With the widespread integration of Machine Learning and AI in our world, Data Science has rapidly gained significant attention.
In this article, we will introduce the fundamentals of Data Science and explore its growing importance.
Data science is an interdisciplinary field that combines statistics and computer science to extract scientifically valuable insights from data.
In the modern era, advancements in information and measurement technologies have led to an explosion in the volume and variety of data, commonly referred to as 'Big Data.'
The role of a data scientist is to analyze this diverse data and provide actionable information that benefits business operations.
Recently, the demand for data analysis has surged in both the public and private sectors, as evidenced by the rise of EBPM (Evidence-Based Policy Making), where governments base policy decisions on rigorous data analysis.
Data science is often perceived as merely using complex statistical models, but a true data scientist requires a balance of the following three elements:
In recent years, the widespread availability of open-source tools like R and Python has significantly lowered the barriers to computational tasks.
Consequently, it is not uncommon for individuals to mistake themselves for data scientists simply because they can use libraries. This leads to frequent situations where their skills cannot be fully utilized, or where organizations struggle to manage and grow such talent.
To avoid these mismatches, it is essential to become a data scientist who masters all three elements, rather than relying solely on computational tools.
If we compare a data scientist to a chef and analysis to cooking, the first essential step is understanding the ingredients: the data itself.
Data can be broadly classified into Qualitative data and Quantitative data.
Qualitative data refers to data categorized by characteristics like gender or preferences. Quantitative data refers to numerical values where the magnitude has significance, such as weight or price.
Data that takes distinct, separate values (e.g., integers) is called Discrete data. Examples include the number of contracts or equipment failures.
Conversely, data that represents values on a continuous scale without gaps is called Continuous data, such as profit rates or physical weight.
Data recorded over regular intervals, such as stock price movements every second, is known as Time series data. Analysis focuses on how these values fluctuate over time.
Measuring multiple subjects at a fixed point in time (e.g., unemployment rates by prefecture in 2021) is referred to as Cross-section data.
Collecting data from multiple subjects over regular intervals results in Panel data. This is analyzed using specific panel data models or multivariate analysis.
In this article, we provided an overview of the fundamentals of Data Science.
Our Data Scientist training programs are specifically designed with recurrent education and reskilling in mind.
Whether you want to rebuild your foundation in mathematics, prioritize hands-on practical training over mere lectures, or aim to obtain international certification as a Data Scientist, we encourage you to explore our courses.
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