comprehensive curriculum
our data science curriculum offers a mix of academic depth and real-world know-how.
introduction to data science
this first course introduces key concepts and provides an overview of data science, its applications across industries and addresses common ethical challenges.
Introduction to the data analytics workflow
Introduction to common tools used in data science and discussion of different roles within the data analytics ecosystem (data analyst, data scientist, data engineers)
Discussion of common ethical challenges in handling and interpreting data
introduction to python for data manipulation and analysis
this course introduces Python programming, data structures, the Pandas library for data manipulation, and exploratory data analysis techniques, culminating in an applied project analyzing a dataset with Pandas.
Basics of Python programming: Variables, data types, loops, and conditionals
Introduction to data structures: Lists, dictionaries, and tuples
Introduction to Pandas library for data manipulation
Introduction to Exploratory Data Analysis (EDA) techniques
data visualization with matplotlib and seaborn
this course covers data visualization principles, storytelling with visualizations, and creating static and interactive visualizations using Matplotlib and Seaborn, culminating in an applied project analyzing and visualizing a dataset with Pandas and Matplotlib.
Storytelling with data visualization
Introduction to data visualization principles
Creating static and interactive data visualizations using Matplotlib and Seaborn
advanced data visualization with powerBI
this course focuses on advanced data visualization and dashboard design using PowerBI, including creating interactive visualizations and preparing Excel data for analysis in PowerBI.
Dashboard design and interactivity
Creating advanced data visualizations using PowerBI
Preparing Excel data for analysis in Power BI
statistical concepts for data analysis
this course covers statistical concepts for data analysis, including descriptive and inferential statistics, hypothesis testing, and confidence intervals, with an applied project to conduct statistical analysis and derive insights from an industry dataset.
Basics of statistics: Descriptive and inferential statistics
Hypothesis testing and confidence intervals
machine learning fundamentals
this course introduces machine learning concepts, covering supervised and unsupervised learning algorithms, with an applied project to build a simple predictive model using scikit-learn.
Introduction to machine learning concepts
Supervised and unsupervised learning algorithms
database management and SQL
this course covers database management and SQL fundamentals for data retrieval and manipulation, culminating in an applied project to analyze an industry dataset using MySQL.
Introduction to data management
SQL fundamentals for data retrieval and manipulation
Analyzing data within a database using SQL and Python
capstone project preparation
the capstone project involves identifying a real-world problem, developing a project proposal, sourcing data, conducting analysis, making recommendations, and applying skills learned in the program.
Identify a real-world problem
Develop a project proposal and source data
Conduct analysis and make recommendations
generative AI tools and platforms for data analysis (bonus)
this bonus course focuses on using generative AI tools like ChatGPT in Python for data pre-processing, cleaning, and conducting exploratory data analysis (EDA).
Pre-processing and cleaning data using ChatGPT in Python
Running Exploratory Data Analysis (EDA) using ChatGPT