Data Science Mastery 2025: Excel, Python & Tableau

Master Tableau's drag-and-drop interface to connect data from Excel, SQL, and cloud sources, build visuals and dashboards, and share insights with stakeholders.

Learn how Tableau, a leading bi tool, turns data from big data, Hadoop, SQL, and the cloud into visually compelling, real-time insights through intuitive dashboards and collaboration.

Explore Tableau Desktop, Public, Prep, Server, and Online to transform data, publish visualizations, prepare data, collaborate securely, and access insights across devices.

Install Tableau Public on Windows by following a step-by-step download and installation, then explore the data pane and main workspace to create visualizations.

Install Tableau Desktop on Windows, following a step-by-step guide from downloading to activation, to run and create your first visualization. Explore interactive dashboards for business intelligence, data analysis, and reporting.

Explore dimensions and measures in Tableau using the Superstore dataset; learn to categorize data with dimensions and quantify it with measures, then build visualizations like sales by region.

Learn how measure names and measure values in Tableau manage and display multiple measures in one view, enabling dynamic visualizations like bar charts, time series, and dual axis charts.

Learn how discrete fields categorize data and how continuous fields measure data on a continuous scale in Tableau, then apply them to pie charts and line charts.

Explore the Show Me toolbar in Tableau for chart recommendations. Learn with the Superstore Sales data to create bar, line, and scatter charts and view helpful tooltips.

Explore how Tableau text charts convert data into clear, text-based visuals, using the HR dataset to display employee counts by department and average salary by position for data-driven insights.

Discover highlight tables in Tableau that use color to reveal patterns and outliers, turning text data into heat maps for HR analytics, including department performance and salary by gender.

Create a bar chart in Tableau to compare recruitment sources by employee count, using color, sorting, and on-bar labels to clearly highlight key differences in an HR data set.

Explore line charts in Tableau to visualize monthly trends in hires and terminations, compare two data series on a dual axis, and gain workforce insights.

Create and interpret pie charts in Tableau to show the composition of a dataset, such as salary distribution by department, using slices, colors, tooltips, and labels for clarity.

Create and analyze multi-dimensional data with bubble charts in Tableau, using position, size, and color to compare relationships across variables such as employee satisfaction by marital status.

Learn to create a histogram in Tableau to visualize the distribution of a continuous variable, using three-year age bins and color to show frequency and insights into data patterns.

Use heat maps to visualize data density and intensity across two dimensions with color, highlighting patterns and correlations such as employee satisfaction across departments and job roles.

Learn to create tree maps in Tableau to visualize hierarchical data as nested rectangles, with area proportional to a metric like employee count, enabling quick comparison of departments and roles.

Explore how to create area charts in Tableau, showing cumulative salaries over time, with color by department to reveal trends, peaks, and budgeting insights.

Explore how a dual axis chart in Tableau combines two measures on separate left and right axes to compare trends and reveal correlations between employee satisfaction and engagement survey.

Explore scatter plots to reveal relationships, correlations, trends, and outliers between variables. See how Tableau visualizes employee satisfaction against special projects, with department color and point size by count.

Learn to create a bullet chart in Tableau that compares a primary measure to targets and benchmarks, using performance reviews and termination status to reveal trends in employee satisfaction.

Explore how to create a waterfall chart in Tableau, showing the cumulative effect of sequential positive and negative values on a starting value, with applications to HR and financial data.

Explore how a Gantt chart visualizes a schedule with bars indicating start dates and durations, and apply it to visualize employee tenure from hire date to termination date in Tableau.

Explore real world use cases of Python across web development, data science, automation, scientific computing, IoT, and game development, highlighting Python's versatility and beginner-friendly syntax.

Install Anaconda on Windows and Mac OS by following the download, license, and installation wizard steps, and enable path access for use from any command prompt.

Learn how variables in Python act as containers to store values, with naming rules, examples, and that variable names are case sensitive and must not start with a number.

Explore Python data types, including integers, floats, and strings, and how Python infers types automatically, use the type and print functions, and perform type casting with int, float, and string.

