Programming for Data Science RTU

Syllabus

Analysis of Algorithms

Unit 1

Introduction

Data Science: Introduction to Data Science – Digital Universe – Sources of Data – Information Commons –
Data Science Project Life Cycle: OSEMN Framework

Unit 2

Data Preprocessing & Concept Learning

Introduction to Data Preprocessing – Reading, Selecting, Filtering Data – Filtering Missing Values –
Manipulating, Sorting, Grouping, Rearranging, Ranking Data Formulation of Hypothesis – Probabilistic
Approximately Correct Learning - VC Dimension – Hypothesis elimination – Candidate Elimination
Algorithm

Unit 3

Essentials of R

R Basics - data types and objects - control structures – data frame -Feature Engineering - scaling, Label
Encoding and One Hot Encoding, Reduction

Unit 4

Model Fit Using R

Regression Models- Linear and Logistic Model, Classification Models – Decision Tree, Naïve Bayes, SVM and
Random Forest, Clustering Models – K Means and Hierarchical clustering

Unit 5

Visualization

Data visualization: Box plot, histogram, scatter plot, heat map – Working with Tableau – Outlier detection – Data
Balancing

Unit 6

Performance Evaluation in R

Loss Function and Error: Mean Squared Error, Root Mean Squared Error – Model Selection and Evaluation criteria:
Accuracy, Precision, F1 score, Recall Score – Binary Predictive Classification – Sensitivity – Specificity.

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Programming For Data Science

Syllabus

Introduction

Data Preprocessing & Concept Learning

Essentials of R

Model Fit Using R

Visualization

Performance Evaluation in R

Complete Material at one Place

Notes

Programming For Data Science Notes

Books

Programming For Data Science Books

Assignment

Programming For Data Science Assignment

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