Data Science Fundamentals

Data Visualization

• Goal: to communicate insights and trends in data to stakeholders
• Types of visualization:
  • Quantitative: numerical data, e.g. bar charts, scatter plots
  • Qualitative: categorical data, e.g. heatmaps, word clouds
  • Temporal: time-series data, e.g. line graphs, stacked charts
• Key considerations:
  • Clarity: avoid clutter and ensure easy interpretation
  • Accuracy: ensure data is accurate and up-to-date
  • Context: provide context for the data being presented

Machine Learning

• Types of machine learning:
  • Supervised: labeled data, e.g. regression, classification
  • Unsupervised: unlabeled data, e.g. clustering, dimensionality reduction
  • Reinforcement: trial-and-error learning, e.g. game playing, robotics
• Key concepts:
  • Model training: fitting a model to data
  • Model evaluation: assessing model performance
  • Overfitting: when a model is too complex and performs poorly on new data
  • Underfitting: when a model is too simple and performs poorly on training data

Reading Graphs

• Types of graphs:
  • Scatter plots: show relationships between two variables
  • Bar charts: compare categorical data across groups
  • Histograms: show distribution of a single variable
  • Box plots: show distribution and outliers of a single variable
• Key considerations:
  • Axis labels: ensure labels are clear and accurate
  • Scale: ensure scales are consistent and appropriate
  • Outliers: identify and consider outliers in data

Steps of the Data Science Process

• Problem definition: identify a problem or opportunity
• Data collection: gather relevant data
• Data cleaning: clean and preprocess data
• Exploratory data analysis: explore data to understand patterns and trends
• Modeling: build and evaluate machine learning models
• Model deployment: deploy models into production
• Model monitoring: continuously monitor and evaluate model performance
• Insight generation: generate insights and recommendations from data