Data Science Online Training
Introduction to Data Science Data Science is a process of extracting knowledge from data. Data science is emerging to meet the challenges of processing large data sets which require versatile skill set and specialized in specific domain. Data scientist analyse the complex problems and ensure rich consistency of data sets with creating visualizations to aid in understanding data. Data science online training is designed to teach the techniques of data mining and gain knowledge on insight of visualization and optimization of data to become a successful Data Scientist.
Course Curriculum Unit-1: Introduction to Data Science Topics- Introduction to Big Data, Roles played by a Data Scientist, Analysing 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 Unit-2: Basic Data Manipulation using R Topics- Understanding vectors in R, Reading Data, Combining Data, subsetting data, sorting data and some basic data generation functions Unit-3: Machine Learning Techniques Using R Part-1 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
Unit-4: Machine Learning Techniques Using R Part-2 Topics- Understanding K-Means Clustering, Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model, Implementing Association rule mining in R. Unit-5: Machine Learning Techniques Using R Part-3 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 Unit-6: Introduction to Hadoop Architecture 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 Unit-7: Integrating R with Hadoop 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
Unit-8: Mahout Introduction and Algorithm Implementation Topics- Implementing Machine Learning Algorithms on larger Data Sets with Apache Mahout Unit-9: Additional Mahout Algorithms and Parallel Processing using R Topics- Implementation of different Mahout algorithms, Random Forest Classifier with parallel processing Library in R Unit-10: Project Topics- Project Discussion, Problem Statement and Analysis, Various approaches to solve a Data Science Problem, Pros and Cons of different approaches and algorithms
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