Data Analytics Nano-Degree Course

About this course

Data analytics is the process of examining and interpreting data to uncover meaningful insights, identify patterns, and inform decision-making. It involves various techniques and tools to collect, clean, and analyze data — enabling organizations to optimize processes, enhance performance, and predict future trends based on historical data.

The ultimate goal of data analytics is to support and enhance decision-making processes. By providing insights and recommendations, organizations can optimize operations, improve customer experiences, identify new opportunities, and mitigate risks.

Data analytics is widely applied across finance, healthcare, marketing, retail, and sports. Overall, it transforms raw data into actionable insights — enabling organizations to harness their data and gain a competitive advantage.

Requirements

No prior skill prerequisites — beginner to advanced
A personal computer — Mac, Windows, or Linux
A stable internet connection for live classes and downloads
Dedication and time

Course curriculum — 14 modules

Module 1

Introduction to Data Analytics

Definition and Importance of Data Analytics
How Companies Leverage Data for Decision-Making
Key Differences Between Data Analytics and Data Science
Career Paths and Job Opportunities
The Four Types: Descriptive, Diagnostic, Predictive, Prescriptive
Understanding Data: Sources, Collection, and Storage
The 4V's of Big Data: Volume, Variety, Velocity, Veracity
Ethical Considerations & Data Privacy (GDPR, CCPA)

Module 2

Microsoft Excel for Data Analytics

Introduction to Excel and Its Role in Data Analytics
Navigating the Excel Interface and Basic Formulas
Formatting Data for Readability and Efficiency
Filtering, Sorting, and Freezing Panes
Essential Functions: SUM, AVERAGE, IF, COUNTIF
Lookup Functions: VLOOKUP, HLOOKUP, XLOOKUP
Data Cleaning: Handling Missing and Duplicate Data

Module 3

Data Analysis with Excel

Core Principles and Rules of Data Analytics
Data Visualization: Charts, Graphs, and Dashboards
Pivot Tables for Dynamic Data Summarization
Project Phase I: 6 Hands-on Projects Using Excel

Module 4

Business Intelligence with Power BI

Introduction to Business Intelligence
Connecting Power BI to Data Sources (Excel, SQL, APIs)
Power Query and Query Editor for Data Manipulation
Building Dashboards and Interactive Reports
Project Phase II: 4 Hands-on Projects

Module 5

Data Visualization with Tableau

Introduction to Tableau and Its Applications
Measures, Dimensions, and Data Modeling
Creating Interactive and Dynamic Visualizations
Forecasting and Predictive Analytics in Tableau
Project Phase III: 7 Hands-on Projects

Module 6

SQL for Data Analysis

Introduction to SQL, Databases, and Queries
SELECT, INSERT, UPDATE, DELETE Keywords
Primary & Foreign Keys, Aggregate Functions
Types of Joins: Full, Left, Right, Inner
Project Phase IV: 3 Projects

Module 7

Project Phase V

Team and Individual Capstone Projects

Module 8

Python for Data Analytics

Introduction to Python and Setup
Jupyter Notebooks / Anaconda Installation
Variables, Naming Conventions, String Operations
Print Statements and String Merging

Module 9

Data Types in Python

Numeric, Sequence, Boolean, Set, Dictionary Types
Indexing, Slicing, and Nested Lists
Tuples (Immutable Sequences) and Sets

Module 10

Control Flow in Python

Comparison and Logical Operators
Conditional Statements: if, else, elif
Loops: For and While
Functions in Python

Module 11

Python Projects & Web Scraping

Project 1: BMI Calculator
Project 2: Web Scraping with BeautifulSoup
Project 3: Scraping Data from a Real Website

Module 12

Data Analysis with Pandas

Introduction to Pandas
Reading CSV, Excel, and Other Files
Group By & Aggregate Functions
Merging DataFrames
Creating Visualizations with Pandas

Module 13

Final Portfolio Project

Comprehensive Project: All Learned Skills
GitHub, DataScience.io, Tableau, Medium
Real-world Applications: Analysis, Web Scraping, Automation

Module 14

Internship & Capstone Projects

Resume & Portfolio Enhancement
Interview Preparation & Technical Assessments
Real-world Case Studies & Industry Applications

Dr. Richards M.

Data Engineer · University of Derby, UK

5.0 · 1,500+ Students

Dr. Richards M. is a skilled Data Analytics professional with a Master's in Big Data Analytics from the University of Derby, UK. He has expertise across various Engineering Tech Stacks, contributing to process optimization and market enhancement for organizations, with a strategic approach focused on business growth and efficiency.

Student reviews

Adenike Idowu

4.9

Empowering and Educative!

Wivina Omolemen

4.9

Life-Changing Educational Journey!

As a recent graduate, Vephla University played an instrumental role in my academic success. The interactive learning modules made difficult concepts easier to grasp, and I loved how I could learn at my own pace.

Data Analytics Nano-Degree

About this course

Requirements

Course curriculum — 14 modules

Student reviews

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