Data Concepts: Data types (nominal, ordinal, interval, ratio), data sources (CRM, ERP, web logs), data quality dimensions (accuracy, completeness, consistency).

Excel:

SUMIFS
COUNTIFS
VLOOKUP
HLOOKUP
XLOOKUP
INDEX
MATCH
FIND
LEFT
RIGHT
IF
AND
OR
AVEREGE
MAX
MIN
TRIM
MID
POWER QUERY

Data Cleaning: Identifying/handling missing values (ISBLANK), duplicate removal, text manipulation (TEXT TO COLUMNS, CONCATENATE).

Basic Visualization: Creating charts (bar, line, pie) for trend analysis and comparisons; conditional formatting.

Case Study: Sales performance analysis for a retail chain using raw transaction data.

Basic SQL Commands (DDL & DML Introduction): 

CREATE DATABASE and USE
CREATE TABLE: Defining columns, data types (INT, VARCHAR, TEXT, DATE, BOOLEAN, DECIMAL), constraints (NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, CHECK, DEFAULT)
INSERT INTO: Single row, multiple rows, inserting from SELECT
SELECT: Basic projection and selection, DISTINCT keyword
UPDATE: Single row, multiple rows, conditional updates
DELETE FROM: Conditional deletion, TRUNCATE vs. DELETE
DROP TABLE, ALTER TABLE (ADD, DROP, MODIFY COLUMN)
COMMENT on tables/columns

Filtering Data with WHERE Clause:

Comparison operators (=, !=, >, <, >=, <=)
Logical operators (AND, OR, NOT)
BETWEEN, IN, LIKE (wildcards: %, _), IS NULL / IS NOT NULL

Ordering and Limiting Results:

ORDER BY: Ascending/Descending, multiple columns
LIMIT / OFFSET (MySQL/PostgreSQL) or TOP (SQL Server) for pagination

Aggregate Functions:

COUNT, SUM, AVG, MIN, MAX
GROUP BY: Grouping data for aggregates
HAVING: Filtering grouped data

Case Expressions and Conditional Logic:

CASE WHEN THEN ELSE END
COALESCE, NULLIF

SQL Join Types:

INNER JOIN
LEFT (OUTER) JOIN
RIGHT (OUTER) JOIN
FULL (OUTER) JOIN
SELF JOIN
CROSS JOIN

Set Operations:

UNION, UNION ALL
INTERSECT
EXCEPT / MINUS

Advanced DDL:

CREATE INDEX
DROP INDEX, ALTER INDEX
CREATE VIEW
DROP VIEW, ALTER VIEW
CREATE SEQUENCE

Data Control Language (DCL):

GRANT: Assigning permissions (SELECT, INSERT, UPDATE, DELETE, CREATE, ALTER, DROP)
REVOKE: Removing permissions

Stored Procedures:

CREATE PROCEDURE / CREATE FUNCTION: Defining parameterized routines
Control flow (IF/ELSE, WHILE loops, CASE statements)
Error handling (TRY/CATCH in SQL Server, EXCEPTION in PostgreSQL/Oracle)

Triggers:

CREATE TRIGGER: Defining actions on DML events (INSERT, UPDATE, DELETE)
FOR/AFTER vs. INSTEAD OF triggers
OLD and NEW row references (PostgreSQL/Oracle) / inserted and deleted tables (SQL Server)
Normalization (1NF, 2NF, 3NF, BCNF)

Window Functions:

ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE()
Aggregate window functions (SUM() OVER(…), AVG() OVER(…))

JSON and XML Data in SQL:

Storing, querying, and manipulating JSON/XML data types
JSON_EXTRACT, JSON_OBJECT, JSON_ARRAY (MySQL/PostgreSQL) / FOR JSON, OPENJSON (SQL Server)

Practice Platforms: PostgreSQL, MySQL, BigQuery (cloud-based SQL).
Case Study: Customer segmentation from a marketing database based on purchase history.

Python Essentials: Data types, control flow, functions, basic data structures (lists, dictionaries).

Libraries :- Numpy,Scipy,Matplotlib,Seaborn,
Pandas for Data Manipulation:-(DataFrames,Series,GroupBy operations, merging, reshaping, handling missing data,outlier detection).
Data Structures :- Efficient use of dictionaries, sets, tuples, and custom classes for data handling.

Pandas Library: DataFrames, series, data loading (CSV, Excel), filtering, sorting, merging, reshaping (pivot_table).
NumPy Library: Array operations, numerical computing.
R Essentials: Vectors, data frames, base R functions.
Tidyverse (R): dplyr for data manipulation (filter, select, mutate, group_by, summarise), ggplot2 for visualization.
Data Acquisition: Basic web scraping (BeautifulSoup) or API calls (Requests).

Case Study: Analyzing customer reviews from e-commerce websites to extract sentiment.

Principles: Choosing appropriate chart types, visual encoding (color, size), avoiding chart junk, Gestalt principles.
Tools: Tableau Desktop/Public, Power BI Desktop (hands-on project building).
Advanced Visuals: Heatmaps, treemaps, scatter plots, geographic maps, dual-axis charts.
Dashboard Design: Layout, interactivity (filters, parameters), drill-down capabilities.
Storytelling: Structuring a narrative with data, identifying key takeaways, audience engagement, delivering actionable recommendations.
Case Study: Building an interactive sales dashboard for a global company, demonstrating regional performance and product trends.

Descriptive Statistics: Measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, IQR).
Probability: Basic concepts, probability distributions (normal, binomial, Poisson).
Inferential Statistics: Hypothesis testing (t-tests, ANOVA, Chi-squared tests), p-values, confidence intervals.
Correlation & Regression: Pearson/Spearman correlation, simple linear regression concepts (R-squared).
EDA Techniques: Histograms, box plots, scatter plot matrices, identifying outliers, assessing data distribution,
Case Study: A/B testing analysis for a website redesign, evaluating user engagement metrics.

Data Cleaning Strategies: Imputation techniques (mean, median, mode, regression), outlier detection (Z-score, IQR method), handling categorical data (one-hot encoding).
Feature Engineering: Creating new variables from existing ones to enhance analysis (e.g., age from birth date).
Version Control: Introduction to Git and GitHub for collaborative projects and code management.
Communication Skills: Presenting findings to non-technical stakeholders, crafting executive summaries and compelling reports.
Ethical Data Use: Data privacy (GDPR, CCPA awareness), bias in data, responsible data collection and reporting.
Career Preparation: Portfolio development, resume tips, interview strategies, mock interviews focusing on technical and behavioural questions.

End-to-End Analysis: Students work on a real-world dataset from an industry of choice (e.g., marketing, finance, healthcare, operations).
Phases: Problem definition, data collection/extraction, cleaning and preprocessing, exploratory analysis, statistical modeling (if applicable), visualization, and final presentation of insights and recommendations.
Tools Utilized: Combination of Excel, SQL, Python/R, and Tableau/Power BI.
Deliverables: Cleaned dataset, SQL queries/Python scripts, interactive dashboard, and a comprehensive report/presentation.