Explore the scope of variables in Python by distinguishing local and global scope, understanding local variables inside functions, and using the global keyword to modify global variables.

Explore Python operators, including arithmetic, assignment, comparison, and logical operators, with practical examples of addition, subtraction, multiplication, division, modulus, exponent, and floor division.

Master python lists and tuples by learning mutable list operations like append, insert, remove, pop, and sort, and immutable tuple concepts such as indexing, concatenation, repetition, and slicing.

Learn python sets and dictionaries, including creation, mutation, and querying with add, update, remove, and in checks; cover union, intersection, and difference, plus nested dictionaries.

Explore stacks and queues, including last in first out and first in, first out principles, with push, pop, enqueue, and dequeue in Python lists and the deque library.

Explore space and time complexity in Python, using big O notation to compare constant, linear, and quadratic cases, with examples of constant, linear, and quadratic space.

Explore bubble sort, selection sort, and insertion sort, showing how adjacent comparisons and swaps sort an array, with bubble sort simple yet inefficient at O(n^2).

Explore linear and binary search algorithms in Python to efficiently locate a target in a list, with sorted data requirements and -1 for not found.

Learn how Python functions use parameters and arguments, define functions with def, and distinguish positional and keyword arguments, including default parameters, while exploring pass by object reference.

Learn how Python modules organize and reuse code, import standard and custom modules, and access functions with the dot operator using math, random, and a calculation module.

Learn how to use Python's filter, map, and zip functions to process iterables, apply conditions, transform data, and combine or unzip tuples in data science tasks.

Discover how Python list, set, and dictionary comprehensions let you build new sequences from existing data, including squaring numbers and filtering evens with concise syntax.

Explore lambda functions, anonymous one-line definitions that enable concise code and inline usage. Apply them in Python for functional programming, callbacks, and data manipulation and feature engineering.

Explore analytical and aggregate functions in Python for exploratory data analysis, data cleaning, and data manipulation. Learn about eval, len, factorial, sort, max, min, sum, mean, median, and mode.

Explore strings in Python, a fundamental data type used to represent text and alphanumeric data. Learn to create, print, index, slice, and concatenate strings to form full names and substrings.

Explore essential Python string methods, including len, upper, lower, replace, strip, split, startswith, endswith, count, and join, with practical examples of manipulating and printing strings.

Master Python string formatting with placeholders and format specifiers, displaying floating point numbers with two decimals, and use the input function to capture and typecast user input.

Explore regular expressions in Python’s re package, focusing on metacharacters like dot, caret, dollar, asterisk, plus, question mark, square brackets, negation, and the pipe for alternation.

Learn about the four built-in regular expression functions in Python's re module: find all function, search function, split function, and sub function, with hands-on examples.

Explore special characters and sets in regular expressions to craft flexible patterns for validation, extraction, and data transformation.

Explore conditional statements in Python, including if, else, and elif, control program flow by evaluating conditions, and observe indentation and the modulo operator in examples.

Learn how to use Python for loops to iterate over lists, tuples, strings, or ranges, executing code for each item and applying simple conditionals as shown.

Master how the Python while loop iterates code while a test condition stays true. Use a counter and increments to avoid infinite loops; learn the pass statement for unimplemented loops.

Learn how break and continue statements control Python loops, terminating a loop or skipping an iteration. See examples with for loops, range, and output.

Explore using conditional statements inside loops in Python to determine even or odd numbers from a list, using for and while loops, if/else, modulus checks, break, and continue.

Explore nested loops and nested conditional statements in Python to handle multi-level iterations and complex decision making, including parity checks for even or odd outcomes.

Explore the fundamentals of classes and objects in Python, including attributes, instantiation, and the init constructor, with practical examples of creating and manipulating a person object.

Explore inheritance in object oriented programming, where derived classes inherit attributes and methods from a base class, enabling code reuse and hierarchy, with Python examples of person and student.

