.<\/p>\n\n\n\n \u201cThrough some Internet sleuthing I discovered the career of data science to be on the rise, and that machine learning was a big part of that,\u201d he says.<\/p>\n\n\n\n What he found most interesting about data science was the idea that a data scientist didn\u2019t just carry out technical tasks. There was more to the job than that. \u201cIt\u2019s important to own what you develop as a data scientist and that you are responsible for what you make, that you are creating an impact,\u201d Joseph says.<\/p>\n\n\n\n Though he had studied computer science and had a good handle on Java and C languages, Joseph\u2019s Python and R capabilities were weak. He realized he wasn\u2019t equipped to apply for an entry-level role as a data scientist out of school. He was inexperienced with scraping languages and applied machine learning, and he had to do something about it.<\/p>\n\n\n\n As Joseph was ready to graduate from Northwestern, instead of applying to jobs in software development or data science\u2014the sorts of jobs he would eventually like to land in\u2014he researched how to bolster his skills to be more prepared for data science roles.<\/p>\n\n\n\n Through his research, he came across NYC Data Science Academy.<\/p>\n\n\n\n There are multiple data science bootcamps students can choose from. So, what made NYC Data Science Academy stand out to a qualified, motivated person like Joseph?<\/p>\n\n\n\n One thing that appealed to him was the breadth and diversity of the NYC Data Science Academy\u2019s Data Science curriculum<\/a>. Joseph wanted to attend a data science program to improve his computer and data science skills, and he found that the academy\u2019s curriculum provided that opportunity to its students. It focused on R and Python, the two languages Joseph wanted to master before applying for jobs.<\/p>\n\n\n\n It also was exciting to him that the school was in New York City. Joseph had always wanted to move to New York, and he knew there was no shortage of exciting opportunities there.<\/p>\n\n\n\n He was admitted to NYC DSA, and throughout his time there, in addition to focusing on R and Python, he developed his portfolio of projects to demonstrate his skill to future employers. He made sure to clearly explain the projects he was involved in on his resume.<\/p>\n\n\n\n He says that these two things\u2014the emphasis on the breadth of learning and his portfolio\u2014were what allowed him to achieve his first role as a data scientist. Within a few months of sending out his applications toward the end of the program, startups started to reach out to him.<\/p>\n\n\n\n Joseph was interviewed by several startups after his time at NYC Data Science Academy, but it was AI industrial company Uptake<\/a> that he decided to go with. He felt there was a strong alignment between what they could offer him as a data scientist and the type of career he wanted.<\/p>\n\n\n\n At Uptake, he found that Python and R were the most highly used languages and that applying them to machine learning was imperative in his work there. Because of his training at the academy, he could work on and assist in various projects. And Joseph was able to engage with more experienced data scientists who took him under their wing and showed him how to grow his skill set based on the solid foundational knowledge he had from NYC DSA.<\/p>\n\n\n\n Even though Joseph had a job in the field, he wasn\u2019t finished learning and growing his knowledge in data science just yet. So, Joseph decided to pursue a Master’s Degree in Data Science at the University of California, Berkeley alongside exceptional students.<\/p>\n\n\n\n Joseph believes that his time at NYC DSA helped prepare him for the program at Berkeley and get accepted into the program. Firstly, it helped him develop his career and skill set. It also helped him get to Uptake. Having work experience is an asset, he says, in applying to Master\u2019s degree programs.<\/p>\n\n\n\n There was also some overlap in the lessons that Berkeley and NYC DSA provided. This overlap is because the academy is functionally breadth-focused, giving those unfamiliar with data science an idea of how they can grow. Because of his previous experience at the bootcamp, he could skip some of the entry classes and focus on the advanced classes he was more interested in now, like data ethics.<\/p>\n\n\n\n Joseph’s career in data science is interesting and informative to prospective students because he has taken many steps to improve his skills throughout his career. He demonstrates first-hand how data science can lead you down many exciting paths in many directions.<\/p>\n\n\n\n It\u2019s up to you to choose where you go.<\/p>\n\n\n\n After his time at Berkeley, Joseph was ready for a change. He\u2019s always been interested in finance and thought there were areas within the finance industry that data science could disrupt, uplift, and innovate. His ambitious nature allowed him to make his dream a reality, which paired well with his desire to return to New York.<\/p>\n\n\n\n He accepted an offer to join a financial services team at McKinsey<\/a>, a prominent management consulting firm. What drew him to this role was the company’s interest in applying data science and solutions engineering to its clients. It also allowed him the opportunity to think about data science beyond the usual technical skillset.<\/p>\n\n\n\n \u201cIt\u2019s one thing to be really good at development and modeling, but it\u2019s very important to own what you make, and ethically market and sell it,\u201d he says.<\/p>\n\n\n\n After working for McKinsey, Joseph began working for Readvantage\/Blackstone<\/a>, which introduced him to the world of real estate.<\/p>\n\n\n\n His ultimate goal, however, was always to found his own company. Through his experiences living and working in New York as a data scientist, he reaped the fruits of building a network of professionals. Working with McKinsey and Blackstone had introduced him to people in the world of real estate that he would have likely not had the opportunity to meet otherwise.<\/p>\n\n\n\n \u201cI was approached by interested parties who liked my background and knew about my ability to deliver data solutions regardless of the industry,\u201d Joseph says.<\/p>\n\n\n\n The interested parties created a role for Joseph as Chief Data Scientist and Co-Founder of reAlpha<\/a>, a machine-driven residential real estate investment company that invests at a large scale.<\/p>\n\n\n\n Now, Joseph has gone from applying to jobs to being the one hiring his employees. So, what does he look for in a job candidate?<\/p>\n\n\n\n \u201cI\u2019m looking for candidates who are interested in taking ownership, developing skill sets so that within a short amount of time, they can operate with minimal overhead. [I\u2019m looking for] someone with pride in what they own and who is ready to continue to take it to the next level,\u201d he says.<\/p>\n\n\n\n
\n\n\n\nBeginning a Data Science Career: A Data Scientist\u2019s Story<\/strong><\/h2>\n\n\n\n
Why Choose NYC Data Science Academy?<\/strong><\/h2>\n\n\n\n
From Attending a Data Science Bootcamp to Becoming a Data Scientist<\/strong><\/h2>\n\n\n\n
Can NYC Data Science Academy Help Prepare You For a Master\u2019s Degree?<\/strong><\/h2>\n\n\n\n
How to Grow Your Data Science Career<\/strong><\/h2>\n\n\n\n
In The Hiring Seat<\/strong><\/h2>\n\n\n\n