Big Data Analytics / Cloud Computing

Description

Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers. Becoming a data scientist takes more than the understanding of basic skills like statistics and programming in various languages. The need to develop one area of technical analytic expertise while being conversant in many others is very crucial. Going beyond descriptive analytics has become essential to meet the complexities of information requirement for decision making as well as developing strategies to drive greater profitability, improved performance and competitiveness. On the other hand, companies are increasingly storing large amounts of data online due to the increase in Big Data. Cloud computing is becoming increasingly vital for not just the software developers but also in the field of big data analytics: cloud computing makes expanding computing power and deploying data solutions much easier and is therefore handy for data scientists who are digging into large datasets. This course builds expertise in advanced analytics, data mining, predictive modeling, as well as cloud computing and Microsoft Azure. The aim of the course is to introduce the trainees to the important big data management techniques, analytical tools and cloud computing (MS Azure). TARGET AUDIENCE Individuals with fundamental knowledge on Database management, Business Intelligence, Statistics and basic knowledge on programming with experience in Python. Having fundamental knowledge in python program- ming will be an advantage TRAINING CONTENT • Data Science Fundamentals • Cloud computing fundamentals • Microsoft Azure concepts and functions • Azure SQL Database • Introduction to Azure Cosmos DB • Customizing Charts on Azure Dashboard • Creating Web Apps in Azure • Managing Virtual Machine via Azure Mobile App • Introduction to Azure Data Factory • Introduction to Visualization • Data Processing and Cleaning using Panda-Python • Managing Big Data using Apache Hadoop and MongoDB • Exploratory Data Analysis and Visualization • Data Mining using Python • Data Extraction for Enterprise Reporting • Advanced Analytics • Linear Regression • Logistic Regression • Big Data Model Diagnostics • Supervised and unsupervised learning • Random Forest, SVM, clustering • Dimensionality reduction • Validation, and Evaluation of Machine Learning Methods • Advanced Analytic Techniques and Text Mining • Simulation of sentimental analysis • Optimization and Causal Mechanistic Analysis • Time Series and Forecasting • Big Data Security • Big Data and Apache Hadoop • Data Science / Big Data frameworks and RDDs • SQL and Data Frames Module