• Vephla
  • Data

Data Analytics Nano-Degree.

Become a Certified Professional Data Analyst by learning modern techniques for Data Analysis. In this course, you will be learning various ways to deal with raw and messy data and derive meaningful information from them. you will also be part of a team to work on a real-life project. This is a beginner to advance course in Data Analytics, positioning you to be ready for the market.


Learn: Introduction | Microsoft Excel | Power BI | Tableau | SQL | Data Analytics with Python | Pandas | github | Data Science io | Medium | Multiple Individual / Team Projects

  • 5.0

  • 100% Live Lessons

  • 1,500+ Students

  • 8 months

Mentor: Dr Michael Richards

40% OFF

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  • Overview
  • Instructor
  • Review

What you'll learn

    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 in Data Analytics
  • Overview of the Four Types of Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
  • Understanding Data: Sources, Collection, and Storage
  • Classification of Data: Qualitative vs. Quantitative, Structured vs. Unstructured
  • The 4V's of Big Data: Volume, Variety, Velocity, and Veracity
  • The Data Lifecycle: Generation, Collection, Processing, Analysis, and Interpretation
  • Ethical Considerations in Data Analytics and Data Privacy Regulations (GDPR, CCPA)
  • Data Integrity: Ensuring Accuracy, Consistency, and Security in Data Handling
  • Key Steps in the Data Analytics Process

    • Module 2: Microsoft Excel for Data Analytics

  • Introduction to Microsoft Excel and Its Role in Data Analytics
  • Navigating the Excel Interface and Understanding Basic Formulas
  • Difference Between Workbooks and Worksheets, Effective Data Entry Methods
  • Formatting Data for Better Readability and Efficiency
  • Filtering and Sorting Techniques for Data Organization
  • Inserting Images, Printing Preparation, and Efficient Data Search (Find & Replace)
  • Freezing Panes for Better Navigation in Large Datasets
  • Essential Excel Functions: SUM, AVERAGE, IF Statements, COUNTIF, and More
  • Conditional Formatting for Data Highlighting and Analysis
  • Lookup Functions: VLOOKUP, HLOOKUP, and XLOOKUP for Advanced Data Searches
  • Data Cleaning Techniques to Handle Missing and Duplicate Data

    • Module 3: Data Analysis with Excel

  • Core Principles and Rules of Data Analytics
  • Data Visualization Techniques: Charts, Graphs, and Dashboards
  • Using Pivot Tables for Dynamic Data Summarization
  • Hands-on Practice: Two Weeks of Real-Life Data Analysis
  • Project Phase I: 6 Hands-on Projects Using Excel

    • Module 4: Business Intelligence with Power BI

  • Introduction to Business Intelligence and Its Role in Data-Driven Decision Making
  • Differences Between Business Intelligence and Data Warehousing
  • Installing and Setting Up Power BI for Analytics
  • Connecting Power BI to Various Data Sources (Excel, SQL, APIs, Cloud Data)
  • Understanding Power Query and Query Editor for Data Manipulation
  • Troubleshooting and Formatting Data in Power BI
  • Append & Merge Queries, Text Transformations, and Numeric Calculations
  • Advanced Power Query Functions for Data Cleaning and Preparation
  • Building Dashboards and Interactive Reports in Power BI
  • 2nd Portfolio Creation & Additions
  • Project Phase II: 4 Hands-on Projects

    • Module 5: Data Visualization with Tableau

  • Introduction to Tableau and Its Applications in Data Analysis
  • Understanding Measures and Dimensions in Data Visualization
  • Formatting Tools and Techniques in Tableau for Better Insights
  • Data Cleaning and Transformation in Tableau
  • Creating Interactive and Dynamic Visualizations
  • Filtering Data and Building Complex Dashboards
  • Data Modeling Concepts, Cardinalities, and Joins in Tableau
  • Using Calculated Fields for Advanced Data Computation
  • Forecasting and Predictive Analytics in Tableau
  • Creating and Presenting Data-Driven Stories in Tableau
  • 3rd Portfolio Creation & Additions
  • Project Phase III: 7 Hands-on Projects

