Data Analytics

Solving Real-World Problems Through a Data-Driven Lens

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Codio’s Data Analytics curriculum empowers learners to explore the world through data. Each module is a step into the analytical mindset—where numbers tell stories, patterns lead to insights, and problems invite powerful solutions. With bite-sized challenges that build toward big wins, learners gain confidence while developing critical thinking and coding skills. Whether wrangling messy datasets, scraping data from the web, creating insightful dashboards, or mastering statistics, learners engage directly with real-world scenarios.

Included in this course:

Introduction:

Introduction Printing, Sorting and Control Structures (If Else) Data Types & Control Structures (For, While) Functions and Descriptive Statistics Logical Operators and List Operations Lab : Movie Night Madness: Data Decisions! Coding Exercises

Exploring Data Storage and Manipulation Techniques Importing a DataFrame Selecting Data Pandas for Data Handling Coding Exercises Getting Started with NumPy Lab: Data in Action Coding Exercise

Fundamentals of Exception Handling Exception Handling in Data Analytics Handling Incomplete Data Sets Detecting and Handling Outliers in Data Analytics Data Validation and Integrity Checks in Analytics Coding Exercises LAB : D.A.T. Coding Exercise

Data Filtering, Sorting, and Selection Techniques Grouping, Aggregation, and Pivot Tables Merges and Joins Text Manipulation and Regular Expressions (Regex) Introducing API and GET Request Coding Exercise Analysis Lab

Dashboard:Using Streamlit GUI: Using Tkinter Version Control and Git

Introduction to Web Scraping and HTML Structure Advanced HTML Parsing and Data Extraction API Interaction for Dynamic Data Extraction Combining Web Scraping and API Data Automating and Scheduling Web Scraping CODING Exercises Lab: WorldLens

Jupyter and Matplotlib Customizing Visualizations Box Plots and Histograms Visualizing Categorical Data with Bar Plots Exploring Data Distribution with Boxplots and Histograms Coding Exercises

Line Charts and Area Charts Scatter Plots and Bubble Charts Pie and Donut Charts Heatmaps, Correlograms and Mosaic plots Time Series and Facet Grids Coding Exercises

Mastering Matplotlib Aesthetics and Effective Data Visualization Mastering Subplots and Advanced Customizations in Matplotlib Mastering Advanced Visualizations and Interactive Plots in Matplotlib Dynamic Visualizations: Interactive and Animated Data Stories Geospatial Data Visualization: Mapping Insights in Data Coding Exercises

Introduction to AI in Data Science and Automating Data Preparation Identifying Trends and Analyzing Data with AI Unstructured Data Analysis and Insights with AI Code Assistance and Workflow Optimization with AI (not done) Enhanced Decision Support and AI-Powered Data Applications

Getting Started with Spreadsheets: Navigating LibreOffice Calc Spreadsheets: Sorting, Filtering, and Basic Calculations Spreadsheets: Visualizing Data with Charts and Graphs Spreadsheets Smarter: Using Logical and Lookup Functions Spreadsheets: Creating and Analyzing Pivot Tables Lab: Down Memory Lane

Hands-On, Insight-Driven Learning

From day one, learners are immersed in a practical journey of discovery. Through Python coding exercises, authentic datasets, and hands-on labs, learners acquire the skills necessary to become confident data-driven problem solvers. Every concept is grounded in application—from parsing JSON APIs to deploying dashboards with Streamlit and Tableau. Learners actively engage with industry-standard tools, including Jupyter Notebooks and Git, reflecting the collaborative and iterative workflows used by data professionals.

Challenge-Based Modules

This curriculum isn’t just about checking boxes—it’s about confronting real-world data challenges. Learners analyze incomplete and noisy datasets, detect and handle outliers, merge complex data sources, and visualize relationships across time, categories, and geography. They will practice web scraping and data mining, automate workflows, and tap into powerful APIs. As they advance, learners integrate AI-assisted tools to streamline data cleaning, automate pattern recognition, and generate synthetic datasets for sophisticated analyses.

[Build] Real-World Coding Skills With Hands-On, Interactive Labs

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