mirror of
https://github.com/kamranahmedse/developer-roadmap.git
synced 2026-10-24 23:06:33 +08:00
chore: cleanup orphaned content files (#9940)
Co-authored-by: jcanalesluna <56018501+jcanalesluna@users.noreply.github.com>
This commit is contained in:
github-actions[bot]
co-authored by
jcanalesluna
jcanalesluna
parent
b6ba0a7655
commit
25842d9638
@@ -1,9 +0,0 @@
|
||||
# AB Testing
|
||||
|
||||
A/B testing is a method of comparing two versions of something (like a website, app, or marketing email) to see which one performs better. It works by randomly showing each version to different groups of people and then analyzing which version achieves the desired outcome, such as more clicks, higher conversion rates, or increased engagement. This allows data-driven decisions about which changes to implement.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Practitioner’s Guide to Statistical Tests](https://vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f#1e3b)
|
||||
- [@article@Step by Step Process for Planning an A/B Test](https://medium.com/data-science/step-by-step-for-planning-an-a-b-test-ef3c93143c0b)
|
||||
- [@feed@Explore top posts about A/B Testing](https://app.daily.dev/tags/ab-testing?ref=roadmapsh)
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
# Machine Learning Algorithms
|
||||
|
||||
Machine learning algorithms are the core tools used to enable computers to learn from data without being explicitly programmed. These algorithms identify patterns, make predictions, and improve their performance over time through experience. They are broadly categorized into classic methods like supervised (where the algorithm learns from labeled data) and unsupervised learning (where the algorithm discovers patterns in unlabeled data), and more advanced techniques like ensemble methods (combining multiple models for better accuracy) and neural networks (complex algorithms inspired by the structure of the human brain).
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@opensource@Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop](https://github.com/gerdm/prml)
|
||||
- [@article@Open Machine Learning Course](https://mlcourse.ai/book/topic01/topic01_intro.html)
|
||||
- [@article@Coursera: Machine Learning Specialization](https://imp.i384100.net/oqGkrg)
|
||||
- [@article@Pattern Recognition and Machine Learning by Christopher Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
# Data Structures and Algorithms (Python)
|
||||
|
||||
Data structures are ways of organizing and storing data in a computer so that it can be used efficiently. Algorithms are step-by-step procedures for solving a problem, often involving manipulating data within these structures. Using Python, common data structures include lists, dictionaries, trees, and graphs, while algorithms encompass searching, sorting, and optimization techniques. These concepts provide the foundational building blocks for creating efficient and scalable solutions.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@roadmap@Visit the Dedicated DSA Roadmap](https://roadmap.sh/datastructures-and-algorithms)
|
||||
- [@article@Learn Algorithms](https://leetcode.com/explore/learn/)
|
||||
- [@article@Leetcode - Study Plans](https://leetcode.com/studyplan/)
|
||||
- [@article@Algorithms Specialization](https://imp.i384100.net/5gqv4n)
|
||||
-9
@@ -1,9 +0,0 @@
|
||||
# Data Understanding, Data Analysis, and Visualization
|
||||
|
||||
Data understanding involves gathering initial insights about a dataset, including its source, content, and potential issues. Data analysis then uses various techniques to extract meaningful information, identify patterns, and test hypotheses within the data. Finally, data visualization presents these findings in a graphical format, making complex data more accessible and understandable for decision-making.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Exploratory Data Analysis With Python and Pandas](https://imp.i384100.net/AWAv4R)
|
||||
- [@article@Exploratory Data Analysis for Machine Learning](https://imp.i384100.net/GmQMLE)
|
||||
- [@article@Python for Data Visualization: Matplotlib & Seaborn](https://imp.i384100.net/55xvzn)
|
||||
-9
@@ -1,9 +0,0 @@
|
||||
# Deployment Models and CI/CD
|
||||
|
||||
Deployment models define how a trained machine learning model is put into production, making it accessible for real-world use. Continuous Integration and Continuous Delivery (CI/CD) are practices used to automate the process of building, testing, and deploying these models, enabling faster and more reliable updates to your AI systems.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Machine Learning Engineering for Production (MLOps) Specialization](https://imp.i384100.net/nLA5mx)
|
||||
- [@article@Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/)
|
||||
- [@feed@Explore top posts about CI/CD](https://app.daily.dev/tags/cicd?ref=roadmapsh)
|
||||
-8
@@ -1,8 +0,0 @@
|
||||
# Differential Calculus
|
||||
|
||||
