Data Science Bootcamp

Data Science Analytics Course Singapore

Bootcamp for Data Science & Analytics Course Singapore

Our training is designed to help the individual gain in-depth knowledge on all the concepts of Data Science with Python, Machine Leaning and Data Science with R Programming from basics to advanced level techniques. You will also get an exposure to work on Tools and real-time industry based projects which are in line with Data Science Certification Exam. Enroll now and get certified in it.

Become a Data Science Expert

Earn certificates as you complete courses covering the entire data science workflow. Whether you’re just starting out or a seasoned pro, you’ll improve your skills in data manipulation, data visualization, statistics, machine learning, and more.

Data Science Bootcamp Objective:

This 3 days training focus on getting started with Data Science technologies. You will learn Azure machine learning studio, R studio, Jupyter Notebook ,Spyder with Python for data science. This course includes real world usage of machine learning for regression, classification and product recommendations.

Our Data Science Training Course is designed in such a way that you can join the course according to your knowledge. The course is divided in 3 level, that is:

First Day: Azure Machine Learning Course
Second Day: Data Science with R Programming
Third Day: Data Science Using Python

Get 50% Discount if Book for Three Days Full Package

Duration: 3 Days

Venue: 10 Anson Road, 26-08A International Plaza, Singapore 079903

  Day 1:- Azure Machine Learning Course Outline

Introduction to Machine Learning

This module introduces machine learning and discussed how algorithms and languages are used.
Lessons

  • What is machine learning?
  • Introduction to machine learning algorithms.
  • Introduction to machine learning languages.

Introduction to Azure Machine Learning

Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
Lessons

  • Azure machine learning overview
  • Introduction to Azure machine learning studio
  • Developing and hosting Azure machine learning applications

Managing Data sets

At the end of this module the student will be able to explore various types of data in Azure machine learning.
Lessons

  • Categorizing your data
  • Importing data to Azure machine learning
  • Exploring and transforming data in Azure machine learning

Building Azure Machine Learning Models

This module describes how to use regression algorithms and neural networks with Azure machine learning.
Lessons

  • Azure machine learning workflows
  • Using regression algorithms
  • Using neural networks

Using Azure Machine Learning Models

This module explores how to provide end users with Azure machine learning
services, and how to share data generated from Azure machine learning models.
Lessons

  • Deploying and publishing models
  • Consuming Experiments

  Day 2:- Data Science with R Programming Course Outline

Introduction To R Programming

  • Using the R console
  • Learning about the environment
  • Writing and executing scripts
  • Object oriented programming
  • Installing packages
  • Working directory
  • Saving your work

Variable Types And Data Structures

  • Variables and assignment
  • Data types
  • Numeric, character, boolean, and factors
  • Data structures
  • Vectors, matrices, arrays,
  • Assigning new values
  • Viewing data and summaries

Base Graphics System in R

  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, titles, axes
  • Exporting graphics to different formats

General Linear Regression

  • Linear and logistic models
  • Regression plots
  • Interaction in regression

Day 3:-Data Science using Python Course Outline

Introduction To Python

  • Python History
  • Users of Python
  • Installing Python
  • Installing IDE

Data Types

  • Numbers
  • Sequences
  • File
  • Tuples
  • Dictionaries

Data Science Introduction

  • Why Python for Data Science
  • Popular packages
  • Use cases
  • Popular Libraries
  • Panda
  • Numpy
  • Matplotlib
  • Scikit-learn

Working With Data

  • Reading & Writing to different data sources
  • Cleaning data
  • Visualisation
  • Data Transformation

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