Data Analytics Masters 2026 - From Basics To Advanced

Explore the basics of generative AI and its impact on the data analytics field. Learn to use ChatGPT and Grok, and master prompt engineering to drive proactive insights.

Map the data analytics workflow from business understanding to data collection and EDA, then leverage generative AI and large language models to shape insights and dashboards.

Discover how this course introduces ai tools like ChatGPT and Grok.com for data analytics, covering data summarization, code generation, debugging, and effective prompt engineering.

Master prompt engineering basics for data analytics, learning to craft prompts for generative AI using zero-shot, one-shot, and few-shot approaches, with context, task, and persona.

Explore practical applications of generative AI for data analytics, including text generation, code generation and debugging with ChatGPT and grok, and generating insights for Power BI dashboards and KPIs.

Explore the ethical considerations of using ai tools and data privacy, learning to mask company and personal data before sharing with ChatGPT or grok and understanding the LLM brain.

Explore what data analytics means, how to analyze data for insights, and how churn examples illustrate descriptive, diagnostic, predictive, and prescriptive analytics.

Explore the importance of data analytics and its descriptive, diagnostic, predictive, and prescriptive approaches to drive product development, market insight, forecasting, and optimized marketing and operations.

Explore the fundamental types of data, distinguishing qualitative (categorical) from quantitative (numerical) data and their subtypes: nominal vs ordinal, discrete vs continuous, within descriptive, diagnostic, predictive, and prescriptive analytics.

Explore the two main types of statistics—descriptive and inferential—built from samples to infer population parameters, and study measures of central tendency (mean, median, mode) and variability.

Understand the problem with business understanding, then assess data understanding, data preparation, modeling, evaluation, and deployment to obtain a data analytics solution.

Define the business problem and objective as the foundation of a data analytics project, aligning business understanding, data understanding, data preparation, modeling, and deployment with domain context.

Analyze customer churn by tracing data understanding from sources to storage, integrate profiles, usage, and feedback, and explore external data to build predictive models that guide retention strategies.

Identify data collection scope across MNCs, mid-level firms, and startups, access diverse databases, and prepare final data sets using public sources, web scraping, and structured formats for analysis.

Learn how data preparation begins with data discovery and profiling, then cleanses, structures, transforms, and enriches data, validates and publishes structured data ready for analysis and modeling.

Explore data collection, data preparation, and moving from unstructured data to structured data. Apply exploratory data analysis, spot anomalies, test hypotheses, and inform predictive modeling.

Evaluate model performance using training and test splits, compare models, and deploy the best one to a live environment, on premises or cloud.

Explore a data analytics and data science led approach to churn, from business understanding to data collection, preparation, exploratory data analysis, predictive modeling, and engaging retention actions.

Learn to install Python, compare local and online options like Google Colab, and use tools such as Anaconda Navigator, Jupyter notebooks, and IDEs to build practical data analytics skills.

Learn to use Google Colab for Python notebooks, understand ipynb vs py files, create and run notebooks with shift enter, and practice with tuples while debugging with ChatGPT.

Learn python debugging with ChatGPT: diagnose errors like list B not defined or pd not defined, receive code fixes, and create files or import libraries when instructors are unavailable.

Explore Python's role as an open source, general purpose language used in data science, back-end development, and IoT, highlighting its object-oriented design, scripting capabilities, cross-platform support, libraries, and strong community.

Learn Python basics by mastering variables and keywords, installation options, and simple data types, with practical examples of assignments and printing.

Explore Python data types, operators and operands, including numeric, sequence types, dictionaries, booleans, and strings. Grasp type casting, input, and operator precedence using Bodmas and Pemdas with practical examples.

Explore Python lists as mutable, ordered data structures, covering indexing, slicing, nested lists, and operations such as extend, append, delete, pop, remove, and sorting.

Explore tuples in Python: their definition, immutability, how to create and access elements, indexing, slicing, joining, nested tuples, and differences from lists, plus practical notes on sorting and dictionary keys.

Explore sets in Python as unordered collections of unique elements defined by curly braces. Use membership checks, avoid duplicates, and perform union, intersection, and difference to derive distinct values.

Explore Python dictionaries as key-value pairs, mastering how to access, update, delete, and sort entries using methods like keys, values, and get.

Explore Python loops and iteration, including for and while loops, iterables and iterators, and practice with lists, strings, dictionaries, and ranges through classic examples and list comprehensions.

