Data Management

ONLINE

Level: Intermediate - Advanced

Learn to analyze, visualize, and manage data using industry-standard tools and platforms like Python, R, SQL, Tableau, Power BI, Azure, and more. This career-focused program equips learners with the skills to drive business insights and make data-driven decisions.

This program includes exam prep for 4 certifications:

  • Microsoft Azure Data Fundamentals (DP-900)

  • Microsoft Azure Data Scientist Associate (DP-100)

  • CompTIA Data+ (DA0-001)

  • Power BI Data Analyst (PL-300)

Upon completing this program, graduates will be able to:

  • Analyze and interpret complex datasets using Excel, Python, R, SQL, and BI tools.

  • Design, implement, and manage data architectures, pipelines, and lakes for enterprise-scale data solutions.

  • Create impactful, interactive visualizations and dashboards for decision-making using Tableau, Power BI, and Python.

  • Apply statistical and mathematical techniques to solve real-world problems and drive business insights.

  • Leverage cloud platforms such as Azure and AWS for scalable data storage, processing, and analytics.

  • Understand data governance, quality, and compliance to ensure secure and reliable data management.

  • Transition from a data analyst to advanced roles like Data Scientist, Machine Learning Engineer, or Data Engineer.

Prerequisites: HS Diploma, G.E.D or equivalent

Required materials: All required materials are included with tuition

Potential Job Roles for Graduates:

  • Data Analyst

  • Data Scientist

  • Data Engineer

  • Business Intelligence Analyst

  • Machine Learning Engineer

  • Analytics Consultant

Delivery: Online

Format: Instructor-led

Duration: 52 weeks (12 months)

  • One 4-hour live session per week

  • 88 hours of self-study

Tuition: $16,170

REGISTER
CLASS SCHEDULE

What’s Included?

  • Exam vouchers

  • Practice exams

  • E-books

Instructor Support

  • Certified instructor

  • Weekly virtual classes

  • Direct access to instructor via email

Curriculum Breakdown

Business Analyst to Data Analyst

Data Analysts interpret data and turn it into information that drives business decisions. This course will guide you in the transition from Business Analyst to a Data Analyst by teaching the required skills.

  • Business Analyst

    • Complete guide to Excel 365

      • Getting Started

      • Working with Charts & Sparklines

      • Using Formatting, Styles, & Themes

      • Linking, Printing, & Protecting Workbooks

      • Validating, Cleaning, & Performing Lookups on Data

      • What-If Analysis, Solver, & Analysis ToolPak

      • Pivot, PowerPivot, & Financial Modeling

    Decision Analyst

    • Tableau for Data Visualization: Introduction

    • Tableau for Data Visualization: Exploring Visualizations & Data Formats

    • Tableau for Data Visualization: Advanced Features

    • Business Reporting: Getting Started with Power BI Desktop for Data Analysis

    • Business Reporting: Visualizing & Merging Data in Power BI

    • Business Reporting: Creating & Formatting Matrix Visualizations in Power BI

    • Business Reporting: Leveraging Treemaps, Matrices, & Slicers in Power BI

    Systems Analyst

    • Google Chart Tools: Basic Charts

    • Google Chart Tools: Interacting with Charts

    • Google Chart Tools: Advanced Visuals with Charts

    • Plotly for Data Visualization: an Introduction to Plotly Chart Studio

    • Plotly for Data Visualization: Exploring Chart Studio Visualization

    • Plotly for Data Visualization: Advanced Charts and Features in Chart Studio

    • SQL Programming with MariaDB: Getting Started with MariaDB for Data Analysis

    • SQL Programming with MariaDB: Analyzing Relational Data

    • SQL Programming with MariaDB: Using Joins, Triggers, and Stored Procedures

    Data Analyst

    • VBA: Getting Started with VBA in Excel

    • VBA: Building User Interfaces with Forms in VBA and Excel

    • VBA: Leveraging VBA to Work with Charts, Stocks, and MS Access

    • Using BigML: an Introduction to Machine Learning and BigML

    • Using BigML: Getting Hands-on with BigML

    • Using BigML: Building Supervised Learning Models

    • Using BigML: Unsupervised Learning

    • Analyzing Data Using Python: Data Analytics Using Pandas

    • Analyzing Data Using Python: Importing, Exporting, and Analyzing Data with Pandas

    • Analyzing Data Using Python: Filtering Data in Pandas

    • Analyzing Data Using Python: Cleaning and Analyzing Data in Pandas

Essential Math for Data Science

Many Data Science elements depend on mathematical concepts such as probability, statistics, calculus, linear algebra, and so on. Explore important concepts of mathematics that form the foundation for Machine Learning algorithms, Data Science and Artificial Intelligence.

