IBM IBSC 2026-04-08 | Module 6 Recap + Week 7 Preview | Data Viz, Dashboards & Machine Learning
Lecture recap and preview for the IBM Data Science Professional Certificate, IBSC 2026-04-01 cohort.
In this video, we review Module 6, which focused on data visualization, dashboards, business intelligence, and data applications. We also preview Module 7, where we begin our transition into machine learning and predictive modeling.
Module 6 Recap
Module 6 was all about moving from analysis to communication. We focused on how data scientists use visualizations to tell clearer stories, identify patterns, and help stakeholders make better decisions.
In this recap, we cover:
• Data visualization as part of the consumption layer of the data science workflow
• Exploratory data analysis vs static reports vs interactive dashboards
• Static visualizations with Matplotlib, Seaborn, and Pandas
• Interactive visualizations with Plotly
• Geographic and mapping visualizations with Folium
• Dashboarding and business intelligence tools such as Tableau, Power BI, Looker, Qlik, and QuickSight
• Data applications with Dash, Streamlit, and Gradio
• Choosing the right chart type for the story: line charts, bar charts, histograms, scatter plots, pie charts, area plots, and more
• Advanced visualization examples including waffle charts, word clouds, regression plots, pair plots, violin plots, facet grids, and swarm plots
• Misleading visualizations, distorted axes, bad labels, spurious correlations, and why ethical visualization matters
• Dashboard design principles, including clarity, context, labels, proportions, relationships, and data-to-ink ratio
• How Dash callbacks work using event-driven coding concepts
The big idea from Module 6: data visualization is not just about making charts. It is about communicating patterns, trends, outliers, and relationships clearly and honestly so that people can make better decisions.
Week 7 Preview
In Week 7, we move from analyzing and visualizing data into building predictive models.
This week introduces supervised machine learning, with a focus on regression and classification. We will start connecting the patterns we found in earlier modules to algorithms that can make predictions and reduce uncertainty.
Topics include:
• The machine learning lifecycle
• Moving from data analysis to predictive modeling
• Regression for predicting continuous values such as price, revenue, or emissions
• Classification for predicting categories or probabilities
• Linear regression and logistic regression
• Why logistic regression is a classification model, despite the word “regression” in the name
• Train/test split workflows
• Fitting models, generating predictions, and evaluating model performance
• Model evaluation metrics including confusion matrices, log loss, F1 score, and classification reports
• Underfitting, overfitting, and misleading model results
• Using scikit-learn to build repeatable machine learning workflows
• How model outputs connect back to dashboards, business decisions, and the broader data science pipeline
We will also spend time reviewing classification concepts such as true positives, false positives, true negatives, false negatives, and type I / type II errors.
By the end of Week 7, you should understand the big-picture workflow of supervised machine learning: preparing data, selecting features, training a model, making predictions, evaluating results, and iterating toward a better solution.
Full course repository:
https://github.com/ABoothInTheWild/ib...
See you in the Week 7 live session!
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