o What does Data Science involve?
o Era of Data Science
o Business Intelligence vs Data Science
o Life cycle of Data Science
o Tools of Data Science
o Introduction to Big Data and Hadoop
o Introduction to Python
o Introduction to Spark
o Introduction to Machine Learning
o Terminologies of Statistics
o Measures of Centers
o Measures of Spread
o Normal Distribution
o Binary Distribution
o Data Analysis Pipeline
o What is Data Extraction
o Types of Data
o Raw and Processed Data
o Data Wrangling
o Exploratory Data Analysis
o Visualization of Data
o What is Machine Learning?
o Machine Learning Use-Cases
o Machine Learning Process Flow
o Machine Learning Categories
o Supervised Learning algorithm: Linear Regression and Logistic Regression
o What are classification and its use cases?
o What is Decision Tree?
o Algorithm for Decision Tree Induction
o Creating a Perfect Decision Tree
o Confusion Matrix
o What is Random Forest?
o What is Clustering & its use cases
o What is K-means Clustering?
o What is C-means Clustering?
o What is Canopy Clustering?
o What is Hierarchical Clustering?
o What is Association Rules & its use cases?
o What is Recommendation Engine & it’s working?
o Types of Recommendations
o User-Based Recommendation
o Item-Based Recommendation
o Difference: User-Based and Item-Based Recommendation
o Recommendation use cases
o The concepts of text-mining
o Use cases
o Text Mining Algorithms
o Quantifying text
o Beyond TF-IDF
o What is Time Series data?
o Time Series variables
o Different components of Time Series data
o Visualize the data to identify Time Series Components
o Implement ARIMA model for forecasting
o Exponential smoothing models
o Identifying different time series scenario based on which different Exponential Smoothing model can be applied
o Implement respective ETS model for forecasting