50 Data Science Acronyms and Their Full Forms

50 Data Science Acronyms

Useful Data Science Acronyms

In the era of minimalism, the IT field is also affected widely by the minimalist culture. Social media trends have started a new culture of using acronyms all around the internet. Acronyms are not new; they have been used in computer science since the beginning.

In this article, we will share the 50 most used data science acronyms that all data professionals must know.

Data Science Acronyms- Part 1
Data Science Acronyms- Part 2

50 Most Used Data Science Acronyms

  1. ANOVA : Analysis of Variance
  2. AUC : Area Under the Curve
  3. BART: Bidirectional and Auto-Regressive Transformer
  4. BDA: Big Data Analytics
  5. BERT: Bidirectional Encoder Representations from Transformers
  6. CFDS: Customer-Facing Data Scientist
  7. CV: Cross Validation
  8. CNN : Convolutional Neural Network
  9. DL: Deep Learning
  10. DNN: Deep Neural Network or Deconvolutional Neural Network
  11. DQ: Data Quality
  12. EDA: Exploratory Data Analysis
  13. ELMO: Embeddings from Language Models
  14. GBM: Gradient Boosting Machine
  15. GLM: Generalized Linear Model
  16. GRU: Gated Recurrent Unit
  17. HMM : Hidden Marcov Model
  18. ICA: Independent Component Analysis
  19. JSON: JavaScript Object Notation
  20. kNN: k-Nearest Neighbors
  21. LB: LeaderBoard
  22. LDA: Latent Dirichlet Allocation or Linear Discriminant Analysis
  23. LLE : Locally Linear Embedding
  24. LOOCV : Leave-One-Out cross-validation
  25. LpO CV : Leave-p-out cross-validation
  26. LSA/LSI: Latent Semantic Allocation/Indexing
  27. LSTM: Long Short Term Memory
  28. MAPE: Mean Absolute Percentage Error
  29. MARGE: Multilingual Autoencoder that Retrieves and Generates
  30. MCMC : Markov Chain Monte Carlo
  31. MDS : Multi-Dimensional Scaling
  32. MSE: Mean Squared Error
  33. NLDR: Non-Linear Dimensionality Reduction
  34. NLP : Natural Language Processing
  35. NMF: Non-Negative Matrix Factorization
  36. OOF: Out Of Fold
  37. PCA: Principal Component Analysis
  38. pLSA: Probabilistic Latent Semantic Allocation
  39. R2 : R-squared
  40. RF: Random Forest
  41. RFE: Recursive Feature Elimination
  42. RMSLE : Root Mean Squared Logarithmic Error
  43. RNN: Recurrent Neural Network
  44. ROC : Receiver Operating Characteristic
  45. SMOTE: Synthetic Minority Over-sampling Technique
  46. SQL: Structured Query Language
  47. SVM: Support Vector Machine
  48. tf-idf: term frequency, inverse document frequency
  49. t-SNE: t-Distributed Stochastic Neighbor Embedding
  50. XML: Extensible Markup Language

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