Complete Data Analyst Bootcamp From Basics To Advanced

Explore Google Colab as a beginner-friendly Jupyter notebook for Python coding. Connect to the runtime, create and run code cells, upload datasets, and download ipynb files.

Explore Python basics: syntax and semantics, comments, case sensitivity, indentation, line continuation, multi-statement lines, and dynamic typing to build reliable data analysis scripts.

Learn how Python variables are declared and assigned, follow naming conventions, and explore types, type checking, and conversions, dynamic typing, practical examples, input handling, and common errors.

Explore basic data types in Python, including integers, floating point numbers, strings, and booleans, and learn type conversion, memory considerations, and common operations to build robust programs.

Learn to use Python conditional statements, including if, elif, and else, with nested structures and real-world examples, plus common errors and best practices.

Explore tuples in Python by creating and accessing tuple elements, understanding their immutable nature, and using packing, unpacking, and nesting with count and index methods for practical data handling.

Explore Python sets, a built-in data type for unique, unordered elements, with creation, membership tests, and key operations (union, intersection, difference, symmetric difference), plus practical examples counting unique words.

Explore dictionaries in Python by creating, accessing, and modifying key-value pairs, learning methods like keys, values, items, and get, plus nested dictionaries and dictionary comprehension.

Explore real world uses of Python lists, a to do list, organizing student grades, inventory management, and user feedback analysis. Learn how append, remove, and membership checks enable data handling.

Master Python functions by defining and calling them, covering syntax, parameters, default values, and variable-length arguments; explore positional and keyword arguments, docstrings, and return statements.

Explore practical Python function examples, including temperature conversion, password strength checks, shopping cart totals, palindrome tests, recursive factorial, file word frequency analysis, and email validation.

Learn how Python's map function applies a function to every item in an iterable, returning a map object for data transformation with examples like square, lambda, and int conversion.

Learn how to manage files and directories with Python's os and os.path, including creating directories, listing contents, and building absolute and relative paths.

Learn to handle errors gracefully in Python using try, except, else, and finally blocks. Explore common exceptions, implement user-friendly messages, and ensure clean resource management with file and database examples.

Explore NumPy, the fundamental Python library for scientific computing, and learn to create, reshape, and perform vectorized operations on arrays and matrices.

Explore how to read data from json, csv, html, excel, and pickle using pandas, and convert between json, dataframes, and tables with read_json, read_csv, read_html, read_excel, and related options.

Learn to visualize data with matplotlib in Python, creating line, bar, histogram, scatter, and pie charts, customizing labels, grids, and subplots, plus a sales data example.

Distinguish population data, the complete set of observations, from sample data, a subset used to estimate population parameters such as mu and sigma square.

Explore probability and non-probability sampling techniques, including simple random, systematic, stratified, cluster, and multi-stage sampling, plus convenient, judgmental, snowball, and quota sampling.

Learn to categorize data into quantitative and qualitative types, then into discrete, continuous, nominal, and ordinal, with practical examples for data analysis and modeling.

Explore the four primary scales of data measurement, nominal, ordinal, interval, and ratio, and learn how each defines data categories, order, intervals, and true zeros.

Explore measure of central tendency, including mean, median, and mode, and learn how they describe the center of a data set while handling outliers and guiding feature engineering.

Explore why sample variance uses n-1 in place of n to achieve an unbiased estimate of population variance, linking x-bar, mu, s^2, sigma^2, and degrees of freedom.

Define a random variable as a function from a random process, illustrated by coin toss and dice; distinguish discrete from continuous variables with probability distribution.

Learn how percentiles and quartiles describe distributions, with formulas to compute values below which a percentage lies and to identify first, second, and third quartiles.

Master the five-number summary—minimum, Q1, median, Q3, and maximum—to build a box plot, compute lower and upper fences, and identify outliers using seaborn in Python.

Learn how histograms visualize numerical data distribution, binning, and kernel density estimation. Examine skewness, mean, median, and mode, and read box plots for outliers.

Explore covariance and correlation as measures of how two continuous variables relate, including positive and negative co-movement, and when to use Pearson or Spearman correlations for linear and non-linear patterns.

Explore the Bernoulli distribution, a discrete binary model with outcomes 0 or 1, defined by p and q, and learn its PMF and mean, variance, and standard deviation.

Learn the binomial distribution, modeling the number of successes in n Bernoulli trials with p. Use the pmf n choose k p^k q^{n-k} to find mean and variance.

Explore the Poisson distribution, a discrete model for the number of events in a fixed time interval, using lambda and the PMF to derive mean and variance.

Explore the normal or Gaussian distribution, a continuous bell-curve distribution with mean equals median equals mode. Learn its symmetric nature, mean and variance, the pdf, and the 68-95-99.7% empirical rule.

Explore continuous and discrete uniform distributions, defined by bounds a and b, with pdf 1/(b-a) or pmf 1/n, and examine their cdf, mean, and variance through candies and dice examples.

Explains Pareto distribution as a power-law, non Gaussian model and outlines Box-Cox transformation to convert it toward a Gaussian distribution, highlighting 80/20 IT examples.

Explore how p values drive hypothesis testing, define alpha, and decide to reject or fail to reject the null using confidence intervals and observed data.

Learn how to conduct z-test hypothesis testing for population means, using known population standard deviation and n≥30, with null and alternative hypotheses, z-scores, p-values, and one- and two-tailed decisions.

Learn how the student t distribution replaces z tests when population standard deviation is unknown, using sample standard deviation, t statistic, and the t table with degrees of freedom n-1.

Determine whether to use t test or z test by checking whether the population standard deviation is known and the sample size, guiding the appropriate test.

Explore Bayes statistics, Bayes theorem, and conditional probability, with examples of independent and dependent events, and apply Naive Bayes to predict outcomes from features.

Explore confidence intervals and margin of error with z and t tests, learn how point estimates like x-bar define population mean ranges.

Explore the chi square test for goodness of fit, a nonparametric method for categorical data, and learn to compare observed versus expected counts for population proportions.

Apply chi-square goodness-of-fit to compare 2010 weight category proportions with 2020 observed counts. With alpha 0.05, compute expected counts from 2010, obtain chi-square 26.66, reject H0, conclude weights differ.

Learn the four key assumptions of anova, including normality of the sampling distribution, absence of outliers, homogeneity of variances, and independent random samples.

Explore the three main types of anova: one-way, repeated measures, and factorial, with examples of independent and dependent levels.

Learn how to perform one-way anova to compare means across three dosage groups using an f test, with null and alternate hypotheses and variance partition.

Master feature engineering to handle missing data, including MCAR, MAR, and not at random, with mean, median, mode, and random-sampling imputation. Learn to decide when to drop rows or columns.

Explore feature engineering by addressing imbalanced datasets in binary classification, using upsampling and downsampling to balance minority and majority classes with practical code examples.

Learn data encoding for categorical features, including one hot, label, and target guided ordinal encoding, converting categories to numerical values and addressing sparse matrices.

Compare label encoding and ordinal encoding for converting categorical data to numbers. See how label encoding assigns unique labels, while ordinal encoding assigns ordered ranks, using sklearn's LabelEncoder and OrdinalEncoder.

Perform exploratory data analysis on the red wine dataset from the UCI repository, examining features such as fixed and volatile acidity, pH, alcohol, and quality score while assessing data quality.