Data Science Academy

IBM AI Enterprise Workflow Data Science Specialist Training


In this program, we decided to take a different approach than traditional product certifications, and instead of building a product-centric certification,

we decided to build a process-centric certification with specific guiding principles:

1.    Using a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution

2.    Leveraging Design thinking as a framework to work through the translation of business goals into AI technical implementations

3.    Bringing together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality

4.    Leveraging python as the tool of choice for building AI models, and bringing in Watson Studio where it adds value on top of Python and other open source tools

Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI

· Explain the difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics

· Describe and explain the key terms in the field of artificial intelligence (Analytics, Data Science, Machine Learning, Deep Learning, Artificial Intelligence etc.)

· Distinguish different streams of work within Data Science and AI (Data Engineering, Data Science, Data Stewardship, Data Visualization etc.)

· Describe the key stages of a machine learning pipeline.

· Explain the fundamental terms and concepts of design thinking

· Explain the different types of fundamental Data Science

· Distinguish and leverage key Open Source and IBM tools and technologies that can be used by a Data Scientist to implement AI solutions

Section 2: Applications of Data Science and AI in Business

· Identify use cases where artificial intelligence solutions can address business opportunities

· Translate business opportunities into a machine learning scenario

· Differentiate the categories of machine learning algorithms and the scenarios where they can be used

· Show knowledge of how to communicate technical results to business stakeholders

· Demonstrate knowledge of scenarios for application of machine learning

Section 3: Data understanding techniques in Data Science and AI

· Demonstrate knowledge of data collection practices

· Explain characteristics of different data types

· Show knowledge of data exploration techniques and data anomaly detection

· Use data summarization and visualization techniques to find relevant insight

Section 4: Data preparation techniques in Data Science and AI

· Demonstrate expertise cleaning data and addressing data anomalies

· Show knowledge of feature engineering and dimensionality reduction techniques

· Demonstrate mastery preparing and cleaning unstructured text data

Section 5: Application of Data Science and AI techniques and models

· Explain machine learning algorithms and the theoretical basis behind them

· Demonstrate practical experience building machine learning models and using different machine learning algorithms

Section 6: Evaluation of AI models

· Identify different evaluation metrics for machine learning algorithms and how to use them in the evaluation of model performance

· Demonstrate successful application of model validation and selection methods

· Show mastery of model results interpretation

· Apply techniques for fine tuning and parameter optimization

Section 7: Deployment of AI models

· Describe the key considerations when selecting a platform for AI model deployment

· Demonstrate knowledge of requirements for model monitoring, management and maintenance

· Identify IBM technology capabilities for building, deploying, and managing AI models

Section 8: Technology Stack for Data Science and AI

· Describe the differences between traditional programming and machine learning

· Demonstrate foundational knowledge of using python as a tool for building AI solutions

· Show knowledge of the benefits of cloud computing for building and deploying AI models

· Show knowledge of data storage alternatives

· Demonstrate knowledge on open source technologies for deployment of AI solutions

· Demonstrate basic understanding of natural language processing

· Demonstrate basic understanding of computer vision

· Demonstrate basic understanding of IBM Watson AI services

For Training Requirement Contact-

Sri Lanka

+94 0716092918

+65 86738158



This course provides a comprehensive introduction to the MATLAB® technical computing environment.

No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

You will learn:

• How to use Matlab to analyze data and report results

• How to load and save information using several file formats including text, binary and Excel

• How to automate analyses using basic programming and built-in functions

• How to create robust, reusable and maintainable code

• To design and present data in graphs and GUI windows

• The basic skills that will enable you to continue learning advanced topics by yourself, at your own pace

1. The Matlab environment

· Learn how to use the Matlab desktop

· The Matlab workspace and command window

· Using the command history

· Using the documentation system and other online resources

2. Using numeric and character-based data

· Matlab’s data types and precisions

· Matlab’s data storage types

· Creating and manipulating data

· Operators and expressions

· Using functions

· Accessing sub-elements and data ranges

3. Matlab Programming

· Creating your first Matlab script

· The Matlab editor

· Using the debugger

· Scripts vs. functions

· Controlling program control flow

· Coding conventions and best practices

4. Analyzing data

· Removing and fixing invalid data

· Fitting data

· Error handling

5. Saving and loading data

· To/from Matlab workspace

· To/from text or binary files

· To/from Excel

· To/from a webpage

6. Visualizing data

· Displaying results in the command window

· Plotting data in 2D and 3D graphs

· Presenting data tables

· Exporting graphics to external applications

For Training Requirement Contact-

Sri Lanka

+94 0716092918

+65 86738158

AI for Business Leaders Workshop Sri Lanka.

AI for Business Leaders Workshop Sri Lanka.

Recently I did AI for Business Leaders Workshop at Orel IT. I covered following topics during the workshop.

AI based business opportunities

AI development life cycle

Cloud based AI technologies

AI Use Cases

AI for Business Leaders Workshop Sri Lanka.

AI for Business Leaders Workshop Sri Lanka.

AI for Business Leaders Workshop Sri Lanka.

For Training Requirement Contact-

Sri Lanka

+94 0716092918


+65 86738158

Online Healthcare Machine Learning workshop at Melbourne.

Online Healthcare Machine Learning workshop at Melbourne.

Recently we had conducted online Machine Learning workshop for St John of God Health Care Doctors at Melbourne.

Following topics covered at the training.

Introduction to Data Science

Introduction to Machine Learning

Machine Learning in Healthcare

Healthcare research in Machine Learning.

Online Healthcare Machine Learning workshop at Melbourne.

Online Healthcare Machine Learning workshop at Melbourne.