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Investigative Data Analytics Course

person icon Penny Li

4.6

Investigative Data Analytics Course

Comparatively learn investigative data analytics in Python and R

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Penny Li

category icon Development ,Data Science,Data Analysis

Lectures -16

Duration -2.5 hours

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4.6

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Course Description

This immersive course delves into Python and R for investigative data analytics, spotlighting techniques such as heatmap generation, clustering algorithms, decision tree analysis, and text analytics. By comparing Python's seaborn and matplotlib with R's ggplot2, students will learn to craft detailed heatmaps and unveiling intricate data patterns. Clustering sessions will demonstrate segmenting techniques using Python's scikit-learn and R's cluster packages, applying K-means to dissect data into significant clusters for insightful analysis in areas such as market research and customer segmentation.

In the decision tree segment, the course contrasts Python's scikit-learn with R's party package, teaching how to build models that illuminate the path from data to decisions. The exploration extends into text analytics, employing Python's plotly express for dynamic visualizations and both languages' capabilities to create expressive word clouds, enabling students to mine and interpret textual data for trend spotting.

Tailored for both budding and seasoned data analysts and researchers, this course interweaves theoretical concepts with substantial hands-on practice. Learners will emerge with a profound understanding of which programming language, Python or R, best fits various data analytics challenges. By fostering a practical learning environment, the course underscores real-world applications, ensuring participants gain the proficiency needed to navigate the complexities of data analytics confidently. This dynamic curriculum is poised to enhance analytical skills, preparing learners for the demands of data-driven decision-making in their professional and academic careers.

Goals

  • Use Pivot Table in Python and R to manipulate data
  • Use Benford's Law to detect anomalies in Python and R
  • Use Heatmap in Python and R to identify correlations between data columns
  • Cluster datasets into subgroups using Kmeans in Python and R
  • Use Image to Text to extract text data from images using Python and R
  • Use Histogram and Word Cloud in Python and R to analyze text data
  • Use machine learning decision trees to identify fraudulent data points in Python
  • Use Plotly in Python to show interactive data structures


Prerequisites

No programming experience needed. All you need to do is follow along.

Investigative Data Analytics Course

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction

1 Lectures
  • play icon Introduction 03:01 03:01

Pivot Table

2 Lectures
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Benford's Law

2 Lectures
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Heatmap

2 Lectures
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Clustering

2 Lectures
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Image to Text

2 Lectures
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Text Analysis

2 Lectures
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Machine Learning

2 Lectures
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Additional Learning: Python Plotly

1 Lectures
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Instructor Details

Penny Li

Penny Li

Penny Li is a Kaggle Notebook Expert, a data app developer with experience developing mobile apps that analyze data. She herself has experience analyzing large volumes of data in class-action lawsuits. Penny has worked 6 years in tax positions.

Penny is a blogger on Medium and a sub stacker.

Penny is an Illinois Certified Public Accountant and a Certified Fraud Examiner.

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