Certified Data Science Practitioner (CDSP)
Course number: CGICDSP40
For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it’s headed. Not only can data reveal insights, it can also inform – by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice.
Course Objectives:
In this course, you will implement data science techniques in order to address business issues.
You will:
• Use data science principles to address business issues.
• Apply the Extract, Transform, and Load (ETL) process to prepare datasets.
• Use multiple techniques to analyze data and extract valuable insights.
• Prepare to train machine learning models.
• Train, tune, and evaluate classification models.
• Train, tune, and evaluate regression and forecasting models.
• Train, tune, and evaluate clustering models.
• Finalize a data science project by presenting models to an audience, putting models into
production, and monitoring model performance.
Prerequisites
- To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.
- You should also have experience working with databases, querying languages like SQL, and high-level programming languages like Python.
Target Audience
This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level, or any number of other data-driven situations.
Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.
This course is also designed to assist students in preparing for the CertNexus® Certified Data Science Practitioner (CDSP) (Exam DSP-110) certification.
Certification
CertNexus® Certified Data Science Practitioner (CDSP)
Exam
CertNexus® Certified Data Science Practitioner (CDSP) Exam DSP-110
The exam will certify that the successful candidate has the knowledge, skills, and abilities required to answer questions by collecting, wrangling, and exploring data sets, applying statistical models and artificial-intelligence algorithms, to extract and communicate knowledge and insights.
Number of Items: 100 (of which 75 count towards final score)
Item Formats: Multiple Choice/Multiple Response
Exam Duration: 120 minutes (including 5 minutes for Candidate Agreement and 5 minutes for Pearson VUE tutorial)
Exam Options: In person at Pearson VUE test centers or online via Pearson OnVUE online proctoring.
Accreditation
Post class completion, students can appear for the CertNexus® Certified Data Science Practitioner (CDSP) Exam DSP-110.
Course Content
- Topic A: Initiate a Data Science Project
- Topic B: Formulate a Data Science Problem
- Topic A: Extract Data
- Topic B: Transform Data
- Topic C: Load Data
- Topic A: Examine Data
- Topic B: Explore the Underlying Distribution of Data
- Topic C: Use Visualizations to Analyze Data
- Topic D: Preprocess Data
- Topic A: Identify Machine Learning Concepts
- Topic B: Test a Hypothesis
- Topic A: Train and Tune Classification Models
- Topic B: Evaluate Classification Models
- Topic A: Train and Tune Regression and Forecasting Models
- Topic B: Evaluate Regression and Forecasting Models
- Topic A: Train and Tune Clustering Models
- Topic B: Evaluate Clustering Models
- Topic A: Communicate Results to Stakeholders
- Topic B: Demonstrate a Model in a Web App
- Topic C: Implement and Test Production Pipelines
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