Data exploration and visualization involves analyzing and presenting data visually to gain insights, identify patterns, and communicate findings effectively.
Data types and formats: Understanding the different types of data, such as numerical, categorical, and ordinal, as well as the various formats data may be presented in, such as CSV or JSON.
Data cleaning and preprocessing: Techniques for cleaning dirty and incomplete data, such as removing null values, dealing with outliers, and standardizing data.
Data visualization: Different types of visualizations, including scatter plots, histogram, bar charts, and heat maps, and how to select the optimal visualization for particular types of data.
Exploratory data analysis: Methods and techniques for carrying out preliminary analysis of data, including descriptive statistics, correlation, and regression analysis.
Dimensionality reduction: Techniques for reducing the number of dimensions in complex datasets, such as principal component analysis (PCA).
Machine learning: Overview of machine learning algorithms, including linear regression, decision trees, and neural networks.
Statistical analysis: Introduction to statistical concepts, such as hypothesis testing, significance testing, and confidence intervals.
Data mining: Introduction to techniques for discovering hidden patterns and trends in data, including association rule mining and clustering.
Big data: Introduction to big data technologies, such as Hadoop and Spark, and the challenges surrounding processing and analyzing large datasets.
Dashboard creation: Techniques for building interactive dashboards to display data to users, including visual design principles and best practices for user experience.
Scatterplots: Used to explore relationships between two continuous variables.
Heatmaps: Shows two-dimensional relationships with color intensity.
Line charts: Shows trends in data over time or other continuous variable.
Bar charts: Displays categorical data with bars of varying lengths.
Pie charts: Displays categorical data in a circular format, with each category represented by a slice.
Area charts: Shows trends in data over time, often used to compare two or more groups.
Histograms: Shows the distribution of one continuous variable.
Box plots: Shows the distribution of data, including outliers and quartiles.
Bubble charts: Shows relationships between three variables using bubble size and color.
Choropleth maps: Displays data by geographic area, often used to compare regions or countries.