Use this quick start guide to collect all the information about IBM Foundations of Data Science using watsonx (C1000-177) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the C1000-177 Foundations of Data Science using IBM watsonx exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual IBM Foundations of Data Science using watsonx certification exam.
The IBM Foundations of Data Science using watsonx certification is mainly targeted to those candidates who want to build their career in Data, Analytics, and AI domain. The IBM Certified watsonx Data Scientist - Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of IBM Foundations of Data Science using watsonx.
IBM Foundations of Data Science using watsonx Exam Summary:
| Exam Name | IBM Certified watsonx Data Scientist - Associate |
| Exam Code | C1000-177 |
| Exam Price | $200 (USD) |
| Duration | 90 mins |
| Number of Questions | 61 |
| Passing Score | 70% |
| Books / Training | IBM Certified watsonx Data Scientist - Associate |
| Schedule Exam | Pearson VUE |
| Sample Questions | IBM Foundations of Data Science using watsonx Sample Questions |
| Practice Exam | IBM C1000-177 Certification Practice Exam |
IBM C1000-177 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Evaluate the Business Problem |
- Translate business objectives into Data Science/ML/AI solutions - Formulate the hypothesis to be tested - Identify appropriate tools for analysis |
16% |
| Perform Exploratory Data Analysis |
- Visually examine the data for data understanding - Assess data characteristics to guide future processing - Conduct statistical analysis of data - Visualize data to identify patterns/trends - Deselect features that have minimal predictive value |
21% |
| Development Tools and Techniques |
- Assess which modeling and statistical techniques are best suited - Select the appropriate environment and libraries |
13% |
| Pre-Processing and Feature Engineering |
- Integrate data from different sources and formats - Normalize data - Mitigate imbalanced data - Handle data anomalies and missing values - Identify the Best Categorical Data Encoding Techniques - Transform Features - Select Relevant Features |
33% |
| Model Selection, Training, Evaluation, and Presentation |
- Identify adequate Machine Learning Model - Split the data to support model evaluation - Choose appropriate model metrics to assess model performance |
17% |
To ensure success in IBM Foundations of Data Science using watsonx certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Foundations of Data Science using IBM watsonx (C1000-177) exam.