up for either the certificate program or courses that the certificate requires.
The options for learning how to code never seem to end, and advanced degrees typically appeal to a particular group of people. While not necessary for either learning to code or obtaining a coding job, an advanced degree can help accelerate your learning and differentiate you from other job candidates. Here are the two types of advanced degree programs:
❯❯ Master’s degree: A technical degree that allows you to explore and specialize in a particular area of computer science such as artificial intelligence, security, database systems, or machine learning. Based on the course load, the degree typically takes one or two years of full-time, in-person instruction to complete. Upon completion, the degree can be a way for a student who pursued a nontechnical major to transition into the field and pursue a coding job. Alternatively, some students use the master’s degree experience as a way to gauge their interest in or improve their candidacy for a PhD program.
❯❯ Doctorate degree: A program typically for people interested in conducting research into a specialized topic. PhD candidates can take six to eight years to earn their degree, so it’s not the most timely way to learn how to code. PhD graduates, especially those with cutting-edge research topics, differentiate themselves in the market and generally work on the toughest problems in computer science. For example, Google’s core search algorithm is technically challenging in a number of ways – it takes your search request, compares it against billions of indexed web pages, and returns a result in less than a second. Teams of PhD computer scientists work to write algorithms that predict what you’re going to search for, index more data (such as from social networks), and return results to you five to ten milliseconds faster than before.
Graduate school computer science curriculum
The master’s degree school curriculum for computer science usually consists of 10 to 12 computer science and math classes. You start with a few foundational classes, and then specialize by focusing on a specific computer science topic. The PhD curriculum follows the same path, except after completing the coursework, you propose a previously unexplored topic to further research, spend three to five years conducting original research, and then present and defend your results before other professors appointed to evaluate your work.
Table 2-2 is a sample curriculum to earn a master’s degree in CS with a concentration in Machine Learning from Columbia University. Multiple courses can be used to meet the degree requirements, and the courses offered vary by semester.
TABLE 2-2 Columbia University MS in Computer Science
The curriculum, which in this case consists of ten classes, begins with three foundational classes, and then quickly focuses on an area of concentration. Concentrations vary across programs, but generally include the following:
❯❯ Security: Assigning user permissions and preventing unauthorized access, such as preventing users from accessing your credit card details on an e-commerce site
❯❯ Machine learning: Finding patterns in data, and making future predictions, such as predicting what movie you should watch next based on the movies you’ve already seen and liked
❯❯ Network systems: Protocols, principles, and algorithms for how computers communicate with each other, such as setting up wireless networks that work well for hundreds of thousands of users
❯❯ Computer vision: Duplicating the ability of the human eye to process and analyze images, such as counting the number of people who enter or exit a store based on a program analyzing a live video feed
❯❯ Natural language processing: Automating the analysis of text and speech, such as using voice commands to convert speech to text
Performing research
Students are encouraged in master’s degree programs and required in PhD programs to conduct original research. Research topics vary from the theoretical, such as estimating how long an algorithm will take to find a solution, to the practical, such optimizing a delivery route given a set of points.
Sometimes this academic research is commercialized to create products and companies worth hundreds of millions to billions of dollars. For example, in 2003 university researchers created an algorithm called Farecast that analyzed 12,000 airline ticket prices. Later, it could analyze billions of ticket prices in real time, and predict whether the price of your airline ticket would increase, decrease, or stay the same. Microsoft purchased the technology for $100 million and incorporated it into its Bing search engine.
In another example, Shazam was based on an academic paper that analyzed how to identify an audio recording based on a short, low-quality sample, usually an audio recording from a mobile phone. Today, Shazam lets a user record a short snippet of a song, identifies the song title, and offers the song for purchase. The company has raised over $100 million in funding for operations and is privately valued at over $1 billion. Both products were based on published research papers that identified a problem that could be addressed with technology and presented a technology solution that solved existing constraints with high accuracy.
Your own research may not lead to the creation of a billion-dollar company, but it should advance, even incrementally, a solution for a computer science problem or help eliminate an existing constraint.
Your classroom work helps create a theoretical foundation but can be divorced from the real world. Actual real-world problems often have inaccurate or incomplete data and a lack of obvious solutions. One way to bridge the gap from the classroom to the real world is to take on an internship.
Internships are 10- to 12-week engagements, usually over the summer, with an employer on a discrete project. The experience is meant to help an intern assess whether the company and the role are a good fit for permanent employment and for the company to assess the intern’s abilities.
The competition for interns is just as strong as it is for full-time employees, so interns can expect to be paid. Top tech companies pay interns between $6,000 and $8,000 per month, with Palantir, LinkedIn, and Twitter topping the list. After the internship is finished, companies offer successful interns anywhere from $5,000 to $100,000 signing bonuses to return to the firm to work full time.
Types of internship programs
Companies structure their internship program differently, but the following configurations are more common than others:
❯❯ Summer internship: The majority of internships happen during the summer. Because of the work involved in organizing an intern class, larger companies
