DataScience using Python Training

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We are the best Training institute for learning DataScience using Python Training in Chennai . We have expert trainers and excellent materials to transform your skills to fit into the job market.

Module 1 : Introduction, Data Science Overview, Recommender Overview

  • Introduction
  • Data Science Overview
  • Use Cases
  • Project Lifecycle
  • Data Acquisition
  • Evaluating Input Data
  • Data Transformation
  • Data Analysis and Statistical methods
  • Fundamentals of Machine Learning
  • Recommender Overview
  • Basic Introduction to Apache Mahout

Module 2 – Use Cases, Project Lifecycle

  • What is Data Science?
  • What Kind of Problems can you solve?
  • Data Science Project Life Cycle
  • Data Science-Basic Principles
  • Data Acquisition
  • Data Collection
  • Understanding Data- Attributes in a Data, Different types of Variables
  • Build the Variable type Hierarchy
  • Two Dimensional Problem
  • Co-relation b/w the Variables- explain using Paint Tool
  • Outliers, Outlier Treatment
  • Boxplot, How to Draw a Boxplot

Module 3 – Data Acquisition

  • 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

Module 4 : 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

Module 5 – Data Analysis and Statistical Methods, Implementing Recommenders with Apache

  • Mahout, Data Transformation
  • 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 and 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

Module 6 – Experimentation and Evaluation, Production Deployment and Beyond

  • 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

Module 7 – Various Algorithms on Business, Simple approaches to Prediction, Model Building,

Deploy the model

  • Machine Learning
  • Importance of Algorithms
  • Supervised and Unsupervised Learning
  • Various Algorithms on Business
  • Simple approaches to Prediction
  • Predict Algorithms
  • Population data
  • sampling
  • Disproportionate Sampling
  • Steps in Model Building
  • Sample the data
  • What is K?
  • Training Data
  • Test Data
  • Validation data
  • Model Building
  • Find the accuracy
  • Rules
  • Iteration
  • Deploy the model
  • Linear regression

Module 8 – Prediction & Analysis Segmentation

  • Clustering
  • Cluster and Clustering with Example
  • Data Points, Grouping Data Points
  • Manual Profiling
  • Horizontal and Vertical Slicing
  • Clustering Algorithm
  • Criteria for take into Consideration before doing Clustering
  • Graphical Example
  • Clustering and 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

Module 9 – Integrating R with Hadoop

  • 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

Projects

Projects 1-Cold Start Problem in Data Science

  • Recommendation Algorithms
  • Two Ways of Recommendation
  • Recommendation Types-Collaborative Filtering Based Recommendation, Content-Based
  • Recommendation
  • Cold Start Problem in Data Science

Project 2-Movie Recommendation, Conclusion

Movie Recommendation

Prediction – Rating Prediction, Item Prediction

  • Two Basic Approaches: Memory Based and Model Based
  • What is User Based Methods in K-Nearest Neighbor?
  • What is Item Based Method?
  • Matrix Factorization
  • Singular Value Decomposition
  • Discuss on Data Science Project
  • Collaboration Filtering
  • What are the Business Variables?

Data Science Assignment

  • Business problem
  • What are the various data sets that I should use to solve this problem?
  • How many variables are related to solve my problem?
  • How do I build my strategy to solve this problem with the available data?
  • Descriptive Statistics

Python

Module 1 – Introduction of Python

  • 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.

Module 2 – Installation Python/Basic Programming

  • 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)

Module 3 – Regular Expressions, Looping, its Packages and Object Oriented Programming

  • 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 and continue
  • The for loop
  • The while loop
  • Control flow
  • What is a function
  • Syntax
  • Documentation
  • Arbitrary of arguments

Module 4 – File Operations and Deep Dive – Functions, Class, DBAPI, Errors and Exception

  • Handling
  • 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

Module 5 – Introduction to Hadoop and Hadoop with Python

  • 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 and Map reduce
  • HDFS feature
  • Map reduce Architecture
  • Map reduce Internals
  • Installation overview of Hadoop

Module 6 – Deep Drive for components and Function

  • Python Basics revisited
  • List components
  • Lamda function
  • List
  • Dict.
  • Numpy
  • Matplot lib

Module 7 – Machine Learning Using Python

  • 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

Module 8 – Manage File System/Sandbox

  • Sandbox
  • How to take remote login of your sandbox
  • How to manage HDFS file system
  • Maper reducer

Module 9 – Machine Learning with Python

  • How to use Scikit-learn
  • Shape method
  • Get Datahome
  • How to do the machine learning
  • Spam detection

Module 10 – Server logs/Firewall logs

  • Server logs
  • Potential uses of server log data
  • Pig script
  • Firewall logs
  • Work flow editor

Module 11 – Detail Project

  • Web Logging in Python and End to end connects with all modules

Why choose SimplyAnalytics for DataScience using Python Training in Chennai?

  • 1. 100% Practical and placement oriented training.
  • 2. We are registered training organization.
  • 3. Expert trainers from IT industries.
  • 4. Placements Assistance.
  • 5. Flexible timings.
  • 6. Weekdays and weekend batches.
  • 7. Affordable fees.
  • 8. Air conditioned classroom.
  • 9. Wi-Fi enabled training institute.
  • 10. Best Lab specialities.

Are you located in any of these areas – Adambakkam, Camp Road, Chromepet, Ekkattuthangal, Guindy, kovilambakkam, Madipakkam, Medavakkam, Nanganallur, Navalur, Nungambakkam, OMR, Pallikaranai, Perungudi, Rajakilpakkam, Saidapet, Sholinganallur,Siruseri, St.Thomas Mount, T. Nagar, Tambaram, Tambaram East, Thiruvanmiyur, Thoraipakkam, Velachery, and West Mambalam.

Our Medavakkam office is just few kilometre away from your location. If you need the best DataScience using Python Training in Chennai travelling of extra kilometres is worth it .

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Course Features

  • Lectures 1
  • Quizzes 1
  • Duration 50 hours
  • Skill level All level
  • Language English
  • Students 0
  • Assessments Self
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