Data Science Certification Training
-
I think the course was a good mixture of theoretical and practical training, it really helped me in all areas that I was previously unsure about, especially concepts like Mahout and Machine. In general, the training was very informative and practical, the pre recorded sessions and assignmemts were very structured as there is a lot of useful information in them to solve any doubts I could have.
← | →
-
This has been a great educational experience that not only has given me an overview of the domain, but also helped me to understand cross technologies and develop an inclination towards it. The trainer had a deep knowledge about the content, and in general the training materials, assignments, project, support and other infrastructures were superb. Definitely I recommend it if you are a Big Data enthusiast.
← | →
Course
Online
*Indicative price
Original amount in USD:
$ 479
Description
-
Type
Course
-
Methodology
Online
-
Start date
Different dates available
Data Science course helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes. You'll perform Big Data Analytics with R Programming, Hadoop and solve real life case studies on Finance, E-Comm, Social Media.
Facilities
Location
Start date
Start date
About this course
After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data.
You will be contacted by our sales team to resolve you queries regarding the course programme. You will also receive emai communication with the course
Reviews
-
I think the course was a good mixture of theoretical and practical training, it really helped me in all areas that I was previously unsure about, especially concepts like Mahout and Machine. In general, the training was very informative and practical, the pre recorded sessions and assignmemts were very structured as there is a lot of useful information in them to solve any doubts I could have.
← | →
-
This has been a great educational experience that not only has given me an overview of the domain, but also helped me to understand cross technologies and develop an inclination towards it. The trainer had a deep knowledge about the content, and in general the training materials, assignments, project, support and other infrastructures were superb. Definitely I recommend it if you are a Big Data enthusiast.
← | →
Course rating
Recommended
Centre rating
Gnana Sekhar Vangara
Balasubramanya SP
Subjects
- Basic
- Server
- IT
- Basic IT training
- SQL
- Installation
- Basic IT
- Algorithms
- Project
- Systems
- Programming
- Media
Course programme
Topics - Introduction to Big Data, Roles played by a Data Scientist, Analyzing Big Data using Hadoop and R, Methodologies used for analysis, the Architecture and Methodologies used to solve the Big Data problems, For example, Data Acquisition from various sources, Data preparation, 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. 2. Basic Data Manipulation using R Learning Objectives - In this module, you will learn the various data manipulation techniques using R.
Topics - Understanding vectors in R, Reading Data, Combining Data, subsetting data, sorting data and some basic data generation functions. 3. Machine Learning Techniques Using R Part-1 Learning Objectives - In this module, you will get an overview of the Machine learning Algorithms, and Supervised and Unsupervised Learning Techniques.
Topics - Machine Learning Overview, ML Common Use Cases, Understanding Supervised and Unsupervised Learning Techniques, Clustering, Similarity Metrics, Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models. 4. Machine Learning Techniques Using R Part-2 Learning Objectives - In this module, you will learn Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, K-Means Clustering, TF-IDF and Cosine Similarity.
Topics - Understanding K-Means Clustering, Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model, Implementing Association rule mining in R. 5. Machine Learning Techniques Using R Part-3 Learning Objectives - In this module, you will learn the Supervised Learning Techniques and the implementation of various Techniques, for example, Decision Trees, Random Forest Classifier etc.
Topics - Understanding Process flow of Supervised Learning Techniques, Decision Tree Classifier, How to build Decision trees, Random Forest Classifier, What is Random Forests, Features of Random Forest, Out of Box Error Estimate and Variable Importance, Naive Bayes Classifier. 6. Introduction to Hadoop Architecture Learning Objectives - In this module, you will learn the HDFS Architecture, MapReduce Paradigm and few data acquisition techniques in Hadoop.
Topics - Hadoop Architecture, 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. 7. Integrating R with Hadoop Learning Objectives - In this module, you will learn the methods to integrate two popular open source softwares for Big Data analytics: R and Hadoop. You will also learn techniques to write your own Mappers and Reducers.
Topics - Integrating R with Hadoop using RHadoop and RMR package, Exploring RHIPE (R Hadoop Integrated Programming Environment), Writing MapReduce Jobs in R and executing them on Hadoop. 8. Mahout Introduction and Algorithm Implementation Learning Objectives - In this module, you will understand Apache Mahout Machine Learning Library and will also gain an insight into the methods to achieve Parallel Processing using Algorithms in Mahout.
Topics - Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout. 9. Additional Mahout Algorithms and Parallel Processing using R Learning Objectives - In this module, you will learn how to implement Random Forest Classifier with Parallel Processing Library in R
Topics - Implementation of different Mahout algorithms, Random Forest Classifier with parallel processing Library in R. 10. Project Learning Objectives - In this module, you will learn various approaches to solve a Data Science problem and How different technologies and Tools (R, Hadoop, Mahout) work together in a typical Data Science Project.
Topics - Project Discussion, Problem Statement and Analysis, Various approaches to solve a Data Science Problem, Pros and Cons of different approaches and algorithms.
Data Science Certification Training
*Indicative price
Original amount in USD:
$ 479