DATA SCIENCE WITH R
Introduction to Data Science
- What is Data Science?
- What does Data Science involve?
- An era of Data Science
- Business Intelligence vs Data Science
- The life cycle of Data Science
- Tools of Data Science
- Introduction to R
- Introduction to Machine Learning
- What is Statistical Inference?
- Terminologies of Statistics
- Measures of Centers
- Measures of Spread
- Normal Distribution
- Binary Distribution
- R and R-Studio Installation
- Data Types and Data Structures
- Arithmetic and Logical Operations
- Conditional Statements
- Packages and Functions in R
- Data Frame Operations
- Getting Data into R
- Practice Exercises
Data Extraction, Wrangling and Exploration
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Exploratory Data Analysis
- Visualization of Data
- Loading different types of a dataset in R
- Arranging the data
- Plotting the graphs
Introduction to Machine Learning
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Supervised Learning algorithms: Linear Regression and Logistic Regression
- Implementing Linear Regression model in R
- Implementing Logistic Regression model in R
- What are classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
- What is Navies Bayes?
- Support Vector Machine: Classification
- Implementing Decision Tree model in R
- Implementing Linear Random Forest in R
- Implementing Navies Bayes model in R
- Implementing Support Vector Machine in R
- What is Clustering & its use cases
- What is K-means Clustering?
- What is C-means Clustering?
- What is Canopy Clustering?
- What is Hierarchical Clustering?
- Implementing K-means Clustering in R
- Implementing C-means Clustering in R
- Implementing Hierarchical Clustering in R
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Types of Recommendations
- User-Based Recommendation
- Item-Based Recommendation
- Difference: User-Based and Item-Based Recommendation
- Recommendation use cases
- Implementing Association Rules in R
- Building a Recommendation Engine in R
- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential Smoothing model can be applied
- Implement respective ETS model for forecasting
- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series Forecasting
- Forecasting for given Time period
- The concepts of text-mining
- Use cases
- Text Mining Algorithms
- Quantifying text
- Beyond TF-IDF
- Implementing Bag of Words approach in R
- Implementing Sentiment Analysis on Data using R
- Reinforced Learning
- Reinforcement learning Process Flow
- Reinforced Learning Use cases
- Deep Learning
- Biological Neural Networks
- Understand Artificial Neural Networks
- Building an Artificial Neural Network
- How ANN works
- Important Terminologies of ANN’s
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