20 Core Data Science Concepts

With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.

  • Dataset
  • Data Wrangling
  • Data Visualization
  • Outliers
  • Data Imputation
  • DataScaling
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Data Partitioning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Model Parameters and Hyperparameters
  • Cross-validation
  • Bias-variance Tradeoff
  • Evaluation Metrics
  • Uncertainty Quantification
  • Math Concepts
  • Statistics and Probability Concepts
  • Productivity Tools

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