Below is an inclusive sample syllabus widely utilized in university or professional certification courses 

(such as Google Data Analytics, Coursera, or university certificates):



 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

Data types: Structured vs. unstructured

Data sources: Databases, APIs, web scraping, surveys

Data warehousing and data lakes

ETL (Extract, Transform, Load) processes introduction

Module 3: Data Cleaning & Preprocessing

Significance of clean data



Dealing with 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

Discovery of trends and patterns

Visualization tools: Matplotlib, Seaborn, Tableau, Power BI

Module 5: Data Visualization & Communication

Best practices for visual storytelling

Visualizations: Bar charts, histograms, scatter plots, dashboards

Making 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

Fundamentals of databases



SQL query writing

Joins, filters, and aggregations

Data manipulation and extraction

Basic syntax and data structures

Data analysis libraries (Pandas, NumPy, Matplotlib)

Reading, analyzing, and visualizing data

Introduction to scripting and automation

Module 9: Predictive Analytics (Advanced/Optional)

Introduction to machine learning

Supervised vs. unsupervised learning

Model building and evaluation

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What is the data analytics syllabus?