fbpx

Download Training Directory (2021) Download Now

Data Scientist

Course Information

Start Date Anytime
End Date 3 Month Access
Mode Self-Paced E-Learning
Fee $1,299 (excluding GST)
Contact 6720 3333 (Ms. Felicia) training.aventis@gmail.com
Register Now
Get Group Quote
LIVE Stream

Course Overview

IBM is the second-largest Predictive Analytics and Machine Learning solutions provider globally (source: The Forrester Wave report, September 2018). A joint partnership with Simplilearn and IBM introduces students to integrated blended learning, making them experts in Artificial Intelligence and Data Science. The Data Science course in collaboration with IBM will make students industry-ready for Artificial Intelligence and Data Science job roles.

IBM is a leading cognitive solutions and cloud platform company, headquartered in Armonk, New York, offering a plethora of technology and consulting services. Each year, IBM invests $6 billion in research and development and has achieved five Nobel prizes, nine US National Medals of Technology, five US National Medals of Science, six Turing Awards, and 10 Inductions in the US Inventors Hall of Fame.

This self-paced E learning course is done in partnership with SimpliLearn with over 220 hours of live interactive learning by industry experts.

Eligibility

The Data Science role requires an amalgam of experience, data science knowledge, and correct tools and technologies. It is a solid career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Science certification course, including:

  • IT Professionals
  • Analytics Managers
  • Business Analysts
  • Banking and Finance Professionals
  • Marketing Managers
  • Supply Chain Network Managers
  • Beginners or Recent Graduates in Bachelors or Master’s Degree
Pre-requisites

Professionals wishing to succeed in this Data Science certification course should have:

  • Basic knowledge of statistics
  • Basic understanding of any programming language
Course Content

Course 1: Data Science with  Python

01: Course Introduction

02: Data Science Overview

03: Data Analytics Overview

04: Statistical Analysis and Business Applications

05: Python Environment Setup and Essentials

06: Mathematical Computing with Python (NumPy)

07: Scientific computing with Python (Scipy)

08: Data Manipulation with Pandas

09: Machine Learning with Scikit-Learn

10: Natural Language Processing with Scikit Learn

11: Data Visualization in Python using matplotlib 

12: Web Scraping with BeautifulSoup

13: Python integration with Hadoop MapReduce and Spark

Practice projects: IBM HR Analytics Employee Attrition Modeling

Free Course: Math Refresher, Statistics Essential for Data Science

 

Course 2: Machine Learning

01: Course Introduction

02: AI & Machine Learning Introduction

03: Data preprocessing

04: Supervised learning

05: Feature engineering

06: Supervised learning classification

07: Unsupervised learning

08: Time series modeling

09: Ensemble learning

10: Recommender systems

11: Text mining

12: Project highlights

13: Practice projects 

Free course: Math Refresher, Statistics Essential for Data Science

 

Course 3: (Section 1: Deep Learning with Keras and TensorFlow (Self-Learning))

01/02: Welcome & Introduction to Tensorflow

03: Convolutional networks

04: Recurrent neural network

05: Restricted Boltzmann Machines (RBM)

06: Autoencoders

07: Course summary

(Section 2: Deep Learning with Keras and Tensor Flow (Live Classes))

01: Course Introduction

02: AI & Deep Learning introduction

03: Artificial Neural network

04: Deep Neural network & tools

05: Deep neural net optimization, tuning & interpretability

06: Convolutional neural network

07: Recurrent neural networks

08: Autoencoders

(Section 3: Practice Projects)

Free Course: Math Refresher

 

Course 4: Tableau Training

01: Course Introduction

02: Getting started with Tableau

03: Core topics in Tableau

04: Creating charts in Tableau

05: Working with Metadata

06: Filters in Tableau

07: Applying analytics to the worksheet

08: Dashboards

09: Modifications to Data Connections

10: Level of Detail

 

Course 5: Data Science Capstone