Application of Data Science for Data Scientists | AIML TM

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.