The Essentials of Data Management

With the emergence of cloud computing, big data, and artificial intelligence, data science has become a rapidly growing profession. The convergence of these technologies has given rise to new and powerful approaches to developing business insights and decision making based on the analysis of vast amounts of data using advanced statistical techniques and complex machine learning algorithms.

Evolving Data Roles

The following courses enable candidates to gain skills in math, statistics, and programming to organize, analyze, and visualize data to uncover hidden solutions that challenge traditional business assumptions and produce entirely new operating and strategic models. Once completed, candidates will be be in a position to take advantage of the increasing opportunities in a wide range of data science and advanced analytics roles.

Prior to taking this course, candidates should have taken the Fundamentals of Information Technology Management course or its equivalent.

Program objectives:

Gain the mathematical and statistical expertise required to carry out data analysis
Develop essential data science skills in the R, Python, and SQL programming languages
Develop a strong understanding of data management, including evaluating, structuring, cleaning of data for analysis, and security considerations
Become familiar with how to apply the tools and techniques used in data visualization
Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling, and time series analysis
Use new generation algorithms for supervised and unsupervised machine learning
Apply a wide range of data science skills, knowledge and techniques within real world contexts, including building a portfolio of work to demonstrate a practical understanding.

Data Positions and Careers

40-Hour Synchronous Data Program Courses; Entry & Advanced Levels

Introduction to Data

The purpose of this introductory course is to provide a comprehensive overview of current and emerging trends in data, big data, and data management. In addition to preparing IT professionals interested in pursuing a career in managing data and business analytics with the important foundations of data, it provides a solid grounding for non-IT professionals.

Workforce Development

This course or its equivalent is a prerequisite for the Entry Level Python program (described below) and the Deploying Data Technologies Certificate.

Prior to taking this course candidates should have taken Fundamentals of Information Technology Management or its equivalent.

Deploying Data Technologies

Course topics include:

Introduction To Data

What is data?
What is big data?
What is information? What is knowledge?
Transaction versus master data
Systems of record vs. systems of engagement
What makes data an ‘asset’?
Private data, purchased/licensed data, open data, personal data
Machine data
Data exhaust
Meta data
Structured data
Unstructured data
Video, audio, text
Volatile data
Sensor data
Data in motion vs. data at rest
Data as service
Security considerations
Other technical considerations including Hadoop open-source software utilities (e.g. IBM, Apache, Linux, Hadoop Distributed File System), Training Data, and programming considerations

Leveraging Data

What does it mean to leverage data?
Real world examples/case studies:

– Using big data to dominate competition

– Fraud detection example

– New business example

– Failed data management example

Data Governance Fundamentals

What is data governance?
Curation
Laws ®egulations
Provenance, lineage, authenticity
Retention
Discovery
Security: authorization, access control, encryption, non-repudiation, auditing
Privacy & Security
Historical data, Data redundancy, archiving, retention
Data standards

Data Management Foundational Practices

Data quality, profiling, cleansing, defect prevention
Master data
Business glossary
Data sharing
Data integration

Advanced Data Management Concepts

Record linkage
Entity relationships
Ontologies
Context computing

Dealing With 3rd Party Data

Licensing private data
Licensing open data (commons)
Data as a service (usage models, licensing)
Managing providers
Data sharing standards — e.g. JSON

Systems Dynamics & Data Strategy

Data driven organization
Data enrichment
Principles of systems dynamics
Applying systems dynamics to data/information architecture
Understanding Business rules
The Information Supply Chain
Data Lifecycle Management
Data architectures & models
Data driven product design

1. Exploratory Data Analysis with Python

Exploratory data analysis enables candidates to perform data health checks and gain initial insights from data, and in this course candidates will gain an understanding of python programming basics and then cover the fundamentals of data management, descriptive statistics, and data visualization using Python. Course Topics:

Python programming essentials
Data management in Python
Descriptive statistics
Data visualization with Python

2. Data Analytics with Excel and SQL

Excel and SQL are a fundamental part of a data analyst’s toolkit. A strong understanding in these tools also provides a basis for more advanced data analytics with other techniques and technologies. In this course, candidates will gain experience in collecting, processing, analyzing, and communicating with data using Excel and SQL. Course Topics:

Excel functions
Data Analysis with Excel
Data management with SQL

3. Data Visualization with Power BI / Tableau

Data visualization is a powerful way to communicate meaning in data while supporting business decision-making. This course will introduce candidates to the main commercial tools used in data visualization such as Tableau, Excel, and Power BI. It will enable candidates to create a wide range of graphs, charts, and dashboards while using them appropriately in context. Candidates will also gain experience in interpreting data graphically and communicating findings effectively to a business audience. Course Topics:

Business intelligence tools
Generate graphs and charts
Build dashboards
Interpret and communicate data visually

4. Statistics for Data Analytics

Statistical inference is the process of drawing conclusions from data using statistical/mathematical techniques. This is at the core of data analytics and data science, and a strong understanding of statistics is an essential ingredient in a competent data analyst. In this course candidates will cover the fundamentals of sampling, statistical distribution, hypothesis testing, and variance analysis, while applying Python code to carry out various statistical tests that derive business solutions from their output. Course Topics:

Fundamental principles of statistical inference
Standard parametric tests
Analysis of Variance (ANOVA)

5. Fundamentals of Predictive Modelling

Solutions to many business problems are related to successfully predicting future outcomes. This course introduces candidates to predictive modelling and provides a foundation for more advanced methods/approaches/techniques. Candidates will gain an understanding of the general approach to predictive modelling while building simple linear regression, multiple linear regression and logistic regression models in Python, to apply their insights in a range of business contexts. Course Topics:

Predictive modelling principles
Build regression models
Python for predictive modelling

6. Introduction to Machine Learning

In this course candidates are introduced to fundamental concepts of machine learning, why machine learning is possible, and a range of real-world machine learning applications. Candidates will then cover common machine learning algorithms and use Python libraries to implement these techniques. Course Topics: