From b90bc43315b5cb4a51b14a9f2371db311f31d593 Mon Sep 17 00:00:00 2001 From: Abhinav Anil <37529325+Abhinav-Anil@users.noreply.github.com> Date: Mon, 6 Jul 2026 15:51:42 +0530 Subject: [PATCH] feat: add learning resources to ai-data-scientist roadmap topics (#10117) Coding, Statistics, Econometrics, and Exploratory Data Analysis had explanatory copy but no resource links. Add official docs, free courses, and articles for each, following the content style guide. --- .../content/coding@XLDWuSt4tI4gnmqMFdpmy.md | 10 +++++++++- .../content/econometrics@Gd2egqKZPnbPW1W2jw4j8.md | 8 +++++++- .../exploratory-data-analysis@l1027SBZxTHKzqWw98Ee-.md | 9 ++++++++- .../content/statistics@4WZL_fzJ3cZdWLLDoWN8D.md | 9 ++++++++- 4 files changed, 32 insertions(+), 4 deletions(-) diff --git a/src/data/roadmaps/ai-data-scientist/content/coding@XLDWuSt4tI4gnmqMFdpmy.md b/src/data/roadmaps/ai-data-scientist/content/coding@XLDWuSt4tI4gnmqMFdpmy.md index 14bee1b39..f9ad195bd 100644 --- a/src/data/roadmaps/ai-data-scientist/content/coding@XLDWuSt4tI4gnmqMFdpmy.md +++ b/src/data/roadmaps/ai-data-scientist/content/coding@XLDWuSt4tI4gnmqMFdpmy.md @@ -1,3 +1,11 @@ # Coding -Programming is a fundamental skill for data scientists. You need to be able to write code to manipulate data, build models, and deploy solutions. The most common programming languages used in data science are Python and R. Python is a general-purpose programming language that is easy to learn and has a large number of libraries for data manipulation and machine learning. R is a programming language and free software environment for statistical computing and graphics. It is widely used for statistical analysis and data visualization. \ No newline at end of file +Programming is a fundamental skill for data scientists. You need to be able to write code to manipulate data, build models, and deploy solutions. The most common programming languages used in data science are Python and R. Python is a general-purpose programming language that is easy to learn and has a large number of libraries for data manipulation and machine learning. R is a programming language and free software environment for statistical computing and graphics. It is widely used for statistical analysis and data visualization. + +Visit the following resources to learn more: + +- [@official@The Python Tutorial](https://docs.python.org/3/tutorial/) +- [@official@The R Manuals (CRAN)](https://cran.r-project.org/manuals.html) +- [@course@Python for Data Science, AI & Development](https://www.coursera.org/learn/python-for-applied-data-science-ai) +- [@course@Kaggle Learn: Python](https://www.kaggle.com/learn/python) +- [@article@Introduction to Data Science with Python (Harvard)](https://pll.harvard.edu/course/introduction-data-science-python) \ No newline at end of file diff --git a/src/data/roadmaps/ai-data-scientist/content/econometrics@Gd2egqKZPnbPW1W2jw4j8.md b/src/data/roadmaps/ai-data-scientist/content/econometrics@Gd2egqKZPnbPW1W2jw4j8.md index d35e19504..92d697824 100644 --- a/src/data/roadmaps/ai-data-scientist/content/econometrics@Gd2egqKZPnbPW1W2jw4j8.md +++ b/src/data/roadmaps/ai-data-scientist/content/econometrics@Gd2egqKZPnbPW1W2jw4j8.md @@ -1,3 +1,9 @@ # Econometrics -Econometrics is the application of statistical methods to economic data. It is a branch of economics that aims to give empirical content to economic relations. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." Econometrics can be described as something that allows economists "to sift through mountains of data to extract simple relationships." \ No newline at end of file +Econometrics is the application of statistical methods to economic data. It is a branch of economics that aims to give empirical content to economic relations. