Statistics-1

Q. what are Statistics?

~ Statistics is a branch of applied mathematics that involves the collection, description, analysis, and inference of conclusions from quantitative data. The mathematical theories behind statistics rely heavily on differential and integral calculus, linear algebra, and probability theory.

                       People who do statistics are referred to as statisticians. They’re particularly concerned with determining how to draw reliable conclusions about large groups and general events from the behavior and other observable characteristics of small samples. These small samples represent a portion of the large group or a limited number of instances of a general phenomenon.

Key Takeaways

• Statistics is the study and manipulation of data, including ways to gather, review, analyze, and draw conclusions from data.

• The two major areas of statistics are descriptive and inferential statistics.

• Statistics can be communicated at different levels ranging from non-numerical descriptor (nominal-level) to numerical in reference to a zero-point (ratio-level).

• Several sampling techniques can be used to compile statistical data, including simple random, systematic, stratified, or cluster sampling.

• Statistics are present in almost every department of every company and are an integral part of investing.

ABOUT STAISTICS-1
Statistics is a branch of math focused on collecting, organizing, and understanding numerical data. It involves analyzing and interpreting data to solve real-life problems, using various quantitative models.

Statistics in data science bridges the gap between raw data and actionable insights, making it a foundational skill for anyone working with data.  

FOUNDATIONAL LEVEL COURSE

Statistics for Data Science I
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam.
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 poison random variables - Bernoulli trials, Independent and identically distributed random variable, Binomial random variable, Expectation and variance of a binomial random variable, Poisson distribution.

WEEK 12:Introduction to continuous random variables - Area under the curve, Properties of pdf, Uniform distribution, Exponential distribution.