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: in-built functions, user defined functions
- Condition: if, if-else, nested if-else, else-if
- List: Different Data Types in a List, List in a List
- Operations on a list: Slicing, Splicing, Sub-setting
| - Condition(true/false) on a List
- Applying functions on a List
- Dictionary: Index, Value
- Operation on a Dictionary: Slicing, Splicing, Sub-setting
- 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, Sub-setting
- Conditional(T/F) on an Array
- Loops: For, While
- Shorthand for For
- Conditions in shorthand for For
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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 sub-setting
- 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
- Re-indexing
- Operations on Data Frame: Slicing, Splicing, Sub-setting
- 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
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6. Data Manipulation & Visualization- Data Aggregation, Filtering and Transforming
- Lamda Functions
- Apply, Group-by
- Map, Filter and Reduce
- Visualization
- Matplotlib, pyplot
- Seaborn
- Scatter plot, histogram, density, heat-map, 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
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11. Random Forest:
- Decision trees
- Cart C4.5
- Random Forest
- Boosted Trees
- Gradient Boosting
12. SVM:
- SVM - Introduction
- Hyper-plane
- Hyper-plane 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 Multi-Layer Perceptron
- TensonFlow with ContribLearn
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