Datascience with R Programming & Python at the Training Center

1.       Introduction to Data Science

Introduction, Demand, Core Components, Life Cycle, Big Data and Data Analytics, Data Science in the Real World, Tools and Technology.

2.       Hadoop

Introduction, Ecosystem, Installation, HDFS, MapReduce API, Applications, Examples

3.       Spark

Introduction, Installation, RDD, Transformations, Actions, Spark Streaming, Examples.

4.       Statistics

Basics of Statistics, Descriptive Statistics, Inferential Statistics, Qualitative vs. Quantitative Analysis, Hypothesis Testing

5.       Machine Learning

Introduction, Types, Supervised Learning, Unsupervised Learning, Predictive Analytics.

6.       Data Science Using R

Introduction, RStudio, CRAN Repository, R and Other Products, Installation, R Packages, R Mathematical Functions, Operators, Data Types, Statements, Functions, Data Structures, Data Interfaces, Data Visualisation using R, Case Studies - Time Series Analysis, Sentiment Analysis.

7.       Data Science Using Python

Basics, Libraries, Tools – Anaconda, IPython, Jupyter, Operators, Data Types, Control Statements, Functions, Lists, Pandas, Numpy, Data Visualisation using matplotlib, Case Study – Automobile Data Exploration

Final Project

Showcase the knowledge and skills acquired during the course. The project takes the form of a challenge in which learner will explore a dataset and develop a machine learning solution.