Courses/AI & Data

Data Analytics Foundations

A complete end-to-end path to becoming a Data Analyst. From Excel to SQL to Python.

What you'll learn

  • Core Concepts
  • Real-world application
  • Best Practices
  • Industry Standards

Syllabus

Module 1: Introduction to Data Analytics

1.1
What is Data Analytics
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1.2
Types of Data
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1.3
Analytics vs Data Science
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1.4
Role of a Data Analyst
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1.5
Analytics Lifecycle
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Module 2: Data Collection & Sources

2.1
Data Sources
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2.2
Databases
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2.3
APIs (Conceptual)
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2.4
Web Data
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2.5
Data Ethics & Privacy
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Module 3: Data Cleaning & Preparation

3.1
Dirty Data Problems
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3.2
Handling Missing Values
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3.3
Removing Duplicates
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3.4
Data Normalization
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3.5
Data Validation Rules
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Module 4: Excel for Data Analysis

4.1
Excel Interface Overview
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4.2
Data Formatting
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4.3
Sorting & Filtering
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4.4
Functions (VLOOKUP, IF)
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4.5
Pivot Tables
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4.6
Charts & Dashboards
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Module 5: SQL Fundamentals

5.1
What is SQL
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5.2
Databases & Tables
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5.3
SELECT Statement
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5.4
WHERE Clause
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5.5
ORDER BY
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5.6
Aggregate Functions
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5.7
GROUP BY
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5.8
JOINS
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5.9
Subqueries
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Module 6: Python for Data Analysis

6.1
Python Basics
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6.2
Data Types
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6.3
Control Flow
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6.4
Intro to NumPy
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6.5
Intro to Pandas
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6.6
DataFrames
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6.7
Pandas Cleaning
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Module 7: Data Visualization

7.1
Principles of Viz
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7.2
Chart Types
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7.3
Matplotlib Basics
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7.4
Seaborn
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7.5
Dashboard Design
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7.6
Storytelling
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Module 8: Statistics for Analytics

8.1
Mean, Median, Mode
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8.2
Variance & Std Dev
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8.3
Probability
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8.4
Correlation vs Causation
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8.5
Hypothesis Testing
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Module 9: Case Studies

9.1
Sales Analysis
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9.2
Customer Churn
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9.3
Marketing Campaign
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Test
Final Certification Exam
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Course Info

Duration
15 Hours
Level
Beginner
Lessons
51 items