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Python for Data Science Training -iPartner
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Python for Data Science Training

  1. Python for Data Science Training
32 hr / 476*
(* including all taxes.)


Key Features
SMALL
BATCHES
MENTORING
BY EXPERTS
FLEXIBLE
SCHEDULE
LEARN
BY DOING
GOAL
ORIENTED



Course Agenda


  • Data Science Overview
  • Reasons to use Data Science
  • Project Lifecycle
  • Data Acquirement
  • Evaluation of Input Data
  • Transforming Data
  • Statistical and analytical methods to work with data
  • Machine Learning basics
  • Introduction to Recommender systems
  • Apache Mahout Overview
  • Discussion on Boxplot- also Explain
  • Example to understand variable Distributions
  • What is Percentile? – Example using Rstudio tool
  • How do we identify outliers?
  • How do we handle outliers?
  • Outlier Treatment: Using Capping/Flooring General Method
  • Distribution- What is Normal Distribution?
  • Why Normal Distribution is so popular?
  • Uniform Distribution
  • Skewed Distribution
  • Transformation
  • Discussion about Boxplot and Outlier
  • Goal: Increase Profits of a Store
  • Areas of increasing the efficiency
  • Data Request
  • Business Problem: To maximize shop Profits
  • What are Interlinked variables
  • What is Strategy
  • Interaction b/w the Variables
  • Univariate analysis
  • Multivariate analysis
  • Bivariate analysis
  • Relation b/w Variables
  • Standardize Variables
  • What is Hypothesis?
  • Interpret the Correlation
  • Negative Correlation
  • Machine Learning
  • Discussion about Boxplot and Outlier
  • Goal: Increase Profits of a Store
  • Areas of increasing the efficiency
  • Data Request
  • Business Problem: To maximize shop Profits
  • What are Interlinked variables
  • What is Strategy
  • Interaction b/w the Variables
  • Univariate analysis
  • Multivariate analysis
  • Bivariate analysis
  • Relation b/w Variables
  • Standardize Variables
  • What is Hypothesis?
  • Interpret the Correlation
  • Negative Correlation
  • Machine Learning
  • Correlation b/w Nominal Variables
  • Contingency Table
  • What is Expected Value?
  • What is Mean?
  • How Expected Value is differ from Mean
  • Experiment – Controlled Experiment, Uncontrolled Experiment
  • Degree of Freedom
  • Dependency b/w Nominal Variable & Continuous Variable
  • Linear Regression
  • Extrapolation and Interpolation
  • Univariate Analysis for Linear Regression
  • Building Model for Linear Regression
  • Pattern of Data means?
  • Data Processing Operation
  • What is sampling?
  • Sampling Distribution
  • Stratified Sampling Technique
  • Disproportionate Sampling Technique
  • Balanced Allocation-part of Disproportionate Sampling
  • Systematic Sampling
  • Cluster Sampling
  • 2 angels of Data Science-Statistical Learning, Machine Learning

  • Multi variable analysis
  • linear regration
  • Simple linear regration
  • Hypothesis testing
  • Speculation vs. claim(Query)
  • Sample
  • Step to test your hypothesis
  • performance measure
  • Generate null hypothesis
  • alternative hypothesis
  • Testing the hypothesis
  • Threshold value
  • Hypothesis testing explanation by example
  • Null Hypothesis
  • Alternative Hypothesis
  • Probability
  • Histogram of mean value
  • Revisit CHI-SQUARE independence test
  • Correlation between Nominal Variable
  • Clustering
  • Cluster and Clustering with Example
  • Data Points, Grouping Data Points
  • Manual Profiling
  • Horizontal & Vertical Slicing
  • Clustering Algorithm
  • Criteria for take into Consideration before doing Clustering
  • Graphical Example
  • Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy Clustering
  • Simple Approaches to Prediction
  • Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity
  • Clustering Algorithm in Mahout
  • Probabilistic Clustering
  • Pattern Learning
  • Nearest Neighbor Prediction
  • Nearest Neighbor Analysis
  • R introduction
  • How R is typically used
  • Features of R
  • Introduction to Big data
  • R+Hadoop
  • Ways to connect with R and Hadoop
  • Products
  • Case Study
  • Architecture
  • Steps for Installing RIMPALA
  • How to create IMPALA packages
  • R introduction
  • How R is typically used
  • Features of R
  • Introduction to Big data
  • R+Hadoop
  • Ways to connect with R and Hadoop
  • Products
  • Case Study
  • Architecture
  • Steps for Installing RIMPALA
  • How to create IMPALA packages
  • Why python?
  • What you need to get started?
  • General purpose
  • Is python a scripting language?
  • Why use python in the RW?
  • What can you do with python?
  • How to install python?
  • Installing python
  • Windows Installation
  • Linux Installation
  • Environment variables
  • What is IDE?
  • ECLIPSE
  • How to download additional Diary?
  • Running python program
  • Data types
  • Object types
  • Python core data types
  • Strings
  • Strings (Methods)
  • Data Types
  • Tuples
  • How to create notebook in Python?
  • Methods of tuple
  • Lists
  • Methods
  • Dictionary
  • Dictionary Methods
  • Advance string methods
  • String formatting
  • Obtaining keyboard input
  • Control flow
  • The if statement
  • Boolean logic
  • Break & continue
  • The for loop
  • The while loop
  • Control flow
  • What is a function?
  • Syntax
  • Documentation
  • Arbitrary of arguments

