Python with Data Science

Python with Data Science

Location: BTM 2ND STAGE, 773,3RD FLOOR, 7TH CROSS 16TH MAIN
City: BENGALURU
Hours: 12:00 PM (IST) ONWARDS
Contact Email:
Mobile No.:
From Date: 02-12-2019
To Date: 31-12-2019

Description:

OVERVIEW

Python Basic :
 
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:
 
List
◦ Introduction to List
◦ List operations
◦ List Comprehensions 
 
Dictionary
◦ Introduction to Dictionary
◦ Dictionaries operations 
 
Set
◦ Introduction to Set
◦ Set Operations 
 
Tuples
◦ Introduction to Tuples
◦ Tuples Operations 
 
Conditionals and Looping
 
Introduction to conditionals
if
if and else
if elif and else
Introduction to looping
for loop
while loop
break
continue
pass
Strings:
 
Introduction to Strings
String Operations
 
Functions modules and Package:
 
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
 
Exception handling in python : 
 
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.
 
 
Some useful packages in Python
 
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 
 
Class and Objects :
 
Class in Python
Constructors
Objects
Inheritance
Namespace
Debugging in Python
 
NumPy :
 
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
 
Matplotlib :
 
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 natural language processing.
Introduction to NLTK
Installing NLTK
Text analysis basics
◦ Tokenization
◦ Stemming 
◦ Stop words 
◦ Part of speech tagging 
◦ Lemmatization 
 
NLTK Corpora
Clustering
 
Pandas :
 
Introduction to Pandas.
Pandas data structure
Operations on Data structures
IO in Pandas
Data summarization and aggregation
Scikit-Learn
 
Introduction :
 
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
 
 
Classification
 
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

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