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# Data Science ProDegree

### Introduction to Data Science

1

What is Data Science?

2

Analytics Landscape

3

Life Cycle of a Data Science Projects

4

Data Science Tools & Technologies

### R for Data Science

1

Intro to R Programming

2

R Base Software

3

Understanding CRAN

4

RStudio The IDE

5

Basic Building Blocks in R

6

Understanding Vectors in R

7

Basic Operations Operators and Types

8

Handling Missing Values in R

9

Subsetting Vectors in R

10

Matrices and Data Frames in R

11

Lapply, sapply, vapply and tapply Functions

### Data Visualization using R

1

Grammar of Graphics

2

Bar Charts

3

Histograms

4

Pie Charts

5

Scatter Plots

6

Line Plots and Regression

7

Word Clouds

8

Box Plots

9

GGPLOT2

### Statistical Learning -1 (Including ANOVA)

1

Measures of Central Tendency in Data

2

Measures of Dispersion

3

Understanding Skewness in Data

4

Probability Theory

5

Bayes Theorem

6

Probability Distributions

7

Hypothesis Testing

### Statistical Learning - 2 (Including ANOVA)

1

Analysis of Variance and Covariance

2

One way analysis of variance

3

Assumption of ANOVA

4

Statistics associated with one way analysis of variance

5

Interpreting the ANOVA Results

6

Two way analysis of variance

7

Interpreting the ANOVA Results

8

Analysis of Covariance

### Exploratory Data Analysis with R

1

IMerge, Rollup, Transpose and Append

2

Missing Analysis and Treatment

3

Outlier Analysis and Treatment

4

Summarizing and Visualizing the Important Characteristics of Data

5

Univariate, Bivariate Analysis

6

Crosstabs, Correlation

### Linear Regression

1

What is Regression Analysis?

2

Limitations of Regression

3

Covariance and Correlation

4

Multivariate Analysis

5

Assumptions of Linearity Hypothesis Testing

6

Limitations of Regression

7

Implementing Simple & Multiple Linear Regression

8

Making sense of result parameters

9

Model validation

10

Handling other issues/assumptions in Linear Regression

11

Handling outliers, categorical variables, autocorrelation, multicollinearity, heteroskedasticity Prediction and Confidence Intervals

### Project 1

1

Property Price Prediction using Linear Regression in R

### Logistic Regression

1

Implementing Logistic Regression

2

Making sense of result parameters: Wald Test, Likelihood Ratio Test Statistic, Chi-square Test Goodness of fit measures

3

Model validation: Cross Validation, ROC Curve, Confusion Matrix

### Project 2

1

Bank Credit Card Default Prediction using Logistic Regression in R

### Decision Trees

1

Introduction to Predictive Modeling with Decision Trees

2

Entropy & Information Gain

3

Standard Deviation Reduction (SDR)

4

Overfitting Problem

5

Cross Validation for Overfitting Problem

6

Running as a solution for Overfitting

### Project 3

1

Churn Analysis in Telecom Industry (Regression Trees

### Random Forest

1

Random Forest

2

Project 4 – Churn Analysis in Telecom Industry (Regression Trees & Classification Trees)

### Linear Discriminant Analysis

1

LDA Objective

2

Why Discriminant Analysis?

3

Discriminant Function

4

Assumption of LDA

5

Advantages & Disadvantages of LDA

6

Applications of LDA

### Project : Wine classification with Linear Discriminant Analysis

1

Project : Wine classification with Linear Discriminant Analysis

### Data Science with Python

### Basics of Python for Data Science

1

Python Basics

2

Data Structures in Python

3

Control & Loop Statements in Python

4

Functions & Classes in Python

5

Working with Data

### Data Frame Manipulation with Pandas

1

Data Acquisition(Import & Export)

2

Indexing

3

Selection and Filtering Sorting

4

Descriptive Statistics

5

Combining and Merging Data Frames

6

Removing Duplicates

7

Discretization and Binning

8

String Manipulation

### Exploration Data Analysis with Python

1

What is EDA?

2

Processes in EDA

3

Handling Data Types

4

Univariate and Bivariate Analysis

5

Hypothesis Testing

### Time Series Forecasting

1

Understand Time Series Data

2

Visualizing TIme Series Components

3

Exponential Smoothing

4

Holt’s Model

5

Holt-Winter’s Model

6

ARIMA

### Project : Forecasting and Predicting the furniture sales using ARIMA

### Clustering

1

What is Clustering?

2

K-means Algorithm

3

Types of Clustering

4

Evaluating K-means Clusters

### Project : Grouping teen students for targeted marketing campaigns

1

Project : Grouping teen students for targeted marketing campaigns

### Dimensionality Reduction

1

Principal Component Analysis (PCA)

2

Scatter plot

3

One-eigen value criterion

4

Factor Analysis

5

Project : Reduce Data Dimensionality for a House Attribute Dataset using PCA

### Machine Learning & Linear Regression

1

Machine Learning Modelling Flow

2

How to treat Data in ML

3

Parametric & Non-parametric ML

4

Types of Machine Learning

5

Introduction to Linear Regression

6

Linear Regression using Gradient Descent

7

Linear Regression using OLS

8

Linear Regression using Stochastic Gradient Descent

9

Project : Real Estate Price Prediction using Linear Regression

### Logistic Regression

1

Introduction to Logistic Regression

2

Logistic Regression using Stochastic Gradient Descent

3

Project 8 Project : Real Estate Price Prediction using Linear Regression

### Model Tuning

1

Performance Measures

2

Bias-Variance Trade-Off

3

Overfitting & Underfitting

4

Optimization Techniques

5

Project : Identifying good and bad customers for granting credit

### K Nearest Neighbor

1

K Nearest Neighbor

2

Understanding KNN

3

Voronoi Tessellation

4

Choosing K

5

Distance Metrics – Euclideam, Manhattan, Chebyshev

6

Project : Case Study: Breast Cancer

### Decision Tree & Random Forest

1

Decision Tree & Random Forest

2

Fundamental concepts of Ensemble

3

Hyper-Parameters

4

Project 11: Case Study : Predicting bank term deposit subscription based on marketing data

### Support Vector Machine

1

Support Vector Machines

2

What is SVM?

3

When to use SVM?

4

What is Support Vector?

5

Understanding Hyperplane

6

Project 12: Predicting credibility of the credit card customers

### SQL

1

Basic SQL

2

Introduction to SQL

3

DDL Statements

4

DML Statements

5

DQL Statements

6

Aggregate Functions

### Advanced SQL

1

Date functions

2

Union, Union All & Intersect Operators

3

Joins

4

Views & Indexes

5

Sub-Queries

6

Project 13 SQL Practice Exercises Creating a Database Schema and Table Relationship for a Logistic Company’s Data

### Tableau

1

Introduction to Visualization

2

Working with Tableau

3

Visualization in Depth

4

Data Organisation

5

Advanced Visualization

6

Mapping

7

Enterprise Dashboards Data Presentation

8

Project 14 Best Practices for Dashboarding and Reporting and Case Study

9

Have a Methodology

10

Know Your Audience

11

Define Resulting Actions

12

Classify Your Dashboard

13

Profile Your Data

14

Use Visual Features Properly

15

Design Iteratively

16

Project : Building Tableau Dashboard

### Resume Building and Interview Prep

1

1:1 Mock Interviews with Industry Veterans to Clear the Technical Round of Interviews to Give You Confidence to Face Real World Scenarios

### 1:1 Mock Interviews

1