datacodecamp

Data Science training curriculum

**Statistics**
	Data Types 
	Sampling
	Measures of Dispersion
	Graphical Techniques
	Probability Distribution Function
	Normal Distribution
	Standard Normal Distribution
	Binomial Distribution
	Z Score
	Central Limit Theorem
	Hypothesis testing
	Confidence Interval
	2 Sample T test
	ANOVA
	2 proportion test
	Chi square test
	Projects & Assignments

**Python**
	Basics of Python
	Input and  output
	Data Types
	Loops and Functions
	Strings & Lists
	Dictionary , Set , Tuples
	Jsons
	Python OOP
	Python Libraries: Numpy Pandas Matplotlib
	Random Data 
	Databases – Mysql , MangoDB
	File Handling
	Exception Handling
	Projects & Assignments

**Machine Learning**
Introduction
Supervised & Unsupervised Learning 
Training , Testing & Validation
Evaluation Metrics – Precision, Recall
ROC Curve 
F-Score
Scikit Learn Introduction
Exploratory Data Analysis:
Regression Algorithms :
	Linear Regression
	Logistic regression
Regularization Algorithms:
	Ridge Regression
	Lasso Regression
Tree Based Models:
	Decision Tree
	Random Forests
	Lightgbm
	XgBoost
	Catboost
	Gradient Boosting
Clustering Algorithms:
	K Means Clustering
	Hierarchical Clustering
	DBScan
Dimensionally Reduction Techniques:
	PCA
	TSNE
Miscellaneous models:
	KNN
	SVM
	Naïve Bayes
Time Series Analysis:
	ARIMA 
	Forecasting
Text Analytics:
Data science pre processing techniques:
Data science Post modelling techniques:
Data Science Ensemblling techniques:
Assignments & Projects:

_**Data will talk to you now,if you are willing to listen**_