1. Introduction Introduction to Data Science
 Roles and Responsibility of a Data Scientist
 Environment Setup and Installations
 Jupytar Notebook
2. Python Crash Course
 Basic Operations in Python
 Variable Assignment
 Functions: inbuilt functions, user defined functions
 Condition: if, ifelse, nested ifelse, elseif
 List: Different Data Types in a List, List in a List
 Operations on a list: Slicing, Splicing, Subsetting
  Condition(true/false) on a List
 Applying functions on a List
 Dictionary: Index, Value
 Operation on a Dictionary: Slicing, Splicing, Subsetting
 Condition(true/false) on a Dictionary
 Applying functions on a Dictionary
 Numpy Array: Data Types in an Array, Dimensions of an Array
 Operations on Array: Slicing, Splicing, Subsetting
 Conditional(T/F) on an Array
 Loops: For, While
 Shorthand for For
 Conditions in shorthand for For

3. Statistical Fundamenatals Statistics & Plotting
 Seabourn & Matplotlib  Introduction
 Univariate Analysis on a Data
 Plot the Data  Histogram plot
 Find the distribution
 Find mean, median and mode of the Data
 Multiple data with different distributions
 Bootstrapping and subsetting
 Making samples from the Data
 Making stratified samples  covered in bivariate analysis
 Find the mean of sample
 Central limit theorem
 Plotting
 Hypothesis testing + DOE
 Bivariate analysis
 Correlation
 Scatter plots
 Making stratified samples
 Categorical variables
 Class variable
 4. Introduction to Numpy
 Numpy Arrays
 Array Indexing
 Numpy Array Indexing
 Numpy Operations
5. Data Analysis with Pandas File I/O
 Series: Data Types in series, Index
 Data Frame
 Series to Data Frame
 Reindexing
 Operations on Data Frame: Slicing, Splicing, Subsetting
 Pandas
 Stat operations on Data Frame
 Reading from different sources
 Missing data treatment
 Merge, join
 Options for look and feel of data frame
 Writing to file
 db operations

6. Data Manipulation & Visualization Data Aggregation, Filtering and Transforming
 Lamda Functions
 Apply, Groupby
 Map, Filter and Reduce
 Visualization
 Matplotlib, pyplot
 Seaborn
 Scatter plot, histogram, density, heatmap, bar charts
7. Data Visualization with Python  Seaborn
 Distribution Plot
 Categorical Plot
 Matrix Plot
 regression Plot
 Grid
 Style and Color
 Panada Data visualization
 Geographical Plotting
 Choropleth Maps
 Introduction to Machine Learning 8. Linear Regression Regression  Introduction
 Linear Regression: Lasso, Ridge
 Variable Selection
 Forward & Backward Regression
9. Logistic Regression Logistic Regression: Lasso, Ridge
 Naive Bayes
10. Unsupervised Learning Unsupervised Learning  Introduction
 Distance Concepts
 Classification
 k nearest
 Clustering
 k means
 Multidimensional Scaling
 PCA

11. Random Forest:
 Decision trees
 Cart C4.5
 Random Forest
 Boosted Trees
 Gradient Boosting
12. SVM:
 SVM  Introduction
 Hyperplane
 Hyperplane to segregate to classes
 Gamma
 13. Big Data and Spark with Python  Big Data and Hadoop Overview
 Spark Overview
 Sprark Setup
 Lambda Expression
 RDD Transformation and Action
14. Neural Nats and deep Learning Neural Network Theory
 Deep Learning
 Tensor Flow
 tensor Flow Installation
 MNIST with MultiLayer Perceptron
 TensonFlow with ContribLearn
