Data Scientist knowledge domain - Entry Level

1. All items on the list: One must know ALL items on this list at least at the level of comprehension, understanding the role and usage of the item in the Data Science domain.
2. Majority of the list: One must have hands-on experience with the majority of the items.
3. Niche: One should have reached mastery with a couple of elements of the list.

Domains of Knowledge

• Programming
  • R programming language
    • ggplot2 / ggally / ggpairs
    • dplyr
    • reshape2
    • data.table
    • shiny
  • Python programming language
    • pandas
    • numpy
    • scipy
    • matplotlib
    • seaborn
    • scikit-learn
    • ipython notebooks
  • JavaScript
    • D3.js
    • three.js
    • JQuery
  • Other
    • C / C++
    • Java / Scala
    • SAS
    • Matlab
    • Excel
    • Weka
• Statistics
  • Descriptive statistics
    • Univariate Analysis
      • Central tendency / Distribution / Dispersion
      • Data distributions
      • Standard Deviation and Variance
      • Hypothesis testing (P-values)
      • Significance testing (Z-test, t-test, chi-squared, ANOVA)
    • Multivariate Analysis
      • M-ANOVA
      • Principal Component Analysis (PCA)
      • Factor Analysis
      • Correlation Analysis
      • (Linear) Discriminant Analysis
      • (Constrained) Correspondence Analysis
  • Inferential statistics
    • Parametric models
    • Non-parametric models
    • Semi-parametric models
    • Frequentist vs. Bayesian inference
  • Experimental design
• Mathematics
  • Probability
  • Linear algebra
  • Matrix manipulation
  • Eigenvalues and Eigenvectors
  • Calculus
• Machine Learning
  • Supervised learning
    • Decision trees
    • Naive Bayes classifications
    • Least Squares regressions
    • Logistic regressions
    • Neural Networks
    • Support Vector Machines
    • Ensemble Methods
    • Supervised dimensionality reduction
  • Unsupervised learning
    • Clustering Algorithms
    • Principal Component Analysis (PCA)
    • Singular Value Decomposition (SVD)
    • Independent Component Analysis (ICA)
    • Unsupervised dimensionality reduction
  • Reinforcement learning
    • Q-Learning
    • TD-Learning
    • Genetic Algorithms
  • Supervised dimensionality reduction
  • Feature selection
• Data Wrangling
  • Shell scripting (sed, awk)
  • Regular Expressions
  • RDBMS / SQL
  • NoSQL / Column Stores / Document Stores / Key-Value Stores
  • Hadoop / Spark
  • Data Pipeline
• Communication / Visualization
  • Edward R. Tufte - The Visual Display of Quantitative Information
  • Static Data Visualization
  • Interactive Data Visualization
  • Jupyter Notebook
  • Apache Zeppelin

http://nbviewer.jupyter.org/gist/lorinc/fea999d83a29cff2cf6e885a022e0755