Data Science Degree Preparation
Get prepared for a degree program in Data Science.
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
Launch your career into Data Science!
This Specialization is intended for individuals who are planning to start their academic education journey in data science. Students will learn the foundational knowledge needed to succeed and to have a good experience in any graduate and undergraduate data science degree program. 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.
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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
- 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.
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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
This module will expose students to intermediate programming in R. Key topics include programming repetitive operations using loops and apply commands and writing functions. After completing this module, participants will be familiar with concepts for writing compact and efficient code for processing data.
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Specific Objectives After completing this module, participants should:
- Know how to program iterative operations
- Know how to write compact, reliable syntax
- Know how to write functions for specific tasks
Key Topics
- Loops
- Vectorization
- Apply commands
- Writing functions
This module will introduce tidyverse methods for handling common but difficult to manage data types: text and geographic data. Key topics include working with text data using regular expressions and obtaining, manipulating, and visualizing geospatial data. After completing this module, participants will be able to apply their existing R skills to these complex data types.
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Specific Objectives After completing this module, participants should:
- Know how to use regular expressions
- Know how to select, filter, and edit text data
- Know how to plot geospatial data
- Know how to obtain and use common shapefiles and census data
Key Topics
- Functions for text data
- Regular expressions
- Geospatial data in ggplot2
- Shapefiles and simple features geometries
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.
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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
This course will continue participants exposure to Python. Key topics of this module will include Python libraries, Python logic, control flow and an introduction Machine learning in python. After completing this module, participants will have learned which are fundamental python libraries, how to code control flow and set up logical statements and use graphlab create – a machine learning environment.
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Specific Objectives After completing this module, participants should:
- Learn which are fundamental python libraries
- Learn to code control flow and set up logical statements
- Learn to use graphlab create – a machine learning environment
Key Topics
- Python libraries
- Python logic
- Control flow
- Introduction Machine learning in python
Intermediate Python 3
This course covers intermediate topics relating to Python programming, including error handling, working with files, working with and analyzing data using the pandas library, and visualizing data using the matplotlib and seaborn libraries. Illustrative examples and live demonstrations of all of these Python topics are also provided.
Key Topics
- Pandas
- Data visualization with Matplotlib Seaborn libraries
- Error handling with Python
Intermediate Python 4
This course covers additional intermediate topics relating to Python, including working with relational databases and SQL using Python, fetching and processing data from websites using the beautiful soup library, building and testing machine learning models using the scipy and scikit-learn libraries, and using APIs from within Python. Illustrative examples and live demonstrations of all of these Python topics are also provided.
Key Topics
- Working with relational databases using Python
- Beautiful soup library
- Building and testing machine learning models
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.
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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
This course presents elementary topics in data analysis through regression.
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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 Data, Big Data Management & Cloud Computing
This course presents the fundamentals of data and database management, ETL, big data, and cloud computing.
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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 SQL 1
This course will cover structured query language (SQL).
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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
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.
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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 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.
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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.
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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
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.
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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
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