Data Science Training
Data Science Training drops an insight on data visualization and techniques of Data Mining. This course gives an overview of the data, and gives answers to all questions, and tools that data analysts and data scientists work with.
Data Science Training Curriculum
Introduction to Data Science
This module will introduce you to Data Science throwing light on Why data science?, Analysing Big Data, Architecture and methods to solve Big Data issues, Data visualization etc…
Introduction to Big Data
Roles played by a Data Scientist
Analysing Big Data using Hadoop and R
Different Methodologies used for analysis in Data Science
The Architecture and Methodologies used to solve the Big Data problems
Data Acquisition from various sources
Data transformation using Map Reduce (RMR)
Application of Machine Learning Techniques, Data Visualization etc.,
Problem statement of few data science problems which we shall solve during the course
Basic Data Manipulation using R in Data Science.
This module teaches how to manipulate data and use R for all kinds of data conversion and restructuring processes that are frequently encountered in the initial stages of data analysis in Data Science Training.
Understanding vectors in R
Sorting data and some basic data generation functions
Machine Learning Techniques Using R Part-1
The goal of machine learning is to create a predictive model, that is indistinguishable from a correct model. This module, starts off giving you an overview about machine learning in Data science Training.
Machine Learning Overview
ML Common Use Cases and techniques
Clustering and Similarity Metrics
Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models
Machine Learning Techniques Using R Part-2
This module is designed to teach you ‘K’ means clustering, association rule mining and much more..
Understanding K-Means Clustering in Data Science
Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
Implementing Association rule mining in R.
Data Science Machine Learning Techniques Using R Part-3
The last part of machine learning module of Data Science course, trains on Decision Tree’s, Random forests concept in Data Science.
Understanding Process flow of Supervised Learning Techniques
Decision Tree Classifier
How to build Decision trees
Random Forest Classifier
What is Random Forests concept in data science
Features of Random Forest
Out of Box Error Estimate and Variable Importance
Naive Bayes Classifier
Introduction To Hadoop Architecture
Understand the Hadoop architecture, its commands, SQOOP and other data loading techniques in this module.
Common Hadoop commands
MapReduce and Data loading techniques (Directly in R and in Hadoop using SQOOP, FLUME, and other data Loading Techniques)
Removing anomalies from the data
Integrating R With Hadoop
This module of Data science course, will give good knowledge on how R is integrated with R, the integrated programming environment and writing MapReduce jobs.
Integrating R with Hadoop using R
Hadoop and RMR package
Exploring RHIPE (R Hadoop Integrated Programming Environment)
Writing MapReduce Jobs in R and executing them on Hadoop
Data Science Mahout Introduction And Algorithm Implementation
By the end of this module, you will be able to implement machine learning algorithms with Mahout
Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout
Additional Mahout Algorithms And Parallel Processing Using R
The aim of the project module is to let you have an idea of what a project is, problem statement, various approaches and solving algorithms.
Problem Statement and Analysis
Various approaches to solve a Data Science Problem
Pros and Cons of different approaches and algorithms.
- 10 Days
- 0 Units
- 0 Hrs