CN-CDSP
Certified Data Science Practitioner
Price:
Duration:
USD 2,550 excl. VAT
5 Days
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 can obtain this level of knowledge by taking the CertNexus DSBIZ™ (Exam DSZ-110) course. 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. You can obtain this level of skills and knowledge by taking the Logical Operations course Using Data Science Tools in Python.
What you’ll learn in this course
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, but 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.
• 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.
Course Outline
Lesson 1: Addressing Business Issues with Data Science
• Topic A: Initiate a Data Science Project
• Topic B: Democratize Data
• Topic C: Formulate a Data Science Problem
Lesson 2: Extracting, Transforming, and Loading Data
• Topic A: Extract Data
• Topic B: Transform Data
• Topic C: Load Data
Lesson 3: Analyzing Data
• Topic A: Examine Data
• Topic B: Explore the Underlying Distribution of Data
• Topic C: Use Visualizations to Analyze Data
• Topic D: Preprocess Data
Lesson 4: Designing a Machine Learning Approach
• Topic A: Identify Machine Learning Concepts
• Topic B: Identify Transformer-Based Deep Learning Concepts
• Topic C: Test a Hypothesis
Lesson 5: Developing Classification Models
• Topic A: Train and Tune Classification Models
• Topic B: Evaluate Classification Models
Lesson 6: Developing Regression Models
• Topic A: Train and Tune Regression Models
• Topic B: Evaluate Regression Models
Lesson 7: Developing Clustering Models
• Topic A: Train and Tune Clustering Models
• Topic B: Evaluate Clustering Models
Lesson 8: Finalizing a Data Science Project
• Topic A: Communicate Results to Stakeholders
• Topic B: Demonstrate Models in a Web App
• Topic C: Implement and Test Production Pipelines
Further information
If you would like to know more about this course please contact us