Explore python functions, from built-in and user defined functions to lambda expressions, with practical examples like bmi calculation, even or odd checks, factorial, and map, filter, reduce previews.

Explore map, reduce, and filter in Python, and learn how lambda expressions simplify data transformations. See practical examples like calculating circle areas and converting Celsius to Fahrenheit.

Master file handling in python by opening, reading, writing, and closing files, using read and read line. Learn append, file permissions, and the role of pandas read_csv for csv data.

Learn control structures in Python, including binary and relational operators, and if-else decision making, plus practical examples like even/odd checks and age-based party access.

Master object oriented programming in Python by learning classes and objects, constructors, self, instance and class variables, methods, inheritance, polymorphism, and encapsulation.

Explore numpy, the Python numerical library for fast multi-dimensional arrays, covering array creation, indexing, shape, dtype, zeros, ones, full, random and identity matrices, and basic arithmetic.

Learn to use pandas for data analytics in Python, including reading csv files, creating and inspecting data frames, describing data, filtering, sorting, derived columns, and joins with iloc and loc.

Discover how data visualization in Python bridges numbers and words, using charts such as bar, area, line, and pie with Matplotlib and Seaborn to reveal trends and insights.

Explore matplotlib for visualizing data in Python, from importing numpy and pandas to plotting histograms, area and bar charts, line, scatter, and pie plots, and converting dictionaries to data frames.

Explore seaborn, a powerful data visualization library, with installation, alias sns, iris data, and plots like kernel density, dist plots, pair plots, and heat maps.

Discover the role of statistics in data analytics and data science, using charts, tables, and graphs to analyze numerical attributes like age and salary and identify trends.

Examine descriptive and inferential statistics and the data types of categorical (qualitative) and numerical (quantitative), as the agenda introduces core concepts.

Describe data with descriptive statistics, covering central tendency (mean, median, mode), variability (variance, standard deviation), and frequency distribution, illustrated with a data set such as customer ages.

Explore inferential statistics, learn to estimate population parameters and conduct hypothesis testing from sample data to infer characteristics of the population.

Explore qualitative data, a key category of data types, including nominal and ordinal data, with examples like gender and economic status, and visualizations using pie and bar charts.

Explore quantitative data, its discrete and continuous forms, with examples like height, weight, and workers, and learn how to plot them using charts such as pie charts or stem-and-leaf plots.

Explore population and samples, understand their differences and sizes, and preview sampling techniques like random and non-probability sampling to prepare for data analytics.

Define population as the entire group to study and a sample as a subset, illustrate with country age examples, and explain why analysts use samples before inferring to the population.

Sampling plays a crucial role in analyzing large populations by using representative samples to infer population metrics, enabling faster, cost-effective analysis and clearer visualizations.

Explore probability and non-probability sampling, including simple random, stratified, cluster, systematic, convenience, purposive, voluntary response, and snowball methods, with a Tokyo population example.

Explore cluster random sampling by dividing the population into diverse clusters, then randomly selecting clusters to collect and analyze data.

Master probability sampling by exploring random sampling, systematic sampling, stratified sampling, and cluster random sampling, and contrast these with non-probability sampling to preserve the population distribution.

Explore non probability sampling techniques, including convenient, purposive, voluntary response, and snowball sampling, and contrast them with probability sampling to understand sample representativeness.

Explore population sampling, define population versus sample, and apply mean, variance, and standard deviation formulas to interpret data.

Explore why sample variance uses n minus one instead of n, via bessel's correction, to provide an unbiased estimate of population variance.

Explore descriptive analytics by examining measures of central tendency and dispersion, including mean, median, mode, range, interquartile range, variance, standard deviation, and mean deviation.

Explore measures of central tendency, including mean, median, and mode. Discover how these measures identify the central location of a data set and when to apply each.

Define mean as the average and show it equals the sum of observations divided by n. Illustrate with a 1,2,3 example and note how missing values are imputed using mean.

Explore the median as a measure of central tendency, learn how to compute it for odd or even data sets, and compare it with the mean to address outliers.

Understand mode, the most frequent value, a measure of central tendency, including unimodal, bimodal, and multimodal cases. Learn to identify it, apply to categorical data, and consider imputation trade-offs.

Explore measures of dispersion to understand data spread from the mean, including range, variance, standard deviation, and interquartile range (IQR), plus mean deviation and percentiles Q1 and Q3.

