Savitribai Phule Pune University, Maharashtra, Computer Engineering Semester 6, Data Science and Big Data Analytics Syllabus

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Unit - 1 Introduction to Data Science and Big Data

Unit 1

Introduction to Data Science and Big Data

1.1 Basics and need of Data Science and Big Data

1.2 Applications of Data Science

1.3 Data explosion

1.4 5 V’s of Big Data

1.5 Relationship between Data Science and Information Science

1.6 Business intelligence versus Data Science

1.7 Data Science Life Cycle

1.8 Data Data Types Data Collection

1.9 Need of Data wrangling

1.10 Methods Data Cleaning

1.11 Data Integration

1.12 Data Reduction

1.13 Data Transformation

1.14 Data Discretization

1.15 Case study Create academic performance dataset of students and perform data pre processing using techniques of data cleaning and data transformation

Unit - 2 Statistical Inference

Unit 2

Statistical Inference

2.1 Need of statistics in Data Science and Big Data Analytics

2.2 Measures of Central Tendency Mean Median Mode Midrange

2.3 Measures of Dispersion Range Variance Mean Deviation Standard Deviation

2.4 Bayes theorem

2.5 Basics and need of hypothesis and hypothesis testing

2.6 Pearson Correlation

2.7 Sample Hypothesis testing

2.8 ChiSquare Tests

2.9 ttest

Unit - 3 Big Data Analytics Life Cycle

Unit 3

Big Data Analytics Life Cycle

3.1 Introduction to Big Data

3.2 Sources of Big Data

3.3 Data Analytic Lifecycle Introduction

3.4 Phase 1 Discovery

3.5 Phase 2 Data Preparation

3.6 Phase 3 Model Planning

3.7 Phase 4 Model Building

3.8 Phase 5 Communication results

3.9 Phase 6 Operation alize

3.10 Case study Global Innovation Social Network and Analysis GINA

Unit - 4 Predictive Big Data Analytics with Python

Unit 4

Predictive Big Data Analytics with Python

4.1 Introduction

4.2 Essential Python Libraries Basic examples

4.3 Data Preprocessing Removing Duplicates

4.4 Transformation of Data using function or mapping

4.5 Replacing values Handling Missing Data

4.6 Analytics Types Predictive Descriptive and Prescriptive

4.7 Association Rules Apriori Algorithm

4.8 FP growth

4.9 Regression Linear Regression Logistic Regression

4.10 Classification Naïve Bayes Decision Trees

4.11 Introduction to Scikitlearn Installations Dataset mat plotlib filling missing values

4.12 Regression and Classification using Scikitlearn

4.13 Case Study Use IRIS dataset from Scikit and apply data preprocessing methods

Unit - 5 Big Data Analytics and Model Evaluation

Unit 5

Big Data Analytics and Model Evaluation

5.1 Clustering Algorithms KMeans

5.2 Hierarchical Clustering

5.3 Timeseries analysis

5.4 Introduction to Text Analysis Textpreprocessing

5.5 Bag of words

5.6 TFIDF and topics

5.7 Need and Introduction to social network analysis

5.8 Introduction to business analysis

5.9 Model Evaluation and Selection Metrics for Evaluating Classifier Performance

5.10 Holdout Method and Random Sub sampling

5.11 Parameter Tuning and Optimization

5.12 Result Interpretation

5.13 Clustering and Timeseries analysis using Scikitlearn sklearn

5.14 metrics Confusion matrix AUCROC Curves Elbow plot

Unit - 6 Data Visualization and Hadoop

Unit 6

Data Visualization and Hadoop

6.1 Introduction to Data Visualization

6.2 Challenges to Big data visualization

6.3 Types of data visualization

6.4 Data Visualization Techniques

6.5 Visualizing Big Data

6.6 Tools used in Data Visualization

6.7 Hadoop ecosystem

6.8 Map Reduce

6.9 Pig

6.10 Hive

6.11 Analytical techniques used in Big data visualization

6.12 Data Visualization using Python Line plot Scatter plot Histogram Density plot Box plot

6.13 Case study ​​Use IRIS dataset from Scikit and plot 2D views of the dataset

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