AI Academy

What is Data Science?

Instructor: Rav Ahuja , Alex Aklson Duration: Approx. 11 hours
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: 4 hours to complete
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

Data Science Methodology

Instructor: Alex Aklson , Polong Lin Duration: 4 hours to complete
Objective 1 Describe what a data science methodology is and why data scientists need a methodology.
Objective 2 Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
Objective 3 Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
Objective 4 Determine appropriate data sources for your data science analysis methodology.
Data Modeling
Jupyter
Predictive Modeling
Data Science
Data Quality
Analytical Skills
Decision Tree Learning
Business Analysis
Stakeholder Engagement
Data Mining
Data Cleansing
Peer Review
User Feedback
Data Storytelling

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

Python Project for Data Science

Instructor: Azim Hirjani , Joseph Santarcangelo Duration: 1 hour 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

Databases and SQL for Data Science with Python

Instructor: Rav Ahuja , Hima Vasudevan Duration: 7 hours to complete
Objective 1 Analyze data within a database using SQL and Python.
Objective 2 Create a relational database and work with multiple tables using DDL commands.
Objective 3 Construct basic to intermediate level SQL queries using DML commands.
Objective 4 Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.
SQL
Relational Databases
Data analysis
Query Languages
Jupyter
Database Design
Data Manipulation
Databases
Pandas (Python Package)
Transaction Processing
Database Management
Stored Procedure

Data Analysis with Python

Instructor: Joseph Santarcangelo Duration: 7 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: 9 hours to complete
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

Machine Learning with Python

Instructor: Joseph Santarcangelo , Jeff Grossman Duration: 3 hours to complete
Objective 1 Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.
Objective 2 Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.
Objective 3 Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.
Objective 4 Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.
Applied Machine Learning
Decision Tree Learning
Machine Learning
Supervised Learning
Scikit Learn (Machine Learning Library)
Statistical Modeling
Regression Analysis
Classification And Regression Tree (CART)
Predictive Modeling
Dimensionality Reduction
Unsupervised Learning
Feature Engineering

Applied Data Science Capstone

Instructor: Yan Luo , Joseph Santarcangelo Duration: 9 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

Generative AI: Elevate Your Data Science Career

Instructor: Rav Ahuja , Abhishek Gagneja , Dr. Pooja Duration: 2 hours to complete
Objective 1 Leverage generative AI tools, like GPT 3.5, ChatCSV, and tomat.ai, available to Data Scientists for querying and preparing data
Objective 2 Examine real-world scenarios where generative AI can enhance data science workflows
Objective 3 Practice generative AI skills in hand-on labs and projects by generating and augmenting datasets for specific use cases
Objective 4 Apply generative AI techniques in the development and refinement of machine learning models
Predictive Modeling
Data Synthesis
Exploratory Data Analysis
Feature Engineering
Generative AI
Data Ethics
Natural language processing
Predictive Analytics
Data Presentation
Data Visualization Software
Data Modeling
Data Cleansing
Data analysis
Data Manipulation
Data Storytelling

Data Scientist Career Guide and Interview Preparation

Instructor: IBM Skills Network Team Duration: 1 hour to complete
Objective 1 Describe the role of a data scientist and some career path options as well as the prospective opportunities in the field.
Objective 2 Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.
Objective 3 Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.
Objective 4 Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.
Portfolio Management
Problem Solving
Professional Development
Presentations
Company, Product, and Service Knowledge
Applicant Tracking Systems
Python Programming
Professional Networking
Data analysis
Communication
Business Research
Interviewing Skills
Job Analysis
Data Science
Talent Sourcing
Writing
Recruitment