Application of Data Science for Data Scientists | AIML TM

Mastering Real-World Data Science Applications and Techniques for Advanced Problem Solving


What you'll learn
Students will learn the fundamentals of Data Science and its applications across various industries.
Students will explore key algorithms and perform exploratory data analysis (EDA).
Students will learn about the roles, skills, and responsibilities of a Data Scientist.
Students will dive into advanced techniques and practical applications used by Data Scientists.
Students will learn the stages of the Data Science process, from problem definition to data collection.
Students will explore model building, evaluation, deployment, and post-deployment strategies.
Students will apply Data Science concepts to solve a real-world case study from start to finish.
Students will learn how to ensure data quality and make their models interpretable.
Students will explore the ethical considerations and responsibilities involved in Data Science.
Students will examine the ethical dilemmas surrounding data collection, privacy, and bias.
Students will understand how to manage and execute a Data Science project from planning to reporting.
Students will learn techniques for selecting and engineering relevant features to improve model performance.
Students will explore how to implement and scale Data Science solutions in real-world applications.
Students will master data wrangling and manipulation techniques to efficiently handle large datasets.



Requirements
Anyone can learn this class it is very simple.

Description
1. Introduction to Data Science
Overview of what Data Science is
Importance and applications in various industries
Key components: Data, Algorithms, and Interpretation
Tools and software commonly used in Data Science (e.g., Python, R)
2. Data Science Session Part 2
Deeper dive into fundamental concepts
Key algorithms and how they work
Exploratory Data Analysis (EDA) techniques
Practical exercises: Building first simple models
3. Data Science Vs Traditional Analysis
Differences between traditional statistical analysis and modern Data Science
Advantages of using Data Science approaches
Practical examples comparing both approaches
4. Data Scientist Part 1
Role of a Data Scientist: Core skills and responsibilities
Key techniques a Data Scientist uses (e.g., machine learning, data mining)
Introduction to model building and validation
5. Data Scientist Part 2
Advanced techniques for Data Scientists
Working with Big Data and cloud computing
Building predictive models with real-world datasets
6. Data Science Process Overview
Steps of the Data Science process: Problem definition, data collection, preprocessing
Best practices in the initial phases of a Data Science project
Examples from industry: Setting up successful projects
7. Data Science Process Overview Part 2
Model building, evaluation, and interpretation
Deployment of Data Science models into production
Post-deployment monitoring and iteration
8. Data Science in Practice - Case Study
Hands-on case study demonstrating the Data Science process
Problem-solving with real-world data
Step-by-step guidance from data collection to model interpretation
9. Data Science in Practice - Case Study: Data Quality & Model Interpretability
Importance of data quality and handling missing data
Techniques for ensuring model interpretability (e.g., LIME, SHAP)
How to address biases in your model
10. Introduction to Data Science Ethics
Importance of ethics in Data Science
Historical examples of unethical Data Science practices
Guidelines and frameworks for ethical decision-making in Data Science
11. Ethical Challenges in Data Collection and Curation
Challenges in ensuring ethical data collection (privacy concerns, data ownership)
Impact of biased or incomplete data
How to approach ethical dilemmas in practice
12. Data Science Project Lifecycle
Overview of a complete Data Science project lifecycle
Managing each phase: Planning, execution, and reporting
Team collaboration and version control best practices
13. Feature Engineering and Selection
Techniques for selecting the most relevant features
Dimensionality reduction techniques (e.g., PCA)
Practical examples of feature selection and its impact on model performance
14. Application - Working with Data Science
How to implement Data Science solutions in real-world applications
Case studies of successful applications (e.g., fraud detection, recommendation systems)
Discussion on the scalability and robustness of models
15. Application - Working with Data Science: Data Manipulation
Techniques for data wrangling and manipulation
Working with large datasets efficiently
Using libraries like Pandas, NumPy, and Dask for data manipulation
This framework covers key aspects and ensures a deep understanding of Data Science principles with practical applications.
Who this course is for:
Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.

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