Data Analytics

Builds job-ready spreadsheet fluency to structure, calculate, and audit real business data before touching code.

• Excel workbook structure, ranges, and data types
• Lookup functions: VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP
• Logical & conditional functions (IF, IFS, SUMIF, COUNTIFS, SUMPRODUCT)
• PivotTables and PivotCharts for summarization
• Data validation, drop-downs, and input controls
• Conditional formatting for exception highlighting
• What-if analysis: Goal Seek, Data Tables, Scenario Manager
• Text functions and data type conversion (TEXT, TRIM, CONCAT, VALUE)
• Introduction to Power Query for import and transformation
• Named ranges, absolute vs relative referencing
• Basic Excel macros and recording for repetitive tasks
• Dashboard-style reporting layouts in Excel
• Version control basics for spreadsheet-driven projects with Git

Equips learners to extract, filter, and combine data directly from production-scale relational databases.

• Relational database concepts and schema design
• SELECT, WHERE, ORDER BY, and filtering fundamentals
• Aggregate functions with GROUP BY and HAVING
• Joins: INNER, LEFT, RIGHT, FULL, and self-joins
• Subqueries and correlated subqueries
• Window functions & CTEs in SQL
• Set operations: UNION, INTERSECT, EXCEPT
• Data definition: CREATE, ALTER, constraints, and indexing basics
• Views and stored procedures for reusable logic
• Query performance and execution plan basics
• Working with dates, strings, and NULL handling in SQL
• MySQL Workbench setup and connecting to live databases
• Writing and tracking SQL scripts with Git version control

Develops programming fluency to automate data workflows and manipulate datasets beyond spreadsheet limits.

• Python syntax, variables, and control flow for data tasks
• Data structures: lists, dictionaries, tuples, and sets
• Functions, loops, and list comprehensions
• NumPy arrays and vectorized operations
• Pandas Series and DataFrame fundamentals
• Reading/writing CSV, Excel, and JSON with Pandas
• Filtering, sorting, and indexing DataFrames
• GroupBy, merge, join, and concatenation of datasets
• Handling dates and time series in Pandas
• Writing reusable analysis scripts and functions
• Interactive analysis workflows in Jupyter Notebook
• Basic error handling and debugging Python scripts
• Version-controlling notebooks and scripts with Git

Trains learners to transform messy, real-world data into analysis-ready datasets with confidence in data quality.

• Data quality dimensions: accuracy, completeness, consistency
• Identifying and handling missing values (dropna, fillna, imputation)
• Detecting and treating duplicate records
• Outlier detection using IQR and z-score methods
• Data type correction and standardization
• String cleaning: regex, trimming, and case normalization
• Merging and reconciling data from multiple sources
• Exploratory Data Analysis: descriptive statistics with Pandas
• Univariate and bivariate analysis techniques
• Correlation analysis and feature relationships
• Data profiling reports and cleaning documentation
• Building reproducible cleaning pipelines in Jupyter
• Data validation rules and sanity-checking datasets

Turns cleaned datasets into clear, persuasive visual narratives using industry-standard plotting tools.

• Principles of effective chart selection and design
• Line, bar, scatter, and histogram plots in Matplotlib
• Statistical plots with Seaborn: boxplot, violin, pairplot
• Customizing figures: subplots, labels, legends, and themes
• Heatmaps for correlation and matrix visualization
• Distribution and trend visualization for time series
• Building interactive dashboards in Tableau
• Connecting Tableau to Excel and database sources
• Calculated fields and parameters in Tableau
• Storytelling with color, annotation, and layout
• Avoiding misleading visualizations and chart junk
• Exporting and embedding charts for reports and decks
• Choosing between Excel, Python, and Tableau per use case

Prepares learners to build enterprise BI dashboards and pass the industry-recognized PL-300 certification.

• Power BI Desktop interface and report workflow
• Connecting to data sources: Excel, SQL, and web connectors
• Power Query Editor: transform, shape, and merge queries
• Data modeling: star schema vs snowflake schema design
• Relationships, cardinality, and cross-filter direction
• DAX fundamentals: calculated columns vs measures
• Core DAX functions: CALCULATE, SUMX, FILTER, ALL
• Time intelligence functions (YTD, QTD, SAMEPERIODLASTYEAR)
• Building interactive reports: slicers, drill-through, bookmarks
• Row-level security and workspace governance
• Publishing to Power BI Service and scheduling refresh
• Performance optimization for large data models
• PL-300 exam-aligned practice: prepare, model, visualize, deploy

Builds the statistical reasoning needed to draw valid, defensible conclusions from data.

• Descriptive statistics: mean, median, mode, variance, std dev
• Probability fundamentals and conditional probability
• Probability distributions: normal, binomial, Poisson
• Sampling methods and sampling distributions
• Central Limit Theorem and its practical implications
• Confidence intervals and margin of error
• Hypothesis testing: null vs alternative hypotheses
• t-tests, chi-square tests, and ANOVA basics
• p-values, significance levels, and Type I/II errors
• Correlation vs causation in analytical reasoning
• Basic linear regression for predictive analysis
• A/B testing design for business experiments
• Applying statistical tests using Python (SciPy/NumPy) in Jupyter

Converts analysis into stakeholder-ready recommendations that drive real business decisions.

• Framing business problems into analytical questions
• KPI and metric selection for business functions
• Building executive dashboards and scorecards
• Cohort analysis and customer segmentation
• Funnel analysis and conversion metrics
• Root-cause analysis frameworks for business metrics
• Structuring data-driven narratives (situation-insight-action)
• Designing slides and reports for non-technical stakeholders
• Sensitivity analysis and forecasting for decision-making
• Competitive and market trend analysis
• Presenting insights with confidence: mock stakeholder reviews
• Case studies across sales, marketing, and operations analytics
• Ethics and bias awareness in business reporting

Wraps the course by teaching learners to build reliable data pipelines and manage analytics code like a professional team.

• ETL vs ELT: concepts and when to use each
• Data extraction from files, APIs, and databases
• Data transformation logic and staging layers
• Loading strategies: full load vs incremental load
• Building a simple ETL pipeline with Python and SQL
• Data pipeline scheduling and automation basics
• Data warehousing concepts: fact and dimension tables
• Error handling and logging in data pipelines
• Git fundamentals: init, clone, add, commit, push
• Branching, merging, and resolving conflicts in Git
• Collaborative workflows with GitHub/GitLab for analytics teams
• Version-controlling notebooks, SQL scripts, and dashboards
• Capstone project: end-to-end pipeline from raw data to dashboard