Python for Data Science with Assignments

Learn how lists and tuples work in Python, including mutable lists, immutable tuples, indexing, and operations such as append, insert, remove, pop, sort, len, concatenation, repetition, and slicing.

Explore Python sets and dictionaries by learning how to create, modify, and query unordered collections of unique elements, and how to work with key-value pairs, nested dictionaries, and common operations.

Explore stacks and queues, learn their LIFO and FIFO behavior, and implement them in Python with lists using push and pop, plus using deque from collections for efficient operations.

Explore time and space complexity in Python with big O notation, examining constant, linear, and quadratic behaviors through examples like constant time access to first element, loops, and matrix generation.

This lecture introduces bubble sort, selection sort, and insertion sort, explains their basic operations in Python, and notes their quadratic time and educational use.

Explore searching algorithms in Python, including linear and binary search, and learn how they locate elements in lists, with linear search scanning, and binary search using a sorted list.

Learn how Python functions use parameters and arguments, define with def, and call with positional or keyword arguments, including default values, and understand pass by object reference.

Explore how python modules organize code, use the standard library and create custom modules, and import modules or specific functions to reuse code across projects.

Learn how to filter elements from iterables with a condition, map functions to transform data, and zip together iterables to form paired tuples in Python for data science.

Explore Python list, set, and dictionary comprehensions to build new sequences from existing data; create squared lists and dictionaries, and filter even numbers.

Learn about lambda functions, anonymous one-line functions defined with the lambda keyword for concise, readable Python code, used in functional programming, inline definitions, and as callbacks, including map and filter.

Explore analytical and aggregate functions in Python, including eval, len, factorial, and sort, and use max, min, sum, mean, median, and mode from the statistics module for exploratory data analysis.

Explore strings in Python as a data type, created with quotes, then practice concatenation with + or += and slicing with 0:4 and 10: to obtain 'John' and 'Junior'.

Learn how to manipulate strings in Python using built-in methods like len, upper, lower, replace, strip, split, startswith, endswith, count, and join.

Explore string formatting with placeholders and two-decimal outputs in Python. Discover how the input function collects data and how typecasting enables numeric operations.

Master metacharacters in regular expressions, such as dot, caret, dollar, asterisk, plus, question mark, square brackets, and the pipe symbol used to match multiple alternatives in Python.

Learn Python's built-in regular expression functions—findall, search, split, and sub—in the re module for data science. Discover how to find matches, locate first results, split text, and replace patterns.

Explore special characters and sets for regular expressions to match the start of a string, word boundaries, and digits. Apply ranges and sets for text extraction in web scraping tasks.

Learn how Python conditional statements control program flow using if and else, evaluate conditions, handle true or false branches, and use indentation with modulo and divisibility examples.

Master for loops in Python by iterating over sequences like lists or strings, printing items, and using conditionals such as the modulo two check for not divisible by two.

Learn the Python while loop, iterating while a condition is true, with an example counting to five, printing values, incrementing to avoid infinite loops, and using pass for placeholders.

Explore how break and continue alter loop flow in Python, showing how break exits a loop when a condition is met and continue skips the current iteration.

Explore how to use conditional statements inside loops in Python, applying for and while loops to check even or odd numbers, and control flow with break and continue.

Explore how nested loops and nested conditional statements in Python enhance control flow, enabling multi-level iterations and complex decision-making for two-dimensional data and matrix operations.

Learn the basics of object-oriented programming in Python, including classes and objects, attributes, the init constructor, self, instantiation, and how to create and manipulate object attributes.

Explore how inheritance in Python enables code reuse, hierarchical class organization, and method overriding through derived classes and the super call, illustrated by person and student examples.

learn how encapsulation wraps variables and methods into a single entity and restricts direct access with private members; see a python class example that updates attributes via modify variable method.

Explore polymorphism in object oriented programming, including compile time polymorphism with method overloading and runtime polymorphism with method overriding, using a common interface to fetch SBI, ICICI and HDFC rates.

Learn to create and inspect Python date and time objects using datetime, including date with year, month, day, today, and time with hour, minute, second.

