Hadoop All in 1, Data Science, Statistics and Probability Training – Combo Course

Expertise Big Data and Data Science using Hadoop and Statistics and Probability at one time Watch Module Sample recording for free. Try before you buy! [row]Hadoop TutorialHive TutorialExisting Learner FeedbackIntroduction to Data ScienceStatistics & Probability Tutorial[/row] Key Features: This is a combo course including: Hadoop Developer Training Hadoop Analyst Training Hadoop Administration Training Hadoop Testing Training Data Science Statistics and Probability 98 hours of High-Quality in-depth Video E-Learning Sessions 146 hours of Lab Exercises Intellipaat Proprietary VM for Lifetime and free cloud access for 6 months for performing exercises. 70% of extensive learning through Hands-on exercises , Project Work , Assignments and Quizzes The training will prepare you for Cloudera Certification: CCA Spark and Hadoop Developer, CCAH, CCP:DS as well as learners can learn how to work with Hortonworks and MapR Distributions 24*7 Lifetime Support with Rapid Problem Resolution Guaranteed Lifetime Access to Videos, Tutorials and Course Material Guidance to Resume Preparation and Job Assistance Step -by- step Installation of Software Course Completion Certificate from Intellipaat About Hadoop All in 1, Data Science, Statistics and Probability Training Course: It is an all-in-one course designed to give a 360 degree overview of Hadoop Architecture, Data Science and Statistics and Probability and their implementation on real-time projects. The major topics include Hadoop and its Ecosystem, core concepts of MapReduce and HDFS, Introduction to HBase Architecture, Hadoop Cluster Setup, Hadoop Administration and Maintenance, advanced modules like Yarn, Flume, Hive, Oozie, Impala, Zookeeper and Hue. It further covers Introduction to Data Science Overview, Project Lifecycle, Data Acquisition, Machine Learning, Data Analysis and Statistical Methods, basics of statistics, data conversion, various Plots techniques, Rules of Probability, Bayes Theorem, Probability Distributions, different types of Sampling and learning through Tables and Analysis. Learning Objectives: This course will help you: Excel in the concepts of Hadoop Distributed File System (HDFS) Implement HBase and MapReduce Integration Learn to write complex MapReduce programs in both MRv1 and Mrv2 Set up Hadoop infrastructure with single and multi-node clusters using Amazon ec2 (CDH4) Monitor a Hadoop cluster and execute routine administration procedures Learn ETL connectivity with Hadoop, real-time case studies Learn to write Hive and Pig Scripts and work with Sqoop Perform data analytics using Yarn and schedule jobs through Oozie Master Impala to work on real-time queries on Hadoop Deal with Hadoop component failures and discoveries Optimize Hadoop cluster for the best performance based on specific job requirements Derive insight into the field of Data Science Work on a Real Life Project on Big Data Analytics and gain hands-on Project Experience Gain deeper insight into concepts of statistics Learn Data Conversion, Data Collection and Data Interpretation Understand various Plotting Techniques Learn rules of Probability and Bayes Theorem Know Probability Distributions and different sampling methods Understand concept of Tables and Data Analysis Perform hands-on exercises and Solve complex queries Learn the basics of Big Data and ways to integrate R with Hadoop Explore steps to install IMPALA Work on two live Projects on Data science and Recommender Systems Gain better insights into the roles and responsibilities of a Data scientist Project Work: Hadoop Projects 1. Project – Working with Map Reduce, Hive, Sqoop Problem Statement – It describes that how to import MySQL data using sqoop and querying it using hive and also describes that how to run the word count MapReduce job. 2. Project – Work on Movie lens data for finding top records Data – Movie Lens dataset Problem Statement – It includes: Write a MapReduce program to find the top 10 movies from the u.data file Create the same top 10 movies using PIG by loading u.data into pig Create the same top 10 movies using HIVE by loading u.data into HIVE 3. Project – Hadoop Yarn Project – End to End PoC Problem Statement – It includes: Import Movie data Append the data How to use sqoop commands to bring the data into the HDFS End to End flow of transaction data How to process the real word data or a huge amount of data using map reduce program in terms of the movie etc. 4. Project – Partitioning Tables Problem Statement – It describes the parting and How to perform portioning. It includes: Manual Partitioning Dynamic Partitioning Bucketing 5. Project – Sales Commission Data – Sales Problem Statement – In this we calculate the commission according to the sales. 6. Project – Connecting Pentaho with Hadoop Ecosystem Problem Statement – It includes: Quick Overview of ETL and BI Configuring Pentaho to work with Hadoop Distribution Loading data into Hadoop cluster Transforming data into Hadoop cluster Extracting data from Hadoop Cluster 7. Project – Multi-node Cluster Setup Problem Statement – It includes following actions: Hadoop Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup Running Map Reduce Jobs on Cluster 8. Project – Hadoop Testing using MR Problem Statement – It describes that how to test map reduce codes with MR unit. 9. Project – Hadoop Weblog Analytics Data – Weblogs Problem Statement – The goal is to enable the participants to have a feel of the actual data sets in a production environment and how to load the data into a Hadoop cluster using various techniques. Once data is loaded, the next goal is to perform basic analytics on this data. Data Science Projects: Project 1-Understanding Cold Start Problem in Data Science Algorithms for Recommender Ways of Recommendation Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation Cold Start Problem Project 2-Recommendation for Movie, Summary Recommendation for movie Two Types of Predictions – Rating Prediction, Item Prediction Important Approaches: Memory Based and Model-Based Knowing User Based Methods in K-Nearest Neighbor Understanding Item Based Method Matrix Factorization Decomposition of Singular Value Data Science Project discussion Collaboration Filtering Business Variables Overview SPT Project – Data Analysis Project Data – Sales Problem Statement –It includes the following actions: Understand the business solutions Discussion with the warehouse team Data Collection & Storage Data Cleaning Build a Hypothesis Tree around the business problem Produce the final result. Recommended Audience: Programming Developers and System Administrators Project managers eager to learn new techniques of maintaining large data and responsible for decision making , research work in the organization Experienced working professionals aiming to become Big Data Analysts Mainframe Professionals, Architects & Testing Professionals Professionals aspiring to be a ‘Data Scientist’ & Machine learning experts Business Intelligence, R / Machine Learning and Big Data Professionals and Business Analysts Statisticians looking forward to implementing statistics practices on Big Data Developers willing to master Machine Learning (ML) Techniques Information Architects to gain expertise in Predictive Analytics domain Marketing Managers who are responsible for fetching data and building reports Entry-level and advanced Software developers Prerequisites: Some prior experience in any Programming Language would be good. Basic commands knowledge of UNIX, SQL scripting. Prior knowledge of Apache Hadoop is not required. Strong interest in learning Data Science Exposure to basic C Programming language and mathematical concepts will be beneficial. Why take Hadoop All in 1, Data Science and Statistics and Probability Training Course? The best ever course to gain in-depth knowledge of the hottest technologies of today, i.e. Hadoop and Data Science. This course provides an exploratory approach to deal with Big data sing concepts of Hadoop, Data science and statistics and Probability. You can learn various statistics and probability rules and techniques to perform Data Analytics. This course certifies you for biggest, top-paid job opportunities in top MNCs working on Data Science. Read More