AI Academy

Python for Data Science, AI & Development

Instructor: Joseph Santarcangelo Duration: 7 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

Python Project for Data Science

Instructor: Azim Hirjani , Joseph Santarcangelo Duration: 2 hours to complete
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

Data Analysis with Python

Instructor: Joseph Santarcangelo Duration: 4 hours to complete
Objective 1 Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning
Objective 2 Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights
Objective 3 Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines
Objective 4 Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven decision-making
Data Import/Export
Exploratory Data Analysis
Data Pipelines
Supervised Learning
Regression Analysis
NumPy
Data-Driven Decision-Making
Pandas (Python Package)
Data Cleansing
Matplotlib
Data analysis
Scikit Learn (Machine Learning Library)
Data Manipulation
Feature Engineering
Predictive Modeling
Statistical Modeling
Data Wrangling
Descriptive Statistics

Data Visualization with Python

Instructor: Saishruthi Swaminathan , Dr. Pooja Duration: Approx. 20 hours
Objective 1 Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story
Objective 2 Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble
Objective 3 Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps
Objective 4 Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library
Dashboard
Data Presentation
Box Plots
Geospatial Information and Technology
Data Visualization
Scatter Plots
Matplotlib
Heat Maps
Plotly
Pandas (Python Package)
Interactive Data Visualization
Data Visualization Software
Data analysis
Seaborn
Histogram

Applied Data Science Capstone

Instructor: Yan Luo , Joseph Santarcangelo Duration: 6 hours to complete
Objective 1 Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders
Objective 2 Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
Objective 3 Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
Objective 4 Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model
Data analysis
Data-Driven Decision-Making
Data Collection
Statistical Modeling
Web Scraping
Data Wrangling
Predictive Modeling
Machine Learning Methods
Plotly
Data Presentation
Pandas (Python Package)
Data Science
Exploratory Data Analysis