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.
This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
There is no specific pre-requisite for the course however exposure to core Java and mathematical aptitude will be beneficial. MAT SOFT will provide you complementary self paced courses covering essentials of Hadoop, R and Mahout to brush up the fundamentals required for the course.
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 o\f Machine Learning Techniques, Data Visualization etc., problem statement of few data science problems which we shall solve during the course.
What is Big Data? What are the challenges for processing big data? What technologies support big data? What is Hadoop? Why Hadoop? History of Hadoop, Use Cases of Hadoop, Hadoop eco Systems, HDFS, Map Reduce.
Getting Up and Running with Spark Installing and setting up Spark locally Spark clusters. The Spark programming model: SparkContext and SparkConf, The Spark shell, Resilient Distributed Datasets, Creating RDDs, Spark operations, Caching RDDs, Broadcast variables and accumulators.
Designing a Machine Learning System Introducing Movie Stream Business use cases for a machine learning system Personalization, Targeted marketing and customer segmentation, Predictive modeling and analytics. Types of machine learning models. The components of a data-driven machine learning system.
At the end of your course, you will work on a real time Project. You will receive a Problem Statement along with a data-set to work.
Once you are successfully through the project (Reviewed by an expert), you will be awarded a certificate with a performance-based grading.
If your project is not approved in 1st attempt, you can take extra assistance for any of your doubts to understand the concepts better and reattempt the Project free of cost.
Classroom & Online Training
|Afternoon: 12:30 pm||Weekdays(Mon-Fri)
Morning: 07:30 am to 08:30 am
|Evening: 08:00 pm to 09:00 pm||Weekends(sat-sun)
morning : 07:00 am to 10:00 am
Morning: 09:30 am to 10:30 am