Download as PDF, PPTX
Presentation.pdf describing data with foundation of data science
- 1.
- 2. ABSTRACT • Descriptive Statistics. •The information (data) from your sample or population can be visualized with graphs or summarized by numbers. • This will show key information in a simpler way than just looking at raw data. It can help us understand how the data is distributed. • Graphs can visually show the data distribution.
- 3. TYPES OF DATA •Nominal, Ordinal, Discrete And Continuous. • Integer (int) It is the most common numeric data type used to store numbers without a fractional component (-707, 0, 707). • Floating Point (float) ... • Character (char) ... • String (str or text) ... • Boolean (bool) ... • Enumerated type (enum) ... • Array. ... • Date.
- 4. TYPES OF VARIABLES •Independent ... • dependent variables... • Active and attribute variables... • Continuous... • discrete and categorical variable... • Extraneous variables and Demographic …
- 6. DESCRIBING DATA WITHTABLES AND GRAPHS • Tables and graphs are used to describe data by summarizing it and making it easier to understand. • Frequency distribution • Bar chart • Histogram • Pie chart • Scatter plot
- 7. DESCRIBING DATA WITHAVERAGES • The mean of a data set is the average of all the data values. • The sample mean x is the point estimator of the population mean μ. • The median of a data set is the value in the middle when the data items are arranged in ascending order. • Mean is the most frequently used measure of central tendency and generally considered the best measure of it.
- 8. DESCRIBING VARIABILITY • Variabilitydescribes how far apart data points lie from each other and from the center of a distribution. • The four main ways to describe variability in a data set are: • Range – The range is the amount between the smallest and largest item in the set. • Interquartile range – The interquartile range s a number that indicates how spread out scores are and tells users what the range is in the middle of a set of scores.
- 9. NORMAL DISTRIBUTIONS ANDSTANDARD (Z) SCORES
- 11. • Normal distribution,also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. • The normal distribution appears as a “bell curve” when graphed. • A normal distribution is a type of continuous probability distribution in which most data points cluster toward the middle of the range, while the rest taper off symmetrically toward either extreme. • The middle of the range is also known as the mean of the distribution.
- 12. CONCLUSION • Not employingstatistical methods can lead to an analysis of data without accounting for variability, which, as a result, can show up wrong estimates. • Data science is the study of data to extract meaningful insights for business • Principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering. • Data processing has become a crucial aspect of modern computing and business operations, allowing organizations to store, analyze, manipulate.
- 13.