Data Science Foundation
Unlock the power of data and pave your way to success with the Data Science Foundation course. Prepare for the exam while engaging in interactive lessons, quizzes, test preps, and hands-on labs that will equip you with the skills to analyze, manipulate, and present data effectively. Become a sought-after data science practitioner and bring invaluable insights to your organization’s decision-making processes.
- Price: $279.99
- Delivery method: eLearning
- DIR Discount: 20%
Submit form to obtain discount
Test Prep
25+ Pre Assessment Questions |
2+ Full Length Tests |
25+ Post Assessment Questions |
50+ Practice Test Questions
Features
37+ LiveLab |
37+ Video tutorials |
01:50+ Hours
The exam contains 100 (of which 75 count towards the final score) questions.
Why choose TOPTALENT?
- Get assistance every step of the way from our Texas-based team, ensuring your training experience is hassle-free and aligned with your goals.
- Access an expansive range of over 3,000 training courses with a strong focus on Information Technology, Business Applications, and Leadership Development.
- Have confidence in an exceptional 95% approval rating from our students, reflecting outstanding satisfaction with our course content, program support, and overall customer service.
- Benefit from being taught by Professionally Certified Instructors with expertise in their fields and a strong commitment to making sure you learn and succeed.
Outline
Lessons 1:
About This Course
- Course Description
- Course Objectives
Lessons 2:
Addressing Business Issues with Data Science
- Topic A: Initiate a Data Science Project
- Topic B: Formulate a Data Science Problem
- Summary
Lessons 3:
Extracting, Transforming, and Loading Data
- Topic A: Extract Data
- Topic B: Transform Data
- Topic C: Load Data
- Summary
Lessons 4:
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
- Summary
Lessons 5:
Designing a Machine Learning Approach
- Topic A: Identify Machine Learning Concepts
- Topic B: Test a Hypothesis
- Summary
Lessons 6:
Developing Classification Models
- Topic A: Train and Tune Classification Models
- Topic B: Evaluate Classification Models
- Summary
Lessons 7:
Developing Regression Models
- Topic A: Train and Tune Regression Models
- Topic B: Evaluate Regression Models
- Summary
Lessons 8:
Developing Clustering Models
- Topic A: Train and Tune Clustering Models
- Topic B: Evaluate Clustering Models
- Summary
Lessons 9:
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
- Summary