Introduction to Statistics
Statistics plays a crucial role in understanding and interpreting data. It covers descriptive and inferential statistics, measures of central tendency and dispersion, probability, distributions, and hypothesis testing, all essential for data science applications.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Statistics helps summarize and make sense of data.
- Central tendency and dispersion describe the shape of data.
- Probability and distributions help model uncertainty.
- Hypothesis testing supports data-driven decisions with confidence.
Key Concepts
- -- Descriptive Statistics
- Statistical methods that summarize and describe data.
- -- Inferential Statistics
- Techniques used to make predictions or inferences about a population based on sample data.
- -- Measures of Central Tendency
- Statistics that describe the center of a dataset, including mean, median, and mode.
- -- Measures of Dispersion
- Statistics that describe the spread of data, including variance, standard deviation, and range.
- -- Probability
- A measure of the likelihood of an event happening, ranging from 0 to 1.
- -- Normal Distribution
- A bell-shaped statistical distribution that is symmetric about the mean.
- -- Binomial Distribution
- A distribution that models the number of successes in a fixed number of trials.
- -- Poisson Distribution
- A distribution that gives the probability of a number of events occurring in a fixed interval of time or space.
- -- Hypothesis Testing
- A statistical method that uses sample data to evaluate a hypothesis about a population parameter.
Additional Learning Materials
Supplementary resources to enhance your learning experience.