Dec 4 / Neeraj Kumar

The Role of Coding Skills in High School Data Science Projects

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As the world continues to generate an enormous amount of data, it is becoming increasingly apparent that learning how to code will be essential for all students, especially those who wish to study something such as data science. This trend has now crossed over to high schools, where students are tasked with data science projects to tackle real-life issues, appreciate data's relevance, and sharpen their critical thinking abilities.

Why is coding important in data science?

Writing code is critical because it empowers students to work smart with data. Handling data on a human scale is relatively slow, error-prone, and highly restricted, especially when handling large amounts of data. For instance, consider a learner who wants to calculate the test scores of countless students from various schools, ages, genders, and socioeconomic backgrounds and their study behavior. To put it mildly, such a task would be challenging, if not impossible, without coding. With coding, students can take it up by finding ways to eliminate task redundancy, conduct data analysis efficiently and accurately, and even present the findings.
Learning how to code also trains students to develop a particular way of thinking in a structured manner. Depending on the assignment they are working on, students may need to simplify the task into various steps, compose a set of commands for a machine to execute, and check that the operations have been performed accurately. Such degrees of logical reasoning come in handy for data science and enhance their problem-solving ability. Additionally, coding is an ability that can be applied in other areas; once a student understands the concept of coding, it ceases to be relevant to data science and includes app development, machine learning, and game design, among other fields. The potential to code presents many opportunities and supports the development of creative thoughts throughout one’s life.

What programming languages should high school students learn for data science?

Critical Languages for Data Science: Python and R
Two programming languages are overused in data science: Python and R. These languages are embraced by the industry and scholars alike due to their flexibility, ease of use, and the many tools available for working with data.
For many high school students inclined towards pursuing studies in data science, one language will most likely be introduced to them in their very early stages: Python. It is apparent why Python will be the first language for students learning data science. Python is simple and easy to read, encouraging people who are timid about coding, especially novices. There is also a vast population of users, meaning that students will have access to numerous tutorials, guides, and forums where help can be sourced. Beyond its accessibility, Python boasts powerful libraries specifically designed for data manipulation, analysis, and visualization. Thanks to libraries like Pandas for data manipulation and Matplotlib and Seaborn for data visualization, students can load, clean, and transform data — in no time. In addition, Python is a multipurpose programming language that students can use outside their data science projects, such as web development, automation, etc., or even Artificial Intelligence (AI).
On the other hand, R was made with the only goal of doing statistical analysis and data visualization; hence, it is very focused and task-oriented regarding data science. If R can be said to appeal to students more with excess maths and statistics training than training on data itself, then it is a more than helpful tool for data. Its libraries include but are not limited to, dplyr for data manipulation and ggplot2 for visualization, enabling students to carry out advanced statistical analysis and produce highly polished and flexibly structured graphics. R enjoys high popularity and penetration into academic and research institutions where correct statistics and complex data modeling are paramount.
The selection of programming languages, R and Python, pivots on the background and aspirations of the student. Students aiming at a more interdisciplinary approach may find a more significant appeal in this general-purpose, constantly evolving programming language. In contrast, R may better suit students interested in statistics and pure data science. However, both languages provide high school students the tools to take their data science projects to the next level.

Coding in Data Science: The Essential Skills

Simply put, coding in data science is not limited to a few lines of code. Data science involves using proper tools to process, examine, and present data worth understanding. These three core aspects, data manipulation, data analysis, and data visualization, are the pillars of any data science work, and each involves a great deal of coding.