Explore encapsulation by wrapping variables and methods into an entity and restricting access with private members. See how a Python class demonstrates controlled updates via a method, reinforcing data protection.

Learn polymorphism, the ability to use a method in different ways, including compile-time overloading and runtime overriding, with a common interface for rate of interest across SBI, ICICI, and HDFC.

Learn to create and validate date and time objects with Python's datetime module, using the date and time classes, date.today, and accessing year, month, day, hour, minute, and second attributes.

Learn how the time delta class represents durations and enables date and time calculations in Python, including adding 90 days to today and computing seconds in a 30-day period.

Explore how statistics describe data, reveal trends, test hypotheses, and guide decisions, with Python's simple libraries like numpy, pandas, and matplotlib enabling practical data analysis.

Explore how statistics collects, cleans, summarizes, and visualizes data to enable inference, hypothesis testing, prediction, and informed decision making.

Discover how Python enables statistical analysis with its beginner-friendly syntax, vast libraries like NumPy, pandas, and seaborn, and practical applications across finance, healthcare, and more.

Identify numerical and categorical data types in statistics, including continuous, discrete, nominal, and ordinal forms, and learn to analyze them in Python with NumPy and pandas.

Explore the measures of central tendency, namely mean, median, and mode, and learn how they describe data distributions, handle outliers, and reveal skew through Python examples.

Explore measures of spread like range, variance, and standard deviation using a monthly income dataset in Python with pandas to assess data variability.

Explore measures of dependence to quantify the relationship between variables, enabling data analysis, prediction, decision making, and risk management, with examples like income and education and correlation coefficients.

Explore measures of shape and position in data, including skewness, kurtosis, and percentiles with quartiles, using Python tools like SciPy and NumPy to analyze distributions.

Explore z scores, which express how many standard deviations a data point lies from the mean, enabling outlier detection, percentile ranking, and Python-based calculation with scipy.stats.

Explore basic probability through real-world examples—from weather forecasts to sports, politics, insurance, games, and lotteries—then compute probabilities in Python with NumPy and pandas using an IPL dataset.

Learn set theory fundamentals, including union, intersection, and difference, visualized with Venn diagrams, and applied to probability, data analysis, and decision making with real-world examples and Python notebook.

Learn conditional probability, defined as P(A|B), its joint probability P(A∩B), and how Python notebooks compute outcomes from data frames.

Learn Bayes theorem and Bayes inference, updating prior beliefs with new evidence via conditional probabilities, using likelihood and posterior calculations in medical, machine learning, and data science contexts.

Explore permutations and combinations, the ways to select objects from a set with or without regard to order, and learn how order defines permutations versus combinations, with formulas and examples.

Explore random variables, including discrete and continuous types, with practical Python examples and concepts like probability distributions, expectation, variance, and PDFs/PMFs.

Describe how probability distribution functions model the likelihood of outcomes, with PMFs for discrete variables and PDFs for continuous ones. Explore binomial, Poisson, normal, exponential, and uniform distributions.

Explore the normal (gaussian) distribution and the 68–95–99.7 rule, estimate mu and sigma, and assess data dispersion, outliers, and implications for hypothesis testing and confidence intervals.

Learn how skewness and kurtosis reveal distribution shape, asymmetry, tail heaviness, and deviations from normality, including positive, negative, zero skewness and mesokurtic, leptokurtic, platykurtic patterns.

Learn how statistical transformations normalize data and reduce skewness using square root, cube root, log, and Box-Cox on a monthly income dataset, with histogram demonstrations and modeling implications.

Explore the difference between sample mean and population mean, learn the formulas for x-bar and mu, and see how samples estimate the population using a Python example.

Explore the central limit theorem, a fundamental inferential principle showing that the sampling distribution of the mean or sum becomes normal with large samples, regardless of population shape.

Explore bias and variance in inferential statistics, how they cause underfitting or overfitting, and examine the bias-variance trade-off with linear and polynomial regression examples.

Learn maximum likelihood estimation to infer model parameters by maximizing the likelihood function, using a data generating process, with applications in regression and survival analysis.

