Information Technology

CertNexus Certified Data Science Practitioner (CDSP)

Instructor-Led 8 Agenda

Description

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 Overview

Course Objective

In this course, you will learn to: 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.

Who Should Attend

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.

Addressing Business Issues with Data Science Lesson Id 1

Addressing Business Issues with Data Science

Extracting, Transforming, and Loading Data Lesson Id 2

Extracting, Transforming, and Loading Data

Analyzing Data Lesson Id 3

Analyzing Data

Designing a Machine Learning Approach Lesson Id 4

Designing a Machine Learning Approach

Developing Classification Models Lesson Id 5

Developing Classification Models

Developing Regression Models Lesson Id 6

Developing Regression Models

Developing Clustering Models Lesson Id 7

Developing Clustering Models

Finalizing a Data Science Project Lesson Id 8

Finalizing a Data Science Project

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