Data Management and Analytics

Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
SQL Core
Python Core

About Course

This comprehensive course empowers learners with foundational and advanced skills in data handling, organization, and analysis to support data-driven decision-making across industries. It delves into database management systems, statistical methodologies, data visualization strategies, and cutting-edge analytics tools, enabling participants to derive actionable insights from intricate datasets through hands-on projects and practical, real-world scenarios.

Data Lifecycle Management: Understanding data creation, storage, transformation, and archiving.
Database Foundations: Practical use of SQL for relational databases and an introduction to NoSQL.
Analytics & Reporting: Core concepts of business intelligence and converting raw data into insights.
Data Visualization: Creating dashboards using industry tools like Power BI or Tableau.
Data Governance: Principles of data quality, integrity, ethics, and regulatory compliance.
Programming (Advanced Tracks): Introduction to Python and R for data manipulation and statistical modeling.

Course Content

Week 1: Introduction to Data Management and Analytics

Data Lifecycle Overview
Data Sources and Characteristics
Data Governance Principles
Ethical Data Considerations
Industry Applications of Data Management
Class Assignment
Week1 Project

Week 2: Database Fundamentals

Week 3: Data Cleaning and Preparation

Week 4: Statistical Foundations – Part 1

Week 5: Statistical Foundations – Part 2 and Data Visualization Introduction

Week 6: Data Visualization

Week 7: SQL for Analytics

Week 8: Programming for Data Analytics – Python Focus

Week 9: Programming for Data Analytics – R Focus and Big Data

Week 10: Big Data Technologies

Week 11: Advanced Analytics and Machine Learning

Week 12: Projects, Ethics, and Career Development

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Course Prerequisite(s)

About Course

What Will You Learn?

Course Content

Week 1: Introduction to Data Management and Analytics

Data Lifecycle Overview

Data Sources and Characteristics

Data Governance Principles

Ethical Data Considerations

Industry Applications of Data Management

Class Assignment

Week1 Project

Week 2: Database Fundamentals

Relational Database Concepts

Database Design and Normalization

SQL Basics for Querying

NoSQL Databases Introduction

Advanced Data Modeling Techniques

Week 2 Assignment

Week 2 Project

Week 3: Data Cleaning and Preparation

Data Quality Assessments

Handling Missing Values

Data Transformation Techniques

Outlier Identification and Management

Data Integration from Multiple Sources

Assignment: Handling errors and ensuring data quality

Project: Data Pipeline

Week 4: Statistical Foundations – Part 1

Descriptive Statistics

Probability Distributions

Hypothesis Testing

Assignment

Week Project

Week 5: Statistical Foundations – Part 2 and Data Visualization Introduction

Regression Analysis

Correlation vs. Causation

Visualization Principles

Chart Types and Best Practice

Week Assignment

Week Project

Week 6: Data Visualization

Dashboard Design

Data Storytelling

Interactive Visualizations

Week Assignment: Interactive Charts

Takeaway Project

Week 7: SQL for Analytics

Advanced SQL Queries

Joins and Subqueries

Aggregation Functions

Window Functions

SQL Performance Optimizatio

Week Assignment: Querying Databases

Take Away project

Week 8: Programming for Data Analytics – Python Focus

Python Fundamentals for Data

Pandas and NumPy Libraries

Scripting for Data Automation

Assignment: Manipulating data for fraud detection

Take Away Project: Automating workflows

Week 9: Programming for Data Analytics – R Focus and Big Data

R Programming Essentials

Advanced R Data Manipulation

Big Data Concepts and Challenges

Assignment: Comparing Statistical Results

Takeaway Project: Surveys

Week 10: Big Data Technologies

Hadoop Ecosystem

Apache Spark Basics

Data Warehouse Design

Cloud Data Platforms and Use Cases

Assignment: Campaign Optimization

Takeaway Project: Warehouse Schema

Week 11: Advanced Analytics and Machine Learning

Supervised Learning Models

Unsupervised Clustering

Classification Algorithms

Time Series Analysis

Model Evaluation Metrics