FTI115 - Data Science Foundations I
Course Overview
Cloud Computing, Generative AI, & Dashboards
This course focuses on cloud computing for cost-effective, scalable data processing. You’ll master technical components like PySpark to integrate Python, SQL, and Spark for handling structured and semi-structured data. Using libraries such as Numpy, Pandas, and PySpark, you’ll work with big data and create stunning visualizations with Python libraries like Seaborn. The course also explores advanced data analysis using generative AI and interactive dashboards, culminating in a project that brings big data to life through visualizations.
What you’ll learn:
- Create a dashboard using data science methodologies with industry standard tool(s)
- Model exploratory data analysis with tools for multiple data sets. (SQL and SQL table relations)
- Utilize programming techniques to process large data samples with (PySpark and Big Data)
Inferential Statistics
This course teaches statistical inference with Python, covering probability distributions, confidence intervals, and hypothesis testing. You’ll apply these techniques to analyze proportions, means, categorical data, and multivariate datasets. The course concludes with a final project where you’ll showcase your ability to analyze a multivariate dataset using various statistical inference methods.
What you’ll learn:
- Integrate statistical inference of data using the technical programming
- Implement methodologies for statistical inference
- Utilize mathematics, statistics, & probability for data science methodologies to derive insights
Regression
This course teaches regression techniques for analyzing real-world datasets. You’ll master linear and multiple linear regression, learning diagnostics, model evaluation, and advanced techniques like transformations, interactions, and regularization methods such as Lasso and Ridge. The course concludes with a project where you’ll build and interpret a multiple linear regression model.
What you’ll learn:
- Perform logistic regression with data sets using programming techniques, lasso and ridge.
- Compare statistical results for different types of regression with data sets, linear, transformations of linear, and multiple linear regressions
- Utilize mathematics, statistics, & probability for data science methodologies to derive insights