Data is one of the hottest topics over the past several years. Organizations are seeing how much information they have at their fingertips and are finding ways to use this information to make smart business decisions. Data Science is the study of that information with the goal of finding insights. As a whole Data Science is a complex field of study comprised of several intricate steps to put this information to good use. For the purpose of this series, we are calling them pillars which are Data Analysis, Data Visualization, Programming, Mathematics, Machine Learning, Web Scraping, Integrated Development Environment (IDE) and Deployment. We will discuss each of these in detail to help you understand how each part of the process works as well as provide a list of how current or future Data Scientists can upskill themselves in these specific areas.
Programming:
Programming and Data Science are interconnected, “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.” Knowing how to navigate and program data hierarchies, or big data, and query certain datasets alongside knowing how to code algorithms and develop models is invaluable to a data scientist. A Data Scientists goal is to use their programming skills to extract useful information out of data. For example, prevent fraud, increase a department’s efficiency or solve problems that are affecting the firm’s profitability. Some of the typical programming Languages used in Data Science are Python, R and Java.
Now lets take a deeper dive into these programming languages and some courses to help you upskill yourself:
Python:
“Python is open source, interpreted, high level language and provides great approach for object-oriented programming.” It is the most commonly used programming language for data scientists because it provides great functionality to deal with mathematics, statistics and scientific function as well as has great libraries tailored to data science application. One of the main reasons why Python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background. It is also more suited for quick prototyping.
Here are a few courses to upskill your python skills:
- Python for Everybody – University of Michigan
- Crash Course on Python – Google
- Advanced Python – Reconnaissance – Infosec
- Advanced Data Science with IBM – IBM Skills Network
- Data Processing Using Python – Nanjing Univesity
R:
R is an open-source programming language that is widely used as a statistical software and data analysis tool and is mainly used for complex data analysis in Data Science. R provides extensive support for statistical modelling; suitable tool for various data science applications because it provides aesthetic visualization tools. “R is heavily utilized in data science applications for ETL (Extract, Transform, Load). It provides an interface for many databases like SQL and even spreadsheets.”
Here are a few courses to upskill your R Skills:
- Data Science: Foundation using R – Johns Hopkins University
- Data Analysis with R – Duke University
- Data Analysis with R Programming – Google
- Application of Data Analysis in Business with R Programming – Coursera Project Network
- Data Analysis with R – IBM Skills Network
Java:
Java is based on object-oriented programming, as a result it stays popular among programmers. While Java cannot be as easy as Python, it is fairly beginner-friendly and easy to understand. Java for data science is perfect when it comes to scaling your products and applications. This makes it the ideal choice when you’re considering building extensive and more complex ML/AI applications. Java is highly functional in several data science processes like data analysis, including data import, cleaning data, deep learning, statistical analysis, Natural Language Processing (NLP), and data visualization. “The majority of code in Java is experimental. Java is a language that is statically typed and compiled, whereas Python is a dynamically organized and analyzed language. This single difference gives Java a faster runtime and more comfortable debugging.“
Here are some courses to upskill your Java skills:
- Java programming and Software Engineering Fundamentals – Duke University
- Analyse data sets with Java streams – Coursera Project Network
- Use Python and Java to create a GUI application – Coursera Project Network
- Object Oriented Programming in Java – University of California San Diego
- The Java Language – LearnQuest
Programming is a crucial skill for data scientists. A thorough understanding of programming languages such as Python, R, and Java, is essential for efficient analysis and modelling of large datasets. Python is a widely used language for data science because of its simple syntax, extensive libraries and easy adaptability for non-engineers. R, on the other hand, is commonly used for complex data analysis and statistical modeling. Java, based on object-oriented programming, is ideal for building extensive and more complex AI/ML applications. There are various online courses available to upskill one’s programming skills and understanding of data science applications. If you are interested in learning more about data science, or exploring career opportunities in the field, feel free to get in touch with us. We would be more than happy to provide guidance and support as you navigate this exciting field.
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