DATASCIENCE -PYTHON
Introduction
Datascience modules:
•	pandas
•	numpy
•	scipy
•	matplotlib
Python Data Processing
•	Python Data Operations
•	Python Data cleansing
•	Python Processing CSV Data
•	Python Processing JSON Data
•	Python Processing XLS Data
•	Python Relational databases
•	Python NoSQL Databases
•	Python Date and Time
•	Python Data Wrangling
•	Python Data Aggregation
•	Python Reading HTML Pages
•	Python Processing Unstructured Data
•	Python word tokenization
•	Python Stemming and Lemmatization
Python Data Visualization
•	Python Chart Properties
•	Python Chart Styling
•	Python Box Plots
•	Python Heat Maps
•	Python Scatter Plots
•	Python Bubble Charts
•	Python 3D Charts
•	Python Time Series
•	Python Geographical Data
•	Python Graph Data
Statistical Data Analysis
•	Python Measuring Central Tendency
•	Python Measuring Variance
•	Python Normal Distribution
•	Python Binomial Distribution
•	Python Poisson Distribution
•	Python Bernoulli Distribution
•	Python P-Value
•	Python Correlation
•	Python Chi-square Test
•	Python Linear Regression

Machine learning syllabus:(using python)
Introduction:
Ø	What is Machine Learning?
Ø	Machine Learning vs. Traditional Programming
Ø	How does machine learning work?
Ø	Machine learning Algorithms and where they are used?
Ø	How to choose Machine Learning Algorithm
Ø	Challenges and Limitations of Machine learning
Ø	Application of Machine learning
Ø	Why is machine learning important?
Python libraries:
Ø	scikit
supervised learning(algorithm implementation with scikit):
Ø	Linear regression	
Ø	Logistic regression	
Ø	Decision tree	
Ø	Naive Bayes	
Ø	Support vector machine 	
Ø	Random forest	
Ø	AdaBoost	
Ø	Gradient-boosting trees
Unsupervised learning
Ø	K-means clustering
Ø	Gaussian mixture model
Ø	Hierarchical clustering
Ø	Recommender system
Ø	PCA/T-SNE