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Does Data Science Involve Coding?

coding in data science

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.

Machine Learning

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

coding in data science

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.

Are Data Science and Computer Science the Same Thing?



No, data science and computer science are not the same thing. They are distinctly different majors with different objectives.

Data science and coding are closely related in that data science requires extensive use of coding. Both computer science majors and data science majors will typically become proficient computer programmers. The computer science major is focused on not only learning to code but also on having a thorough understanding of the architecture and theory of computer science. Computer science majors could easily become successful data scientists, but they also have a broad variety of other choices for career paths.

The successful data science major’s end goal is typically using their computing skills on behalf of their future employers to extract useful information out of data; they will then attempt to use the data to prevent fraud, increase operating efficiency or otherwise solve the problems hindering their organization from maximizing profitability. A good data science program will teach all the skills necessary to achieve this goal, and computing is only a small part of it. Statistics is also an essential part of the program.

What Are the Top Coding Languages for Data Science and Machine Learning?

coding in data science

The top coding languages for data science tend to be the ones that enable the data scientist to quickly and easily collate massive quantities of data and then cull through it. There are multiple languages that data scientist can use to achieve this objective and their other related goals. The following programming languages are the ones that data scientists tend to use most frequently:

Python

Python is the coding language that data science majors at university are likely to learn first. It is a versatile, multi-purpose, open source programming language that offers data scientists a number of benefits. One of its main advantages is that it is extremely easy to learn, use and debug. Learning some programming languages is literally like learning to speak a foreign language; in contrast, for native English speakers, learning to code in Python can seem almost as comfortable as reading and writing in English. This characteristic and its open-source status ensure that Python is widely used amongst data scientists and many other tech professionals.

Data scientists frequently have a need to save time by automating various tasks. When automation is required, Python is a reliable, time-saving tool to use.

According to Zdnet.com, Python is currently the most important programming language data scientists are using. It offers data scientists a number of useful libraries such as Pandas, NumPy, Scikit-learn, Gradio, SciPy, Keras, Statsmodels, TensorFlow, MatplotLib, Seaborn, Plotly and others. The data scientist can use each of these libraries for achieving different objectives.

Pandas is one of libraries data scientists use most frequently because this library’s primary purpose is empowering data analysis. When a data scientist looks at a mountain of new, unexplored structured data that needs to be cleaned up and analyzed quickly, Pandas is a reliable tool that’s ideal to use for accomplishing that job. NumPy is another library that’s useful for helping data scientists to clean up data and manipulate it in various ways.

When it comes to machine learning, Scikit-learn is a top priority Python library for data scientists to master. To get the most out of this library, it’s helpful to first take the data to be modeled and clean it using tools such as Pandas or NumPy. Then the predictive modeling tools included in the Scikit-learn library can be used for building either supervised or unsupervised machine learning models.

SQL

After Python, SQL is the second most important programming language for data science, according to Zdnet.com. This is such a high priority language to know because it is the industry standard language for interacting with relational databases. Querying databases is a crucial skill for data science professionals, and so it’s critical for the aspiring data scientist to gain a working knowledge of SQL. Furthermore, data scientists typically need to work with this language in cases where structured data is involved.

Data scientists might write SQL scripts or queries to automate tasks such as aggregating data, calculating averages or determining the maximum and minimum in a given data set. SQL is also useful for storing data in databases and extracting data from databases.

R

R is a sophisticated, open-source, broadly supported scripting language that can handle massive, complex data sets. This is the language a data scientist might want to use when statistical computing, mathematics and graphics are all involved. This language offers its programmers a massive collection of packages, libraries and other tools that are suitable to use for quantitative applications. A few of these include Esquisse, Dplyr, Ggplot2, BioConductor, Shiny, Lubridate and R Studio.

JavaScript

Like Python, JavaScript is an adaptable object-oriented programming language that offers data scientists a broad variety of libraries to work with. This is a language worth learning for a variety of reasons; besides just data science, people are also using this language for web development, creating mobile apps and designing new computer games.

A data scientist would want to prioritize learning to code in the Javascript language because it is one of the best tools available for creating visualizations that explain and describe the data being manipulated. The downside for data scientists is that JavaScript does not offer a broad variety of data science-specific libraries, tools or packages like some other languages such as R and Python do.

Java

A data scientist might choose to use the Java programming language for tasks relating to machine learning, data analysis and data mining. It’s an especially appropriate choice in cases where these applications need to be integrated into larger development projects.

Java also offers extensive libraries for data mining and machine learning applications.

A data scientist who uses Java might also find it helpful to learn Scala, which is an extension of the Java programming language. Scala enhances the data science professional’s capacity for manipulating high volume datasets and handling large volumes of siloed data. Scala also offers a vast number of well-supported, useful libraries.

C/C++

Many of the latest programming languages draw on C/C++ as their codebase, so it makes sense for a data scientist to have a strong foundation in C. Beyond that, C/C++ offers benefits such as a capacity for compiling data quickly and efficiently. A data scientist would want to consider using C/C++ for projects that require high performance and massive scalability.

Julia

Julia is a multi-purpose, high-performance programming language that data scientists use for numerical analysis. It can be useful for empowering visualization and manipulation of complex, multi-dimensional datasets. Furthermore, this is a great language to work with when conducting risk analysis operations. This program includes built-in support for a useful package manager.

Other Programming Languages That Can Sometimes Be Useful for Data Science

Other programming languages that some data science professionals use frequently include Bash/Shell, Typescript, PHP, Rust, HTML, CSS and Go.

Is Computer Science the Best Major for Becoming a Data Scientist?

degree for data scientist

Data science and coding go hand in hand, so computer science is an extremely useful major course of study for aspiring data scientists. However, computer science isn’t the only acceptable major that aspiring data science professionals might want to consider pursuing.

Data science is a relatively new major available to bachelor’s degree candidates. This major course of study wasn’t broadly available when data science initially gained prominence as a viable career path in the corporate world. With the rising popularity of “big data” and data analytics, increasing numbers of universities are now offering dedicated bachelor’s degree programs in data science. Data science master’s degree programs have also become widely available.

Data science is the ideal major for students who are already reasonably sure they want to work as data scientists after graduation. For those who aren’t as committed to that specific career path, computer science might still be a better choice of majors. This is because a computer science degree offers many of the needed skills for data science, plus it also offers a broader level of versatility in case the student later decides that it would be better to follow a different career path.

Statistics is another appropriate major for students who wish to pursue data science careers. Aspiring data scientists who do not choose statistics as a major will need to either declare it as a minor or take as many statistics electives as possible. This is one of the most important subjects for data science professionals to master.

It’s also possible to succeed with a career in data science after pursuing a major in another subject such as applied mathematics, economics, engineering, physics, chemistry, biology, microbiology, one of the social sciences or a related field. The major course of study doesn’t have to be specifically focused on data or math to provide a good foundation for acquiring a data scientist’s skill set.

However, an aspiring data science professional does need to know how to code. The student must also have the ability to apply data analysis techniques to whatever course of study they select.

Let’s say that an aspiring data scientist wants to pursue a data-driven career in the field of healthcare. The student in this case might opt to study microbiology or premed studies at the undergraduate level. This could provide the student with the necessary underlying foundational knowledge to understand how the human body works. The student would then be better equipped to understand the types of problems that healthcare professionals might have a realistic chance at using data and machine learning to solve.

In cases like these, it would be appropriate for the student to choose computer science, statistics, data analytics or applied mathematics as a minor course of study. It would also be beneficial for such a student to prioritize studying additional electives that involve statistics, probability, data analytics, machine learning and computer programming.

Conclusion

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.

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