Learn Python, Excel,Deep Learning, Power BI, SQL, Artificial Intelligence,Business Statistics, Capstone Projects
What you will learn
Build a portfolio of data science projects to apply for jobs in the industry
Learn how to create pie, bar, line, area, histogram, scatter, regression, and combo charts
Create your own neural networks and understand how to use them to perform deep learning
Understand and apply data visualisation techniques to explore large datasets
Use data science algorithms to analyse data in real life projects such as Mushroom classification and image recognition
Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more
Add-On Information:
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- Master the End-to-End Data Lifecycle: Go beyond isolated skills to gain a comprehensive understanding of how data flows from ingestion and cleaning through analysis, modeling, and finally, deployment for real-world impact.
- Develop Strategic Data Thinking: Cultivate the ability to frame business problems as data science challenges, identifying key metrics and selecting appropriate methodologies to drive actionable insights.
- Build Robust Data Pipelines: Learn to construct efficient and scalable data processing workflows, ensuring data quality and reliability for downstream analysis and model building.
- Implement Advanced Statistical Modeling: Gain proficiency in applying statistical principles to interpret complex datasets, identify patterns, and quantify uncertainty in your findings.
- Unlock the Power of Generative AI: Explore foundational concepts and practical applications of generative AI models, enabling you to create novel content and solve complex problems through automated generation.
- Deploy Models into Production: Understand the principles and tools required to take your trained machine learning models from the development environment to operational use, making them accessible for business applications.
- Drive Business Decisions with Data Storytelling: Effectively communicate your analytical findings and model results to diverse audiences, translating technical jargon into compelling narratives that influence strategic choices.
- Navigate the Modern Data Science Ecosystem: Become familiar with the landscape of cutting-edge data science tools and frameworks, enabling you to adapt to evolving industry standards and technologies.
- Engineer Data for Optimal Model Performance: Learn techniques for feature engineering and selection, transforming raw data into formats that maximize the accuracy and efficiency of your machine learning models.
- Interpret Model Behavior and Explainability: Develop the capacity to understand why your models make certain predictions, fostering trust and enabling responsible AI deployment.
- Collaborate Effectively in Data Teams: Gain insights into best practices for working with other data professionals, fostering efficient collaboration and knowledge sharing within data-driven organizations.
- PROS:
- Comprehensive Skill Acquisition: Covers a broad spectrum of essential data science and machine learning topics, providing a well-rounded skillset.
- Practical, Project-Based Learning: Emphasizes hands-on application through portfolio-building projects, enhancing job readiness.
- Exposure to Modern Tools: Integrates industry-standard libraries and frameworks, ensuring relevance in the current job market.
- CONS:
- Intensive Learning Curve: The breadth of topics may require significant dedication and self-discipline due to the fast-paced nature of the bootcamp.
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