Data Analytics

Course: Data Analytics
Program Length: 16 Weeks
Certificate: Upon successful completion of the course, learners will receive a Data Analytics Certificate, accredited by the American Council of Training & Development (ACTD), validating their skills in data analysis, data visualization, SQL, Python, and statistical techniques, and demonstrating readiness to work on real-world data projects.
Start Date: 2026-03-21
Application Fee: $250
Location: Virtual
Course Image

Course Description

Are you ready to harness the power of data to drive insightful decision-making? Join our immersive Data Analysis course at OdumareTech and embark on a transformative journey into the world of data-driven insights and analytics. This comprehensive program will equip you with the essential skills to manipulate, analyze, and visualize data using popular tools such as Excel, SQL, Power BI, and Python. The course begins with a strong focus on Excel, where you'll learn the foundations of data manipulation, basic formulas, and data visualization techniques we'll teach you how to efficiently handle large datasets and extract meaningful information to gain valuable insights. Moving forward, we'll delve into the powerful world of SQL. You'll learn how to write queries to extract, filter, and aggregate data from databases. Power BI, a leading business intelligence tool, will be your next frontier. We'll guide you through creating interactive and visually appealing dashboards that effectively communicate your data findings. Python, a versatile programming language, will be introduced to you as a powerful tool for data manipulation, analysis, and visualization. We'll cover essential Python libraries such as Pandas and Matplotlib, enabling you to tackle more complex data analysis tasks efficiently. Statistics and exploratory data analysis will be the backbone of your analytical skills. You'll learn how to use statistical methods to uncover patterns, relationships, and anomalies within datasets, ensuring that your data-driven decisions are accurate and reliable. By the end of our Data Analysis course, you'll have a strong foundation in Excel, SQL, Power BI, and Python. You'll be well-prepared to handle diverse datasets, perform in-depth analyses, and provide valuable insights to businesses across various industries.

Admission Requirements

  • Ability to communicate in English
  • Access to a computer or laptop
  • Reliable internet connection

Course Curriculum

10 Sections • 36 Lectures

Week 1–2: Introduction to Data Analytics
3 lectures

  • 1 Importance and applications in business and industry
  • 2 Overview of the analytics workflow: Data Collection → Cleaning → Analysis → Visualization → Reporting
  • 3 Tools and software for data analytics

Week 3–4: Data Collection & Excel Basics
5 lectures

  • 1 Data types and sources
  • 2 Introduction to Excel for data analytics
  • 3 Basic formulas, functions, and data organization
  • 4 Sorting, filtering, and pivot tables
  • 5 Exercise/Project: Summarize sales or survey data in Excel

Week 5–6: Data Cleaning & Preparation
4 lectures

  • 1 Handling missing data, duplicates, and errors
  • 2 Data transformation and standardization
  • 3 Introduction to Python for data cleaning (pandas library)
  • 4 Exercise/Project: Clean a messy dataset using Excel and Python

Week 7–8: Data Analysis Fundamentals
6 lectures

  • 1 Descriptive statistics: mean, median, mode, standard deviation
  • 2 Data aggregation and summarization
  • 3 Data aggregation and summarization
  • 4 Introduction to SQL for querying databases
  • 5 Filtering, sorting, and joining tables
  • 6 Exercise/Project: Analyze sample sales or customer data using SQL and Python

Week 9–10: Data Visualization
5 lectures

  • 1 Principles of data visualization
  • 2 Charts and graphs in Excel
  • 3 Python visualization libraries: Matplotlib, Seaborn
  • 4 Introduction to Tableau/Power BI for interactive dashboards
  • 5 Exercise/Project: Visualize data trends using Python and Tableau/Power BI

Week 11–12: Advanced Data Analytics Concepts
2 lectures

  • 1 Correlation and regression analysis
  • 2 Exploratory Data Analysis (EDA)

Week 13: Data-Driven Decision Making
3 lectures

  • 1 Translating data into insights
  • 2 Reporting and storytelling with data
  • 3 Dashboards and KPI tracking

Week 14: Introduction to Python for Analytics
2 lectures

  • 1 Python basics: variables, data types, loops, functions
  • 2 Working with pandas and NumPy

Week 15: Capstone Project Preparation
3 lectures

  • 1 Defining the project scope and objectives
  • 2 Planning visualizations and reports
  • 3 Collecting, cleaning, and analyzing project data

Week 16: Capstone Project Presentation
3 lectures

  • 1 Complete the data analytics project
  • 2 Prepare a report and/or interactive dashboard
  • 3 Present findings and actionable insights

Cost

Installment

$100

Pay $100 upfront, then weekly until the full $250 is covered.

Most Popular
Full Payment

$250

Pay once and enjoy full access. Save more with a one-time payment.

Course Instructor

Instructor Image

Ridwan Kolawole

0 Students  

Highly analytical and process-oriented data analyst with in-depth knowledge of Microsoft Excel, Power BI, Tableau, Python and SQL. Proficient in data gathering, data cleaning and data visualization.
Experienced in the analysis of marketing, sales, customer and financial data.

