Level 1: Be Aware of Data Science

In today's data-driven world, understanding how to extract valuable information is essential. This introductory course demystifies data science, making it accessible to everyone. You'll gain an intuitive understanding of how data science models create value, even if you're a complete beginner.

 

By the end of this course, you will be able to:

• Understand how data science turns data into valuable information.

• Understand what cognitive biases are and how data science helps us fight them.

• Identify and avoid spurious correlations.

• Conduct data-driven business experiments to verify the impact of changes.

• Address the challenges posed by big data and unpurposed data collections.

• Discover the role and skills of data scientists and what it takes to become one.

• Create scientific models through experimentation and observation.

• Utilize basic data science methods such as descriptive statistics and correlation measures.

• Develop a strong intuition about how machine learning models work.

• Understand how data science models simplify human decision-making.

 

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Level 2: Associate of Data Science

This course provides a comprehensive, non-technical overview of data science projects, from initial ideas to deploying models in real-world settings. It is designed for business analysts, technical experts from related fields (e.g., databases, cloud engineering), initiators of data science projects, and anyone new to data science seeking a solid foundation.

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In this course, you'll gain the skills to become a valuable contributor to data science projects. You'll be able to:

• Identify and prioritize promising project ideas

• Formulate actionable hypotheses and solution plans

• Help integrate projects into the organization's broader goals

 

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Level 3: Baseline Data Science

In this hands-on course, you will learn to apply essential data science techniques to structured data using Python. You will learn how to preprocess, explore, visualize data and build basic machine learning models. This course is ideal for aspiring junior data analysts or junior data scientists, professionals working with data, managers of data science teams, and anyone looking to learn Python for data science applications. 

 

By the end of this course, you will:

• Know how to perform advanced data analysis in Python, surpassing basic Excel capabilities.

• Be able to preprocess data using techniques like data merging, aggregation, transformation, and handling missing values.

• Know how to visualize data with libraries like Matplotlib and Seaborn, creating univariate, bivariate, and multivariate visualizations.

• Know how to apply feature engineering and feature selection techniques for predictive modeling.

• Build and evaluate basic machine learning models from structured data.

• Implement supervised learning techniques such as linear regression, decision trees, and logistic regression.

• Apply unsupervised learning techniques including clustering, dimensionality reduction, and anomaly detection.

• Have knowledge about advanced topics like neural networks, computer vision, and natural language processing.

 

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Level 4: Modeler of Data Science

This advanced course takes you beyond the basics of machine learning. You will learn about specialized techniques to handle complex scenarios, unusual datasets, and the nuances of model selection and evaluation. It is designed for junior and professional data scientists looking to solidify their modeling expertise. 

 

By the end of this course, you will have the ability to:

• Take full ownership of predictive modeling projects.

• Confidently select and apply the most suitable machine learning techniques for diverse use cases.

• Utilize advanced techniques for processing structured data and handling imbalanced datasets.

• Apply powerful ensemble methods such as boosting and stacking.

• Automate machine learning pipelines using scikit-learn.

• Implement uplift modeling and advanced model interpretation techniques.

• Use specialized libraries like Woodwork, Featuretools, and EvalML for complex datasets.

• Create and train neural networks using the Keras library.

• Use autoencoders and train convolutional neural networks for computer vision tasks.

• Utilize natural language processing techniques including text embeddings and transfer learning.

• Tackle time series analysis and forecasting with specialized machine learning libraries.

• Deploy machine learning models utilizing best practices and cloud infrastructure.

 

5. Data Science for Product Owners & Leaders

This dynamic crash course equips product owners and leaders with the practical knowledge and skills needed to effectively navigate and lead data science initiatives. Tailored for those overseeing and participating in data-driven projects, it provides a comprehensive overview of data science concepts, methodologies, and applications. This course is ideal for product owners responsible for data-driven products, leaders seeking to integrate data science into their teams, and anyone involved in overseeing data science projects. No prior experience is required, just a motivation to learn new concepts.

 

Upon completing this course, you will be equipped to:

• Understand data science fundamentals, terminology, and key approaches.

• Foster a data-driven organizational culture and successfully adopt data science initiatives.

• Critically evaluate data, interpret visualizations, and identify patterns and biases.

• Apply both basic (descriptive & exploratory) and advanced (inferential & predictive) data science methods.

• Generate valuable data science project ideas, formulate actionable hypotheses, and frame viable solutions.

• Calculate the business value of data science projects, set KPIs, and ensure cost-effective implementation.

• Navigate the challenges and considerations of deploying and maintaining machine learning models in production.

• Understand applications of Natural Language Processing (NLP) and Visual Recognition (VR) in projects.