Data Science for Business
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Learn how the data science workflow, data teams, dashboards, A/B tests and machine learning support business decisions, through interactive exercises.
What data science is, and how an organization can use it, is the opening question. You follow the data science workflow through real business problems, look at the skills a data team needs and how to structure it, see how company data is sourced, protected and stored, and meet the dashboards, A/B tests and machine learning methods used to analyze it.
What You Will Achieve
- Map a business problem onto the data science workflow and assign each task to the right role on a data team
- Sort data sources by risk and classify sensitive records such as PII for security
- Write a directed dashboard request or an ad hoc analysis request that a data team can act on
- Judge when a problem calls for supervised learning, clustering or deep learning, and read basic model evaluation results
Program Overview
Data Science Workflow and Data Teams
Defines data science, follows its workflow through business cases, and looks at how to build and run a data team.
- Defining data science and its workflow
- Business uses such as customer segmentation, chatbots and investment research
- Building a team, team sprints and job postings
- Matching skills to jobs and classifying data tasks
Data Collection, Storage and Dashboards
Covers where company data comes from, how it is kept and retrieved, and how dashboards, ad hoc requests and A/B tests put it to use.
- Data sources, security classification and PII
- Solicited data, focus groups and Net Promoter Score
- Data storage, cloud platforms and database types
- Dashboards, ad hoc requests and A/B testing
Machine Learning for Prediction
Introduces supervised and unsupervised machine learning, then special topics that include time series prediction, sentiment analysis, deep learning and explainable AI.
- Features, labels and model evaluation
- Clustering and cluster size selection
- Time series prediction and natural language processing
- Deep learning and explainable AI
Certificate Awarded (Paid)
Job Outlook
These skills apply wherever an organization relies on a data team, in work on business decisions, customer insight, product testing and the planning and staffing of data projects.
Good for: Managers and team leads who commission data work and want to understand the workflow, the roles on a data team and what to ask for.
Keep in mind: Expect decisions more than techniques: the exercises ask you to classify tasks, choose a dashboard or judge whether deep learning fits a problem.
Bottom line: A business-side look at data science that ties each step of the workflow to team decisions, data requests and A/B tests.
Frequently Asked Questions
Which business examples does the course use?
The first chapter works through customer segmentation, a customer service chatbot, improving OKRs and investment research. Later exercises bring in Net Promoter Score, an asthma frequency case and sentiment analysis as further examples.
How does the course handle personal data?
The data collection chapter looks at the risks that come with each data source, how to classify data for security and how to protect PII before the data is stored and queried.
What does the course teach about A/B testing?
A/B tests are presented as an analytics tool that lowers the risk of a decision. You build an A/B testing workflow, consider the sample size a test needs and look at how to read intermediate results.
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