|Tools||Spark, Hive, HBase, Hadoop, Scala, Java|
|Learning Mode||Classroom, online|
Who should do this course?
Candidates from various quantitative backgrounds, like ETL, data warehousing, Admin, BI who are willing to switch their career in Bigdata domain.
- Strong interest in programming.
- Good in analytics.
- Strong background in java.
- Python/Java programming experience or understanding of programming concepts such as variables, functions, loops, and basic python data structures like lists and dictionaries etc.
- Understand data and know how to extracts insights from it.
INTRODUCTION TO HADOOP
- Introduction to Big data
- Hadoop and HDFS
BIGDATA PROCESSING FRAMEWORKS
- Map Reduce Advanced
- Programming in Hive
UNSTRUCTURED DATA PROCESSING FRAMEWORK
- NoSQL concept
BIG DATA PROCESSING FRAMEWORK
- Spark streaming
- Spark internals
INTRODUCTION TO DATA SCIENCE
- What is data Science?
- Introduction. Importance of Data Science.
- Demand for Data Science Professional.
- Brief Introduction to Big data and Data Analytics. Lifecycle of data science.
- Tools and Technologies used in data Science.
- Business Intelligence vs Data Science.
- Role of a data scientist.
- R programming
- Spark programming
INTRODUCTION TO STATISTICS
- Fundamentals of Math and Probability
- Descriptive Statistics
- Inferential Statistics
- Hypothesis Testing
- Hands on with assignment & case studies
UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING
- Introduction to Machine Learning
- Linear Regression
- Logistic Regression
- Decision Trees and Supervised Learning
- Unsupervised Learning
- Introduction to Deep Learning
- Natural language Processing