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Data Science

Section: 01 - Introduction to Data Science

1
• What is Data Science?
2
• Data Science Life cycle
3
• Different types of Data Science tasks

Section: 02 - Introduction to R-Programming

1
• Importance of R
2
• R and R-studio installation
3
• Data types and Data structures
4
• Arithmetic, logical operations
5
• Creating new variables or updating existing Variables
6
• Conditional statements and loops
7
• String manipulations
8
• Packages and functions in R
9
• Data Frame operations
10
• Getting data into R from flat files
11
• Data Inspection and Manipulation

Section: 03 - Business Statistics

1
• Probability refresher
2
• Descriptive statistics
3
• Measures of central tendency
4
• Measures of spread
5
• Sampling
6
• Need for sampling?
7
• Different types of sampling
8
• Sampling Distributions
9
• Normal Distribution
10
• Characteristics of Normal Distribution
11
• Binomial distribution
12
• Inferential statistics
13
• Hypothesis testing
14
• Type I error
15
• Type II error
16
• Null and alternate hypothesis
17
• Reject or acceptance criterion
18
• Correlation, covariance, associations, odds ratio

Section: 04 - Exploratory Data Analysis

1
• Getting data into R
2
• Cleaning and preparing the data
3
• Data conversions
4
• Handling missing values
5
• Outlier Detection and Management
6
• Binning and Data normalization
7
• Cross Validation
8
• Dimensionality Reduction

Section: 05 - Data Visualization

1
• Visualization in R using ggplot2 (plots and charts)
2
• Histogram
3
• Bar chart
4
• Box plot
5
• Scatter plot
6
• Correlation
7
• Spurious correlation
8
• Correlation vs. Causation
9
• Visualization using Tableau (Introduction)

Section: 06 - Supervised Learning

1
• Introduction
2
• Steps in Supervised Learning
3
• Regression and Classification
4
• Training, Validation and Testing
5
• R-square, RMSE for Regression
6
• Confusion Matrix
7
• F-1 Score, Accuracy, Precision and Recall
8
• Sensitivity and Specificity
9
• ROC and AUC

Section: 07 - Linear Regression

1
• Simple Linear Regression
2
• Cost Functions
3
• Sum of Least Squares
4
• Gradient Descent Approach
5
• Variable Selection
6
• Model Development and interpretation
7
• Model Validation and Diagnostics
8
• Advantages and Disadvantages

Section: 08 - Logistic Regression

1
• Need for Logistic Regression
2
• LOGIT link function
3
• Variable Selection Methods
4
• Model Development and interpretation
5
• Measurements of Accuracy
6
• Model Validation
7
• Advantages and Disadvantages

Section: 09 - Decision Trees

1
• Classification and Regression Trees
2
• Process of Tree building
3
• Choosing variables for Decision nodes
4
• Measures of Impurity
5
• Entropy, GINI Index and Information Gain
6
• Over fitting and Pruning
7
• Advantages and Disadvantages

Section: 10 - Re-Sampling and Ensembles Methods

1
• Bagging
2
• Random Forests
3
• Boosting
4
• Gradient Boosting Machines (GBM)
5
• Extreme Gradient Boosting-XG Boost

Section: 11 - KNN (K-nearest Neighbors’)

1
• Selection of K
2
• Normalization of variable
3
• Model Development and Validation
4
• Advantages and Disadvantages of KNN

Section: 12 - Support Vector Machines

1
• Kernels-Linear and Non Linear
2
• Support Vector Classifier

Section: 13 - Probabilistic Methods (Naïve Bayes)

1
• Conditional Probabilities
2
• Classification using Naïve Bayes Approach

Section: 14 - Neural Networks

1
• Network Topology
2
• Feed Forward and Backward Propagation models

Section: 15 - Text Mining

1
• Introduction to Natural Language Processing
2
• Corpus
3
• Cleaning Text Data
4
• Tokenization
5
• Word Cloud
6
• Term Document Matrix
7
• Sentiment Analysis
8
• Text Classification

Section: 16 - Unsupervised Learning

1
• Clustering (Segmentation)
2
• Hierarchical Clustering
3
• K-Means Clustering
4
• Distance Measures

Section: 17 - Dimensionality Reduction Techniques

1
• Principal Component Analysis (PCA)
2
• Principal Component Analysis (PCA)

Section: 18 - Time Series Analysis-Forecasting

1
• Moving Averages
2
• Components of Time Series
3
• Exponential Smoothing
4
• Trend, Seasonality, Randomness
5
• ARIMA, ARIMAX

Section: 19 - Association Rules (Market Basket Analysis)

1
• APRIORI

Section: 20 - Recommender Systems

1
• Collaborative filtering
2
• User Based Collaborative filtering
3
• Item Based Collaborative filtering

Section: 21 - Introduction to Python for Data Science

1
• Python Introduction
2
• Python installation
3
• Comparison with R
4
• Data types and Data structures
5
• Functions
6
• Numpy, Pandas, Data frames
7
• Matplotlib
8
• Scikit –Learn
9
• Use Cases

Section: 22 - Introduction to Big Data Analytics

1
• Hadoop: Distributed File System
2
• Map reduce, Hive, Pig

Section: 23 - Spark

1
• Introduction
2
• Spark Frame Work
3
• RDD
4
• Spark
5
• PySpark

Section: 24 - Tensor Flow

1
• Introduction to Tensor Flow
2
• Introduction Deep Learning with Tensor Flow
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