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Mastering Real-World Data Science Applications and Techniques for Advanced Problem Solving
Updated on Sep, 2026
IT and Software , Other IT and Software, Python
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Because it is so big, this course exposes aspirant and practicing data scientists to high-level skills and their applications in practice. Following this streamlined approach, participants will master how to deal with the data, building models for prediction, and deploying the solution to a real case. 1. Introduction to Data Science Data Science Concepts and Key Components: Data, Algorithms, Interpretation. Importance and applicability across industries . Tools: Introduction to Python, R. 2. Data Science Session Part 2 Algorithms and what they do Application of EDA techniques and realizing to make simple models 3. Data Science vs Traditional Analysis Statistics as they were before and Data Science as it is today Examples how Data Science can outdo old-fashioned ways in a few respects 4. Data Scientist Part 1 What is a Data Scientist and how we work? Key techniques: Machine learning, data mining Little introduction to creating simple models. 5. Data Scientist Part 2 Data Science techniques and tools Big Data, cloud computing and predictive modeling. 6. Overview of Data Science Process Detailed description of the Data Science process from problem definition to data collection. Real best practice examples from industry. 7. Overview of Data Science Process Part 2 Learn how to build models, evaluate them and get them live in production. 8. Data Science in Action - Case Study Real-world Application Case Study: Inclusive Workflow - Data Collection to Model Interpretation in Data Science. 9. Data Science at Work: Data Quality & Model Interpretability Dealing with Missing Data & Downsampling the Data Tune your Model to be Interpretive & Invariant 10. Introduction to Data Science Ethics Data Science Ethical Issues Frameworks that might be helpful in responsible decisions for Data-Driven Projects 11. Ethical Issues during Data Collection and Curation How to Avoid Violations of Privacy, Ownership, and Bias in Datasets. 12. Data Science Project Life Cycle Project planning for the whole life cycle, execution and reporting 13. Feature Engineering and Selection Feature selection algorithms and techniques for dimensionality reduction impacting on the performance of the model 14. Application - Working with Data Science Applications of Data Science solutions; fraud detection and recommendation systems 15. Application - Working with Data Science: Data Wrangling Handling data wrangling and mass manipulation of large datasets with hands-on approaches in Pandas, NumPy, and Dask. The students at the end of the course will be very well equipped with the best tools, techniques, and expertise to become very effective data scientists who can contribute fruitfully to projects cut across multiple industries.
Core concepts in data science: deep foundational understanding of principles behind data science: analysis, algorithms and interpretation. Important data science tools and techniques: mastering work with very top tools and software, Python and R, SQL, and a set of libraries like Pandas, NumPy and Seaborn. Acquire hands-on practice in data science: Hands-on practice on the derivation in data preprocessing, feature engineering and model building with real datasets. Apply Advanced Machine Learning Methods: Methods of supervised and unsupervised learning which include regression, classification, clustering, and dimensionality reduction. Address Ethical and Practical Challenges: Know the importance of quality of data and interpretability of the model and good practices on ethical issues involving gathering and curating data. Data Science Programme: Manage Full Data Science Projects: Learning to manage the whole Data Science project lifecycle from the problem definition to deployment, as well as post-deployment monitoring. Real-world applications of data science will be explained through case studies and hands-on practice with real-world applications within the realms of finance, healthcare, and other areas. A Career Guide to a Data Scientist Know what you know not to become an effective practicing data scientist using large datasets to solve complex business problems.
Anyone can learn this class it is very simple.
Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.
Check out the detailed breakdown of what’s inside the course
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