DATA ANALYST COURSE

Data Analyst Course

Industry-Oriented Data Analyst Course Curriculum

CADD Info Technology – Karad
Power of BI with Excel (Basics to Dashboards) + SQL + Python for Data Analysis.

Power of BI with Excel

Basics to Dashboards

Introduction to Data & Analytics (What is Data Analysis? Role of Excel & Power BI, Business Intelligence (BI), real-life use cases, Installing Power BI Desktop)

What is Data Analysis?
Role of Excel & Power BI
What is Business Intelligence (BI)?
Real-life use cases of Power BI
Installing Power BI Desktop

Excel Fundamentals for Power BI

Excel interface & worksheets
Data types (Text, Number, Date)
Tables vs Ranges
Basic Excel formulas: SUM, AVERAGE, COUNT
IF, VLOOKUP / XLOOKUP
Data cleaning in Excel
Formatting data correctly for Power BI

Getting Data into Power BI

Power BI interface overview
Connecting Power BI to Excel files
Import vs DirectQuery
Understanding Power BI data model
Refreshing data

Power Query (Data Cleaning & Transformation)

What is Power Query?
Removing duplicates & errors
Changing data types
Splitting and merging columns
Filtering rows, Sorting data

Power Query (Continued)

Creating new columns
Applying transformations

Data Modeling

Understanding tables and relationships
Primary key and foreign key
Star schema basics
Managing relationships, Cardinality (One-to-One, One-to-Many)

DAX (Data Analysis Expressions) – Basics

What is DAX? Calculated column vs Measures
Basic DAX: SUM, COUNT, AVERAGE
CALCULATE, FILTER
Time Intelligence basics: Date, Time, Month, Year

Data Visualization in Power BI

Bar chart, Column chart, Line chart
Pie & Donut chart, Table & Matrix
Choosing the right visual

Dashboards & Reports

Report design principles, Interactive dashboards
Drill-through, Tooltips, Filters

Module : SQL for Data Analysis

Databases + Queries + Joins

Introduction to Databases
What is SQL?
Database & Table Concepts
DDl, DML, DQL

SELECT, WHERE
ORDER BY
GROUP BY, HAVING
JOIN (INNER, LEFT, RIGHT), Subqueries, Aggregate Functions

Module : Python Basics for Data Analysis

Foundation + Files + IDE Setup

Introduction to Python
Installing Python & IDE
Variables & Data Types
Collections
Working with Files

Module 6: Python Libraries for Data Analysis

NumPy + Pandas (Detailed)

NumPy (for Data Analysis) – What to Teach

1. Introduction to NumPy (Why used, NumPy vs Python List, Installing & importing) • 2. NumPy Arrays (1D/2D, shape/size/dimension, dtype, type conversion) • 3. Array Operations (Arithmetic, broadcasting, functions: sum/mean/min/max/std) • 4. Indexing & Slicing (rows/columns, boolean indexing) • 5. Array Manipulation (reshape, flatten, concatenate/split, copy vs view) • 6. Random Module (random numbers, sampling, seed) • 7. Missing Values (NaN, checking, replacing)

Pandas (for Data Analysis) – What to Teach

1. Introduction to Pandas (Why used, Pandas vs Excel, Installing & importing) • 2. Data Structures (Series, DataFrame, DataFrames from dictionary/list/CSV/Excel) • 3. Data Exploration (head, tail, info, describe, shape & columns) • 4. Data Selection & Filtering (loc, iloc, conditions) • 5. Data Cleaning (isnull, fillna, dropna, duplicates, rename, change types) • 7. Working with Dates & Text (DateTime conversion, year/month/day extraction, string functions)

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