AI Academy

Introduction to Data Analytics

Instructor: Rav Ahuja Duration: 4 hours to complete
Objective 1 Explain what Data Analytics is and the key steps in the Data Analytics process
Objective 2 Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst
Objective 3 Describe the different types of data structures, file formats, and sources of data
Objective 4 Describe the data analysis process involving collecting, wrangling, mining, and visualizing data
Data Warehousing
Big Data
Relational Databases
Apache Spark
Data Lakes
Data Visualization Software
Data Cleansing
Apache Hadoop
Data analysis
Statistical Analysis
Apache Hive

Excel Basics for Data Analysis

Instructor: Sandip Saha Joy , Steve Ryan Duration: 4 hours to complete
Objective 1 Display working knowledge of Excel for Data Analysis.
Objective 2 Perform basic spreadsheet tasks including navigation, data entry, and using formulas.
Objective 3 Employ data quality techniques to import and clean data in Excel.
Objective 4 Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.
Excel Formulas
Microsoft Excel
Data Cleansing
Data Import/Export
Pivot Tables And Charts
Information Privacy
Data Manipulation
Data Quality
Data analysis
Google Sheets
Data Wrangling
Data Visualization Software
Spreadsheet Software

Data Visualization and Dashboards with Excel and Cognos

Instructor: Sandip Saha Joy , Kevin McFaul , Steve Ryan Duration: Approx. 15 hours
Objective 1 Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.
Objective 2 Explain the important role charts play in telling a data-driven story.
Objective 3 Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.
Objective 4 Build and share interactive dashboards using Excel and Cognos Analytics.
Dashboard
Histogram
Tree Maps
Data Storytelling
Data Visualization Software
IBM Cognos Analytics
Scatter Plots
Pivot Tables And Charts
Microsoft Excel

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: 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

Databases and SQL for Data Science with Python

Instructor: Rav Ahuja , Hima Vasudevan Duration: Approx. 18 hours
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: Approx. 15 hours
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: 6 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

IBM Data Analyst Capstone Project

Instructor: Rav Ahuja , Ramesh Sannareddy , IBM Skills Network Team Duration: 3 hours to complete
Objective 1 Apply techniques to gather and wrangle data from multiple sources.
Objective 2 Analyze data to identify patterns, trends, and insights through exploratory techniques.
Objective 3 Create visual representations of data using Python libraries to communicate findings effectively.
Objective 4 Construct interactive dashboards with BI tools to present and explore data dynamically.
Data analysis
Box Plots
Exploratory Data Analysis
Data Storytelling
Dashboard
Web Scraping
Statistical Analysis
Data Presentation
Data Wrangling
Scatter Plots
IBM Cognos Analytics
Data Manipulation
Data Collection
Data Cleansing
Histogram
Pandas (Python Package)

Generative AI: Enhance your Data Analytics Career

Instructor: Dr. Pooja , Abhishek Gagneja , Rav Ahuja Duration: Approx. 14 hours
Objective 1 Describe how you can use Generative AI tools and techniques in the context of data analytics across industries
Objective 2 Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools
Objective 3 Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights
Objective 4 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics
Generative AI
Data analysis
Query Languages
SQL
Data Visualization Software
Data Ethics
Python Programming
Data Storytelling
ChatGPT
Prompt Engineering
Analytics
Dashboard
Artificial Intelligence and Machine Learning (AI/ML)
OpenAI

Data Analyst Career Guide and Interview Preparation

Instructor: IBM Skills Network Team Duration: 1 hour to complete
Objective 1 Describe the role of a data analyst 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.
Analytics
Presentations
Relationship Building
Data analysis
Professional Development
Talent Recruitment
LinkedIn
Portfolio Management
Interviewing Skills
Professional Networking
Recruitment