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MCS 2403 Intro to Data Science

(LTU-MCS2403.AJ1)
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Skills You’ll Get

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1

Data Science Terminology

  • What is data science?
  • The data science Venn diagram
  • Some more terminology
  • Data science case studies
  • Summary
2

Types of Data

  • Structured versus unstructured data
  • The four levels of data
  • Summary
  • Questions and answers
3

The Five Steps of Data Science

  • Introduction to data science
  • Exploring the data
  • Summary
4

Data Mining Patterns

  • Cluster analysis
  • Anomaly detection
  • Association rules
  • Questions
  • Summary
5

Data Mining Sequences

  • Patterns
  • Questions
  • Summary
6

Text Mining

  • Packages
  • Questions
  • Summary
7

Data Analysis – Regression Analysis

  • Packages
  • Questions
  • Summary
8

Data Analysis – Correlation

  • Packages
  • Questions
  • Summary
9

Data Analysis – Clustering

  • Packages
  • K-means clustering
  • Questions
  • Summary
10

Data Visualization – R Graphics

  • Packages
  • Questions
  • Summary
11

Data Visualization – Plotting

  • Packages
  • Scatter plots
  • Bar charts and plots
  • Questions
  • Summary
12

Data Visualization – 3D

  • Packages
  • Generating 3D graphics
  • Questions
  • Summary
13

Machine Learning in Action

  • Packages
  • Dataset
  • Questions
  • Summary
14

Predicting Events with Machine Learning

  • Automatic forecasting packages
  • Questions
  • Summary
15

Supervised and Unsupervised Learning

  • Packages
  • Questions
  • Summary

1

Data Science Terminology

  • Extracting and Analyzing Cashtags in Tweets
2

Types of Data

  • Exploring CSV Data
  • Analyzing Temperature Data Using Statistical Methods
3

The Five Steps of Data Science

  • Performing Time-Based Analysis
  • Mastering Data Insights
4

Data Mining Patterns

  • Plotting a Graph by Performing k-means Clustering
  • Calculating K-medoids Clustering
  • Displaying the Hierarchical Cluster
  • Plotting Graphs By Performing Expectation-Maximization
  • Plotting the Density Values
  • Computing the Outliers for a Set
  • Calculating Anomalies
  • Using the apriori Rules Library
5

Data Mining Sequences

  • Using eclat to Find Similarities in Adult Behavior
  • Finding Frequent Items in a Dataset
  • Evaluating Associations in a Shopping Basket
  • Determining and Visualizing Sequences
  • Computing LCP, LCS, and OMD
6

Text Mining

  • Manipulating Text
  • Analyzing the XML Text
7

Data Analysis – Regression Analysis

  • Performing Simple Regression
  • Performing Multiple Regression
  • Performing Multivariate Regression Analysis
8

Data Analysis – Correlation

  • Performing Tetrachoric Correlation
9

Data Analysis – Clustering

  • Estimating the Number of Clusters Using Medoids
  • Performing Affinity Propagation Clustering
10

Data Visualization – R Graphics

  • Grouping and Organizing Bivariate Data
  • Plotting Points on a Map
11

Data Visualization – Plotting

  • Displaying a Histogram of Scatter Plots
  • Creating an Enhanced Scatter Plot
  • Constructing a Bar Plot
  • Producing a Word Cloud
12

Data Visualization – 3D

  • Generating a 3D Graphic
  • Producing a 3D Scatterplot
13

Machine Learning in Action

  • Finding a Dataset
  • Making a Prediction
14

Predicting Events with Machine Learning

  • Using Holt Exponential Smoothing
15

Supervised and Unsupervised Learning

  • Developing a Decision Tree
  • Producing a Regression Model
  • Understanding Instance-Based Learning
  • Performing Cluster Analysis
  • Constructing a Multitude of Decision Trees

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