Unlocking Data Insights: The Mathematics Behind Successful Analysis

In data science and data analytics, statistics is the cornerstone of the entire discipline. The degree of mathematical proficiency required depends largely on the depth and complexity of the tasks involved, but a solid foundation in mathematics is a must for professionals working in these fields.

Data Analytics: Data analytics involves organizing and exploring large amounts of data to generate insights that can help solve problems and improve business decisions. To do data analysis, you need to understand how to collect and organize data, how to extract the information you want, and how to interpret the results.

Data Science: Data science is the role in data management that requires very advanced mathematical skills. Given that this is the most familiar role in the industry, this may give the impression that accounting requirements are prevalent throughout the industry, but this is not the case.

The difference between a data analyst and a data scientist is that, while a data analyst is more of a generalist who uses domain knowledge to gain insights and make recommendations, a data scientist is an expert who puts effort into applying advanced analytics to solve problems automatically.

Here is a breakdown of key mathematical concepts commonly encountered in data science and data analytics:

Basic coding: Learn simple languages ​​like Python and R. I suggest R if you are starting from scratch, and Python if you have at least some programming background.

Data visualization: Almost any Data Science works. You should know how to work with tools like ggplot2 (in R), or matplotlib's pyplot (in Python).

Statistics with R: Integrated coding in a simple language like R, with some mathematical logic, can be very appropriate, and many classes make this integration possible.

Basic Calculus:

Data Science doesn't need much math, except as a prerequisite for probability and statistical theory. Knowledge of derivatives, gradients, and optimization techniques like gradient descent is valuable for training machine learning models.

Linear algebra: Linear algebra because it is the basis of modern practical mathematics. Least squares, dimensionality reduction, collinearity, etc. can all be understood in terms of linear algebra.

Linear mathematical model: A secondary course in statistics often involves so-called "multistage statistics" that is, mastery of least-squares techniques in statistics and minimalist learning is one of those tricky, yet most powerful tools out there. Concepts like matrices, vectors, eigenvalues, and eigenvectors are used extensively in algorithms for dimensionality reduction, clustering, and classification.

Machine Learning/Deep Learning/IA: These are more advanced methods of forecasting. I think it's best to focus on case studies and application areas to study these techniques because the theoretical rationale for these tools lags behind the state of the art in industry practice well.

Conclusion: While a strong understanding of mathematics is essential for success in data science and data analytics, specific mathematical requirements may vary depending on the type of work but mathematics, linear algebra, calculus, probability theory, discrete calculus, and statistical methods are the primary tool for solving the challenges of these rapidly developing fields.