Foundational Level Course

Statistics for Data Science I

The students will be introduced to large datasets. Using this data, the students will be introduced to various insights one can glean from the data. Basic concepts of probability also will be introduced during the course leading to a discussion on Random variables. Course ID: BSCMA1002

Course Credits: 4

Course Type: Foundational

Prerequisites: None

What you’ll learnVIEW COURSE VIDEOS

Frame questions that can be answered from data in terms of variables and cases.
Describe data using numerical summaries and visual representations.
Estimate chance by applying laws of probability.
Translate real-world problems into probability models.
Calculating expectation and variance of a random variable.
Describe and apply the properties of the Binomial Distribution and Normal distribution.

Course structure & Assessments

12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

 WEEK 1 Introduction and type of data, Types of data, Descriptive and Inferential statistics, Scales of measurement WEEK 2 Describing categorical data Frequency distribution of categorical data, Best practices for graphing categorical data, Mode and median for categorical variable WEEK 3 Describing numerical data Frequency tables for numerical data, Measures of central tendency - Mean, median and mode, Quartiles and percentiles, Measures of dispersion - Range, variance, standard deviation and IQR, Five number summary WEEK 4 Association between two variables - Association between two categorical variables - Using relative frequencies in contingency tables, Association between two numerical variables - Scatterplot, covariance, Pearson correlation coefficient, Point bi-serial correlation coefficient WEEK 5 Basic principles of counting and factorial concepts - Addition rule of counting, Multiplication rule of counting, Factorials WEEK 6 Permutations and combinations WEEK 7 Probability Basic definitions of probability, Events, Properties of probability WEEK 8 Conditional probability - Multiplication rule, Independence, Law of total probability, Bayes’ theorem WEEK 9 Random Variables - Random experiment, sample space and random variable, Discrete and continuous random variable, Probability mass function, Cumulative density function WEEK 10 Expectation and Variance - Expectation of a discrete random variable, Variance and standard deviation of a discrete random variable WEEK 11 Binomial and poisson random variables - Bernoulli trials, Independent and identically distributed random variable, Binomial random variable, Expectation and variance of abinomial random variable, Poisson distribution WEEK 12 Introduction to continous random variables - Area under the curve, Properties of pdf, Uniform distribution, Exponential distribution

Prescribed Books

The following are the suggested books for the course:

Introductory Statistics (10th Edition) - ISBN 9780321989178, by Neil A. Weiss published by Pearson

Introductory Statistics (4th Edition) - by Sheldon M. Ross 