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Kalibur Info solutions (P) Ltd.
Plot # 75, Tanasha Nagar,

Next to HDFC Bank,, Manikonda, Hyd - 500089

Telangana, INDIA

E-mail: infoe@kalibur.in



+91 789.328.2707

Copyright © 2016 Kalibur Info Solutions. All rights reserved.

1 Section-1 1 Hour Course Introduction Course Resources Installation setup and overview Setting up a Big Data cluster in
AWS with Apache Spark and Python 2 Section-2 1 Hour 2.1 Python Introduction and Basics Values and Variable Expression and Arithmetic Command-Line Programming Times and Dates Control Structures and Conditional Branching Looping Exception Handling 2.2 Function Function Basics Standard Mathematical Functions Custom Functions Recursion Lambda Expressions 2.3 Data Types Identifiers and Keywords Integral Types Booleans Floating-Point Types Complex Numbers Booleans Decimal Numbers 2.4 Strings Comparing Strings Slicing and Striding Strings String Operators and Methods Built-in String Methods 3 Section-3 1 Hour 3.1 Collection Data Types Sequence Types
Named Tuples
Lists Set Types
Frozen Sets Mapping Types
Default Dictionaries
Ordered Dictionaries Iterating and Copying Collections
Iterators and Iterable Operations and Functions
Copying Collections 4 Section-4 1 Hour 4.1 Regular Expressions 4.2 File Handling
Writing and Reading Binary Data
Writing and Parsing Text Files
Writing and Parsing XML Files 4.3 Object-Oriented Programming Object-Oriented Concepts and Terminology Classes Magic Methods Inheritance and Multiple Inheritance Abstract Base Classes Advance Python 5 Section-5 1 Hour 5.1 Data visualization in python with Matplotlib nad Seaborn Basic plots
Line, Bars
stacked column chart, bubble plot and pies
 Plotting distribution
Box plot, violin plot
heat map 6 Section - 6 1 Hour Data exploration using Pandas Data exploration using NumPy Playing with SciPy for scienti_c Computing 7 Section - 7 1 Hour Web Scraping With Python
Playing with HTML and XML document With Beutiful Soup
Web Crawling
Different Web API calling using Python
Django basics
 8 Section - 8 2 Hours Machine Learning With Python Scikit Learn
Recommendation system Introduction
Dimension Reduction Algorithms
Regression Algorithms
Classification Algorithms
Clustering Algorithms
Association Rule Mining
 9 Section 9 1 Hour 9.1 Natural Language Processing With Python
Spliting and Tokenizing Text
Data preprocessing
Hands on with di_erent Natural language processing tools like NLTK,
Stanford Core NLP, Open NLP etc
Named Entity Recognition
Relationship Extraction
Part of Speech Tagging
Sentiment Analysis
Text Summarization 
10 Section - 10 4 Hours Big Data Processing with Python and Spark 2.0.1
Installation and Basic Setup to integrate python and Apache Spark
Introduction Big Data, Distributed Computing with Apache Spark
Dataframes,Dataset, RDD, Spark SQL, HiveQL: Overview
RDD Operations- Transformation and Actions
Creating DataFrame from RDD and from exteral Data Sources
Applying SQL queries on DataFrame
Pandas vs Pyspark DataFrames
Spark Streaming Basics
Machine Learning With Apache Spark and Python
Graph processing using Apache Spark and Python 11 Section - 11 25 Hours 11.1 Project 
1. Twitter Data - Sentiment Analysis using Spark Streaming and Python Collect live tweets and process those tweets using DataFrame and Spark SQL modules to find out the sentiment associated with each Tweet 2. Building a Recommendation Engine using Spark ML library and Python Introduces the collaborative filtering techniques used by many online re-tailers to recommend products or media. The Lecture includes a section on recommending links to people from a social bookmarking site, and building a movie recommendation system from the MovieLens dataset. 3. Credit Card Defaulter Founder This project corresponds to a real life scenario found in Credit Card companies. Customers who own credit cards are expected to pay their balances monthly. But, they do default (not pay), which forces the bank into financial situations. Banks want to know which customer would possibly default in the future, so they can take necessary actions (such as closing their card, reducing their spending limits etc. This problem involves a specific bank who wants to analyze their customer's payment patterns and narrow down to cases where they are most likely to default. This problem has a dataset that contains information about Credit Card customers for the past 6 and a set of questions that the bank has. Your assignment is to analyze the data and come up with answers to these questions using Apache Spark and Python 4. Building Price Models Approaches the problem of predicting numerical values rather than classifications using k-nearest neighbors techniques, and applies the optimization algorithms. These methods are used in conjunction with the eBay API to build a system for predicting eventual auction prices for items based on a set of properties. 5. Information Extraction, relationship extraction and Text Summerization From live Textual Data collected from Scrapping the Web using Python. A natural Language Project where one will learn to extract informations like Locations , Name Organisations present in the text files as well as the relationship among them.



Umacx Building 2nd floor

1-23/1/2, Rajiv Gandhi Nagar

Gachibowli, Hyderabad - 500032, TS

After Gachibowli Central on the road going from Gachibowli to Kondapur, Opposite Tata Motors






      +91 789.328.2707

Copyright © 2018 Kalibur. All rights reserved.

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