Learn how to construct confidence intervals for population parameters from observed sample data, using the standard normal distribution and a 95% confidence level, with margin of error.

Explore correlation and causation, learn about Pearson, Kendall, and Spearman coefficients for linear and monotonic relationships, and analyze positive, negative, and zero correlations with examples.

Learn how random, systematic, and stratified sampling select representative samples from a population, using sampling intervals, random generators, and proportional allocation to reduce bias.

Learn the fundamentals of hypothesis testing, including formulating null and alternate hypotheses, testing with examples, and understanding type I and II errors and p values.

Explore the student's t distribution and apply one-sample, two-sample, and paired t tests to compare means, interpret p-values, and understand degrees of freedom.

Learn the z test for comparing population means when the population standard deviation is known, including one-sample and two-sample cases, z scores, p values, and hypothesis testing.

Explore the chi square test, including goodness of fit and independence, to assess relationships between categorical variables using observed versus expected frequencies and p-values.

Explore anova to compare means across three or more groups using the f-test, while noting independence, homogeneity of variances, and normality, with one-way and two-way designs.

Master numerical computing with numpy by learning the ndarray, multi-dimensional arrays, broadcasting, and essential operations like linear algebra, Fourier transforms, and random number generation.

Explore NumPy operations on arrays, including element-wise addition, multiplication, exponentials, and aggregations, then reshape, transpose, concatenate, split, and apply broadcasting for efficient data manipulation.

Explore the pandas library in Python, its data frames, and tools for data manipulation, missing data handling, and time series analysis.

Learn pandas series and data frames, two fundamental labeled data structures for tabular data; create series with custom indexes and build data frames from dictionaries for analysis and manipulation.

Import the pandas library and learn to read csv and json data into a dataframe using read_csv and read_json, inspect with head, and customize with delimiter, header, and orient options.

Learn to analyze data with pandas by inspecting an employee dataset: import pandas, check shape and columns, preview with head and tail, and derive descriptive statistics with describe.

Learn to index, slice, and filter pandas dataframes to explore, clean, subset, and visualize data, using boolean masks and label or integer indexing.

Learn how to merge and concatenate data frames in pandas using inner, outer, left, and right joins, and stack frames vertically or horizontally with the merge and concat functions.

Explore correlation in Pandas, quantify relationships with the correlation coefficient, and plot iris data using bar, pie, and line charts.

Explore map, apply, and lambda functions in Python to transform data frames, using map on a series, apply on dataframes, and simple lambda expressions to divide values by ten.

Explore grouping operations on Python data frames with groupby to aggregate by department, identify employees with the highest monthly income, and apply functions like mean and max.

Use cross tabulation in pandas to analyze the relationship between attrition and department, visualize frequency distributions with bar plots, and reveal department-level patterns.

Master filtering operations in Pandas to extract subsets from Python data frames using boolean masks. Apply filters to explore and clean data with examples on monthly rate and department.

Discover interactive grouping and filtering with pandas and ipywidgets in Jupyter notebooks, using the interact decorator to group by columns, compute the mean values, and update results in real time.

Discover how effective data visualization enhances comprehension, storytelling, and decision making by choosing the right chart types, ensuring data accuracy, and enabling interactive exploration.

Explore univariate data analysis with histograms, bar charts, pie charts, and line charts to reveal distribution, central tendency, and variability, using Python with pandas, matplotlib, and seaborn.

Learn bivariate analysis of variables, including categorical vs numerical and numerical vs numerical, using scatter plots and Seaborn line plots to compare average monthly income by department.

Learn to visualize two categorical variables with grouped and stacked bar charts to compare attrition and department frequencies, using pandas crosstab to cross-tabulate and plot.

Explore multivariate data analysis and visualization to reveal relationships among multiple variables using pandas and seaborn, with line, bar, and scatter plots that show gender gaps and income differences.

Explore heat maps and pair plots to visualize correlations, patterns, and distributions in data using Seaborn and pandas; identify relationships, outliers, and feature relationships across variables.

