Project Description
Data Science
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities can be build with it. Data science is ultimately about using data in creative ways to generate business value:
This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It’s about surfacing hidden insight that can help enable companies to make smarter business decisions
 What is DataScience
 Install Python and Anaconda.
 Installing packages: numpy, pandas, matplotlib, sklearn)
 Introduction to Python
 Flow Control (If, for, while) Statements
 Data Structures
 Numbers
 Lists
 Tuples
 Dictionary
 Strings
 Functions and classes in Python
 Ndarray Object
 Data Types in Numpy
 Array Attributes and Manipulation in Numpy
 Indexing & Slicing
 Iterating Over Array
 Binary Operators in Numpy
 Mathematical Functions in Numpy
 Arithmetic Operations in Numpy
 Matrix Library in Numpy

 Data Structures in Pandas
 Series and DataFrame in Pandas
 Basic Functions in Pandas
 Iteration
 Sorting
 Reindexing
 Indexing and Selecting Data
 Missing Data
 Groupby
 Merging/ Joining
 Concatination
 Categorical Data

 2D Plotting with matplotlib
 Plotting with keyword strings
 Plotting with categorical variables

 Regression
 Simple Linear Regression
 Multiple Linear Regression
 Support Vector Regression
 Decision Tree Regression
 Random Forest Regression
 Classification
 Support Vector Classification(SVM)
 K – Nearest Neighbour Algorithm(KNN)
 Naive Bayes Classification
 Decision Tree Classification
 Random forest Classification
 Kmeans Clustering Algorithm
 Hierarchical clustering
 Regression