Fractal Data Science

Explain the lifecycle of DS project and role of structured thinking in DS project. Define a problem statement using the SMART framework. Construct MECE issue tree to break down business problems. Explain the role of human-centered design during solutioning of business problems.

Learn to extract relevant data from databases efficiently, build powerful SQL queries, analyze and manage large datasets, and derive inferences from complex relational databases.

Master the selection and use of relevant charts for appropriate data problems, connect with diverse data formats, communicate key insights from business problems using Reports and Dashboards, and create advanced visualizations using DAX.

Understand the significance of Python in data science and its applications, apply Python for data manipulation and analysis, create informative data visualizations, and develop a comprehensive data preparation workflow for machine learning.

Gain insights into human behavior when given data to calculate results, understand how perceptions, prejudices, and biases affect human decision making, and learn to collaborate effectively with AI to overcome biases.

Construct machine learning models, apply appropriate metrics to assess model performance, develop regression and tree-based models, and analyze business problems where unsupervised machine learning models could be used.

Employ regularization techniques for enhanced model performance, leverage ensemble methods to improve predictive accuracy, implement hyperparameter tuning and feature engineering, and combine diverse models for superior predictions.

Understand the importance of data storytelling, apply various visualization techniques, and craft a compelling narrative to create captivating data stories.