Data Science With Machine Learning Training
Participants in the Data Science with Machine Learning Training gain a thorough understanding of machine learning techniques, predictive modeling, and data analysis. It covers the fundamental tools and methods for training machine learning models, drawing conclusions from massive datasets, and formulating predictions based on data. Participants gain knowledge in data visualization, model deployment, and programming languages like R and Python.
- 10+ Courses
- 30+ Projects
- 400 Hours
Data Science With Machine Learning Training is suitable for the following target audiences:
Aspiring Data Scientists: People who wish to work in data science and machine learning, honing their technical abilities in model construction and data analysis.
Engineers and IT Professionals: Professionals in engineering or IT who want to specialize in data science, learn more about machine learning, and improve their problem-solving abilities.
Business analysts: To enhance forecasts and decision-making, business analysts attempt to incorporate machine learning methods into their analytical work.
Software developers: Those who wish to expand their knowledge of machine learning tools and methods to create more intelligent, data-driven apps.
Scholars and Researchers: Researchers who wish to use machine learning for business or scientific research projects.
Data Scientist: Building machine learning models, analyzing data, and offering insights to help with decision-making are all tasks data scientists perform.
Machine Learning Engineer: System scaling, algorithm optimization, and designing and implementing machine learning models in practical applications.
Data analyst: Gathering, analyzing, and interpreting data to use machine learning insights to assist businesses in making well-informed decisions.
AI Engineer: Developing algorithms, automating tasks with machine learning models, and working on artificial intelligence initiatives.
Company Intelligence Analyst: Applying machine learning to improve prediction capabilities and produce meaningful insights from company data.
Data scientists and machine learning specialists have excellent employment prospects in the United States and Canada. Openings are available in areas such as government, e-commerce, healthcare, finance, and technology. In the rapidly changing field of data science, these positions provide excellent pay and substantial opportunities for career advancement.
- Agile Overview
- Agile Manifesto & Principles
- Agile vs. Traditional (Waterfall) Approaches
- Scrum, Kanban, XP – overview of frameworks
- Role of a Business Analyst in Agile BA responsibilities in Agile teams
- Collaboration with Product Owner, Scrum Master, Dev Team
- Scrum Deep Dive
- Roles, Events, Artifacts
- Product Backlog, Sprint Planning, Review, and Retrospective
- Kanban Basics (WIP limits, continuous flow)
- Scaling Agile (SAFe, LeSS, Nexus) – intro level
- Tools included : Jira / Trello setup basics
- Gathering Requirements in Agile
- Just-in-time requirements
- Collaborative discovery
- User Stories & Acceptance Criteria
- INVEST criteria and Definition of Ready / Done
- Story Mapping & Backlog Grooming
- Jira / Confluence Training
- Backlog creation, story linking, boards
- Wireframing & Prototyping
- Tools: Balsamiq / Figma / Draw.io
- Facilitation Techniques
- Agile workshops (Story Mapping, Impact Mapping)
- Agile Planning Levels (Roadmap, Release, Sprint planning)
- Estimation Techniques (Planning Poker, T-Shirt sizing)
- Prioritization Methods (MoSCoW, Kano Model, WSJF)
- Performance Metrics (Velocity, Burnup/Burndown charts , Lead/Cycle Time)
- BA Contribution Metrics (Requirements churn, value tracking)
- How to create and maintain Dashboards in Jira
- Case Study: Analyse sprint reports & suggest improvements
- Stakeholder Management
- Effective Communication
- Facilitation & Conflict Resolution
- Agile Project Simulation End-to-end product backlog creation
- Resume Building for Agile BA
- Mock Interviews & Job Placement
This course is ideal for aspiring Business Analysts, working professionals transitioning to Agile roles, Product Owners, Project Managers, and anyone interested in Agile practices and business analysis.
No technical background is required. The course is designed to help non-technical individuals understand Agile concepts and tools in a practical, easy-to-follow manner.
The duration for Agile Business Analyst training is 2 months (8 weeks), with sessions held 2 times per week (either during week or weekends), including theory, hands-on practice and project work.
Yes, upon successful completion, you’ll receive a Certificate of Completion from Checkmate IT Tech. We also guide you on pursuing global certifications like IIBA-AAC and ICAgile-BA.
Absolutely! The course includes hands-on exercises, case studies, and a capstone project to simulate real Agile environments.
We offer online training classes to promote easy access to all candidates. Recordings are also made available for revision or if you miss a session.
Yes. We provide resume reviews, mock interviews, LinkedIn optimization, and guidance on job portals to help boost your chances in the job market.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
https://checkmateittech.com/
Email info@checkmateittech.com OR Call Us +1-347-4082054
- Submit Form
Job opportunities in USA and Canada
Data Scientist: Building machine learning models, analyzing data, and offering insights to help with decision-making are all tasks data scientists perform.
Machine Learning Engineer: System scaling, algorithm optimization, and designing and implementing machine learning models in practical applications.
Data analyst: Gathering, analyzing, and interpreting data to use machine learning insights to assist businesses in making well-informed decisions.
AI Engineer: Developing algorithms, automating tasks with machine learning models, and working on artificial intelligence initiatives.
Company Intelligence Analyst: Applying machine learning to improve prediction capabilities and produce meaningful insights from company data.
Data scientists and machine learning specialists have excellent employment prospects in the United States and Canada. Openings are available in areas such as government, e-commerce, healthcare, finance, and technology. In the rapidly changing field of data science, these positions provide excellent pay and substantial opportunities for career advancement.
