Python from Basics till Advance for Data Science

Explore how Python functions work, from defining functions with def, to calling with mandatory, optional, and keyword arguments, including *args and **kwargs; learn return behavior, scope, and lambda basics.

Explore Python functions, including def and lambda forms, and how to call them and return values. Learn to handle mandatory, optional, and keyword arguments, including *args and **kwargs.

Explore Python modules, packages, and libraries, including creating inbuilt and user defined modules. Learn importing methods (import, from module import function, star) and the package structure with __init__.py.

Learn Python file handling: open and manage files, explore read and write modes, read lines, use with statements, and master seek and other file attributes through practical examples.

Master Python regular expressions to search, match, substitute, and compile patterns, exploring flags, greediness, character classes, groups, and lookahead/lookbehind for text analytics.

Master Python debugging with the Python debugger (PDB) and breakpoint function, learning to set breakpoints, inspect frames, and step through code using the pdb commands.

Master Python exceptions by understanding error types and handling them with try/except and finally, raising custom errors, inspecting exception messages, and debugging issues like division by zero.

Learn to enable Python logging, use file and console handlers, set levels, format logs with time, module name, line number, and capture exceptions for multi-module apps.

Explore Python memory management from allocation to automatic garbage collection, including stack vs heap, reference counts, id() memory locations, and how gc handles cycles and manual collection.

Master Python threading fundamentals, including daemon versus non-daemon threads, joining and thread lifecycle; manage race conditions with locks and context managers, handle critical sections, and log with Python's logging module.

Learn to install and use numpy for data science, explore array creation, data types, reshaping, random number generation, and basic operations essential for data analysis and machine learning.

Explore numpy fundamentals: creating and reshaping arrays, dtype inference and casting with mixed data, random, linspace, and logspace values, zeros, ones, identity, diagonal matrices, and transposing, stacking and splitting arrays.

Explore advanced NumPy techniques like indexing, reshaping, broadcasting, inserting, concatenating, and vectorized operations, with practical examples on data manipulation, statistics, and conditional filtering.

Explore NumPy's core matrix operations and linear algebra concepts, including ellipsis indexing, dot product, eigenvalues and eigenvectors, determinants, inverses, and plotting sine and cosine curves.

Learn to use pandas to manipulate tabular data, create dataframes and series, select columns and rows, and perform basic data analysis.

Pandas part two teaches dataframe basics, viewing top/bottom rows, renaming and adding columns, filtering by conditions, and applying descriptive statistics plus transformations with map, apply, and lambda.

Explore advanced pandas techniques in pandas part-3, including describe for data summaries, memory information for large datasets, and handling missing values with fillna and replace.

Explore advanced Pandas techniques for merging, joining, and concatenating datasets; create and manipulate data frames, convert types, encode categories, group data, plot, and work with time series.

This lecture teaches matplotlib essentials for data visualization, including line, bar, scatter, histogram, pie charts, and heat maps, with notebook plotting, labeling, color, transparency, and interpolation.

Learn to customize Matplotlib plots with scatter properties, labeling, and markers; draw vertical and horizontal lines, explore subplots, save figures, and create 3D, dual-axis, and animated plots.

Matplotlib part-3 demonstrates animated 2d and 3d plots, including rotating graphs to reveal data separation and animated line, bar, and trend plots driven by z values.

Learn to visualize data with seaborn, using built-in datasets, plotting multi-dimensional data with bar, line, histogram, density, scatter, box, violin plots, heatmaps, and confusion matrices.

Explore seaborn part 2, creating univariate, bivariate, and joint plots, kernel density and regression visuals, and grid-based groupings to reveal patterns in data.

Master natural language processing in Python data science by learning NLP fundamentals, text preprocessing, tokenization, stop words, stemming, POS tagging, and exploring NLP libraries like NLTK with case studies.

Master NLP preprocessing and tokenization by cleaning text, removing unwanted characters, and lowercasing. Compute word frequency and visualize results with a bar plot, focusing on stop words.

Explore stemming and its limitations in NLP, comparing Porter, Snowball, and Lancaster stemmers, and learn how tokenization, lowercasing, and stop-word removal reduce features for text-based machine learning.

For Python data science students, this NLP part covers part-of-speech tagging, chunking, and named-entity recognition; build grammar trees and extract noun phrases from text corpora.

Explore natural language processing fundamentals by extracting synonyms, antonyms, and word similarities from corpora using WordNet, with practical workflows from tokenization to cosine similarity.

Learn how spaCy offers fast natural language processing through a configurable pipeline that tokenizes text, tags parts of speech, and performs named entity recognition.

Explore spaCy part 2 concepts including noun chunks and root word lemmatization, visualize dependencies with display C function, filter named entities, remove stop words and punctuation, and save html visualizations.

Explore spaCy pipelining, create and manage custom pipelines with decorators, read files, and display named entities using a practical walkthrough of enabling, disabling, and restructuring pipelines.

Explore n-gram concepts such as bigrams and trigrams and their applications in text processing. Generate and customize word clouds with stop words and masks to visualize text data.