  • Introduction to Math

    • Introducing Sets and Set Operations

    • Introducing Graphs and Graph Operations

    • Solving Optimization Problems Using Linear Programming

    • Solving Optimization Problems Using Integer Programming

    • Getting Started with Derivatives

    • Derivatives with Linear and Quadratic Functions and Partial Derivatives

    • Understanding Integration

    • Exploring Linear Algebra

    • Getting Started with Matrix Decomposition

    • Using Eigen Decomposition and Singular Value Decomposition

    Statistics and Probability

    • An Overview of Statistics and Sampling

    • Statistics and Sampling with Python

    • Getting Started with Probability

    • Understanding Joint, Marginal, and Conditional Probability

    • Creating Bayesian Models

    • Getting Started with Probability Distribution

    • Uniform, Binomial, and Poisson Distributions

    • Understanding Normal Distributions

    • Getting Started with Hypothesis Testing

    • Using the One-sample T-test

    • Performing Two-sample T-tests and Paired T-Tests

    • Using Non-parametric Tests and ANOVA Analysis

    Math Behind ML Algorithms

    • Getting Started with Linear Regression

    • Using Gradient Descent and Logistic Regression

    • An Exploration of Decision Trees

    • Overview of Distance-based Metrics and Algorithms

    • Implementing Distance-based Algorithms

    • A Conceptual Look at Support Vector Machines

    • Building and Applying SVM Models in Python

    • Understanding the Mathematics of a Neuron

    • Exploring the Math Behind Gradient Descent

    Advanced Math

    • ML & Dimensionality Reduction: Performing Principal Component Analysis

    • Recommender Systems: Under the Hood of Recommendation Systems

Data Analyst to Data Scientist

This course provides a foundation of data architecture, statistics, and data analysis programming skills using Python and R which will be the first step in acquiring the knowledge to transition away from using disparate and legacy data sources.

  • Data Analyst

    • Data Architecture Getting Started

    • Data Engineering Getting Started

    • Python - Introduction to NumPy for Multi-dimensional Data

    • Python - Advanced Operations with NumPy Arrays

    • Python - Introduction to Pandas and DataFrames

    • Python - Manipulating & Analyzing Data in Pandas DataFrames

    • R Data Structures

    • Importing & Exporting Data using R

    • Data Exploration using R

    • R Regression Methods

    • R Classification & Clustering

    • Simple Descriptive Statistics

    • Common Approaches to Sampling Data

    • Inferential Statistics

    • Apache Spark Getting Started

    • Hadoop & MapReduce Getting Started

    • Developing a Basic MapReduce Hadoop Application

    • Hadoop HDFS Getting Started

    • Introduction to the Shell for Hadoop HDFS

    • Working with Files in Hadoop HDFS

    • Hadoop HDFS File Permissions

    • Data Silos, Lakes, & Streams Introduction

    • Data Lakes on AWS

    • Data Lake Sources, Visualizations, & ETL Operations

    • Applied Data Analysis

    Data Wrangler

    • Python - Using Pandas to Work with Series & DataFrames

    • Python - Using Pandas for Visualizations and Time-Series Data

    • Python - Pandas Advanced Features

    • Cleaning Data in R

    • Technology Landscape & Tools for Data Management• Machine Learning & Deep Learning Tools in the Cloud

    • Data Wrangling with Trifacta

    • MongoDB Querying

    • MongoDB Aggregation

    • Getting Started with Hive

    • Loading & Querying Data with Hive

    • Viewing & Querying Complex Data with Hive

    • Optimizing Query Executions with Hive

    • Using Hive to Optimize Query Executions with Partitioning

    • Bucketing & Window Functions with Hive

    • Filtering Data Using Hadoop MapReduce

    • Hadoop MapReduce Applications With Combiners

    • Advanced Operations Using Hadoop MapReduce

    • Data Analysis Using the Spark DataFrame API

    • Data Analysis using Spark SQL

    • Data Lake Framework & Design Implementation

    • Data Lake Architectures & Data Management Principles

    • Data Architecture Deep Dive - Design & Implementation

    • Data Architecture Deep Dive - Microservices & Serverless Computing

    Data Ops

    • Data Science Tools

    • Delivering Dashboards: Management Patterns

    • Delivering Dashboards: Exploration & Analytics

    • Cloud Data Architecture: Cloud Architecture & Containerization

    • Cloud Data Architecture: Data Management & Adoption Frameworks

    • Data Compliance Issues & Strategies

    • Implementing Governance Strategies

    • Data Access & Governance Policies: Data Access Governance & IAM

    • Data Access & Governance Policies: Data Classification, Encryption, & Monitoring

