Investigative Data Analytics: Python Vs. R

Comparatively learn investigative data analytics in Python and R

What you'll learn

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

Requirements

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

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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Pivot Table

Lecture 2 Python

Lecture 3 R Code

Section 3: Benford's Law

Lecture 4 Python

Lecture 5 R Code

Section 4: Heatmap

Lecture 6 Python

Lecture 7 R Code

Section 5: Clustering

Lecture 8 Python

Lecture 9 R Code

Section 6: Image to Text

Lecture 10 Python

Lecture 11 R Code

Section 7: Text Analysis

Lecture 12 Python

Lecture 13 R Code

Section 8: Machine Learning

Lecture 14 Python

Lecture 15 R Code

Section 9: Additional Learning: Python Plotly

Lecture 16 Plotting with Plotly in Python

Data analysts who want to develop investigation specific skills.