Learn how to use the time delta class in Python's DateTime module to compute durations, add or subtract days, and derive seconds in a 30-day period.

Explore how statistics reveals stories in data, from describing central tendencies and variability to predicting outcomes and testing hypotheses with Python libraries like NumPy, pandas, and Matplotlib.

Explore how statistics powers data analysis by guiding data collection, cleaning, summarization, visualization, and inference. Learn to predict outcomes, test hypotheses, inform decisions, and ensure quality control across industries.

Discover how Python enables statistical analysis with readable syntax, NumPy to Seaborn libraries, and essential concepts like variables, data types, loops, and functions for data visualization across finance and healthcare.

Explore data types in statistics, including numerical and categorical data with continuous, discrete, nominal and ordinal subtypes, and learn to analyze them using Python tools like NumPy and Pandas.

Explore the measures of central tendency—mean, median, and mode—and learn how they describe the center of data and inform analysis in Python with a monthly income example.

Explore measures of spread in Python using pandas to analyze salary data, including range, variance, and standard deviation, and interpret what these dispersion metrics reveal about the data.

Explore measures of dependence to quantify relationships between variables, including how strong and in which direction, such as a correlation of about 0.77 between income and working years.

Explore measures of shape and position to describe data distributions, including skewness, kurtosis, percentiles, and quartiles, and apply Python tools to assess central tendency, tails, and outliers.

Explore measures of standard scores, or z-scores, to standardize data, identify outliers, compare distributions, and interpret deviations from the mean using Python's SciPy zscore.

Explore basic probability concepts, from weather forecasts to sports and lottery, and learn to compute probabilities in Python with NumPy and Pandas on an IPL dataset.

Explore set theory foundations, including union, intersection, and difference, with Venn diagrams and applications in probability, data analysis, and decision making, illustrated using Python with IPL data.

Learn conditional probability and its notation P(A|B) and P(A∩B), with Python examples on the Mumbai Indians dataset computing outcomes like winning given toss or field, 57% and 13.8%.

Explore Bayes theorem as a tool for updating probabilities with new evidence. Apply prior, likelihood, and posterior concepts to data science, medical diagnostics, and machine learning using practical examples.

Explore permutations and combinations, distinguishing when order matters from when it does not. Apply formulas for permutations and combinations, including nPk and nCr, with repetition and factorial basics.

Explore random variables in probability, distinguishing discrete and continuous types, with Python examples using the random module to model outcomes, uncertainty, and basic statistics.

Explore probability distribution functions, including pmf and pdf, and model discrete and continuous random variables with examples like binomial distribution, Poisson distribution, normal distribution, exponential distribution, and uniform distribution.

Explore the normal distribution, its bell curve and empirical rule, with mu and sigma, and fit and visualize it using Python tools like NumPy, Pandas, Matplotlib, and SciPy.

Explore skewness and kurtosis as measures of distribution shape, symmetry, and tail heaviness, including positive, negative, and zero skewness, and mesokurtic, leptokurtic, and platykurtic distributions.

Explore statistical transformations to normalize skewed data, applying square root, cube root, log, and Box-Cox to the monthly income variable and visualizing results with histograms.

Learn to distinguish a sample from a population, compute the sample mean and population mean, and estimate the population using the formula x̄ = sum x_i / n.

Explore how the central limit theorem describes sampling distributions becoming normal as sample size grows. Apply this to confidence intervals, hypothesis testing, and estimation in data science.

Explore bias and variance in inferential statistics, and how underfitting and overfitting affect accuracy. Compare linear and polynomial regression to understand the bias-variance trade-off and practical model selection.

Learn maximum likelihood estimation by maximizing the likelihood function to estimate model parameters, given a data generating process and observed data.

Learn how to estimate a population parameter from sample data using confidence intervals, the margin of error, and the x bar plus or minus Z*s/sqrt(n) formula at 95% level.

Explore correlations and causation with practical examples, learn how Pearson, Kendall, and Spearman coefficients measure strength and direction, and understand monotonic relationships.

Explore random, systematic, and stratified sampling methods used in statistics and data science to draw representative samples from a population.