Mastering Data Analysis: Do We Actually Need to Learn How to Code?

Written by: Timotius Abian

What is Data Analysis?

According to Shamoo and Resnik (2009), data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Moreover, there are four type of data analysis:

1. Descriptive Analysis: This type of analysis focuses on summarizing and describing the features of a dataset, providing insights into what has happened based on historical data.
2. Diagnostic Analysis: This analysis delves deeper to understand the reasons behind past outcomes. It identifies causes and correlations by exploring the data more comprehensively.
3. Predictive Analysis: Predictive analysis uses statistical models and machine learning techniques to forecast future outcomes based on historical data.
4. Prescriptive Analysis: This type of analysis goes beyond prediction and recommends actions you can take to affect desired outcomes. It combines insights from predictive analysis with actionable strategies.

Why Should We Learn Data Analysis?

From understanding global economics to international policies, data analysis skills would be the tools to make informed decisions and provide data-driven recommendations. By excelling in data analysis, you can significantly enhance your career journey in the International Fields, such as being a policy analyst and a research analyst. 

By the definition above, you might be wondering, is data analysis really that hard? Does it really require us to code?  While it may sound difficult, with the right approaches and resources, people with any background can learn it. There is nothing you should worry about, because we’ve got the tips to learn data analysis from zero!

Tips to Master Data Analysis From Zero

1. Review the Basics of Statistics: You might want to look back again at your high school and college mathematics and statistics books! Understanding fundamental statistical concepts is crucial. Statistics would help you interpret data accurately and draw meaningful conclusions. Here are the key resource
   • Probability: Understanding basic probability rules and distributions.
   • Descriptive Statistics: Summarizing and describing data sets using measures like mean, median, mode, variance, and standard deviation.
   • Estimation: Techniques for point estimation and constructing confidence intervals to infer population parameters.
   • Hypothesis Testing: Methods for testing hypotheses using p-values, t-tests, chi-square tests, and ANOVA.
   • Regression Analysis: Simple and multiple linear regression, logistic regression, and understanding assumptions of regression models.
   • Multivariate Analysis: Techniques like Principal Component Analysis (PCA), Factor Analysis, and Cluster Analysis.
   • Time Series Analysis: Analysis of time series data using models like ARIMA and methods for identifying trends, seasonality, and cycles.
   • Non-parametric Methods: Techniques for analyzing data that do not require assumptions about the distribution of the variables.
2. Learn the Right Programming Language:
   In order to learn data analysis, you should actually learn data analysis. But don’t worry, it will not be as complicated as it seems!
   • Python: Widely used for its simplicity and powerful libraries like pandas, NumPy, and matplotlib.
   • R: Preferred in academia for its statistical capabilities and graphical representation.
   • SQL: Essential for database management and querying large datasets.
3. Take Online Courses: If you’re struggling to learn by yourself, platforms like Coursera, MySkill, and RevoU offer specialized courses in data analysis and specific programming languages. These courses provide structured learning paths, expert mentors, and hands-on projects to enhance your skills.
4. Do hands-on practice with datasets: After learning the fundamental theory, you should start to engage with some datasets to have a practical experience. You can look up Kaggle and Google Dataset Service for some free open source dataset platforms.
5. Use Data Visualization Tools: Data analysis is more than just analyzing numbers. It’s about making your analysis accessible and understandable for all. Learn to use tools like Tableau, Power BI, or the visualization libraries in Python and R to present your data clearly and effectively.
6. Utilize AI in your learning journey: AI or Artificial Intelligence, such as ChatGPT and Gemini would enhance your learning journey in mastering data analysis. AI may provide personalized learning experiences and advanced visualization capabilities, making it easier to grasp the data concepts in real-world scenarios.

In conclusion, mastering data analysis would be a powerful way to enhance your capabilities and stand out as an International Relations student. By investing time in learning relevant programming languages and analytical techniques, you can unlock huge career opportunities in the data field.

References

Baesens, Bart. Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications. Wiley, 2014.

Hastie, Trevor, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2001.

Shamoo, Adil E., and David B. Resnik. Responsible Conduct of Research. Oxford University Press, 2009.