Data Manipulation

Imagine a high school student working with a large dataset on climate change, containing thousands of rows of temperature, precipitation, and wind speed data across multiple years and regions. Before any meaningful analysis occurs, the data needs to be organized, cleaned, and prepared. This is where coding for data manipulation comes in.
In Python, students can use the Pandas library. This versatile tool enables them to import a database into their workstation in a tabular format called a data frame. It does other things, such as sifting, combining, ordering, and modifying the data. For instance, suppose that in an analysis, one needs to take care of missing temperature measurement values or where outliers shift the analysis. With the aid of Pandas, all these can be done within a few lines of code. This is the case also with R, where with the help of the dplyr package, functions are available to students for performing data manipulation within the shortest time possible, such as row selection with the use of filter(), new column creation through mutate() and summary of data through summarize(). This kind of data management skill is mandatory for students to perform reliable and accurate analyses.
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Data Analysis

Upon cleaning and organizing the raw data, it is subjected to analysis to identify specific patterns, trends, or relationships. This analytic process can be coded, thus allowing students to reduce the time to conduct these analyses and eliminate human errors. For instance, a student can write a short program to efficiently determine various parameters' mean, median, and standard deviation depending on a particular data set.

In the Python programming language, computational numerical tasks can be performed with the help of the NumPy library, and students can create predictive analysis machine learning algorithms by utilizing the Scikit-learn library. Regarding work documents addressing regression for forecasting or clustering, coding makes it possible to do so with higher-grade algorithms. The stats package incorporated into R has hundreds of statistical procedures, including hypothesis testing, regression, etc.

Data Visualization

The ability to visualize data is arguably one of the most enjoyable aspects of a data science project. Students appreciate their efforts when they see that their work is worth the time and effort they contributed through plots, graphs, and charts relating to exciting events. Visualization, however, is not simply a decoration of data; it can also be effective insight communication. For this reason, coding is also necessary because students need to customize their visualizations and make several plots illustrating some central aspects of data.

In Python, the Matplotlib and Seaborn libraries are primarily used to draw all types of plots, such as heatmaps, scatter plots, and simple line charts. For example, when a student is interested in discovering how temperature has changed in various regions at different times, Seaborn can provide such data in clear visual patterns that are simple and understandable. In the case of R, when there’s a need for complex and easy-to-edit graphs, ggplot2 is the tool for the job. Students can choose the best way to portray data by varying the colors, themes, and layouts.

Online Courses

Platforms like Coursera, edX, AIBrilliance, and Udemy offer structured courses to help you learn Python or R for data science.

  • Coursera: Check out “Python for Everybody” from the University of Michigan, which is perfect for beginners, or “Data Science: Foundations using R” from Johns Hopkins University, which is an excellent choice for learning R.
  • edX: Explore courses such as Microsoft’s “Introduction to Python for Data Science” or Harvard’s “Data Science Essentials” for a solid foundation in both languages.
  • AIBrilliance: Explore AIbrilliance's offerings, such as courses like “Introduction to Data Science and Machine Learning,” “Python, Matrices, and Linear Algebra for Data Science and ML,” or “K-12 Data Science and Machine Learning,” all taught by Rahul Rai.
  • Udemy: Popular courses include Jose Portilla’s “Complete Python Bootcamp,” which is a comprehensive guide to Python, and Kirill Eremenko’s “R Programming A-Z™: R For Data Science With Real Exercises!,” which offers practical R exercises.

Tutorials

For those who prefer to learn at their own pace, there are plenty of free, high-quality resources available:

  • W3Schools: Offers interactive tutorials on Python, covering everything from the basics to advanced concepts.
  • Kaggle: An excellent platform for hands-on tutorials and coding exercises in Python and R, specifically focused on data science and machine learning.
  • Python.org: The official Python site provides a wide range of tutorials and documentation for learners at all levels.

Books

Books can be an excellent resource for deepening your understanding of programming concepts. Here are two highly recommended titles for beginners:

  • “Python for Data Analysis” by Wes McKinney: This book is a comprehensive guide to working with data in Python, covering data manipulation, processing, and analysis.
  • “R for Data Science” by Hadley Wickham and Garrett Grolemund: This book offers a thorough introduction to R, with a strong focus on data visualization, transformation, and modeling.

Conclusion

For adolescent students who participate in data science projects, they need to have coding skills. Learning to program in Python or R is one of the ways through which students become capable of cleaning, analyzing, and visualizing data efficiently and effectively. These skills promote the success of their data science endeavors and enable them to acquire encore opportunities in their future education or even career. This allows the students to analyze and present the data to make the report usable and actionable, presenting trends and conclusions that will lead to innovation and decision-making.

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