Explore range, the simplest measure of dispersion, defined as the difference between maximum and minimum values. See how it informs control charts in quality assurance and highlights extreme data values.

Explore the interquartile range, the dispersion measure for the middle 50% of data; learn to compute Q1 and Q3 and detect outliers with clear examples.

Explore variance and standard deviation as dispersion measures around the mean, and learn their population and sample formulas, including sigma square and sigma.

Compute mean deviation by averaging the absolute deviations from the mean, using the formula sum of |xi − μ| over n; the example yields mean 3.4 and deviation 0.88.

Explore probability as the measure of how likely an event is to occur, with coin toss and die examples, and key terms like trial, event, and sample space.

Explore the addition rule of probability, including union and intersection, and apply P(A or B)=P(A)+P(B)−P(A∩B) to die and classroom examples.

Explore independent events in probability, apply the multiplication rule P(A and B)=P(A)P(B), and compare independent versus dependent events using die and coin examples.

Learn to apply cumulative probability, the likelihood that a random variable lies in a range [a, b], using two-coin and two-dice examples and P(X ≤ 1) calculations.

Learn conditional probability and the rule P(B|A) = P(A intersection B) divided by P(A) with replacement vs without replacement through urn marble examples and sports drink and snacks scenarios.

Describe Bayes theorem as an extension of conditional probability and show how to relate P(A|B) to P(B|A) and P(A), including P(A∩B) = P(B|A) P(A) and P(A|B) = P(B|A) P(A)/P(B).

Use Bayes' theorem to compute the probability that a randomly drawn black ball came from bag x among two bags, yielding 7/12.

Explore probability distributions, including uniform, binomial, Poisson, and normal, and learn feature scaling techniques to convert a normal distribution to a standard normal.

Explore the uniform distribution, comparing discrete and continuous types and showing how equal probabilities form a rectangular sample space; illustrate with a die example of 1/6 probability per face.

Learn the binomial distribution as a discrete model for independent trials with two outcomes, using the binomial formula nCx p^x (1-p)^(n-x) to compute exact probabilities, illustrated by coin-toss examples.

Explain the Poisson distribution, a discrete probability model for the number of events in a time period, using lambda and the formula e^{-lambda} lambda^x / x! with call center examples.

Explore the normal distribution, also known as the Gaussian or bell curve, and the empirical rule. Learn mean and standard deviation, mu and sigma, and outliers beyond three sigma.

Explains computing the mean and standard deviation, evaluating normal distribution and outliers, and discusses skewness, kurtosis, and transformation techniques for skewed data.

Explore skewness in data distributions, distinguish right and left skew from the normal bell curve, and apply transformation techniques using Python, pandas, and NumPy to approach normality.

Explore kurtosis as a measure of distribution thickness and symmetry, including mesokurtic, leptokurtic, and platykurtic shapes. Learn how transformations such as log, reciprocal, and square root address kurtosis alongside skewness.

Explore probability with z scores for the normal distribution and understand standard normal distribution, then apply feature scaling through standardization and normalization using mu, sigma, and z values.

Use the z table to compute z scores for the normal distribution; a 700 score (mu 600, sigma 150) yields z ≈ 0.67, showing John outperforms 150 of 200 peers.

Apply z-score calculation for a normal height distribution to determine that 4.75% of students are shorter than 1.5 m, using mu=1.6 and sigma=0.06 and a z-table.

Explore covariance and correlation and examine how they relate to each other, including the possible relationship between them and why it matters in data analytics.

Understand covariance, a measure of direction between two variables; positive covariance signals a direct relationship and negative covariance signals an inverse relationship, while magnitude does not matter, only direction.

Explore correlation and covariance, their linear relationship, and signs, using Pearson's coefficient and practical examples such as height and weight.

Explore covariance versus correlation and how two variables move together, noting that covariance is unbounded while correlation ranges from -1 to 1 and reflects strength.

Explore hypothesis testing, a method that uses sample data to evaluate the null hypothesis against the alternative hypothesis, two mutually exclusive population statements, in the context of normal distribution.

Compare one-tailed and two-tailed hypothesis tests, detailing their rejection regions and examples like 800 marks or 80%, and prepare to explore p value versus significance value.

Explain p value, significance level or alpha, and how they guide rejecting the null hypothesis in hypothesis testing, with a coin-toss example.

Learn to select and apply key statistical tests—t test, z test, anova, chi-square, and correlation—while framing null and alternate hypotheses and matching tests to data types.

