项目实训营
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获得数据科学项目经验

Tigerair 数据科学项目实训营

数据科学专家指导项目,8 周获得数据科学项目经验

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feature增加项目经验
feature数据科学实践
feature导师每周辅导
feature全面的技术栈
feature增加项目经验
feature数据科学实践
feature导师每周辅导
feature全面的技术栈

Tigerair 数据科学项目实训营亮点

starTigerair 的真实案例研究
starDS 行业专家指导
star 全面数据科学技能培养
star解决航空公司的核心问题
star团队合作
star扩展职业网络

为什么选择 Tigerair 数据科学项目实训营

参与这个实训营,将获得 Tigerair 丰富数据集的实践经验,挖掘可能彻底改变乘客体验的优化方案。它是您在数据科学职业生涯的垫脚石。您将发展在各行各业都需求的技能,为未来的挑战做好准备。 ...

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CAREER SERVICES

通过我们帮你获得理想工作

有机会参加Career Coaching Bootcamp,2个月找工作陪伴,为面试做好准备,并与招聘人员建立联系。

100+
Hiring partners
85%
Employment rate
5,000+
Offers

导师团队

Saisai Ma
Senior Data Scientist

Saisai was awarded a Ph.D. degree in Computer Science (Causa ...

价格选项

1
Upfront Early Bird
33%off

$1999

$2999

课程知识点

数据科学Kaggle实战班

数据科学Kaggle实战班

Machine Learning

Machine Learning

Data Wrangling

Data Wrangling

MySQL

MySQL

R

R

Kaggle

Kaggle

Web Crawler

Web Crawler

Data Visualisation

Data Visualisation

Spark

Spark

Quantitative Analysis

Quantitative Analysis

Deep Learning

Deep Learning

NumPy

NumPy

Kafka

Kafka

Spatiotemporal Data Analysis

Spatiotemporal Data Analysis

Python

Python

Matplotlib

Matplotlib

Exploratory Data Analysis

Exploratory Data Analysis

谁应该参加我们的Tigerair 数据科学项目实训营?

starDS graduate
starDS equivalent

项目介绍

In the highly competitive airline industry, Tigerair has always been committed to providing its passengers with an exceptional flying experience. However, with the ever-changing market dynamics and the increasing diversity in passenger needs, Tigerair is faced with the challenge of enhancing passenger satisfaction, strengthening brand loyalty, and increasing market share. Despite various measures already in place, the company recognizes that to maintain a leading position in the fierce market competition, it needs to deeply understand the satisfaction levels of its passengers and the factors that influence them, and based on these insights, implement effective strategies.

项目内容

Launch your data science career in just 8 weeks. This comprehensive project delves into core machine learning principles and practical applications, encompassing key areas such as problem formulation, exploratory data analysis (EDA), preprocessing, feature engineering, and the development and evaluation of machine learning models. You will hone your development skills to execute each phase of the data science lifecycle, utilizing Python and Jupyter Notebook.

The curriculum also covers advanced techniques, data/ML pipeline, specifically focusing on the widely utilized pipeline employed by major artificial intelligence companies.

Beyond merely introducing these concepts, the project is designed to bolster your practical skills and real-world experience.

Guiding you through this immersive learning experience is Dr. Saisai Ma, the Data Science Director (acting) at the Australian Taxation Office (ATO). Dr. Ma is dedicated to providing hands-on guidance to help you forge your path in the data science field.

导师介绍

Saisai was awarded a Ph.D. degree in Computer Science (Causal Data Mining) at the University of South Australia. Currently he is an Assistant Director Data Scientist at Australian Taxation Office. He has around 10 years’ experience on data science projects, in both academic and industrial domains.

技术栈

1. Data Analysis & Preprocessing

  • Programming Languages: Python and Jupyter notebook
  • Libraries: Pandas for data manipulation, NumPy for numerical operations
  • Tech Stack: Handling missing values, dealing with duplicate records, managing data types, addressing inconsistencies

2. Data Visualization

  • Libraries: Matplotlib and Seaborn for Python
  • Techniques: Exploratory data analysis (EDA), visualizing variation and covariation among variables

3. Feature Engineering & Selection

  • Libraries: scikit-learn and feature-engine for Python
  • Techniques: Creating new features, selecting significant features, dimensionality reduction

4. Machine Learning

  • Concepts: Basic to advanced ML concepts, model selection
  • Libraries: scikit-learn for Python
  • Techniques: Implementing various ML models, bias-variance tradeoff understanding

5. Hyperparameter Tuning

  • Techniques: Grid search, random search, Bayesian optimization
  • Libraries: scikit-learn's GridSearchCV and RandomizedSearchCV for Python

6. Model Evaluation

  • Techniques: Cross-validation, understanding and applying various metrics (e.g., accuracy, precision, recall, F1 score)

7. Pipelines

  • Libraries: scikit-learn's Pipeline for Python
  • Components: Preprocessing pipeline, feature engineering pipeline, training pipeline, scoring pipeline

8. Understanding & Framing DS Problems

  • Skills: Translating business problems into data science problems, identifying key objectives and metrics

项目目标

Tigerair aims to invite students and professionals from different backgrounds to a data science boot camp, utilizing the company's extensive collection of flight and passenger satisfaction survey data to analyze and identify the key factors affecting passenger satisfaction. The goal of the project is to uncover the relationship between passenger satisfaction and various aspects of the service, and to predict which factors most significantly impact passenger satisfaction. Through this project, Tigerair hopes to:

  1. Identify Key Satisfaction Factors: Determine which service attributes (e.g., ease of online booking, seat comfort, in-flight entertainment) have the most significant impact on passenger satisfaction.
  2. Optimize Service Experience: Formulate specific improvement measures based on the analysis results to enhance the overall satisfaction of passengers with Tigerair flights.
  3. Provide Personalized Services: Develop more personalized services by analyzing the needs of different passenger groups to meet their specific requirements.
  4. Improve Operational Efficiency: Identify strategies to reduce delays and improve punctuality by analyzing the relationship between flight delays and passenger satisfaction, thereby enhancing both passenger satisfaction and company efficiency.

Customer satisfaction analysis/prediction is one of the most common problems in all kinds of companies, to better understand customer needs and improve service quality and customer stickiness. If you are seeking employment with a company that manufactures products or offers services, then this project is indispensable for you.

案例展示

指导方式

Groups of 4 people will receive 3 hours of project coaching per week.
The project will be developed over a period of 8 weeks, with each week being a different task, following the pace of the tutor throughout the project.

通过项目实训营,你将能够

  • 掌握数据科学全流程,从数据预处理到模型部署
  • 解锁高级技术与策略,提高解决实际问题的能力
  • 与行业专家进行互动,获得实际工作中的宝贵经验
  • 提升个人简历,为未来职业生涯开辟新道路

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