The Software & Data Spectrum

The Software & Data Spectrum Substack offers educational content focused on the foundational aspects of data science, statistical analysis, and programming within the tech field. It covers statistical methods, particularly Bayesian statistics, programming in R for data manipulation and visualization, and fundamentals of linear algebra involving matrices and vectors.

Bayesian Statistics Programming in R Data Science Linear Algebra Statistical Analysis Data Visualization Mathematical Foundations of Machine Learning Tech Educational Content

The hottest Substack posts of The Software & Data Spectrum

And their main takeaways

What is Bayesian Statistics? The Beginner Math Guide (Part One)

78 implied HN points • 13 Apr 23
🔬 Science Statistics Probability Logic Combinatorics
  1. Bayesian Statistics is used in various fields like Machine Learning, Engineering, Data Science, and more.
  2. Bayesian Thinking involves observing data, holding prior beliefs, forming hypotheses, gathering evidence, and comparing hypotheses.
  3. Probability is a way to measure belief strength, and calculating probabilities involves counting outcomes and using ratios of beliefs.

Introduction to R for Data Science (Part Seven Final)

39 implied HN points • 06 Apr 23
🕹 Technology Data science Visualization Themes
  1. Boxplots are common for visualizing data like stock pricing, and you can customize them with colors and flips.
  2. Variable plotting can include heat maps to show occurrences, and you can adjust the appearance with features like scale_fill_gradient().
  3. Coordinate your graphs using functions like coord_cartesian() and facet them based on specific variables for more detailed insights.

Introduction to R for Data Science (Part Five)

39 implied HN points • 30 Mar 23
🕹 Technology Data science Data Manipulation Programming
  1. Using apply functions in R like lapply and sapply can help apply functions to elements in a vector or list.
  2. Math functions in R like abs(), sum(), mean(), and round() are useful for basic calculations and rounding numbers.
  3. Data manipulation in R using dplyr involves functions like filter(), arrange(), select(), and mutate() to filter, sort, and create new columns in datasets.
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Introduction to R for Data Science (Part Four)

39 implied HN points • 26 Mar 23
🚌 Education Functions
  1. In R, if, else, and else if statements have similar logic to other programming languages but with different syntax.
  2. While loops in R allow a program to run continuously until a condition is met, and can include if statements and break statements.
  3. For loops in R iterate over objects and execute code for each loop, distinguishing from while loops as they execute code for each variable in the object.

What is Bayesian Statistics? The Beginner Math Guide (Part Two)

19 implied HN points • 16 Apr 23
  1. Beta distribution helps estimate probabilities when only data is available.
  2. Conditional probability is about the likelihood of an event given another event occurred.
  3. Bayes' Theorem is a way to update beliefs based on new data and observations.