Certified Data Science Practitioner™ (CDSP)

Lesson 1: Addressing Business Issues with Data Science - Initiate a Data Science Project - Formulate a Data Science Problem Lesson 2: Extracting, Transforming, and Loading Data - Extract Data - Transform Data - Load Data Lesson 3: Analyzing Data - Explore the Underlying Distribution of Data - Use Visualizations to Analyze Data - Preprocess Data - Designing a Machine Learning Approach Lesson 4: Designing a Machine Learning Approach - Identify Machine Learning Concepts - Test a Hypothesis Lesson 5: Developing Classification Models - Train and Tune Classification Models - Evaluate Classification Models Lesson 6: Developing Regression Models - Train and Tune Regression Models - Evaluate Regression Models Lesson 7: Developing Clustering Models - Train and Tune Clustering Models - Evaluate Clustering Models Lesson 8: Finalizing a Data Science Project - Communicate Results to Stakeholders - Demonstrate Models in a Web App - Implement and Test Production Pipelines

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. This course includes hands on activities for each topic area. 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

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™ course. You should have also 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)