    • Streaming Data Architectures: An Introduction to Streaming Data in Spark

    • Streaming Data Architectures: Processing Streaming Data with Spark

    • Scalable Data Architectures: Getting Started

    • Scalable Data Architectures: Using Amazon Redshift

    • Scalable Data Architectures: Using Amazon Redshift & QuickSight

    • Building Data Pipelines

    • Data Pipeline: Process Implementation Using Tableau & AWS

    • Data Pipeline: Using Frameworks for Advanced Data Management

    • Data Sources: Integration from the Edge

    • Data Sources: Implementing Edge Data on the Cloud

    • Securing Big Data Streams

    • Harnessing Data Volume & Velocity: Turning Big Data into Smart Data

    • Data Rollbacks: Transaction Rollbacks & Their Impact

    • Data Rollbacks: Transaction Management & Rollbacks in NoSQL

    Data Scientist

    • The Four Vs of Data

    • Data Driven Organizations

    • Raw Data to Insights: Data Ingestion & Statistical Analysis

    • Raw Data to Insights: Data Management & Decision Making

    • Tableau Desktop: Real Time Dashboards

    • Storytelling with Data: Introduction

    • Storytelling with Data: Tableau & Power BI

    • Python for Data Science: Basic Data Visualization Using Seaborn

    • Python for Data Science: Advanced Data Visualization Using Seaborn

    • Data Science Statistics: Using Python to Compute & Visualize Statistics

    • Advanced Visualizations & Dashboards: Visualization Using Python

    • R for Data Science: Data Visualization

    • Data Recommendation Engines

    • Data Insights, Anomalies, & Verification: Handling Anomalies

    • Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

    • Data Science Statistics: Applied Inferential Statistics

    • Data Research Techniques

    • Data Research Exploration Techniques

    • Data Research Statistical Approaches

    • Machine & Deep Learning Algorithms: Introduction

    • Machine & Deep Learning Algorithms: Regression & Clustering

    • Machine & Deep Learning Algorithms: Data Preparation in Pandas ML

    • Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

    • Creating Data APIs Using Node.js

Data Visualization

In this course, the focus will be on data visualization with Python using Matplotlib, Bokeh and Plotly.

  • Data Visualization with Excel

    • Data Visualization: Best Practices for Creating Visuals

    • Excel Visualization: Getting Started with Excel for Data Visualization

    • Excel Visualization: Building Column Charts, Bar Charts, & Histograms

    • Excel Visualization: Visualizing Data Using Line Charts & Area Charts

    • Excel Visualization: Plotting Stock Charts, Radar Charts, Treemaps, & Donuts

    • Excel Visualization: Building Box Plots, Sunburst Plots, Gantt Charts, & More

    Data Visualization with BI Tools

    • QlikView: Getting Started with QlikView for Data Visualization

    • QlikView: Creating Line Charts, Combo Charts, Pivot Tables, & Block Charts

    • QlikView: Creating Mekko Charts, Radar Charts, Gauge Charts, & Scatter Charts

    Creating Infographics for Data Visualization

    • Infogram: Getting Started

    • Infogram: Advanced Features

    • Visme: Introduction

    • Visme: Exploring Charts

    • Visme: Designing a Presentation

    Data Visualization with Python

    • Python & Matplotlib: Getting Started with Matplotlib for Data Visualization

    • Python & Matplotlib: Creating Box Plots, Scatter Plots, Heatmaps, & Pie Charts

    • Data Visualization: Building Interactive Visualizations with Bokeh

    • Data Visualization: More Specialized Visualizations in Bokeh

    • Data Visualization: Getting Started with Plotly

    • Data Visualization: Visualizing Data Using Advanced Charts in Plotly

Data Analysis with R

R programming language is widely used for statistical analysis and modeling and data mining. 

