Data science is one of the most popular career choices for technically inclined college graduates, and working in the data science industry requires strong coding skills. Data scientists use artificial intelligence, or machine learning, algorithms to detect patterns in large sets of data. Without these machine learning programs, the useful information contained in the data would be imperceptible to the human eye. Data scientists use statistics and probability theory to make claims about data sets with a specified degree of confidence.
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Implementing a machine learning program is less demanding than creating a commercial software application, so data scientists don’t need to have the same software engineering skills that application developers must have. Machine learning algorithms can be quickly implemented using ready-made libraries in Java, C, Python, and other programming languages. Data scientists don’t need to be experts in programming artificial intelligence code because the machine learning logic is already implemented by developers who specialize in that type of programming. Data scientists specialize in using machine learning code to detect patterns in data, so they must thoroughly understand how the functions work and how to include them in a script or application, according to the Bureau of Labor Statistics.
When the appropriate functions of a machine learning library have been added to a program, the data is fed to the functions from a file or folder on the hard drive. The machine learning algorithm analyzes the data for as many iterations as necessary to reach a conclusion with the level of confidence specified by the program. This type of program is referred to as a “black box” because the steps taken by the program to reach a conclusion are unknown. The machine “learns” how to arrive at the answer it’s looking for and returns the answer when it has enough confidence in its calculations. The data scientists executing the program may set the required level of confidence to 95 percent or another appropriate value.
Data Structures and Algorithms
A thorough understanding of data structures and algorithms is necessary to create efficient code that can analyze large sets of data. To the extent that a data scientist is a programmer, the job of a data scientist is to produce the most efficient and accurate code possible. Professional data scientists typically have computer science degrees, so they learn essential programming skills as well as the theory of data structures and algorithms during their undergraduate years. Data structures are patterns implemented in code to store data sets. The choice of data structure depends on the situation, and programmers choose the appropriate data structures and algorithms by analyzing the time complexity of the program. The most common data structure is an array.
Other choices include trees, lists, dictionaries, maps, heaps and hash tables. While it takes only one step to locate a value stored in a hash table, a search function must iterate over all data points stored in an array to find the correct value, in the worst case. Data values stored in a tree can be located in a length of time equal to the logarithm of the size of the tree.
Data science is a rapidly growing industry, and advances in technology will continue to increase demand for this specialized skill. While data science does involve coding, it does not require extensive knowledge of software engineering or advanced programming.