Data Science for Business
Curriculum
Required Courses
Our Data Engineering course will provide you with a technical overview of how to understand, leverage, and report data. You will be taught how to source, prepare, and manage historical data. You will also learn about the history and principles of database systems, how to clean raw data, and how to use SQL to load and query data in databases.
You will learn to:
- Work with databases and data classification, formats, and profiles.
- Identify the principles and best practices of relational databases.
- Build and extract insights from document databases.
- Design a system, known as data warehousing, used for reporting and data analysis.
- Apply data privacy and security, ingestion, and quality and preparation techniques.
- Explore NoSQL database types, supported data formats, and data models using the MongoDB application.
- Implement data cleaning and validation techniques that ensure information reaches users properly for exploitation.
- Create reports and dashboards in Tableau using an analytical datastore.
- Develop a coherent, concise, and realistic analysis, and apply your knowledge to create an automated end-to-end data pipeline.
The Python for Data Science course introduces the core concepts of Python as a programming language. This technical program is project-based and will equip you to create and run your own Python projects.
You will learn to:
- Perform advanced data analysis and processing.
- Write production-level Python code to efficiently process large datasets.
- Create persistence models to be deployed as an API or used for batch scoring.
- Design code that runs in parallel using multiprocessing and multithreading functionalities.
- Optimize Python code for performance and speed.
- Prepare, train, and evaluate machine learning models.
- Discuss advanced Python functionalities like classes and functions.
Statistics for Data Science is a highly practical course that will provide you with the foundational tools to solve data science problems and prepare you to take the next steps in the world of machine learning.
You will learn to:
- Understand R and RStudio and their applications.
- Analyze the concept of hypothesis testing, work with datasets, build classification models, and interpret results.
- Comprehend the intricacies of logistic regression, evaluate its outputs, and understand how a link function works.
- Perform a Principal Components Analysis (PCA) and multiple pairwise comparisons and analyze models with multiple categorical predictors.
- Present a start-to-finish analysis with meaningful insights on a dataset using exploratory analysis, dimension reduction, linear models, and classification models.
Our Artificial Intelligence and Machine Learning course focuses on the mathematical and theoretical principles that support machine learning’s function in the business landscape. Students will acquire a foundation in data investigation and exploration, as well as supervised and unsupervised learning. They will also learn how to transform big data into informed, actionable insights.
You will learn to:
- Identify big data-related solutions.
- Understand the basic concepts of predictive analytics and machine learning.
- Design classification and regression models for prediction and reasoning.
- Develop a comprehensive grasp of model interpretation and evaluation.
- Use scripting programming languages, including Python, to process, visualize, and analyze large datasets.
- Implement machine-learning solutions.
This course will provide you with the techniques and tools you need to turn insights into compelling narratives. Over eight weeks, you will learn the art of conveying data in a meaningful way to support stakeholder decision-making and drive action.
You will learn to:
- Analyze data to determine patterns, key insights, action items, and storytelling potential.
- Identify which business needs can be addressed with data.
- Describe the key elements of a successful data story: knowing one’s audience, defining the goal, maintaining engagement, and being explicit about the takeaways.
- Distill data into key points using infographics, dashboards, reports, and stories.
- Anticipate and manage questions from a variety of audiences.
- Enhance decision-making through appropriate cues and indicators for specific audiences.
Methodologies and Techniques
Alteryx
Anaconda
Beautiful Soup
Gephi
GitHub
Google Cloud
Jupyter Notebook
MongoDB
MySQL
Neo4J
NumPy
OpenRefine
Python
Seaborn
Sklearn
Tableau
Online Format Features
- Self-paced interactive learning modules with a variety of engaging learning activities, assignments, and resources.
- Live sessions that bring you, your peers, and your instructor together to learn collaboratively about the current state of the field, engage with real-world problems, and explore authentic solutions.
- Continuous support from your instructional assistant, who will accompany you on your journey through the content, answer your questions, and provide feedback on your work.