  • Getting Started with R Programming

    • R Programming for Beginners: Getting Started

    • R Programming for Beginners: Exploring R Vectors

    • R Programming for Beginners: Leveraging R with Matrices, Arrays, & Lists

    • R Programming for Beginners: Understanding Data Frames, Factors, & Strings

    Applying and Using R Programming Structures

    • Using R Programming Structures: Leveraging R with Control Flow & Looping

    • Using R Programming Structures: Functions & Environments

    • Using R Programming Structures: Object Systems

    Working with Datasets in R

    • Datasets in R: Loading & Saving Data

    • Datasets in R: Transforming Data

    • Datasets in R: Selecting, Filtering, Ordering, & Grouping Data

    • Datasets in R: Joining & Visualizing Data

    Statistical Analysis and Modeling in R

    • Working with Probability Distributions

    • Understanding & Interpreting Statistical Tests

    • Statistical Analysis on Your Data

    • Performing Regression Analysis

    • Performing Classification

    • Performing Clustering

    • Building Regularized Models & Ensemble Models

Graph Analytics

This course is designed to make data professionals proficient in the latest graph technologies. It gives a comprehensive view of how data represented in the form of graphs help businesses leverage complex and dynamic relationships in highly connected data to generate insights and competitive advantage

  • Getting Started with Graphs

    • Graph Data Structures: Understanding Graphs & Knowledge Graphs

    • Graph Data Structures: Representing Graphs Using Matrices, Lists, & Sets

    • Graph Data Structures: Implementing Graph Traversal & Shortest Path Algorithms

    Graph Analytics with Neo4j

    • Graph Analytics with Neo4j: An Introduction to Graph Databases & Neo4j

    • Graph Analytics with Neo4j: Administering a Neo4j Database

    • Graph Analytics with Neo4j: Managing Databases with the Neo4j Browser

    • Cypher Query Language: Creating Nodes & Relationships with Cypher

    • Cypher Query Language: Basic Reads & Writes with Cypher

    • Cypher Query Language: Advanced Operations with Cypher

    • Working with Neo4j Bloom: Analyzing Graphs

    • Graph Modeling with Neo4j: An Introduction to Modeling Graphs

    • Graph Modeling with Neo4j: Automating & Refactoring Graph Models

    • Database-as-a-Service with Neo4j: The AuraDB Cloud Database Service

    Graph Data Science with Neo4j

    • Neo4j: Building Graphs with Neo4j's Graph Data Science Library

    • Neo4j: Managing Graphs with the Graph Data Science Library

    • Neo4j: Applying Graph Algorithms on In-memory Graphs

    Graph Modeling with Apache Spark

    • Graph Modeling on Apache Spark: Working with Apache Spark GraphFrames

    • GNNs: An Introduction to Graph Neural Networks

    • GNNs: Classifying Graph Nodes with the Spektral Library

Data for Leaders and Decision Makers

Designed to raise the awareness of managers, leaders, and decision-makers on data and modern data technologies, this course gives a comprehensive view of modern data sources, modern data infrastructures and groundbreaking technologies, that are emerging for addressing a wide range of business needs.

  • Data Primer

    • Data Nuts & Bolts: Fundamentals of Data

    • Traditional Data Architectures: Relational Databases

    • Traditional Data Architectures: Data Warehousing and ETL Systems

    • New Age Data Infrastructures: Factors Driving Data Infrastructures

    Big Data Infrastructures

    • Big Data Concepts: Getting to Know Big Data

    • Big Data Concepts: Big Data Essentials

    • Non-relational Data: Non-relational Databases

    • Techniques for Big Data Analytics

    • Spark for High-speed Big Data Analytics

    Raw Data to Insights

    • Data Mining and Decision Making: Modern Data Science Lifecycle

    • Data Mining and Decision Making: Data Preparation & Predictive Analytics

    • Data Mining and Decision Making: Data Mining for Answering Business Questions

    • Data Mining and Decision Making: Predictive Analytics for Business Strategies

    Emerging New Age Architectures

    • Cloud Data Platforms: Cloud Computing

    • Cloud Data Platforms: Cloud-based Applications & Storage

    • Cloud Data Platforms: AWS, Azure, & GCP Comparison

    • Data Lakes

    • Modern Data Warehouses• Azure Databricks & Data Pipelines

    Data Governance and Management

    • Modern Data Management: Data Management Systems

    • Modern Data Management: Data Governance

    • Modern Data Management: Data Quality Management

    Emerging Data Trends

    • Navigating the Latest Trends in Data for Leaders

    • Unveiling the Power of Practical Data Fabric

    • Unlocking Data Observability

    • Converged & Composable Systems

    • AI TRiSM Unleashed

Microsoft Azure Data Fundamentals (DP-900)

Designed for individuals who are new to data services and want to demonstrate foundational knowledge of data concepts and how they are implemented using Microsoft Azure. This certification is ideal for those who are exploring a career in data management, analytics, or business intelligence and is perfect for beginners who want to understand core data concepts, Azure data services, and how to work with data in the cloud.

