• This module is designed to promote key foundational concepts of being an effective analyst or data scientist in the workplace. 

  Participants in this module will understand the steps of using data to solve business problems, introductory concepts in statistics, and how to promote reproducibility in analytic findings. Moreover, students will install analytic software and be introduced to sample data problems that will carry over to other modules in the certificate program. 

  Content Overview

  1. The current climate of big data in the workplace
  2. The process of using data to solve business problems
  3. Analytics 101: structures, distributions, sampling, and common models
  4. Getting familiar with Microsoft Azure Data Studio ©
  5. The science of analytics: reproducibility

  Learning Objectives

  By the conclusion of this course module, students will be able to:

  1. Understand common analytic challenges currently facing business organizations
  2. Recognize the principles of solving business problems and grasp how analytics should be applied to facilitate the problem-solving process.
  3. Demonstrate knowledge on ethically sound practices of the use and analysis of data in the workplace.
  4. Understand foundational principles of data structure, distributions, and sampling procedures.
  5. Demonstrate introductory knowledge in the functionality of the cross-platform tool Microsoft Azure Data Studio.
  6. Employ best practices in promoting reproducibility in analytic findings and know the utility of notebook programming platforms in the workplace.

The Data Exploration and Management module focuses on the data management lifecycle of extracting, translating, and loading data into a structured query language (SQL) database. The module will be both high-level, focusing on data management challenges and considerations, as well as low-level, focusing on hands-on SQL interactions.

Students will gain familiarity with data management techniques for the movement of data in and out of a SQL data repository. These techniques are key to being able to examine data quality, missing data, data mappings, and data cleaning. Bad data = bad conclusions, so proper data management and understanding of data are key to being able to draw meaningful and accurate data analytics.

The course will incorporate problem-based learning and an application component that connects back to the previous modules in this course.

Content Overview

1. Types of Data
2. Data Preparation
3. Why is Prep Important
4. Data Wrangling vs ETL
5. SQL Data Importing
6. SQL Data Querying, Ordering, and Preliminary Data Analysis
7. Data Aggregation and Cleansing Based on Preliminary Analyses
8. SQL Joins
9. SQL Data Profiling
10. SQL Data Indexing
11. SQL Data Exporting

Learning Outcomes

By the completion of this module, you will be able to:

• Understand types of data and challenges loading and working with various types of data.
• Utilize Microsoft SQL Server Enterprise Management Studio to load, query, and manipulate data.
• Perform SQL data operations and understand the associated query components.
• Import/export data from SQL.
• Analyze long-running queries and use SQL indexes to optimize queries.

Analytical models are key to our understanding of data and are the instruments that allow for evidence-based decisions. Without this capability, making insightful predictions and discovering underlying patterns of data can be difficult or even intractable. This module will educate students on a variety of common predictive modeling and pattern discovery techniques that can be considered a “core” set of tools for an analyst.

Content Overview

1. Linear Regression
2. Logistic Regression
3. Classification and Regression Trees
4. Pattern Discovery

Learning Outcomes

By the completion of this module, you will be able to:

• Understand model structure and concepts
• Fit appropriate models for a given set of data and associated problem statement
• Interpret model outcomes and assessments
• Understand pattern discovery techniques

Learning Outcomes

Upon completion of this module, students will:

• Know how to install and work with the Anaconda Python distribution, a Python distribution tailored for data science and analytics work
• Understand basic data types and flow control methods available in Python
• Understand how to use Pandas for working with data in flat files and databases
• Understand how to create basic visualizations using matplotlib and seaborn
• Know how to implement regression, classification, and clustering models using popular Python libraries

The Business Intelligence module focuses on building creative and technical skills to transform data into intelligence. This involves the integration of visualizations that employ appropriate design principles to curate a meaningful story and foster shared understanding.

Participants will gain familiarity with data visualization concepts and exploratory and explanatory techniques for data storytelling. This process includes strategic visual encoding and mapping data attributes to graphical attributes based on accepted properties of visual perception.

The course will incorporate problem-based learning and an application component that connects back to the previous modules in this course.

Content Overview

1. Visualization as a core component of the business intelligence process
2. Data visualization and visual perception
3. Fundamentals of visualization
4. Typography and chromaticity in data visualization design
5. From data to intelligence – owning your data story

Learning Outcomes

By the completion of this module, you will be able to:

• Use knowledge of visual encoding and cognition to evaluate visualization design alternatives.
• Make informed decisions about typography and color palettes based on principles of perception.
• Use the interface/paradigm of a proprietary data visualization tool to:
  • design and effectively create powerful visualizations
  • conduct exploratory data analysis
• Apply data transformations such as aggregation and filtering for effective data storytelling.