MA1002 · Foundation · 4 credits

Statistics for Data Science I

Descriptive statistics, probability, distributions and the first principles of statistical reasoning.

Instructor Prof. Usha Mohan Term Every term Prereq none

§ 1Calendar

  • Quiz 1—
  • Quiz 2—
  • End-term examination—

§ 2Previous Year Questions

§ 3Resources

Course material7

Notes5

Books1

TA resources2

MA1002 · Foundation · 4 credits

Syllabus

The official course syllabus, transcribed from the IITM BS Data Science & Applications course booklet.

§ 1Course Description

Students will be introduced to large datasets and the insights that can be derived from them. The course shall also cover fundamental concepts of probability leading to a discussion on random variables.

Lectures-Tutorials-Practicals-Credits: 4-0-0-4

§ 2Faculty

  • Prof. Usha Mohan, Department of Management Studies, IIT Madras

§ 3Week-by-week

Week 1: Introduction to data types; Descriptive and inferential statistics; Scales of measurement.

Week 2: Categorical data - description, frequency distributions, best practices for graphing; Mode and median.

Week 3: Numerical data - frequency tables; 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 variables - categorical (contingency tables, relative frequencies) numerical (scatterplot, covariance, Pearson correlation, point-biserial correlation).

Week 5: Basic principles of counting and factorial concepts - addition and multiplication rules; Factorials.

Week 6: Permutations and combinations.

Week 7: Probability - basic definitions, events, properties.

Week 8: Conditional probability - multiplication rule, independence, law of total probability, Bayes' theorem.

Week 9: Random Variables - random experiment, sample space, discrete and continuous random variable; Probability mass function; Cumulative distribution function.

Week 10: Expectation and variance - expectation, variance, and standard deviation of discrete random variables.

Week 11: Discrete Distributions - Bernoulli trials (i.i.d), binomial (expectation and variance), Poisson, geometric, and negative binomial distributions.

Week 12: Continuous random variables - area under the curve; Properties of probability density functions; Uniform, exponential, and Normal distributions.