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Data Analytics Masterclass

Master business analytics, SQL, Excel, Power BI, and Tableau to make data-driven decisions. From fundamentals to advanced techniques with real-world business scenarios.

8
Modules
45+
Hours
120+
Examples
8
Assignments
Excel SQL Power BI Tableau Python Statistics

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Course Outline

1Introduction to Data Analytics

0/3
Data Analytics vs Data Science
Types of analytics (descriptive, diagnostic, predictive, prescriptive)
Analytics lifecycle and workflow
Career paths in analytics
Real-world use cases across industries
Understanding business objectives
Key performance indicators (KPIs)
Financial metrics (ROI, revenue, profit margins)
Customer metrics (CAC, LTV, churn rate)
SMART goals and metric selection

2Excel for Data Analytics

0/5
Data entry and formatting best practices
Cell references (relative, absolute, mixed)
Named ranges and tables
Data validation and conditional formatting
Keyboard shortcuts for efficiency
Mathematical functions (SUM, AVERAGE, COUNT)
Logical functions (IF, AND, OR, NOT)
Lookup functions (VLOOKUP, HLOOKUP, INDEX-MATCH)
Text functions (CONCATENATE, LEFT, RIGHT, MID)
Date and time functions
Creating pivot tables from raw data
Grouping, filtering, and sorting data
Calculated fields and measures
Pivot charts and slicers
Dashboard design principles
Finding and removing duplicates
Handling missing values
Text to columns and Flash Fill
Find and replace with wildcards
Power Query basics

3SQL for Analytics

0/5
Database concepts and RDBMS
SELECT statements and WHERE clause
Filtering with comparison and logical operators
Sorting with ORDER BY
LIMIT and DISTINCT keywords
Understanding table relationships
INNER JOIN for matching records
LEFT, RIGHT, and FULL OUTER JOIN
CROSS JOIN and self-joins
Join performance optimization
Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
GROUP BY for data summarization
HAVING clause for filtered aggregations
Multiple grouping columns
CASE statements for conditional logic
Subqueries (scalar, row, table)
Common Table Expressions (CTEs)
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Date and string manipulation
UNION and UNION ALL

4Statistics for Analytics

0/4
Measures of central tendency
Measures of spread and variability
Distribution shapes (normal, skewed)
Outlier detection methods
Five-number summary and box plots
Probability fundamentals
Conditional probability and independence
Bayes' theorem in business
Probability distributions
Expected value and risk assessment
Null and alternative hypotheses
p-values and significance levels
t-tests for mean comparisons
Chi-square tests for categorical data
A/B testing fundamentals

5Power BI Fundamentals

0/5
Power BI ecosystem (Desktop, Service, Mobile)
Interface and navigation
Data import and connection options
Report vs dashboard differences
Data transformation steps
Merge and append queries
Column operations and data types
Custom columns and conditional logic
M language basics
Star schema and snowflake schema
Relationships and cardinality
Active vs inactive relationships
Date tables and hierarchies
Calculated columns vs measures
Basic DAX functions (SUM, AVERAGE, COUNT)
CALCULATE and filter context
Time intelligence functions
RELATED and RELATEDTABLE

6Tableau Visualization

0/4
Tableau interface and workspace
Connecting to data sources
Dimensions vs measures
Creating basic charts
Show Me feature and chart types
Calculated fields
Table calculations
LOD expressions (FIXED, INCLUDE, EXCLUDE)
Parameters for interactivity
Dashboard best practices
Layout containers and objects
Actions (filter, highlight, URL)
Stories and narratives
Publishing to Tableau Server/Public

7Python for Analytics

0/4
DataFrames and Series basics
Data selection and filtering
GroupBy operations
Merge and join operations
Handling time series data
Matplotlib basics
Seaborn statistical plots
Plotly interactive charts
Chart customization
Automating data pipelines
Schedule tasks with cron/Task Scheduler
Email reports automatically
Web scraping for data collection

8Real-World Projects

0/5
Requirements gathering and KPI definition
Data preparation and modeling
Building interactive visualizations
Customer segmentation analysis
Churn prediction and prevention
Lifetime value calculations
P&L statement analysis
Budget vs actual variance reporting
Financial forecasting models
Employee attrition analysis
Recruitment metrics and trends
Performance and productivity tracking