Essential Notes for Organising, Visualising, and Describing Data (CFA Level I)
Get ready to dive into the fascinating world of data analysis! We’re here to guide you through the key lessons of Organising, Visualising, and Describing Data for the CFA Level 1 exam. So, without further ado, let’s get started!
Peek into the world of data types and organization! Learn the difference between numerical and categorical data, uncover the mysteries of cross-sectional and time series data, and get acquainted with structured and unstructured data. It’s a wild ride, so buckle up!
► Explore data types like a boss here.
Dive into the art of data summarization and visualization! Discover the differences between population and sample, learn to create frequency distributions, and master various data visualization tools like bar charts, line charts, and heat maps. Your data will be as beautiful as a Picasso painting!
► Unleash your inner data artist here.
Unleash your central tendency superhero! Save the day with mean, mode, median, harmonic mean, and geometric mean. Tackle quantiles head-on, and use box and whisker plots to visualize your data like a boss. It’s a bird, it’s a plane, it’s Central Tendency Hero!
► Soar to new central tendency heights here.
Dive into the world of measures of dispersion, including range, mean absolute deviation, variance, standard deviation, downside deviation, and the coefficient of variation. Unravel the mysteries of investment risk and make smarter decisions like a pro!
► Master the measures of dispersion here.
Embrace the thrilling universe of skewness and kurtosis to understand returns distributions like a master. Uncover the secrets of normal distributions, asymmetry, and peakedness, and get ready to conquer the world of risk management!
► Unleash your inner distribution guru here.
Unravel the mysteries of covariance and correlation as you learn how to calculate and interpret them like a seasoned analyst. Get ready to visualize relationships using scatter plots, and remember to keep an eye on the limitations of correlation analysis!
► Delve into the dazzling domain of covariance and correlation here.