Apply one-sample and two-sample t tests to real data, testing mean ages against 30 and exploring gender differences at a 0.05 significance level.

Learn how the z test compares two sample means and tests the null hypothesis at 0.05 significance, using Excel toolpak, with a case comparing mean ages of males and females.

Explore ANOVA, a parametric analysis of variance, to test whether means across three drug-dose groups differ. Learn hypotheses, f statistics, p-values, and decision rules in Excel.

Explore the chi square test for categorical data, compute observed and expected values, frame null and alternate hypotheses, and interpret p-values with degrees of freedom using a pivot table approach.

Explore how correlation measures the linear relationship between variables, and compare it to covariance. Learn to compute and interpret correlation values in Excel using scatter plots and the corr function.

Master exploratory data analysis (eda) from data sourcing and cleaning to univariate, bivariate, numerical analyses. Derive metrics and insights through visual analytics, charts, and use cases, with Python demonstrations.

Master the data analytics and data science process, from data collection and cleaning to EDA and model building. Learn how to translate insights into a final data product and visualizations.

Explore and visualize data with eda using visual methods to understand data structure, detect anomalies, and form a baseline model, preparing insights for business decisions and potential machine learning deployment.

Visualize data in graphical form to help end customers and managers understand insights clearly. Use bar charts, pie charts, line charts, and scatter charts to reveal distributions and trends.

Learn the steps of data sourcing in the EDA process, distinguishing public versus private data and how to access them across organizations, from startups to multinational companies.

Clean data after sourcing to produce high-quality data for accurate predictive models. Learn handling missing values, feature scaling, and outlier and invalid data management for effective EDA and modeling.

Learn how to handle missing values in data cleaning, including when to delete rows or columns and how to impute with mean, median, or mode.

Handle missing values in a churn modeling dataset using pandas: identify nulls, decide between dropping columns or rows, and impute with mean, median, or forward/backward fill.

Explore feature scaling as a data cleaning technique essential for predictive modeling and sometimes for EDA, using normalization and standardization to bring features to comparable scales.

Learn standardization by converting data to z-scores using the mean and standard deviation, with mu and sigma, Python tools, and a typical -3 to 3 range.

Demonstrate normalization through min–max scaling to prevent bias in a predictive model, using income data, and preview Python libraries for standardization and normalization in the next video.

Explore feature scaling using standardization and normalization with sklearn's StandardScaler and MinMaxScaler, applying fit_transform to age and tenure after imputing null values with the mean.

Identify outliers with box plots, histograms, and scatter plots, guided by the normal distribution, and explore removal, quantile replacement, or robust models in predictive analytics and time series forecasting.

Apply the three sigma technique to identify outliers. Use log transforms and box plots to reduce skewness and better understand data.

Explore strategies to handle invalid data during cleaning, including encoding corrections, data type conversions, and filtering or correcting values that break expected structure or range.

Explore the two main data types: qualitative (categorical) and quantitative (numerical). Distinguish nominal and ordinal categories and discrete and continuous measurements, with examples such as gender, location, and temperature.

Learn univariate analysis, the simplest one-variable approach, applicable to categorical and numerical data, to understand data characteristics and summarize numerical columns.

Explore univariate analysis, analyzing one variable at a time for numerical and categorical data. Use summary statistics and distributions to reveal patterns such as gender balance and age trends.

Explore bivariate analysis across two numerical, two categorical, or mixed variables to uncover relationships using scatter plots and box plots, illustrated with churn and demographic insights.

Explore multivariate analysis to uncover deeper insights by analyzing more than two variables, including both categorical and numerical attributes, as part of exploratory data analysis.

Explore numerical data analysis with pandas: single-variable statistics, multi-variable correlation via scatter plots and heat maps, plus kernel density estimates and histograms.

Practice data analysis in Python across univariate, bivariate, and numerical methods, exploring churn with EDA and visualizations like count plots, hist plots, KDE, and heat map.

Create derived metrics by deriving new features from existing data to reveal insights. Apply domain knowledge, feature binning, and encoding techniques such as one-hot, label, dummy, target, and hash encoding.

Learn the theory of feature binning, converting a continuous numerical variable into categorical bins using equal width or equal frequency, to reveal patterns in data during EDA.

Learn practical feature binning using pandas, pd.cut, and age bins to transform continuous data, drop irrelevant columns, validate bins, and visualize results.

