About The Course
This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.
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
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
Who should go for this course?
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.The following professionals can go for this course :
1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts wanting to understand Machine Learning (ML) Techniques
5. Information Architects wanting to gain expertise in Predictive Analytics
6. ‘R’ professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
9. Statisticians looking to implement the statistics techniques on Big data
There is no specific pre-requisite for the course however exposure to core Java and mathematical aptitude will be beneficial. LearnChase will provide you complementary self paced courses covering essentials of Hadoop, R and Mahout to brush up the fundamentals required for the course.
Project#1:FLIGHT Delay Prediction
Industry : Aviation
Description : The goal of this project is to predict the Arrival Time of a FLIGHT given the parameters like:”UniqueCarrier”, “DepDelay”, “AirTime”, “Distance”, “ArrDelay”, etc. Whether these attributes affect the arrival delay and if yes, to which extent? Construct a model and predict the arrival delay.
Compute the (Source Airport – Destination Airport) mean scheduled time, actual and inflight time with the help of MapReduce in R and visualize the results using R.
Project #2: Stock Market Prediction
Industry : Finance
Description : This problem is about making predictions on the stock market data.The dataset contains the daily quotes of the SP500 stock index from 1970-01-02 to 2009-09-15 (10,000+ daily sessions). For each day information is given on the Open, High, Low and Close prices, and also for the Volume and adjusted close price.
Project #3: Twitter Analytics
Industry : Social Media
Description : This problem is about social media analytics. This can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. This problem is mainly about how to use twitter analytics to find meaningful data by performing Sentiment analysis of the tweets obtained and visualizing the conclusions.
Project #4: Recommendation System
Industry : e-commerce
Description : The problem of creating recommendations given a large data set from directly elicited ratings is a widely potential area which was lately boosted by players like AMAZON, Netflix, Google to name a few. In this project, you are given a collection of real world data from the different users involving the products they like, rating assigned to the product, etc. and you have to create and come up with recommendations for the users.
Project #5: NFL Data Analysis
Industry : Sports
Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.
1. Introduction to Data Science
Learning Objectives – This module will give you an understanding of Big Data and the Roles and Responsibilities of a Data Scientist. You will learn how Hadoop and R are used in Big Data Analytics and what are the methodologies used in the Analysis. This module will cover common Big Data as well as non-Big Data problems and available methods in Data Science to solve these problems. We will also solve few real-life data sets a Data Scientist encounter in his day to day work using R, Hadoop and Mahout.
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.
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.
- 10 Days
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