Explore color scales, including sequential, diverging, qualitative, and cyclical palettes, and apply facet grids and subplots for advanced data visualization.

Discover what 3D charts are and create interactive 3D visualizations in Python with Plotly Express, including a 3D scatter plot of iris data set using x, y, and z axes.

Explore interactive data visualizations in Python to filter, explore, and analyze datasets with real-time updates. Build engaging visuals using Plotly and ipywidgets to support storytelling, collaboration, and insights.

Explore how maps in data visualization reveal spatial patterns, support geospatial analysis, and inform location based decisions through an interactive choropleth map in Python.

Learn to create funnel and Gantt charts with Plotly in Python, visualizing funnel stages for sales and web analytics and project timelines with dependencies.

Learn to build animated data visualizations in Python with a bubble plot, using Gapminder data to explore life expectancy, GDP per capita, and population across continents over time, with interactivity.

Defines missing values and explains their causes, including data entry errors, non-response, data loss, and skip patterns, and shows how they bias analyses and impact data cleaning and model performance.

Identify and classify missing data into four types: missing completely at random (mcar), missing at random (ma), not at random (na), and missing by design, guiding subsequent handling choices.

Learn practical rules for deleting rows or columns with missing values before imputing, based on dataset size, missing-value thresholds, and correlation with the target variable.

Impute missing values with mean, median, and mode using real-world data. Learn when to apply each statistic, use fillna, and verify zero missing values on the Melbourne dataset.

Apply business logic to impute missing values using domain knowledge and data context. Tailor strategies by data type—numeric uses mean, median, or regression; categorical uses mode—while documenting and validating results.

Explore what outliers are and how they affect mean, variance, and model robustness. Learn how to detect and handle outliers with removal, transformation, or robust techniques to improve predictions.

Identify univariate outliers in the Big Mart data set using box plots, then apply capping, deletion, or winsorization to key variables such as item weight and item outlet sales.

Explore label encoding and ordinal encoding to convert categorical data into numerical features for machine learning, including practical benefits and key limitations.

Explore binary and base n encoding to convert categorical features into numerical representations, compare dimensionality and interpretability, and apply with real data examples.

Explore target encoding, which converts categories into numerical values using the mean of the target variable, and compare it with label, ordinal, and one-hot encodings for high-cardinality data.

Learn data manipulation in Python with pandas and NumPy to clean, transform, and reshape data for analysis and modeling. Master reindex, set_index, reset_index, and sort_index in Jupyter notebooks.

Master replace and drop level functions to clean data in a data frame, replacing old values and removing levels from a multi-level (hierarchical) index during data cleaning.

Explore split and strip functions in Python for data manipulation, parsing csv/tsv data, tokenization, url and config parsing, and data cleaning, illustrated by extracting movie genres and release year.

Learn to reshape data with stack and unstack in Python using NumPy and pandas, transforming data frames by turning columns into index levels and vice versa to create multi-level indexes.

Explore how melt, explode, and squeeze reshape and clean data in pandas, turning tables into analysis-ready formats, expanding lists into rows, and extracting scalar values.

Master selecting data with at time and between time functions on a time series, using time strings, axis options, and include start and end booleans to filter values.

Explore Pandas' nlargest and nsmallest functions to extract top and bottom records from a data frame, using keep options (first, last, all) and understanding ordering and duplicates.

Remove unnecessary columns as a feature engineering step, dropping constant values and the employee number, and use a heat map to drop highly correlated features.

Master date and time features in Python by converting date variables to datetime, then extracting year, month, day, and day of the week to boost time-based machine learning predictions.

Learn to decompose categorical features using the FIFA dataset by narrowing the nationality column to the top five countries and treating the rest as others, reducing dimensionality.

Learn how to bin numerical features to improve model learning and predictions, using examples of age, distance from home, and total working years categorized into meaningful groups.

Learn how to aggregate features from existing data using domain knowledge to create new features like total score, percentage, and grade, and apply feature engineering to improve model performance.