Application of Data Science for Data Scientists | AIML TM

About 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.