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Foundations of Data Science and Analytics

(Interlaken, Switzerland - Alvin Wong)


- Overview

Data science and data analytics are both fields that involve working with data to gain insights. Data science is an umbrella term for all aspects of data processing, including collection, modeling, and insights. Data analytics is a subset of data science that focuses on statistics, mathematics, and statistical analysis. 

Data science and data analytics can be considered different sides of the same coin, and their functions are highly interconnected. Here are some differences between data science and data analytics:

  • Data science: Involves building, cleaning, and organizing datasets. Data scientists use data to understand the future, model data to make predictions, identify opportunities, and support strategy. Data science often involves using data to build models that can predict future outcomes.
  • Data analytics: Involves understanding datasets and gleaning insights that can be turned into actions. Data analysts work with the data as a snapshot of what exists now, solving problems and spotting trends. Data analytics tends to focus more on analyzing past data to inform decisions in the present. Business users perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes.


Please refer to the following for more information:


- The Mathematical Foundations of Data Science

The mathematical foundations of data science include topics such as: linear algebra, calculus, statistics, probability, optimization, number theory, numerical linear algebra, scientific computing. 

Data scientists use these mathematical foundations to analyze large amounts of data and extract meaningful insights for business. 

Linear algebra is an essential part of coding and thus of data science and machine learning (ML). Calculus is key to understanding the linear algebra and statistics needed in ML and data science. 

Data science is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence (AI), and computer engineering.

According to UC Berkeley, the foundations of data science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. 

The four pillars of data science are: domain knowledge, math and statistics skills, computer science, and communication and visualization.

Mathematics is a core educational pillar for data scientists. It's crucial for statistical analysis, mathematical modeling, ML, and data visualization. 

Here are some roles of mathematics and key foundations in data science:

  • Make sense of data: Mathematics helps uncover patterns, identify relationships, and draw conclusions from data. It also plays an important role in developing algorithms for ML and AI.
  • Solve problems: Mathematics can help you solve problems, optimize model performance, and interpret complex data that answer business questions.
  • Build accurate models: A strong foundation in mathematics is essential to building accurate models, making informed decisions, and communicating insights to non-technical stakeholders.
  • Communicate complex ideas: Understanding the mathematical principles underlying data science and AI enables us to make better decisions, optimize processes, and effectively communicate complex ideas.

University of Chicago_050222A
[University of Chicago]

- Characteristics of Data Science

Here are some characteristics of data science:

  • Data analysis: A core skill that involves analyzing data to gain insights and make better decisions.
  • Data visualization: A key stage in the data science process that provides a first glance at data in a graphical style.
  • Exploratory data analysis: An essential aspect of data science that allows you to understand data sets, develop hypotheses, and uncover hidden patterns.
  • Data exploration: An important and time-consuming step in the data science life cycle that involves extracting patterns from data to solve problems.
  • Classification: A fundamental concept in data science that involves using machine learning to predict class labels for data inputs.
  • Cluster analysis: A staple of unsupervised machine learning and data science that automatically finds patterns in data without the need for labels. 


- The Life Cycle of Data Analytics

Data analytics (DA) is a process that involves inspecting data to draw conclusions and improve software systems. The DA life cycle has six phases:

  1. Data discovery and formation
  2. Data preparation and processing
  3. Design a model
  4. Model building
  5. Result communication and publication
  6. Measuring of effectiveness

The data analytics life cycle also has other life stages, including creation, testing, consumption, and reuse. Each stage has its own characteristics and significance. 

Here are some steps in the DA life cycle:

  • Data exploration: An important first step where an analyst tries to understand an unfamiliar dataset.
  • Data extraction: An essential step for many DA processes, where the necessary data is extracted from any data sources.
  • Data modeling: A fundamental component of analytics that helps organizations collect and manage accurate data sources.
  • Model evaluation: Involves evaluating the performance of the predictive model to ensure that it is accurate and reliable.
  • Model deployment: The fourth stage in the model development life cycle, but is usually the most cumbersome for data scientists as it takes time and resources.


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