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference." Econometrics can be described as something that allows economists "to sift through mountains of data to extract simple relationships." + +Visit the following resources to learn more: + +- [@official@Econometrics (MIT OpenCourseWare)](https://ocw.mit.edu/courses/14-382-econometrics-spring-2017/) +- [@course@Econometrics: Methods and Applications](https://www.coursera.org/learn/erasmus-econometrics) +- [@article@Lecture Notes and Short Texts in Econometrics](https://economicsnetwork.ac.uk/teaching/Lecture%20Notes%20and%20Short%20Texts/Econometrics) \ No newline at end of file diff --git a/src/data/roadmaps/ai-data-scientist/content/exploratory-data-analysis@l1027SBZxTHKzqWw98Ee-.md b/src/data/roadmaps/ai-data-scientist/content/exploratory-data-analysis@l1027SBZxTHKzqWw98Ee-.md index d36d3bdac..4f825edfb 100644 --- a/src/data/roadmaps/ai-data-scientist/content/exploratory-data-analysis@l1027SBZxTHKzqWw98Ee-.md +++ b/src/data/roadmaps/ai-data-scientist/content/exploratory-data-analysis@l1027SBZxTHKzqWw98Ee-.md @@ -1,3 +1,10 @@ # Exploratory Data Analysis -Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. EDA is used to understand what the data can tell us beyond the formal modeling or hypothesis testing task. It is a crucial step in the data analysis process. \ No newline at end of file +Exploratory Data Analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. EDA is used to understand what the data can tell us beyond the formal modeling or hypothesis testing task. It is a crucial step in the data analysis process. + +Visit the following resources to learn more: + +- [@official@pandas User Guide: Essential Basic Functionality](https://pandas.pydata.org/docs/user_guide/basics.html) +- [@course@Exploratory Data Analysis with Python and Pandas](https://www.coursera.org/projects/exploratory-data-analysis-python-pandas) +- [@article@Exploratory Data Analysis in Python](https://towardsdatascience.com/exploratory-data-analysis-in-python-c9a77dfa39ce/) +- [@article@Step-by-Step Exploratory Data Analysis (EDA) using Python](https://www.analyticsvidhya.com/blog/2022/07/step-by-step-exploratory-data-analysis-eda-using-python/) \ No newline at end of file diff --git a/src/data/roadmaps/ai-data-scientist/content/statistics@4WZL_fzJ3cZdWLLDoWN8D.md b/src/data/roadmaps/ai-data-scientist/content/statistics@4WZL_fzJ3cZdWLLDoWN8D.md index abecc3481..428c49397 100644 --- a/src/data/roadmaps/ai-data-scientist/content/statistics@4WZL_fzJ3cZdWLLDoWN8D.md +++ b/src/data/roadmaps/ai-data-scientist/content/statistics@4WZL_fzJ3cZdWLLDoWN8D.md @@ -1,3 +1,10 @@ # Statistics -Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is used in a wide range of fields, including science, engineering, medicine, and social science. Statistics is used to make informed decisions, to predict future events, and to test hypotheses. It is also used to summarize data, to describe relationships between variables, and to make inferences about populations based on samples. \ No newline at end of file +Statistics is the science of collecting, analyzing, interpreting, presenting, and organizing data. It is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It is used in a wide range of fields, including science, engineering, medicine, and social science. Statistics is used to make informed decisions, to predict future events, and to test hypotheses. It is also used to summarize data, to describe relationships between variables, and to make inferences about populations based on samples. + +Visit the following resources to learn more: + +- [@course@Statistics and Probability (Khan Academy)](https://www.khanacademy.org/math/statistics-probability) +- [@book@OpenIntro Statistics (free textbook)](https://www.openintro.org/book/os/) +- [@course@Statistics with Python Specialization](https://www.coursera.org/specializations/statistics-with-python) +- [@video@StatQuest with Josh Starmer](https://www.youtube.com/@statquest) \ No newline at end of file