Master feature encoding to convert categorical x variables into numerical inputs for predictive models. Learn label, one hot, and dummy encoding, with get dummies and drop first or last options.

Practice, with a churn dataset, feature encoding techniques including handling gender nulls by mode, label encoding, and one hot or dummy encoding using pandas and sklearn.

Explore customer churn analysis through end-to-end exploratory data analysis, data cleaning, and feature engineering, using univariate, bivariate, and multivariate insights to inform predictive modeling.

Explore customer churn through an end-to-end exploratory data analysis workflow. Import, inspect, and clean a churn dataset, identify data quality issues, and uncover key drivers behind churn.

Learn data cleaning by backing up the data frame, converting invalid types to numeric, handling missing values, and applying feature binning for analysis readiness.

Explore univariate analysis on a cleaned telecom dataset, using an automated loop to generate charts for features like gender, senior citizen, and contract, deriving churn insights.

Perform a bivariate analysis of churn using monthly charges, total charges, tenure, and payment method, including cross tabs, correlation plots, kernel density visuals, and heatmaps.

Explore bivariate analysis to uncover churn insights by comparing churners and non-churners across gender and partner status, using reusable EDA plots and end-to-end data storytelling report.

Develop an end-to-end EDA report for churn analysis, outlining business understanding, data understanding, missing data handling and imputation, graph-based findings, heatmaps and correlation maps, and final insights.

Install MySQL by setting up MySQL Workbench and MySQL Installer with a root password to access the local database, then learn SQL, DBMS, and file-server and client-server architectures.

Compare file server and client-server architectures; explain how file servers lock files to prevent data loss and why client-server databases process sql statements in order, with versioning managed by dbas.

Master introduction to sql, the standard language for relational databases, and learn to read, insert, update, and delete data; explore data types like integer and varchar, and four sql categories.

Learn how constraints govern data in tables, including unique constraints, not null, and primary key constraints, and explore how primary keys relate to foreign keys to link tables.

Master table basics and the data definition language in SQL by creating, altering, and dropping tables, defining columns, data types, and constraints.

Master the fundamentals of data query language (DQL) with select statements, create table and insert basics, and learn filtering, aliases, and simple aggregations like count on sample tables.

Explore data manipulation language (DML) in SQL, focusing on insert, update, and delete commands, with practical table examples and safe update considerations.

Explore SQL joins, including inner, left, right, full outer, and cross joins, and learn how common columns enable linking tables. Practice self joins and real-world examples to master join scenarios.

Learn how to import and export data to sql databases using manual and command-line methods, including MySQL workbench routines, csv and excel files, and exporting data in chunks.

Explore group by, count, min, max, and average in SQL, with examples on gender, contract type, and total charges, and see how rounding clarifies totals.

Explore string functions in SQL, including concat, trim, substr or substring, upper and lower, character length, and mid, with examples using the customers table.

Explore SQL date and time functions, including date diff, date format, day, month, year, quarter, and date add or subtract, with a hands-on transaction details example.

Discover how regular expressions replace the like operator for pattern matching in SQL queries, using regex with square brackets and ranges to filter customer data.

Explore nested queries, or subqueries, to combine data from multiple tables using inner and outer queries, execute the inner query first, and harness aggregate functions like average to filter results.

Learn what views are in sql: virtual tables that do not store data and display data from tables, offering data privacy, access control, and options like read-only and materialized views.

Explore stored procedures in SQL, including creating, calling, passing in and out parameters, and using top n queries, updates, and counts with examples in MySQL workbench.

Master windows function in SQL to compute aggregates across related rows using over and partition by, with row number, rank, and first value examples.

Learn SQL Python connectivity using the Pi MySQL connector to pull data from a MySQL database into pandas data frames for EDA and visualization, and connect SQL to Power BI.

Learn how to apply predefined functions in Excel, including round, square root, min, max, average, left, right, and rank, and perform aggregation across columns.

Discover essential date time functions in Excel and Power BI, including date, end-of-month, networkdays, weekday, today, and date diff, with practical DAX examples.

Explore essential string functions for data analysis, including find, replace, substitute, mid, search, and concat, with Excel and DAX examples for real-world text tasks.

Explore essential Excel mathematical functions for data analysis, including product (dot product), modulus, square root, factorial, round up/down, floor and ceiling, sumif/sumifs, and averageif/averageifs, with practical examples.

Master lookup functions in Excel, including vlookup and hlookup, to retrieve values from a table array via leftmost column or top row, and use match and index for flexible lookups.

