Python with Data Science

Introduction to Python

Running Python from eclipse

IDLE

Data type in Python

Integer

Long

Float

Complex

None

Typecasting data

Operators in python

Collections in python:

◦ Introduction to List

◦ List operations

◦ List Comprehensions 

◦ Introduction to Dictionary

◦ Dictionaries operations 

◦ Introduction to Set

◦ Set Operations 

◦ Introduction to Tuples

◦ Tuples Operations 

if

if and else

if elif and else

Introduction to looping

for loop

while loop

break

continue

pass

Strings:

String Operations

 

Inbuilt Functions

User Defined Functions and its definition

return statement

Local and global variables

Default argument

Variable length arguments

Anonymous functions

Modules in Python

Packages in python

Writing packages

importing packages

Introduction to exception

Raising Exception

try and except

Different type of exception

Multiple except

 Role of finally

try, except and finally together

assert

IO in python

Reading from and writing to console.

Reading from a file

Difference between read() and readLine() function.

Writing to a file

Reading and Writing csv (Comma Separated Files)

Reading and Writing JSON files.

Reading pdf files

Reading and writing excel files.

os

◦ Joining path

◦ Creating new directory 

◦ Absolute and relative path 

◦ File size 

◦ Getting content of a folder 

shutil

◦ Copying files and directories

◦ Deleting files and directories 

◦ Renaming files and directories 

Constructors

Objects

Inheritance

Namespace

Debugging in Python

Introduction to NumPy.

NumPy data type

NumPy array and its operations

NumPy ndarray

Indexing slicing and Stacking of array and ndarray

Manipulating array shapes

Splitting arrays

Matrices and its operation in NumPy

Linear systems and NumPy

File IO in NumPy

Introduction to Matplotlib.

Line plot multiple line plot

Scatter plot

Bar plot

Histograms

Box plot

Error bars

Contour plot

Piplot

Different aspects of coloring

Violin plots

Text plot

Word Cloud

NLTK :

Introduction to NLTK

Installing NLTK

Text analysis basics

◦ Tokenization

◦ Stemming 

◦ Stop words 

◦ Part of speech tagging 

◦ Lemmatization 

Clustering

 

Introduction to Pandas.

Pandas data structure

Operations on Data structures

IO in Pandas

Data summarization and aggregation

Scikit-Learn

Introduction to Data Mining and Machine learning

Supervised and unsupervised learning.

Some use cases on machine learning.

Introduction to Scikit-Learn

Installing Scikit-Learn

What is data?

Steps in data analysis.

Types of data.

Data preprocessing and its importance.

Exploratory analysis of data :

Importance of data visualization

Initial questions in model building.

Model selection

Linear regression

Introduction to simple linear regression.

Assumption to simple linear regression.

Parameter estimation

Simple linear regression with Scikit-Learn

Multiple linear regression with Scikit-Learn

Model verification and linear regression assumption testing.

Data transformation and polynomial regression

Introduction to Ridge regression

Ridge regression and Scikit Learn

Introduction to Lasso Regression

Lasso Regression and Scikit Learn

 

 

Introduction to classification

Classification use cases

K Nearest neighbor

◦ Introduction to K Nearest neighbor

◦ K Nearest neighbor in Scikit Learn 

◦ Strength and weakness of K Nearest neighbor 

Logistic Regression

◦ Introduction to logistic regression

◦ Use cases of logistic regression 

◦ Mathematical description of logistic regression 

◦ Logistic regression with Scikit Learn 

Naive Bayes Classifier

◦ Introduction to Bayes theorem

◦ Bayes theorem in classification 

◦ Bayes classifier with Scikit Learn 

Decision Tree

◦ Introduction to decision tree.

◦ Use cases of decision tree 

◦ Partition algorithms for decision tree 

▪ ID3

▪ Gini Index 

▪ Cart 

◦ Tree pruning 

◦ Scikit Learn and decision tree 

Ensemble methods

Random forest with Scikit Learn

Neural Networks

◦ Introduction to brain

◦ Introduction to neural network 

◦ Perceptron 

◦ Back propagation algorithm 

◦ MLP and Scikit Learn 

Classifier performance evaluation

◦ Confusion matrix

◦ Cohen kappa 

◦ Precision, recall and F-measures 

◦ Receiver operating characteristic (ROC) 

Clustering

Introduction to clustering

Use cases of clustering

K-means clustering

Hierarchical clustering

◦ Different linkage type: Ward, complete and average linkage

DBSCAN

Clustering performance evaluation

◦ Adjusted Rand index

◦ Mutual Information based scores 

◦ Homogeneity, completeness and V-measure 

◦ Fowlkes-Mallows scores 

◦ Silhouette Coefficient 

 

 

 

NLTK :

Introduction to natural language processing.

Introduction to NLTK

Installing NLTK

Text analysis basics

◦ Tokenization

◦ Stemming 

◦ Stop words 

◦ Part of speech tagging 

◦ Lemmatization 

NLTK Corpora

Clustering