Introduction to Data Analytics for Managers

Learn the fundamentals of business analytics and data science from a managerial perspective.

Cost

$159* per course (regular price)

$119* per course (discounted price for eligible students)

(*plus 2.5% registration fee)

Duration

8-10 weeks

Mode

Online, Zoom meetings

Level

Beginner

Manage with confidence through data based decision making!

This Specialization is designed for future business leaders and managers that want to gain a beginner level understanding of data analytics and how these capabilities can be leveraged by their teams at work. Tools and methodologies in agile project management, data preparation, data analysis, data visualization and influencing decision making with storytelling will be covered. Upon completion of a minimum of 12 courses, students will be issued a shareable, verifiable, and printable digital badge in Foundations for Data & Analytics.

Recommended Courses for This Specialization

Beginner R1

This course will expose students to the underlying concepts of “R”. Key topics include the RStudio IDE, R fundamentals, installing and using packages, working with vectors and data frames, and running basic models. After completing this module, participants will learn to work with R environment, conduct basic of programming in R, and use the Tidyverse for data manipulation.

Learn more...

Specific Objectives After completing this module, participants should:

  • Learn to work with R using RStudio
  • Learn to install packages and work will libraries in R
  • Learn the basic of programming in R
  • Learn tidyverse approaches to data manipulation
Key Topics

  • RStudio IDE
  • Working with packages
  • Conditional statements
  • Vectors and data frames
  • Loading Data

Beginner R2

This course will continue participants exposure to R focusing on visualizing, summarizing, tidying data. After completing this module, participants will be familiar with generating graphs with ggplot2 and aggregating, joining, and reshaping data using dplyr and tidyr.

Learn more...

Specific Objectives After completing this module, participants should:

  • Learn to summarize (aggregate) and reshape data
  • Learn to join data frames (tables)
  • Learn to create visualizations (graphs)

Key Topics

  • Visualization with ggplot2
  • Summarizing and joining with dplyr
  • Reshaping with tidyr
Beginner Python 1

This course will expose students to the underlying concepts of Python. Key topics include Python basics, lists, functions and numpy. After completing this module, participants will learn the how to code in Python, work with key python data structures, learn to code functions in Python, and work with a python library.

Learn more...

Specific Objectives After completing this module, participants should:

  • Learn the how to code in Python
  • Work with key python data structures
  • Learn to code functions in Python
  • Work with a python library

Key Topics

  • Python Basics
  • Lists
  • Functions
  • numpy
Beginner Math, Probability and Statistics 1

This course will expose students to the underlying concepts of probability, statistics, and graphing necessary for simple data reports and data visualizations. It also serves as background introduction for future MSBA classes.

Learn more...

Specific Objectives After completing this module, participants should:

  • Prepare and interpret visual data representations
  • Define and interpret different data summarization techniques
  • Prepare and interpret a data summarization report in R
  • Prepare and interpret data visualization in R

 

Key Topics

  • Statistical Data Visualization
  • Statistical Data Summarization
Beginner Math, Probability and Statistics 2

This course presents elementary topics in data analysis through regression.

Learn more...

Specific Objectives After completing this module, participants should:

  • Explain the difference between different types of probability distributions
  • Calculate and interpret the confidence interval of different data types
  • Create and interpret different types of hypothesis tests
  • Prepare data for OLS regression analysis in R
  • Describe the different assumptions of OLS regression
  • Perform a simple OLS regression in R
  • Interpret and present the results of simple OLS regression

 

Key Topics

  • Probability
  • Confidence Intervals
  • Hypothesis Testing
  • Simple OLS Regression
Beginner Business Foundations

This course provides students a grounding in the basic terminology and theories of business, including accounting, finance, marketing, and operations management.

Learn more...

Specific Objectives After completing this module, participants should:

  • Be able to understand the basic economic theories of supply and demand and their applications to business
  • Describe different types of marketing theories and their translation to strategy
  • Understand the six fundamental principles of finance
  • Understand the purpose for accounting laws
  • Be able to interpret and use basic accounting tools such as income statements and balance sheets
  • Describe the role of management in business

 

Key Topics

  • Goals of Business
  • Economics – Supply and Demand
  • Marketing – Branding and market strategies
  • Financial Accounting – Income statement, balance sheet, statement of cash flows
  • Finance – Time value of money, risk-return tradeoff, cost of capital, IRR
  • Management – Theories and applications
Beginner Information Technology/Systems

This course provides students a grounding in the basic terminology and theories of business, including accounting, finance, marketing, and operations management.

