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Data Analytics Syllabus
Module 1: Introduction to Data Analytics
What is data analytics?

Role of a data analyst

Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive

Data-driven decision making

Module 2: Data Collection & Data Sources
Types of data: Structured vs. unstructured
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Data sources: Databases, APIs, web scraping, surveys

Data warehousing and data lakes

Introduction to ETL (Extract, Transform, Load) processes

Module 3: Data Cleaning & Preprocessing
Importance of clean data

Handling missing or inconsistent data

Data transformation and normalization

Tools: Excel, Python (Pandas), R

Module 4: Exploratory Data Analysis (EDA)
Descriptive statistics: Mean, median, mode, standard deviation

Data profiling

Identifying trends and patterns

Visualization tools: Matplotlib, Seaborn, Tableau, Power BI

Module 5: Data Visualization & Communication
Best practices for visual storytelling


Creating reports and dashboards

Tools: Tableau, Power BI, Google Data Studio

Module 6: Statistical Analysis
Probability and distributions

Hypothesis testing

Correlation vs. causation

Linear and logistic regression

Module 7: SQL for Data Analytics
Basics of databases

Writing SQL queries

Joins, filters, and aggregations
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Data manipulation and extraction

Basic syntax and data structures

Data analysis libraries (Pandas, NumPy, Matplotlib)

Reading, analysis, and visualization of data

Introduction to scripting and automation

Module 9: Predictive Analytics (Advanced/Optional)
Introduction to machine learning

Supervised and unsupervised learning

Model construction and assessment

Algorithms: Decision trees, clustering, regression

Module 10: Data Ethics & Governance
Data privacy regulations and compliance (e.g., GDPR)

Ethical usage of data

Bias in data and algorithms

Capstone Project
Work with a real-world dataset

Conduct full-cycle data analysis

Communicate insights through a written report or dashboard

Would you prefer a version of this syllabus for beginners, business users, or data science professionals?
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Data analytics = using data to understand,

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