This course delves into the intricacies of data analytics using Google Sheets, equipping participants with the skills to analyze and visualize data effectively. Participants will engage in a project-based learning environment, where they will explore various analytical techniques and tools available within Google Sheets. The hands-on approach ensures that learners can apply their knowledge in real-world scenarios, enhancing their employability and practical skill set.
Throughout the course, participants will work on comprehensive projects that culminate in a final presentation of their findings, which can be published in Cademix Magazine. This opportunity not only showcases their work but also builds their professional portfolio. By the end of the program, learners will possess a thorough understanding of data manipulation, visualization, and interpretation, making them valuable assets in any business setting.
Data Collection and Management Techniques
Data Cleaning and Preparation Strategies
Utilizing Formulas and Functions for Analysis
Creating Dynamic Charts and Graphs
Implementing Pivot Tables for Data Summarization
Advanced Data Visualization Techniques
Automating Reports with Google Sheets Scripts
Collaborative Features and Sharing Data
Case Studies on Business Analytics
Final Project: Analyzing a Real-World Dataset with Google Sheets

This course focuses on the creation of dynamic and engaging visualizations using Plotly, a powerful library for interactive data visualization in Python. Participants will delve into the core functionalities of Plotly, learning to transform complex datasets into insightful graphical representations. The project-based approach ensures that learners not only grasp theoretical concepts but also apply them in real-world scenarios, culminating in a final project that showcases their skills. By the end of the course, participants will be equipped to produce professional-grade visualizations suitable for diverse applications, including business presentations and research reports.
Throughout the program, learners will engage in hands-on activities that promote collaboration and creativity. The interactive format encourages participants to explore their data, experiment with various visualization techniques, and share their findings. Additionally, participants are encouraged to publish their results in Cademix Magazine, providing an opportunity for visibility within the professional community. This course is ideal for those looking to enhance their data storytelling capabilities and make informed decisions based on visual data analysis.
Introduction to Data Visualization Principles
Overview of Plotly and its Capabilities
Setting Up the Plotly Environment
Creating Basic Charts: Line, Bar, and Scatter Plots
Customizing Visualizations: Layouts, Colors, and Annotations
Interactive Features: Hover Effects, Tooltips, and Legends
Advanced Visualizations: Heatmaps, 3D Plots, and Dashboards
Integrating Plotly with Pandas for Data Manipulation
Best Practices for Effective Data Storytelling
Final Project: Develop an Interactive Visualization Project

This course focuses on equipping participants with the essential skills to utilize Jupyter Notebooks for data analysis and visualization. Through a project-based and interactive approach, learners will engage in hands-on exercises that reinforce theoretical knowledge and practical application. Participants will explore various data manipulation techniques, visualization libraries, and analytical methods, culminating in a final project that showcases their ability to derive insights from complex datasets.
As participants navigate through the course, they will also have the opportunity to publish their findings in Cademix Magazine, fostering a sense of community and professional recognition. By the end of the program, graduates will possess a robust understanding of data analysis workflows, enabling them to make informed decisions based on data-driven insights in their respective fields.
Syllabus:
Introduction to Jupyter Notebooks and environment setup
Data import and export techniques using Pandas
Data cleaning and preprocessing methods
Exploratory Data Analysis (EDA) fundamentals
Visualization techniques using Matplotlib and Seaborn
Time series analysis and forecasting
Statistical analysis and hypothesis testing
Introduction to machine learning concepts with Scikit-learn
Building interactive visualizations with Plotly
Final project: Comprehensive data analysis and visualization report