Data Science Fundamentals

Study Notes

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

Description

Test your knowledge of data visualization, machine learning, and the data science process. Covers types of visualization, machine learning concepts, reading graphs, and the steps of the data science process.