    Module 6: SQL for Data Analysis

  • Introduction to SQL, Databases, and Queries
  • Application Dashboards & Schemas
  • Creating Tables with SQL (Methods)
  • SELECT, INSERT, UPDATE, DELETE Keywords
  • Understanding Primary & Foreign Keys
  • Data Import in SQL
  • Using Operators, Indenting, Toggle, AND/OR/NOT Keywords
  • IN, BETWEEN, LIKE, ALIAS, LIMIT, ORDER BY Keywords
  • Aggregate Functions, GROUP BY, HAVING, ALIAS
  • CASE Syntax, Types of Joins: Full, Left, Right, Inner
  • 4th Portfolio Creation & Additions
  • Project Phase IV - 3 Projects

    • Module 7: Project Phase V

  • Team and Individual Capstone Projects

    • Module 8: Python for Data Analytics

  • Introduction to Python
  • Overview and Setup
  • Jupyter Notebooks / Anaconda Installation
  • Variables, Naming Conventions (CamelCase, PascalCase, Snake_case)
  • String Concatenation, Arithmetic Operations, Multiple Variable Assignment
  • Print Statements, String Merging with Variables

    • Module 9: Data Types in Python

  • Numeric Types: Integers, Floats, Complex Numbers
  • Sequence Types: Strings, Lists, Tuples
  • Boolean, Set, and Dictionary Data Types
  • Best Practices for Data Types
  • Indexing, Slicing, and Nested Lists
  • Tuples (Immutable Sequences) and Sets

    • Module 10: Control Flow in Python

  • Comparison Operators: Equality, Inequality, Greater/Less Than
  • Logical & Membership Operators
  • Conditional Statements: if, else, elif
  • Loops: For and While
  • Functions in Python

    • Module 11: Python Projects & Web Scraping

  • Data Type Conversions
  • Python Project 1: BMI Calculator
  • Python Project 2: Web Scraping Basics (BeautifulSoup + Requests)
  • Python Project 3: Scraping Data from a Real Website

    • Module 12: Data Analysis with Pandas

  • Introduction to Pandas
  • Reading CSV, Excel, and Other Files
  • Filtering Columns & Rows, Indexing in Pandas
  • Group By & Aggregate Functions
  • Merging DataFrames
  • Creating Visualizations with Pandas
  • Python Project

    • Module 13: Final Portfolio Project

  • Building a Comprehensive Project Showcasing All Learned Skills
  • Github
  • Datascience.io
  • Tableau
  • Medium
  • Real-world Applications: Data Analysis, Web Scraping, Automation

    • Module 14:Internships Capstone Projects

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

Requirements

  • This course has no skill prerequisites; however, having a basic familiarity with computer operations is beneficial.
  • Personal computer—whether it's a Mac, Windows PC, or a Linux machine
  • A stable internet connection is essential for engaging in virtual classes, downloading required softwares, and for individual practice.
  • Time

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. Through data analytics, businesses can make data-driven decisions, improve operational efficiency, and gain a competitive edge.
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 various industries, including finance, healthcare, marketing, retail, and sports. Companies use it to analyze customer behavior, improve operational efficiency, manage risks, and drive strategic decisions.
Overall, data analytics is a powerful tool that transforms raw data into actionable insights, enabling organizations to harness the potential of their data to gain a competitive advantage and achieve their goals.

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Instructor

Dr Richards Michael

Data Engineer

  • 5.0
  • 1,500+ Students

Mentor Richards is a skilled Data Analytics professional with a Master's in Big Data Analytics from the University of Derby, UK. He has expertise in various Engineering Tech Stacks, contributing to process optimization and market enhancement for organizations. His strategic approach focuses on promoting business growth and efficiency. Additionally, Richards is committed to knowledge-sharing and continuous learning within the tech community, advancing data analytics and technology.

Review
Adenike Idowu
4.9

Empowering and Educative!

I am grateful for the invaluable resources that Vephla University provided throughout my studies. The platform’s comprehensive curriculum was a perfect fit for my diploma program, offering both essential knowledge and specialized skills needed in my field. The self-paced learning format was incredibly beneficial, especially during my hectic finals season— I could manage my time while diving deep into complex topics.

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  • Not helpful
Wivina Omolemen
4.9

Life-Changing Educational Journey!

As a recent graduate, I can confidently say that Vephla University played an instrumental role in my academic success. The platform offered a diverse range of courses that not only enriched my knowledge but also developed my practical skills. The interactive learning modules made difficult concepts easier to grasp, and I loved how I could learn at my own pace.

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This course includes:

  • Job summit

  • Portfolio creation

  • Projects

  • Live classes

  • Full lifetime access

  • Diploma certification

  • Nano-Degree certification

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