Differential calculus is a branch of mathematics concerned with the study of rates at which quantities change. It involves finding derivatives, which represent the instantaneous rate of change of a function with respect to its input variable. Key concepts include limits, continuity, differentiation rules, and applications such as optimization and related rates.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Algebra and Differential Calculus for Data Science](https://imp.i384100.net/LX5M7M)
|
||||
- [@video@Calculus Youtube Course](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
# Neural Network Architectures
|
||||
|
||||
These are different types of neural networks, each designed with a specific structure to excel at particular tasks. Fully Connected Networks (also known as Dense Networks) have every neuron connected to every neuron in the adjacent layers. Convolutional Neural Networks (CNNs) are particularly effective for image recognition by using convolutional layers to extract features. Recurrent Neural Networks (RNNs) are designed for sequential data, like text or time series, by maintaining a hidden state that captures information about past inputs. Long Short-Term Memory networks (LSTMs) are a special type of RNN that mitigate the vanishing gradient problem and are better at capturing long-range dependencies in sequential data. Transformers rely on the attention mechanism to weigh the importance of different parts of the input sequence, proving highly effective in natural language processing. Finally, Transfer Learning (TL) involves leveraging knowledge gained from solving one problem to a different but related problem, improving efficiency and performance.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
|
||||
- [@article@Attention is All you Need](https://arxiv.org/pdf/1706.03762.pdf)
|
||||
- [@article@Deep Learning Book](https://www.deeplearningbook.org/)
|
||||
- [@article@Deep Learning Specialization](https://imp.i384100.net/Wq9MV3)
|
||||
-8
@@ -1,8 +0,0 @@
|
||||
# Hypothesis Testing
|
||||
|
||||
Hypothesis testing is a statistical method used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for an entire population. It involves formulating a null hypothesis (a statement of no effect or no difference) and an alternative hypothesis (the statement you are trying to find evidence for), then using sample data to assess the likelihood of observing the data if the null hypothesis were true. Based on this likelihood, a decision is made to either reject the null hypothesis in favor of the alternative, or fail to reject the null hypothesis.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Introduction to Statistical Analysis: Hypothesis Testing](https://imp.i384100.net/vN0JAA)
|
||||
- [@feed@Explore top posts about Testing](https://app.daily.dev/tags/testing?ref=roadmapsh)
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
# Increasing Test Sensitivity
|
||||
|
||||
Test sensitivity refers to the ability of a test to correctly identify individuals who truly have the condition or characteristic being tested for. It essentially measures the proportion of true positives that are correctly identified by the test. Increasing test sensitivity means improving the test's ability to detect more true positives, reducing the number of false negatives (cases where the test incorrectly says someone doesn't have the condition when they actually do).
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Minimum Detectable Effect (MDE)](https://splitmetrics.com/resources/minimum-detectable-effect-mde/)
|
||||
- [@article@Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://kdd.org/kdd2016/papers/files/adp0945-xieA.pdf)
|
||||
- [@article@Improving Experimental Power through Control Using Predictions as Covariate — CUPAC](https://doordash.engineering/2020/06/08/improving-experimental-power-through-control-using-predictions-as-covariate-cupac/)
|
||||
- [@article@Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://www.researchgate.net/publication/305997925_Improving_the_Sensitivity_of_Online_Controlled_Experiments_Case_Studies_at_Netflix)
|
||||
-10
@@ -1,10 +0,0 @@
|
||||
# Learn Python Programming Language
|
||||
|
||||
Python is a versatile, high-level programming language known for its readability and extensive libraries. It supports multiple programming paradigms, including object-oriented, imperative, and functional styles. Python's syntax emphasizes code clarity, making it easier to learn and use, and its vast ecosystem of packages caters to a wide range of applications.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@roadmap@Visit the Dedicated Python Roadmap](https://roadmap.sh/python)
|
||||
- [@article@Kaggle — Python](https://www.kaggle.com/learn/python)
|
||||
- [@article@Google's Python Class](https://developers.google.com/edu/python)
|
||||
- [@feed@Explore top posts about Python](https://app.daily.dev/tags/python?ref=roadmapsh)
|
||||
@@ -1,10 +0,0 @@
|
||||
# Learn SQL
|
||||
|
||||
SQL, or Structured Query Language, is the standard language for interacting with databases. It allows you to retrieve, update, and manage data stored in relational database management systems (RDBMS) like MySQL, PostgreSQL, and SQL Server. You use SQL to write queries to filter, sort, and aggregate data, as well as to define and manipulate the structure of the database itself.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@roadmap@Visit Visit the Dedicated SQL Roadmapthe](https://roadmap.sh/sql)