Practice solving Python interview questions by reading problem statements, identifying input and output, and writing functions to pass diverse test cases, including leap year logic.

learn to build a Python program that reads a number of student records, stores name and marks in a dictionary, and computes the average with two decimals using type casting.

Flip all bits of a 32-bit unsigned integer by padding to 32 bits, inverting each bit, and converting the result back to decimal. Handle multiple queries and 0b prefixes.

Tackle a Python problem that counts how many apples and oranges land on Sam's house by applying distance-based rules, input parsing, and optional list comprehension techniques.

Explore a Python quiz that prints a doormat pattern using symbols, dots, and hyphens, by parsing input, building lines with lists, and printing top, middle, then bottom reversed.

Master print formatting in Python by generating a function that prints, for each number from 1 to n, its decimal, capitalized hexadecimal, and binary representations.

Explore regular expressions in Python to count and compare American and British spellings, focusing on words ending in -ize vs -ise, and practice pattern replacement across multi-line inputs.

Master regex in Python to validate usernames using a pattern with look ahead and look behind. Apply positive look ahead to determine valid and invalid usernames.

Solve the migratory birds problem by counting sightings across bird types 1–5, identifying the maximum frequency, and returning the smallest id among those with that frequency.

Learn how to compute highlighted text area by finding the maximum letter height from a 26-letter height array and multiplying by the word length, using Python.

Count students with arrival time less than or equal to zero as on time, compare this count to the threshold, and return yes if the class is cancelled.

Learn to count beautiful days in a range by reversing numbers, computing their difference, and checking divisibility by a given k, all without using strings.

Explore statistics basics for data science, covering descriptive and inferential methods, data cleaning, visualization, hypothesis testing, confidence intervals, and regression and correlation analyses.

Explore mean, median, and mode, their calculations and limitations with outliers, and visualize distributions using kernel density plots in Python with NumPy, pandas, seaborn, and matplotlib.

Explore percentile, quantile, and interquartile range concepts with box plots and the five-number summary. Learn practical calculations using numpy and interpret quartiles in data.

Learn how histograms visualize frequency, then compute pdf and cdf, and apply kernel density estimation to smooth data using seaborn and iris data.

Explore violin plots that fuse kernel density estimation with five point summary, and learn population versus sample, including how the sample mean converges to population mean as size grows.

Explore measure of variability by analyzing range, variance, and standard deviation using a sample data set like iris petal length to understand distribution.

Explore the gaussian normal distribution, the empirical rule (68, 95, 99.7 percent within 1–3 sigma), and Chebyshev's theorem for non-normal data.

Explore the gaussian distribution by computing and visualizing its pdf and cdf with norm, mu zero, and sigma one, and understand data within 1–3 sigma.

Convert data to the standard normal variate by subtracting the mean and dividing by the standard deviation; obtain a zero-mean, unit-variance distribution.

Explore symmetric distribution, skewness, and kurtosis through visualizations of normal and non-normal data, compare mean, median, and mode, and learn how left and right skew affect data spread and tails.

Learn how the central limit theorem makes the distribution of sample means approach normal with large samples (n≥30), guiding estimates of population mean and variance.

Explore how a q-q plot uses quantile vs quantile to assess whether data follows a gaussian normal distribution, highlighting linearity on the reference line.

Explore discrete and continuous uniform distributions, comparing discrete values like a fair die to continuous values, and apply area under the curve with pdf, pmf, and cdf in Python.

Apply the Box-Cox transform to convert non-Gaussian data into a Gaussian distribution by estimating lambda in Python. Use q-q plots and probability plots to verify mean zero and unit variance.

Learn what covariance is, how it measures positive or negative relationships between two variables, and how to compute and visualize it with the covariance formula and heatmaps.

Explore covariance drawbacks and how Pearson correlation addresses them, then learn Spearman rank correlation for nonlinear, monotonic data by ranking x and y.

Learn to compute confidence intervals from gaussian normal data using mu and sigma, with t distribution when sigma is unknown, and apply central limit theorem for sample means.

Explore hypothesis testing concepts, including null and alternate hypotheses, significance level, p-values, critical values, two- and one-tailed tests, and common errors with practical examples.

Explore the Titanic dataset through exploratory data analysis, performing data wrangling, cleaning missing values, memory optimization via typecasting, and dropping nonessential features, then visualize correlations and distributions for insights.

Explore automotive data with pandas to perform eda: load, inspect shape and features, handle missing values, optimize memory, and visualize correlations with heatmaps and pair plots.

Explore exploratory data analysis of Twitter text data to detect cyberbullying. Clean text, remove stop words, visualize with word clouds, and encode categories for machine learning.

Explore exploratory data analysis on a US data science salary dataset using Python, covering data loading, cleaning, min and max salary extraction, visualization, and memory optimization.

Explore customer service chat data with practical EDA in Python, clean missing values, and convert chat duration to minutes to reveal patterns in browser, OS, and transfer behavior.

Master exploratory data analysis on a disaster tweet dataset in Python, covering data import, missing values, text cleaning, and visualizations like a word cloud for disaster vs not.