Introduction to Data Science and Analytics
This first course in this series will introduce you to fundamentals of data and its analysis, statistics and machine learning basics.
Introduction to Mathematics for Data Science
This course will cover the foundational mathematics for data science. You will learn mathematical concepts such as standard deviation, confidence intervals, derivates and integrals, correlations, and linear algebra.
Analyzing and Visualizing Data with Excel
Learn to analyze and visualize data using Excel, one of the leading data tools. In this course, you’ll learn to import and merge data sets and prepare data for analysis. This course will also cover essential Excel functions and the DAX calculation engine.
Microsoft Power BI Data Analyst – PL-300
You will dive deeper into using Power BI to analyze and visualize data. Based on twelve modules, you’ll cover desktop data transformation, desktop modeling, desktop visualization, Power BI service, connecting and collaborating with Excel, direct connectivity, develop API and mobile application.
Project 1: Exploratory Data Analysis
For the first project, students are going to develop an exploratory data analysis report using excel or Power BI. The overall goal of this project is to produce a report as a data scientist.
Introduction to Python for Data Science
Beginning with Python fundamentals, this course will cover the basics of Python syntax and functions. You will also cover NumPy, plotting with Matplotlib, Seaborn, and Pandas.
Essential Math for Machine Learning: Python Edition
Designed to support students in machine learning coursework, this course covers essential math concepts, including neural networks, decision trees, clustering and logistic regression models.
Data Science Research Methods: Python Edition
This course will focus on essential research methods, specifically for use with Python. The coursework will cover data gathering and scraping, analysis and documentation, how to formulate a hypothesis, developing a research plan, and analyzing conclusions.
Application of Machine Learning: Python Implementation
In this course, you will learn about the application of machine learning and Python concepts covered in previous coursework. You will study regression, model tuning, clustering, logistic regression, neural network and decision tree implementation.
Project 2: Machine Learning
For this project students will be applying their knowledge of supervised machine learning to build a classifier. This project will fit well in students portfolio – prediction is a highly sought-after skill.
Querying Data with SQL
This course will cover querying tables with SELECT, along with use functions, subqueries, table expressions, data modification, and error handling.
Data Presentation and Visualization
To prepare students to best present data findings, this course will focus on data visualization, communication, and display skills.
Project 3: Data Querying and Cleaning
In this project students are going to be applying their SQL and Python skills to explore baseball statistics. In the process students will practice a vital data science skill – the ability to research new tools and read documentation.
Ethics and Law in Data and Analytics
During this course, you will learn about ethics in data science, GDPR compliance, law, morals and ethics in machine learning, and parameters around personal data use.
Analytics Storytelling for Impact
In this course, you will learn how to convey the story your data presents.
Capstone Project: Covid-19
The objective of this capstone project is to develop a ML model to predict if a patient of a confirmed COVID-19 case will require admission to the ICU.