  • File handling
  • File system
  • Opening files
  • Opening other file types
  • Exception handling
  • What are exceptions?
  • Object oriented programming (OOP)
  • OOP basics
  • Defining a class
  • Special methods
  • Python DB API
  • SQLite
  • SQLite in python
  • Panda quick overview
  • The state of Data
  • Hadoop
  • Component of Hadoop
  • Why Hadoop is scalable?
  • Hadoop eco system
  • Sqoop
  • Ambari
  • Zookeeper
  • Hadoop Incubator
  • Stack Implementation
  • Architecture of HDFS & Map reduce
  • HDFS feature
  • Map reduce Architecture
  • Map reduce Internals
  • Installation overview of Hadoop
  • Review of Python basics
  • Components
  • Lamda function
  • List
  • Python Dictionary
  • Numpy Overview
  • Matplot library
  • Sandbox
  • How to take remote login of your sandbox
  • How to manage HDFS file system
  • Maper reducer
  • Panda
  • Introduction of panda
  • Key feature of panda
  • Importing the panda
  • Data structure in Panda
  • Importing data
  • Read table
  • Skip rows
  • Machine learning
  • Definition
  • Machine learning algorithms
  • Unsupervised learning
  • Real world example of machine learning
  • Statistical learning problem
  • How to use Scikit-learn
  • Shape method
  • Get Datahome
  • How to do the machine learning
  • Spam detection
  • Server logs
  • Potential uses of server log data
  • Pig script
  • Firewall logs
  • Work flow editor

Learn & Get

  • Understand the concepts of Data science and Python
  • You will have an idea of Statistical and Analytical methods to deal with huge data sets.
  • You will be able to create business algorithms and data models using Python and it’s techniques.
  • You will gain an expertise on Regular Expressions, looping functions and concepts of Object Oriented Programming.
  • You will be able to use Python in Discovering Data.
  • Work on Real-life Projects will help you to get a practical experience of real scenarios of IT Industry.

Payment Method

PAYMENT METHODS
You need to pay through PayPal. We accept both Debit and Credit Card for transaction.
SCHOLARSHIPS
We subsidize our fees by 10% for military personnel, and college students with exceptional records. To apply for a scholarship, email info@ipartner.ca
FREQUENTLY ASKED QUESTIONS
In our iPartner self-paced training program, you will receive the training assessments, recorded sessions, course materials, Quizzes, related softwares and assignments. The courses are designed in such a way that you will the get real world exposure; the solid understanding of every concept that allows you to get the most from the online training experience and you will be able to apply the information and skills in the workplace. After the successful completion of your training program, you can take quizzes which enable you to check your level of knowledge and also enables you to clear your relevant certification at higher marks/grade where you will be able to work on the technologies independently.
In Self-paced courses, the learners are able to conduct hands-on exercises and produce learning deliverables entirely on their own at any convenient time without a facilitator whereas in the Online training courses, a facilitator will be available for answering queries at a specific time to be dedicated for learning. During your self-paced learning, you can learn more effectively when you interact with the content that is presented and a great way to facilitate this is through review questions and quizzes that strengthen key concepts. In case if you face any unexpected challenges while learning, we will arrange a live class with our trainer.
All Courses from iPartner are highly interactive to provide good exposure to learners and gives them a real time experience. You can learn only at a time where there are no distractions, which leads to effective learning. The costs of self-paced training are 75% cheaper than the online training. You will offer lifetime access hence you can refer it anytime during your project work or job.
Yes, at the top of the page of course details you can see sample videos.
As soon as you enroll to the course, your LMS (The Learning Management System) Access will be Functional. You will immediately get access to our course content in the form of a complete set of previous class recordings, PPTs, PDFs, assignments and access to our 24*7 support team. You can start learning right away.
24/7 access to video tutorials and Email Support along with online interactive session support with trainer for issue resolving.
Yes, You can pay difference amount between Online training and Self-paced course and you can be enrolled in next online training batch.
Please send an email. You can join our Live chat for instant solution.
We will provide you the links of the software to download which are open source and for proprietary tools, we will provide you the trail version if available.
You will have to work on a training project towards the end of the course. This will help you understand how the different components of courses are related to each other.
Classes are conducted via LIVE Video Streaming, where you get a chance to meet the instructor by speaking, chatting and sharing your screen. You will always have the access to videos and PPT. This would give you a clear insight about how the classes are conducted, quality of instructors and the level of Interaction in the class.
Yes, we do keep launching multiple offers that best suits your needs. Please email us at: info@ipartner.ca and we will get back to you with exciting offers.
We will help you with the issue and doubts regarding the course. You can attempt the quiz again.
Sure! Your feedbacks are greatly appreciated. Please connect with us on the email support - info@ipartner.ca.