Learn more...

Specific Objectives After completing this module, participants should:

  • Understand the fundamentals of information systems in context of organizational strategies
  • Explore what information systems skills and knowledge are essential
  • Become familiar with the major trends in management information systems & infrastructures (Cloud, Big Data, ERPs, Mobile, IoT, CRM, SCM, cognitive computing/AI) and how these evolutions will affect workplaces and business strategies
  • Learn the major computer hardware, data storage, input and output technologies used

 

Key Topics

  • Service oriented enterprise, architecture
  • Information technology
  • Management information systems
  • Cloud computing

Beginner Data, Big Data Management & Cloud Computing

This course presents the fundamentals of data and database management, ETL, big data, and cloud computing.

Learn more...

Specific Objectives After completing this module, participants should:

  • Understand the fundamentals of transactional (OLTP) and decision support systems (OLAP)
  • Learn the relationship between data, information, and knowledge
  • Learn the fundamentals of data and database management
  • Learn how to create a database, and how to access it
  • Learn how database, data warehousing, data lakes, big data, and business intelligence are connected, and how they are used to support smart decision making
  • Learn the fundamentals of Cloud Technology and cloud services used for Data Management.

 

Key Topics

  • Data, information, knowledge
  • Database
  • Data Warehousing
  • Big Data
  • ETL
  • OLTP vs OLAP
  • On-premise vs cloud solutions
  • Data Management in the cloud
Beginner Data Modeling

This course will cover conceptual, logical and physical database modeling, entity relationship diagrams, relational database modeling, and dimensional database modeling.

Learn more...

Specific Objectives After completing this module, participants should:

  • Understand what is data modeling
  • Create a conceptual, logical and physical database model
  • Understand fundamentals of relational vs. dimensional modeling, and how they are being used
  • Hands-on exercise to design a data model with various data modeling tools

 

Key Topics

  • Data modeling
  • Entity relationship diagrams
  • Relational modeling
  • Dimensional modeling

Beginner SQL 1

This course will cover structured query language (SQL).

Learn more...

Specific Objectives After completing this module, participants should:

  • Learn fundamentals of structured query language
  • Learn how to obtain information from a database with SQL
  • Update database content with SQL and transaction handling
  • Retrieve data with filter conditions and from multiple tables using various types of join

 

Key Topics

  • SQL
  • Data Input
  • Data Manipulation
  • Data Retrieval
Beginner Excel for Data Analytics 1

This course will expose students to fundamental topics in Excel, such as formula creation, cell referencing, chart creating, and data manipulation. It will then explore applications of data analytics through linear programming and Solver.

Learn more...

Specific Objectives After completing this module, participants should:

  • Enter Data into Excel in multiple ways
  • Create and interpret formulas
  • Create and interpret charts
  • Prepare Linear Programming problems
  • Interpret Linear Programming Problems

 

Key Topics

  • Data Entry and Manipulation in Excel
  • Chart Creation
  • Linear Programming

Beginner Data Visualization using Power BI 1

This course will cover the basics of art and science of data visualization and how to create insightful dashboards using Power BI. After completing this module, participants should be able to identify and create most appropriate diagrams for a given dataset and business purpose, which contributes to better data exploration, more informed decision making and more compelling communications.

Learn more...

Specific Objectives After completing this module, participants should:

  • Utilize data visualization principles and avoid common mistakes
  • Be aware of different graph types and identify the most effective one given a business context
  • Create and publish Power BI dashboards
  • Customize dashboards in Power BI

Sample list of software that will be used:

  • Power BI
  • Microsoft Excel

 

Key Topics

  • Best practices and pitfalls
  • Graph types
  • Power BI capabilities
  • Dashboard creation in Power BI

Beginner Storytelling with Data

This course will cover the basics of Storytelling for Analytics Innovation. Students will be introduced to the elements of crafting a good story around the presentation of data (visualization, narrative and context) to improve the conversion of data insights into action by their stakeholders.

Learn more...

Specific Objectives After completing this module, participants should:

  • Understand the importance of crafting a story around the presentation of data to improve memorability, persuasiveness and engagement.
  • Have a fundamental understanding of tools and best practices (visualization, narrative and context) in the creation and sharing of a data story.

