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Week 1-2
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.
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Week 3-4
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.
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Week 5
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.
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Week 6
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.
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.
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Week 7-8
Project 1: Exploratory Data Analysis
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Week 9-10
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.
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Week 11-12
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.
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Week 13-14
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.
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Week 15-16
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.
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Week 17-18
Project 2: Machine Learning
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Week 19-20
Querying Data with SQL
This course will cover querying tables with SELECT, along with use functions, subqueries, table expressions, data modification, and error handling.
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Week 21-22
Data Presentation and Visualization
To prepare students to best present data findings, this course will focus on data visualization, communication, and display skills.
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Week 23-24
Project 3: Data Querying and Cleaning
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Week 25
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.
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Week 26
Analytics Storytelling for Impact
In this course, you will learn how to convey the story your data presents.
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Week 27-28
Capstone Project: Covid-19