AI Academy

Introduction to Data Engineering

Instructor: Rav Ahuja , Priya Kapoor Duration: Approx. 13 hours
Objective 1 List basic skills required for an entry-level data engineering role.
Objective 2 Discuss various stages and concepts in the data engineering lifecycle.
Objective 3 Describe data engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines.
Objective 4 Summarize concepts in data security, governance, and compliance.
Big Data
Databases
Data Lakes
Data Pipelines
Extract, Transform, Load
SQL
Data Security
Apache Hadoop
NoSQL
Data Architecture
Relational Databases
Data Store
Data Warehousing
Data Governance
Apache Spark

Excel Basics for Data Analysis

Instructor: Sandip Saha Joy , Steve Ryan Duration: 1 week at 10 hours a week
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

Statistical Analysis Fundamentals using Excel

Instructor: Murtaza Haider , IBM Skills Network Team Duration: 1 week to complete at 10 hours a week
Objective 1 Describe the fundamental concepts of statistics and apply them to business and data analytics settings.
Objective 2 Apply data collection, analysis, and interpretation techniques to derive actionable insights for making informed business decisions.
Objective 3 Apply descriptive and inferential analysis methods to derive insights and actionable recommendations from data.
Objective 4 Apply hypothesis testing, regression analysis, and forecasting to support business decision-making processes.
Statistical Methods
Predictive Analytics
Statistical Analysis
Probability
Data analysis
Microsoft Excel
Probability Distribution
Forecasting
Spreadsheet Software
Business Analytics
Regression Analysis
Statistics
Descriptive Statistics

Data Visualization and Dashboards with Excel and Cognos

Instructor: Sandip Saha Joy , Kevin McFaul , Steve Ryan Duration: 3 hours to complete
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

Introduction to Relational Databases (RDBMS)

Instructor: Rav Ahuja , Sandip Saha Joy Duration: 2 weeks at 10 hours a week
Objective 1 Describe data, databases, relational databases, and cloud databases.
Objective 2 Describe information and data models, relational databases, and relational model concepts (including schemas and tables).
Objective 3 Explain an Entity Relationship Diagram and design a relational database for a specific use case.
Objective 4 Develop a working knowledge of popular DBMSes including MySQL, PostgreSQL, and IBM DB2
Database Management Systems
Data Integrity
PostgreSQL
SQL
Database Architecture and Administration
IBM DB2
Command-Line Interface
Databases
Relational Databases
Data Manipulation
Data Modeling
MySQL
Data Management
Database Design

SQL: A Practical Introduction for Querying Databases

Instructor: Rav Ahuja Duration: 2 weeks at 10 hours a week
Objective 1 Analyze data within a database using SQL.
Objective 2 Create a relational database on Cloud and work with tables.
Objective 3 Write SQL statements including SELECT, INSERT, UPDATE, and DELETE.
Objective 4 Build more powerful queries with advanced SQL techniques like views, transactions, stored procedures and joins.
Database Systems
Transaction Processing
SQL
IBM DB2
Data Manipulation
Relational Databases
MySQL
Databases
Stored Procedure
Database Management
Data analysis
Query Languages
Microsoft SQL Servers

Data Warehouse Fundamentals

Instructor: Ramesh Sannareddy , Rav Ahuja Duration: Approx. 15 hours
Objective 1 Job-ready data warehousing skills in just 6 weeks, supported by practical experience and an IBM credential.
Objective 2 Design and populate a data warehouse, and model and query data using CUBE, ROLLUP, and materialized views.
Objective 3 Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.
Objective 4 How to design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.
Snowflake Schema
PostgreSQL
Data Modeling
Data Architecture
Star Schema
Data Quality
Data Warehousing
Query Languages
SQL
Database Design
IBM DB2
Database Systems
Data Validation
Data Cleansing
Data Integration
Data Lakes
Extract, Transform, Load
Data Mart

Getting Started with Tableau

Instructor: Skill-Up EdTech Team , Dr. Pooja Duration: 1 week to complete at 10 hours a week
Objective 1 Understand Tableau's fundamental concepts, significance in data visualization, diverse product range and key features crucial for data professionals.
Objective 2 Analyze and evaluate the capabilities of Tableau Public as a powerful Business Intelligence (BI) tool for data visualization and analysis.
Objective 3 Implement relationships between data tables, use Tableau calculations for analytics, empower user interaction, and apply filters and highlighting.
Objective 4 Develop expertise in Tableau dashboard design principles, organize visual elements effectively, and apply best practices for interactive dashboards.
Tableau Software
Data Storytelling
Data Import/Export
Business Intelligence
Dashboard
Data Transformation
Data Manipulation
Interactive Data Visualization
Real Time Data
Data Cleansing
Data Visualization
Data Visualization Software

Data Integration, Data Storage, & Data Migration

Instructor: Skill-Up EdTech Team Duration: 8 hours to complete Recommended experience
Objective 1 Build valuable applied data storage, integration, and migration skills employers need.
Objective 2 Gain hands-on experience using industry-specific data tools.
Objective 3 Demonstrate you understand data-related best practices and can apply methodologies through industry-standard processes.
Objective 4 Showcase your ability to solve problems related to data processes that you can talk about in interviews.
Data Security
Data Pipelines
Data Migration
Data Transformation
Disaster Recovery
Data Architecture
Data Storage
Data Management
Data Infrastructure
Cloud Storage
Data Integration
File Systems

Data Privacy, Security, Governance, Risk and Compliance

Instructor: Skill-Up EdTech Team Duration: 9 hours to complete Recommended experience
Objective 1 Develop and implement effective data privacy and security strategies.
Objective 2 Understand and apply security measures to protect and govern organizational data.
Objective 3 Conduct risk assessments and implement appropriate risk management practices.
Objective 4 Navigate and comply with relevant legal and regulatory compliance requirements.
Security Strategy
Incident Response
Security Controls
Risk Analysis
Personally Identifiable Information
Risk Management
Encryption
Compliance Management
Data Architecture
Data Governance
Data Integrity
Threat Detection
Threat Management
Data Security
Data Quality

Data Management Capstone Project

Instructor: Skill-Up EdTech Team Duration: 16 hours to complete 3 weeks at 5 hours a week
Objective 1 Integrate valuable applied data management skills employers need for a wide range of data-related roles in a culminating project.
Objective 2 Gain hands-on experience using industry-specific data tools including Excel, SQL, PostgreSQL, Tableau, etc.
Objective 3 Demonstrate best practices and apply those methodologies through industry-standard processes, data design, governance, security, and reporting.
Objective 4 Showcase your ability to solve problems relevant to working with data that you can discuss with colleagues and prospective employers.
Data Presentation
Data Security
Dashboard
Personally Identifiable Information
Data Management
Data Visualization Software
Role-Based Access Control (RBAC)
MySQL
Data Integration
SQL
Data Transformation
Relational Databases
Database Design
Data Cleansing
Tableau Software
Data Warehousing
Microsoft Excel

Practice Test for CompTIA Data+ Certification

Instructor: Skill-Up EdTech Team Duration: 6 hours to complete Recommended experience
Objective 1 Evaluate your CompTIA Data+ exam readiness with practice exercises using real-world scenarios and statistical methods for data-driven decisions.
Objective 2 Summarize the CompTIA Data+ (DA0-001) certification process, including eligibility criteria, objectives, resources, and associated costs.
Objective 3 Explain core competency areas of the certification, including data mining, manipulation, basic statistical methods, and governance standards.
Objective 4 Identify and apply tools and techniques for analyzing complex datasets to derive insights aligned with business requirements.
Statistical Analysis
Data-Driven Decision-Making
Data Transformation
Data Governance
Data Security
Data Literacy
Data Quality
Data Visualization Software
Data Mining
Data analysis