Career Outcome

Upon completing this course, learners will be equipped to analyze, interpret, and visualize data to support business decisions. Graduates can pursue roles such as Data Analyst, Business Intelligence Analyst, Junior Data Scientist, or Data Visualization Specialist, and will have the skills to work on real-world data projects across industries.

Course Description

Are you ready to harness the power of data to drive insightful decision-making? Join our immersive Data Analysis course at OdumareTech and embark on a transformative journey into the world of data-driven insights and analytics. This comprehensive program will equip you with the essential skills to manipulate, analyze, and visualize data using popular tools such as Excel, SQL, Power BI, and Python. The course begins with a strong focus on Excel, where you'll learn the foundations of data manipulation, basic formulas, and data visualization techniques we'll teach you how to efficiently handle large datasets and extract meaningful information to gain valuable insights. Moving forward, we'll delve into the powerful world of SQL. You'll learn how to write queries to extract, filter, and aggregate data from databases. Power BI, a leading business intelligence tool, will be your next frontier. We'll guide you through creating interactive and visually appealing dashboards that effectively communicate your data findings. Python, a versatile programming language, will be introduced to you as a powerful tool for data manipulation, analysis, and visualization. We'll cover essential Python libraries such as Pandas and Matplotlib, enabling you to tackle more complex data analysis tasks efficiently. Statistics and exploratory data analysis will be the backbone of your analytical skills. You'll learn how to use statistical methods to uncover patterns, relationships, and anomalies within datasets, ensuring that your data-driven decisions are accurate and reliable. By the end of our Data Analysis course, you'll have a strong foundation in Excel, SQL, Power BI, and Python. You'll be well-prepared to handle diverse datasets, perform in-depth analyses, and provide valuable insights to businesses across various industries.

Cost

Installment

$100

Pay $100 upfront, then weekly until the full $250 is covered.

Most Popular
Full Payment

$250

Pay once and enjoy full access. Save more with a one-time payment.

Admission Requirements

  • Ability to communicate in English
  • Access to a computer or laptop
  • Reliable internet connection

Course Instructor

Instructor Image

Ridwan Kolawole

0 Students  

Highly analytical and process-oriented data analyst with in-depth knowledge of Microsoft Excel, Power BI, Tableau, Python and SQL. Proficient in data gathering, data cleaning and data visualization.
Experienced in the analysis of marketing, sales, customer and financial data.

)

Career Outcome

Upon completing this course, learners will be equipped to analyze, interpret, and visualize data to support business decisions. Graduates can pursue roles such as Data Analyst, Business Intelligence Analyst, Junior Data Scientist, or Data Visualization Specialist, and will have the skills to work on real-world data projects across industries.

Course Curriculum

10 Sections • 36 Lectures

Week 1–2: Introduction to Data Analytics
3 lectures

  • 1 Importance and applications in business and industry
  • 2 Overview of the analytics workflow: Data Collection → Cleaning → Analysis → Visualization → Reporting
  • 3 Tools and software for data analytics

Week 3–4: Data Collection & Excel Basics
5 lectures

  • 1 Data types and sources
  • 2 Introduction to Excel for data analytics
  • 3 Basic formulas, functions, and data organization
  • 4 Sorting, filtering, and pivot tables
  • 5 Exercise/Project: Summarize sales or survey data in Excel

Week 5–6: Data Cleaning & Preparation
4 lectures

  • 1 Handling missing data, duplicates, and errors
  • 2 Data transformation and standardization
  • 3 Introduction to Python for data cleaning (pandas library)
  • 4 Exercise/Project: Clean a messy dataset using Excel and Python

Week 7–8: Data Analysis Fundamentals
6 lectures

  • 1 Descriptive statistics: mean, median, mode, standard deviation
  • 2 Data aggregation and summarization
  • 3 Data aggregation and summarization
  • 4 Introduction to SQL for querying databases
  • 5 Filtering, sorting, and joining tables
  • 6 Exercise/Project: Analyze sample sales or customer data using SQL and Python

Week 9–10: Data Visualization
5 lectures

  • 1 Principles of data visualization
  • 2 Charts and graphs in Excel
  • 3 Python visualization libraries: Matplotlib, Seaborn
  • 4 Introduction to Tableau/Power BI for interactive dashboards
  • 5 Exercise/Project: Visualize data trends using Python and Tableau/Power BI

Week 11–12: Advanced Data Analytics Concepts
2 lectures

  • 1 Correlation and regression analysis
  • 2 Exploratory Data Analysis (EDA)

Week 13: Data-Driven Decision Making
3 lectures

  • 1 Translating data into insights
  • 2 Reporting and storytelling with data
  • 3 Dashboards and KPI tracking

Week 14: Introduction to Python for Analytics
2 lectures

  • 1 Python basics: variables, data types, loops, functions
  • 2 Working with pandas and NumPy

Week 15: Capstone Project Preparation
3 lectures

  • 1 Defining the project scope and objectives
  • 2 Planning visualizations and reports
  • 3 Collecting, cleaning, and analyzing project data

Week 16: Capstone Project Presentation
3 lectures

  • 1 Complete the data analytics project
  • 2 Prepare a report and/or interactive dashboard
  • 3 Present findings and actionable insights