  • Describe core data concepts

    • Structured Data

    • Semi-Structured and Unstructured Data

    • Azure Data Storage

    • Data Storage Considerations

    • Azure Analysis Services

    • Analytical Workloads

    • Online Transaction Processing (OLTP)

    Describe considerations for working with non-relational data on Azure

    • Relational and Non-Relational Databases

    • Azure Database for MySQL and PostgreSQL

    • Azure SQL Database

    • SQL Server on Azure VM

    Describe an analytics workload on Azure

    • Azure Blob Storage

    • Azure Data Lake Storage

    • Azure File Storage

    • Azure File Shares

    • Azure Table Storage

    • Azure Cosmos DB

    • Azure Cosmos DB for MongoDB, Apache Cassandra, and Gremlin

    Azure Data Services: Ingestion, Transformation, and Business Intelligence

    • Data Ingestion and Processing

    • Data Warehousing Services

    • Azure Synapse Analytics

  • Duration: 45 minutes 

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

Microsoft Azure Data Scientist Associate (DP-100)

This course prepares learners for the DP-100 exam by covering the design and implementation of data science solutions on Microsoft Azure. Topics include machine learning models, Azure ML services and workspaces, data storage and orchestration, and model deployment and monitoring.

    • Machine Learning

    • ML Services

    • ML Regression Models

    • ML Classification Models

    • ML Clustering Models

    • Project Jupiter & Notebooks

    • Azure ML Workspaces

    • Azure Data Platform Services

    • Azure Storage Accounts

    • Storage Strategy

    • Azure Data Factory

    • Non-relational Data Stores

    • ML Data Stores & Compute

    • ML Orchestration & Deployment

    • Model Features & Differential Privacy

    • ML Model Monitoring

    • Azure Data Storage Monitoring

    • Data Process Monitoring

    • Data Solution Optimization

    • High Availability & Disaster Recovery

  • Duration: 100 minutes 

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.

CompTIA Data+ (DA0-001)

Entry-level certification designed for individuals who want to demonstrate their foundational knowledge and skills in data management, data analytics, and data-driven decision-making.

  • Data Concepts and Environments

    • Understanding Databases

    • Database Concepts

    • Understanding Data

    • Data Analytics Tools

    Data Mining

    • Data Acquisition and Cleansing

    • Understanding Data Manipulation

    • Data Manipulation Techniques

    • Query Optimization

    Data Analysis

    • Descriptive Statistical Methods

    • Inferential Statistical Methods

    • Data Analysis Types and Techniques

    Visualization

    • Data Visualization Reports

    • Data Visualization Dashboards

    • Creating Charts and Graphs

    Data Governance, Quality, and Controls

    • Data Governance

    • Data Quality and Master Data Management

  • Number of questions: maximum of 90 questions

    Types of questions: multiple-choice and performance-based

    Duration: 90 minutes

    Passing score: 675 (on a scale of 100-900)

    Recommended experience: 18–24 months in a report or business analyst job role, with exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience

Power BI Data Analyst (PL-300)

Designed to equip you with the skills and knowledge necessary for effective data analysis and visualization using Microsoft Power BI.

  • Prepare the data

    • Power BI for Data Analysis

    • Loading and Transforming Data in Power BI

    • Preparing Data for Visualizations in Power BI

    Model the data

    • An Overview of Data Modeling in Power BI

    • Applying the DAX Formula Language in Power BI

    • Working with Filters in Power BI

    • Using Time Intelligence in Power BI

    • Advanced Modeling Techniques in Power BI

    Visualize and analyze the data

    • Understanding Data Visualization

    • Creating and Formatting Charts in Power BI

    • Leveraging Ribbon, Line, Column, and Pie Charts in Power BI

    • Maps, Waterfall Charts, and Scatter Plots in Power BI

    • Matrix and Treemap Controls in Power BI

    • Using the Power BI Service

    Deploy and maintain items

    • Analysis and Sharing Features in Power BI

    • Extracting Insights from Data Using Power BI

    • Applying Power BI’s Advanced Analysis Features

    • Sharing Power BI Reports and Workspaces

  • Duration: 100 minutes

    This exam will be proctored. You may have interactive components to complete as part of this exam. To learn more about exam duration and experience, visit: Exam duration and exam experience.