 

 

Key Topics

  • Why tell a story with data?
  • Best practices for telling a story with data in business

Beginner Predictive Analytics – Data Mining & Machine Learning 1

This course will expose students to the underlying concepts of machine learning pipelines. Key topics include predictive modeling, the split/train/score/evaluate framework, and basic data cleaning and preparation. After completing this module, participants will be able to generate their own machine learning web service which can be called from a Microsoft Excel spreadsheet.

Learn more...

Specific Objectives After completing this module, participants should:

  • Generate their own machine learning pipelines
  • Choose the best algorithm for their data
  • Choose the best features to predict an outcome
  • Create machine learning web services

 

 

Key Topics

  • Predictive modeling
  • Split/train/score/evaluate process
  • Cleaning missing data
  • Deploying machine learning
Beginner Business Intelligence, Analytics and Data Science

Today’s dynamic marketplace demands quick business decisions based on data, analysis and facts, and intuition. This course will introduce, and expose students to the fundamentals of business intelligence, business analytics and data science in the era of big data and cloud computing for digital transformation.

Learn more...

Business Analytics, or BA, is neither a product nor a system. Business Analytics refers to a dynamically evolving strategy, vision, architecture, technologies, applications, processes and practices for the collection, integration, analysis and presentation of data with analytics to generate information and knowledge for efficient and effective evidence base management. Setting up a business intelligence program with analytics takes more than just installing the technology. A successful BA program involves a set of concepts and methods designed to make informed business decisions that execute corporate strategy, improve performance and ultimately produce the best possible results by putting targeted information into the hands of those who need it most and empowering people, at whatever level they occupy, from strategic to tactical and then operational.

Specific Objectives After completing this module, participants should:

  • Evaluate the concepts with innovative uses of data, information, knowledge and analytics to support managerial decision-making
  • Analyze fundamentals of OLTP vs. OLAP solutions
  • Synthesize the directions in which BA is evolving. Which are the cutting-edge practices and solutions (e.g. mobile, social, cloud intelligence) within BA through which competitive advantage can be built?
  • Evaluate the foundations of analytics with different levels of analytics, such as graphs, standard reporting, ad hoc reports, score cards, key performance indicators, dashboards, alerts, statistical analysis, forecasting, predictive modeling and data/text mining.
  • Recognize and analyze ethical dilemmas and social responsibilities. 

Key Topics

  • Overview of business intelligence, data analytics and data science
  • Competing on data analytics
  • How to map goals and objectives to tactics and metrics with data analytics
  • Overview of descriptive, diagnostic and predictive analytics
  • Career pathways in data analytics
  • Future trends in data analytics, e.g. ML, AI, IA, IoT, smart services
Beginner Agile Project Management for Data & Analytics

This course will introduce, and expose students to the fundamentals of agile project management principles and explain the application of agile principles to software development and data analytics projects. The course will also cover differences between waterfall and agile projects, and the various roles required in an agile framework.   

Learn more...

Business Analytics, or BA, is neither a product nor a system. Business Analytics refers to a dynamically evolving strategy, vision, architecture, technologies, applications, processes and practices for the collection, integration, analysis and presentation of data with analytics to generate information and knowledge for efficient and effective evidence base management. Setting up a business intelligence program with analytics takes more than just installing the technology. A successful BA program involves a set of concepts and methods designed to make informed business decisions that execute corporate strategy, improve performance and ultimately produce the best possible results by putting targeted information into the hands of those who need it most and empowering people, at whatever level they occupy, from strategic to tactical and then operational.

Specific Objectives After completing this module, participants should:

  • Be able to understand the artifacts, and documentations associated with agile projects
  • Understand the roles, and responsibilities of agile team members
  • Know what user stories are, and how to develop them
  • Understand the tracking, and reporting requirements of an agile project
  • Understand how to deal with changes on agile projects

Key Topics

  • A brief history, and origin of agile management
  • Differences between waterfall and agile projects
  • The types of projects that are best suited for agile projects
  • The various roles involved on agile projects
  • Agile time, cost, and schedule management with Scrum and Kanban
  • Implementing Agile PM for data analytics projects

Do you need more information?

 

Connect to an advisor

8 + 11 =