Data Science Management
CERTIFICATE
Managing a team of Data Scientists can be tough. Often the most talented Data people think, and act, in a “non-traditional” manner. Expecting a data team to work like a typical 9-5 employee can be disastrous. Managing these unique individuals takes a special skill set.
In this certificate, you’ll learn not only how to think like your team, but how to empower them to innovate and excel at their jobs. You’ll also learn how to communicate in a manner that completes the task(s) at hand but allows for creative problem solving as well.
Finding Data Science people can be tough. Retaining those people can be even harder.
Once a (good) data science person finds the right fit with management and work environment, they stay for a long time.
CERTIFICATION REQUIREMENTS
Prerequisite:
MUST BE IN THE ROLE of a Data Scientist (or equivalent) for at least 1 year with aspirations of future management
OR
Fundamentals of Data Science Certificate
Complete ALL of the following:
DATA VISUALITION CERTIFICATE
Fundamentals of Data Visualization (Tableau 101)
3 hour sessions, two days/week for 4 weeks
In this class, students will learn the fundamentals of expressing data visually using Tableau. We will teach you data design and how humans digest data–specifically, the fundamentals of data visualization design and construction, as well as best practices needed to implement them. This class uses Tableau, an industry-wide benchmark for quality visualization tools.
or
Power BI – Business Intelligence (BI 101)
3 hour sessions, two days/week for 4 weeks
Power BI is a major tool for most Data Analysts to use in Data visualization, especially when the data needs significant preparation prior to visualization. This class will teach how to visualize data using Power BI and how to collect, arrange, and prepare data for visualization.
BUSINESS INTELLIGENCE ANALYST CERTIFICATE
Data Manipulation and Management (SQL 101)
3 hour sessions, two days/week for 4 weeks
This class teaches a student how to store and transform data specifically to be used in modeling. Students will learn database design, SQL queries, different schemas, data cleaning techniques, and data appending. The class also will introduce a tool called Dataiku, a data platforming tool used for easier data engineering and visual/drag and drop data science. This module, taken individually, does not earn a student a certificate.
Introduction to Python Programming (Python 101)
3 hour sessions, two days/week for 4 weeks
In this class, students will be introduced to some of the major concepts of Data Science (Python Programming, Database Management, Modeling, and Data Visualization) and some of the tools used in the profession. The tools include a crash course in the basics of programming, data structures and object oriented design, basic web development, Jupyter Notebooks, GitHub, and web scrapers, as well as functional programming concepts and key Python libraries (Numpy and Pandas). This module, taken individually, does not earn a student a certificate.
MACHINE LEARNING CERTIFICATE
Basic Model Building (Model 202)
3 hour sessions, two days/week for 4 weeks
Fundamentally, data science is using statistics and economic modeling to predict what is likely to happen next. This class will teach the student the fundamentals of how to build common algorithms inside of an industry-leading data science platform called Dataiku. This will include the basics of model evaluation, choosing target variables and characteristics, and basic machine learning. This module, taken individually, does not earn a student a certificate. This module, taken individually, does not earn a student a certificate.
Mathematics of Model Evaluation (Eval 202)
3 hour sessions, two days/week for 4 weeks
This class will dive into the metrics behind evaluating an analytics model’s performance using F1, Accuracy, Precision, Recall, AUC, Cost matrix, and Cumulative Lift. Students also will learn to show the steps to building, testing, evaluating, adjusting/rebuilding, re-testing, and re-evaluating a model. Finally, students will learn which model to use, avoiding the pitfalls of just using accuracy as an indicator. This module, taken individually, does not earn a student a certificate.
API & Cloud Database (Data 202)
3 hour sessions, two days/week for 4 weeks
Cloud Infrastructure and API Integration for Data Science focuses on leveraging the power of cloud platforms for scalable data science applications. It also covers the importance of APIs for data retrieval, sharing, and integration. Students will understand how to build, deploy, and manage data science pipelines in cloud environments. This module, taken individually, does not earn a student a certificate.
Advanced Modeling in Python (Python 202)
3 hour sessions, two days/week for 4 weeks
Data science has gone from needing to know how to code to most modeling techniques having standardized libraries that can be pasted into a program. This means that one may do data science without understanding what the models mean or actually do. This class will drill into how to program models the traditional way. Students will use Word2Vec to scrape, debug, and enhance data science models. They also will learn how to use Python to solve other gaps such as calculations, other data manipulation, and random number population. This module, taken individually, does not earn a student a certificate.
Complete the following:
Data Science Management (DS Mgmt 505)
3 hour sessions, two days/week for 4 weeks
Data Scientists are a new type of employee and require a new type of leadership, understanding, and management. Taught in workshop format alongside the book, “Leading a Data Driven Organization – A Practical Guide to Transforming Yourself and Your Organization to Win the Data Science Revolution,” this class walks through the concepts of data science to equip students to succeed as leaders in the field.
Total Time/Cost to complete certification:
4 weeks/$3,300 if you have the necessary prerequisite
what does a Data Science Manager do?
A DATA SCIENCE MANAGER manages the design and implementation of big data solutions for the organization. They also oversee the team responsible for predictions, models, visualizations, APIs, and databases associated with the models. They typically report to C-Suite.
COMMON TASKS OF A DATA SCIENCE MANAGER:
- Creating and managing repositories for data inside the organization
- Manipulate that data to better analyze.
- Build and manage models to predict outcomes
- Research potential issues and insight inside the data
- Implement those models/findings into the enterprise for automation and use at scale.
- Manage Data Science team, including staff development
- Oversee model selection, model implementation, and management
- Present findings to C-Level executives