Explore logical and error functions in Excel, using if-else blocks to create derived columns and age bins, and diagnose common errors like hash div by zero and hash name.

Explore Excel's statistical functions, including mean, median, mode, correlation, standard deviation, and variance, along with count, count blank, and countifs to analyze data, handle missing values, and inform imputation strategies.

Insert images into Excel cells, including logos and icons, and use format picture settings to move and size with cells for reliable sorting and filtering.

Master the basics of Excel formatting for data analysis. Learn to format cells, apply colors, borders, and fonts, and manage numbers, text, and date formats.

Learn how to apply custom formatting in Excel to create thousand separators, enforce four-digit product codes, hide zeros, and display numbers with chosen decimals using the four-part syntax.

Learn conditional formatting in Excel to visualize data with color scales, icons, and data bars; apply thresholds, manage rules, and highlight top values.

Explore how to visualize data in Excel using charts for both categorical and numerical data. Learn basic EDA concepts, chart types, correlation, pivot tables, and recommended charts.

Learn to perform correlation analysis and tests like ANOVA, t tests, and z tests in Excel using the Analysis ToolPak, visualize with scatter plots and heatmaps, and generate descriptive statistics.

Learn to analyze data with pivot tables in Excel, using drag-and-drop fields, filters, and slicers. Create pivot charts to compare salaries across gender and geography, and perform exploratory data analysis.

Master dashboarding in Excel by creating dashboards with interactive charts, pivot tables, and form controls such as a combo box and a slicer to deliver clear insights.

Explore practical excel tips beyond DAX, including error checking, show formulas, and text to columns. Learn data validation, sort and filter, wrap text, and merging cells to enhance spreadsheets.

Explore how Excel what-if tools—scenarios, scenario manager, goal seek, and data tables—let you change cell values to analyze outcomes and identify the scores needed to reach a target.

Discover Power BI as a business intelligence tool to visualize data, share insights, and build dashboards, and learn its desktop, service, and mobile components from data sources to published reports.

Discover the basics of Power BI, compare Power BI Desktop and Power BI Pro, and learn about a cautionary life hack to access Pro licenses via Office 365 trial.

Power BI desktop introduction that covers getting data from 80-plus sources, analyzing and modeling with DAX measures, visualizing with 150+ visuals, and publishing to the cloud to enable collaboration.

Explore Power BI service, including publishing from desktop to service, data sources, gateways, security, and integration with Excel, Teams, and embedding reports for collaborative insights.

Explore Power Query Editor, the heart of Power BI, to transform data, add columns, replace values, manage and merge queries, and perform data profiling with tracked applied steps.

Explore data profiling tools in Power BI’s Power Query Editor, including column quality, distribution, and profile, and learn how to identify and impute nulls using mean, median, or mode.

Explore how group by works in SQL and Power BI, from counting gender to grouping by payment method, and learn how Power Query handles group by dialogue.

Explore how applied steps in Power BI's Power Query Editor log every transformation. Rename steps, inspect replaced values and changed type, and edit steps directly to avoid repeated undos.

Learn how to append and merge queries in Power Query Editor, mastering inner, left, right, and outer joins, and understand how to stack rows and join tables for Power BI.

Explore Power BI visuals and building blocks—visualizations, data sets, reports, dashboards, and tiles—and see how dynamic visuals respond to interactions.

Explore Power BI charts and visualizations, from bar and pie charts to area, funnel, maps, tables, and slicers, and learn when to use time series versus categorical data.

Explore data analysis expressions (DAX) in Power BI, focusing on measures and calculated columns, and how row and filter contexts shape calculations with core functions like sum and calculate.

Learn how implicit measures use calculated columns or other measures to compute results, illustrated with examples like tenure in months and total charges in Power BI.

Master DAX by exploring aggregations and filtering, with examples like sum, average, min, max. See how Power BI validates outputs and builds measures and dashboards.

Explore basic dax functions in power bi, including sum, average, min, max, and count, and learn to create measures for dashboard cards and monthly charges.

Explore date functions in DAX, such as date diff and date add, and apply today, year, and month calculations in a Power BI dashboard using the air passengers dataset.

Explore DAX calendar functions to create a date table with daily dates, build a date hierarchy, and map to data for robust time-based analysis.

Explore Power BI contexts by comparing row context for calculated columns with filter context used by measures, using a tenure example and round function to illustrate differences.

