Certified Data Science Practitioner (CDSP) (Exam DSP-110)

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 its direction.

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.

Objectives

In this course, you will implement data science techniques in order to address business issues.

You will learn:

• 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.

Outline

Lesson 1: Addressing Business Issues with Data Science

Topic A: Initiate a Data Science Project

Topic B: 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: 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

Pre-Requisite

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.

In addition to programming, you should also have experience working with databases, including querying languages like SQL. Several Logical Operations courses can help you attain this experience:

• Database Design: A Modern Approach
• SQL Querying: Fundamentals (Second Edition)
• SQL Querying: Advanced (Second Edition)

Methodology

• Batch-wise training
• Practical hands-on training with real-time examples