AI Academy

The Data Science Profession – Student View

Instructor: Robert Zimmer Duration: 4 hours to complete 3 weeks at 1 hour a week
Objective 1 In this course you learn how Data Science is applied in the real world, what we mean by data, and what we mean by machine learning.
Machine Learning
Machine Learning Algorithms
Data Science
Data analysis
Applied Machine Learning
Unsupervised Learning

What is Data Science?

Instructor: Rav Ahuja , Alex Aklson Duration: 4 hours to complete
Objective 1 Define data science and its importance in today’s data-driven world.
Objective 2 Describe the various paths that can lead to a career in data science.
Objective 3 Summarize  advice given by seasoned data science professionals to data scientists who are just starting out.
Objective 4 Explain why data science is considered the most in-demand job in the 21st century.
Digital Transformation
Big Data
Data analysis
Data Mining
Data-Driven Decision-Making
Cloud Computing
Machine Learning
Deep Learning
Artificial Intelligence
Data Science
Data Literacy

Tools for Data Science

Instructor: Aije Egwaikhide , Svetlana Levitan , Romeo Kienzler Duration: 1 week at 10 hours a week
Objective 1 Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Objective 2 Utilize languages commonly used by data scientists like Python, R, and SQL
Objective 3 Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
Objective 4 Create and manage source code for data science using Git repositories and GitHub.
Data Visualization Software
Version Control
Jupyter
Data Science
Git (Version Control System)
Application Programming Interface (API)
Other Programming Languages
Query Languages
Machine Learning
Big Data
GitHub
Cloud Computing
Statistical Programming
R Programming

Problems, Algorithms and Flowcharts

Instructor: Robert Zimmer Duration: 8 hours to complete 3 weeks at 2 hours a week
Objective 1 In this course you will learn the history of algorithms, discretisation and pseudocode and Euclidean algorithm in pseudocode.
Program Development
Pseudocode
Computer Science
Algorithms
Data Structures
Computational Thinking

Python for Data Science, AI & Development

Instructor: Joseph Santarcangelo Duration: 4 hours to complete
Objective 1 Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.
Objective 2 Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.
Objective 3 Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.
Objective 4 Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.
Computer Programming
Python Programming
Data Structures
NumPy
Programming Principles
Application Programming Interface (API)
Jupyter
Data Literacy
Pandas (Python Package)
Data Manipulation
Data Import/Export
Restful API
Scripting
Web Scraping
File Management
Object Oriented Programming (OOP)
Data analysis

Statistics and Clustering in Python

Instructor: Robert Zimmer Duration: 2 weeks to complete at 10 hours a week
Objective 1 In this course you will engage in a variety of mathematical and programming exercises while completing a data clustering project.
Matplotlib
Pandas (Python Package)
Statistical Analysis
Probability & Statistics
Unsupervised Learning
Statistics
Data Science
Jupyter
Data Manipulation
NumPy
Descriptive Statistics
Python Programming
Data Visualization Software
Machine Learning Algorithms
Data analysis

Data Science Project Capstone: Predicting Bicycle Rental

Instructor: Robert Zimmer Duration: 7 hours to complete
Objective 1 In this course you will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day.
Regression Analysis
Statistical Modeling
Data Science
Data-Driven Decision-Making
Data Collection
Correlation Analysis
Forecasting
Exploratory Data Analysis
Time Series Analysis and Forecasting
Predictive Modeling
Data analysis

Python Project for Data Science

Instructor: Azim Hirjani , Joseph Santarcangelo Duration: Approx. 8 hours
Objective 1 Play the role of a Data Scientist / Data Analyst working on a real project.
Objective 2 Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.
Objective 3 Apply Python fundamentals, Python data structures, and working with data in Python.
Objective 4 Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.
Data Manipulation
Data Science
Python Programming
Web Scraping
Data Visualization Software
Data Collection
Dashboard
Pandas (Python Package)
Matplotlib
Data Processing
Jupyter
Data analysis