2 Day (Sat/Sun) Predictive Analytics and Data Science using Spark, R & Python
Predictive Analytics course in Data Science using Spark, R & Python:
This is a 2 Day fast paced course in which attendees will understand the emerging world of Predictive Analytics, Data Science, using Spark, R & Python.
Prerequisite: Some Scripting and programming skills, Basic Statistics, Able to code algorithms
Schedule: 2 Days, Sat/Sun 9am5pm (March 19 & 20)
Training Location: Embassy Suites, 2885 Lakeside Dr, Santa Clara CA
DataInquest training is one of a kind training in the Big Data industry today.
Our training is unique in the following aspects
•Experts in the Big Data from the Silicon Valley with hands on experience and knowledge provide the training. 2 days of intense training (Sat/Sun) with multiple use cases to practice.
•Trainees will get handon experience with Hadoop, Spark, R, and Python
•Our training is offered at comfortable location in an auditorium at Hilton Embassy Suites at Santa Clara, California.
Course Curriculum
1. Introduction to Data Science
Topics Introduction to Data Science, Roles played by a Data Scientist, Typical use cases, Intro to predictive analytics, supervised and unsupervised methods, Overview of regression and classification algorithms.
2. Using R for data exploration and data visualization
Topics R Overview, Data import using text files, spreadsheets, databases, and web data. Using lists, matrix, and data frames in R. Describing data using simple statistical measures and visualizing using R. Plotting and describing data using ggplot2 package (biplots, box plots, word cloud, histogram with density and more)
3. R Project
4. Machine Learning Techniques  1
Topics

Linear regression

Logistic regression

Predictor selection and testing

Classification

Nonlinear methods

Lab using R
5. Machine Learning Techniques  2
Topics

Decision Trees

Random Forest

Naive Bayes Classifier

Lab using R
6. Machine Learning Techniques  3
Topics

Time series analysis

Moving Average based forecasting, ARIMA, ARMA models, Exponential Smoothing, Winter’s method


Outlier Detection

Text mining, Sentiment Analysis

Lab using R
7. Data Science workshop with Python

Python Programming Introduction

NumPy, SciPy, Scikit Learn, Matplotlib, Pandas etc.

Machine Learning implementation in Python

Use of Jupyter and iPython notebook
8. Apache Spark

Advantages of Using Spark for Big Data

Using Spark MLlib for machine learning

Linear regression, Logistic Regression, SVM, Naive Bayes, KMeans, Recommendation Engine


Lab using Apache Spark, Python
9. Deep Learning with Tensorflow

Train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects.
10. Data Science workshop with IoT (Internet of Things)

Implementation of Machine Learning algorithms on Realtime Streaming IoT data
11. Scalable Data Science on Cloud with R and Python

Implementation of machine learning algorithm using AWS and Microsoft Azure
12. Project
Who Should Attend: Statisticians, Big Data Engineering, Data Scientists, Business Intelligence professionals, PM, Teaching Staff, Delivery Manager, Product Manager, and Data Enthusiasts.