|
||||
- [@course@Master SQL with Roadmap](https://roadmap.sh/courses/sql)
|
||||
- [@article@SQL Tutorial](https://www.sqltutorial.org/)
|
||||
- [@feed@Explore top posts about SQL](https://app.daily.dev/tags/sql?ref=roadmapsh)
|
||||
-9
@@ -1,9 +0,0 @@
|
||||
# Linear Algebra, Calculus, and Mathematical Analysis
|
||||
|
||||
Linear algebra deals with vectors, matrices, and linear transformations, providing the foundation for representing and manipulating data in higher dimensions. Calculus focuses on continuous change, covering concepts like derivatives and integrals, which are essential for optimization and modeling. Mathematical analysis rigorously studies the concepts underlying calculus, such as limits, continuity, and sequences, providing a deeper understanding of the theoretical underpinnings of many algorithms.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Mathematics for Machine Learning Specialization](https://imp.i384100.net/baqMYv)
|
||||
- [@video@Linear Algebra Youtube Course](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
|
||||
- [@feed@Explore top posts about Math](https://app.daily.dev/tags/math?ref=roadmapsh)
|
||||
-7
@@ -1,7 +0,0 @@
|
||||
# Pre-requisites of Econometrics
|
||||
|
||||
Econometrics uses statistical methods to analyze economic data and test economic theories. Before diving into econometrics, a solid foundation in several areas is needed. This includes a grasp of basic mathematics like calculus and linear algebra, a strong understanding of probability and statistics, and familiarity with economic principles. These pre-requisites provide the necessary tools for understanding econometric models and interpreting their results.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/)
|
||||
-8
@@ -1,8 +0,0 @@
|
||||
# Probability and Sampling
|
||||
|
||||
Probability is the measure of how likely an event is to occur, expressed as a number between 0 and 1. Sampling involves selecting a subset of individuals from within a statistical population to estimate characteristics of the whole population. These techniques are fundamental for understanding data distributions, making predictions, and drawing inferences from data. They provide the basis for hypothesis testing, confidence intervals, and many machine learning algorithms.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Probability and Statistics: To p or not to p?](https://imp.i384100.net/daDM6Q)
|
||||
- [@feed@Explore top posts about Statistics](https://app.daily.dev/tags/statistics?ref=roadmapsh)
|
||||
@@ -1,8 +0,0 @@
|
||||
# Ratio Metrics
|
||||
|
||||
Ratio metrics compare two numerical quantities by dividing one by the other. This results in a relative measure that can be used to understand the proportional relationship between the two values. Common examples include conversion rates, profit margins, or even simple metrics like students per teacher. The resulting ratio provides a standardized way to compare values across different groups or time periods, even when the underlying absolute values vary significantly.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas](https://arxiv.org/pdf/1803.06336.pdf)
|
||||
- [@article@Approximations for Mean and Variance of a Ratio](https://www.stat.cmu.edu/~hseltman/files/ratio.pdf)
|
||||
-13
@@ -1,13 +0,0 @@
|
||||
# Regressions, Time series, Fitting Distributions
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@opensource@Blockchain.com Data Scientist TakeHome Test](https://github.com/stalkermustang/bcdc_ds_takehome)
|
||||
- [@article@10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/)
|
||||
- [@article@Dougherty Intro to Econometrics 4th edition](https://www.academia.edu/33062577/Dougherty_Intro_to_Econometrics_4th_ed_small)
|
||||
- [@article@Econometrics: Methods and Applications](https://imp.i384100.net/k0krYL)
|
||||
- [@article@Kaggle - Learn Time Series](https://www.kaggle.com/learn/time-series)
|
||||
- [@article@Time series Basics : Exploring traditional TS](https://www.kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts#Hierarchical-time-series)
|
||||
- [@article@How to Create an ARIMA Model for Time Series Forecasting in Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python)
|
||||
- [@article@11 Classical Time Series Forecasting Methods in Python](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/)
|
||||
- [@article@Linear Regression for Business Statistics](https://imp.i384100.net/9g97Ke)
|
||||
@@ -1,7 +0,0 @@
|
||||
# Central Limit Theorem (CLT)
|
||||
|
||||
The Central Limit Theorem (CLT) states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the population's distribution. This means if you take many independent random samples from any population, calculate the mean of each sample, and then plot those means, the resulting distribution will tend towards a bell curve, even if the original population wasn't normally distributed. The theorem requires a sufficiently large sample size, which is often cited as a minimum of 30.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Introduction to Statistics](https://imp.i384100.net/3eRv4v)
|
||||
Reference in New Issue
Block a user