Data Science With Python Training
Training in data science with Python gives people the knowledge to analyze, visualize, and comprehend complicated data using Python. This course covers Pandas, NumPy, Matplotlib, and other crucial Python libraries, along with machine learning strategies for resolving practical data problems. Participants will learn how to manage and work with big datasets, create predictive models, and get valuable insights from data using Python.
- 10+ Courses
- 30+ Projects
- 400 Hours
Data Science With Python Training is suitable for the following target audiences:
Aspiring Data Scientists: People who wish to use Python for statistical modelling, machine learning, and data analysis to pursue a career in data science.
Software developers: Developers want to add Python-based data analytics to their applications and broaden their data science skill set.
Business analysts: Analysts who want to use Python to improve their data processing and analytical skills to make better business decisions.
Mathematicians and statisticians: Experts wishing to include Python in their predictive modelling and statistical analysis work.
IT and Data Professionals: People who wish to improve their handling and analysis skills in big data and are employed in database, data management, or IT roles.
Data Scientist: Analyzing and interpreting big datasets using statistical models and machine learning.
Data Analyst: Data analysis is the process of gathering, sanitising, and analysing data to assist companies in making informed decisions.
Machine Learning Engineer: Machine learning engineers create and execute machine learning algorithms to enhance business procedures and results.
Business Intelligence Analyst: Business intelligence analysts use Python to gather data and create visuals that aid strategic decision-making.
Data Engineer: Data engineering involves creating and managing systems that enable data scientists to process and analyse data effectively.
Data workers with Python skills are in high demand across healthcare, banking, retail, and technology industries. These fields offer competitive pay and opportunities for advancement as data-driven decision-making becomes increasingly crucial.
- 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: Analyzing and interpreting big datasets using statistical models and machine learning.
Data Analyst: Data analysis is the process of gathering, sanitising, and analysing data to assist companies in making informed decisions.
Machine Learning Engineer: Machine learning engineers create and execute machine learning algorithms to enhance business procedures and results.
Business Intelligence Analyst: Business intelligence analysts use Python to gather data and create visuals that aid strategic decision-making.
Data Engineer: Data engineering involves creating and managing systems that enable data scientists to process and analyse data effectively.
Data workers with Python skills are in high demand across healthcare, banking, retail, and technology industries. These fields offer competitive pay and opportunities for advancement as data-driven decision-making becomes increasingly crucial.