Learn how to use the DAX calculate and filter functions in Power BI to compute sums and counts within filtered contexts, via measures.

Apply if else and nested if blocks in DAX to create age and tenure bins in Power BI, using Power Query Editor and practical examples.

Explore time intelligence functions in DAX and Power BI, learning to compute year-to-date, quarter-to-date, month-to-date, and last year metrics using YTD, QTD, MTD, and rolling 12 months.

Explore x versus non x functions in Power BI, focusing on sumx and averagex as iterator functions and their use as measures vs calculated columns under filter context.

Learn how tooltips and drill-throughs in Power BI enrich reports by using tooltip pages, hover details, and drill-through navigation for focused analysis.

Explore Power BI relationships, linking tables with primary keys and cardinality, including one-to-many and many-to-one, and apply left, right, inner, and outer joins within a star schema.

Learn how to use Power BI KPIs to compare actual versus target, assess variance, and visualize trends with KPI visualizations.

Explore administration options in Power BI, manage users and dashboards, assign roles (admin, member, contributor, viewer) in workspaces, and control access to reports and data.

Explore row level security in Power BI by creating roles, applying geography filters, and validating RLS rules in Power BI Desktop and Power BI service to restrict data access.

Learn dynamic row level security in Power BI, replacing tedious static security with user-based access using roles, measures, and group-based management across reports and workspaces.

Learn how Power BI data flows consolidate multiple tables from Snowflake into a single data flow, enabling one refresh for all reports and streamlined data source management.

Explore how to format visuals, dashboards, and reports in Power BI Desktop and Power BI service, including data labels, colors, backgrounds, borders, shadows, tooltips, and table styling.

Explore the top five Power BI best practices, including separate blank queries for Dax measures, limited visuals, tooltips, drill through, and using certified visuals for performance.

Explore exploratory data analysis (EDA) concepts in Power BI, including data visualization, data cleaning, and univariate to multivariate analyses for numerical and categorical data insights.

Explore end-to-end telecom churn analysis with practical EDA, data cleaning, and dashboard creation in Power BI, linking business understanding to reports and predictive insights.

Learn to navigate Power BI service, a cloud-based business analytics platform for creating, sharing, and managing reports and dashboards. Explore workspaces, deployment pipelines, and access roles to empower collaboration.

Explore the Power BI service layout on app.powerbi.com, including workspaces, reports, dashboards, and the co-pilot feature, tied to fabric capacity, with development, testing, and production workflows.

Create and manage collaborative workspaces in Power BI and Fabric to organize dashboards and data sets, with personal and shared options, access roles, and publishing capabilities.

Explore Power BI workspace roles including admin, member, contributor, and viewer, and learn who can edit and view reports, publish or unpublish, and add or remove users.

Set up a workspace with fabric capacity by creating a subscription and capacity in portal.azure.com, then attach the workspace to the fabric capacity and experiment with Copilot in Power BI.

Learn how to delete fabric capacity, cancel its subscription, and understand that copilot becomes disabled and you cannot deploy on capacity, while the workspace remains visible.

learn how a Power BI deployment pipeline streamlines development, testing, and production across dev, testing, and production stages with version control, collaboration, and quality assurance.

Create a three-environment deployment pipeline using Azure MySQL databases for dev, testing, and production, publish a Power BI dashboard, and sync data sources across stages.

Explore deploying and validating data reports and a semantic model across development, testing, and production environments using an Azure MySQL backend, with credential configuration and SSL considerations.

Explore configuring deployment rules in a Power BI deployment pipeline across development, testing, and production stages, including data source rules and fabric capacity setup to manage environment-specific configurations.

Troubleshoot deployment rules in a Power BI data pipeline when using Azure MySQL, and apply a workaround by switching to a supported data source such as Azure SQL.

Explore building a Power BI deployment pipeline with Azure SQL DB, switching from Azure MySQL, and syncing dev, test, and prod datasets using deployment rules and shared data sources.

Explore Power BI's report and dashboard concepts, contrasting multi-page, interactive reports with single-page, summarized dashboards built from pinned visuals across datasets.

Create a report directly in Power BI service, replicating desktop capabilities. Connect to data sources, build visuals, publish to a workspace, and schedule refresh.

Learn to create reports on the Power BI service using AI Copilot in a Fabric Capacity workspace. Prompt Copilot for churn KPIs and visuals, and explore service and desktop workflows.