Success in a data driven world
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.
In this course, you will implement data science techniques to address business issues.
- 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.
- Design a machine learning approach to address business issues.
- 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.
This course is designed for business professionals who leverage data to address business issues. The typical student on 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.
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 with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.
In addition to programming, you should also have experience working with databases, including querying languages like SQL.
- 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 Models
- Topic B: Evaluate Regression Models
- Topic A: Train and Tune Clustering Models
- Topic B: Evaluate Clustering Models
- Topic A: Communicate Results to Stakeholders
- Topic B: Demonstrate Models in a Web App
- Topic C: Implement and Test Production Pipelines
Appendix A: Mapping Course Content to CertNexus® Certified Data Science Practitioner (CDSP) (Exam DSP-110)
This training is preparing you for CertNexus® Certified Data Science Practitioner (CDS) (Exam DSP-110.
The Certified Data Science PractitionerTM (CDSP) is an industry-validated certification which helps professionals differentiate themselves from other job candidates by demonstrating their ability to put data science concepts into practice. Data can reveal insights and 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.
This certification validates candidates’ ability to use data science principles to address business issues, use multiple techniques to prepare and analyze data, evaluate datasets to extract valuable insights, and design a machine learning approach. In addition, it will validate skills to design, finalize, present, implement and monitor a model to address issues regardless of business sector.
The exam is included in the price.
- Passing Score: 70%
- Number of questions: 100, of which 75 count toward your score
- Duration: 120 minutes
- Format: Multiple Choice/Single Response
Upon successful credentials candidates will earn the CertNexus CDSP credentials.
Do you have any questions please contact
- Mette Rosenløv Vad
- +45 72202432