Foundations of Data Science using IBM watsonx C1000-177 Questions Replace C1000-144

Understand IBM C1000-177 Exam Overview

IBM C1000-177 exam is the new replacement test of C1000-144, which has been retired. C1000-177 exam is a required test for earning IBM Certified watsonx Data Scientist – Associate certification. To earn the certification, an Associate Data Scientist has fundamental data science skills and knowledge using IBM watsonx.ai to solve business problems with machine learning solutions.

Number of questions: 61
Number of questions to pass: 43
Time allowed: 90 minutes
Available Language: English
Price: $200

Master Key Topics Covered in C1000-177 Exam

The C1000-177 exam content is divided into five sections, each focusing on different stages of the data science workflow:

Evaluate the Business Problem (16%)
Perform Exploratory Data Analysis (21%)
Development Tools and Techniques (13%)
Pre-Processing and Feature Engineering (33%)
Model Selection, Training, Evaluation, and Presentation (17%)

IBM C1000-177 Exam Preparation Tips

To prepare effectively for the IBM C1000-177 Foundations of Data Science using IBM watsonx exam, consider the following tips to build and reinforce your understanding of the exam objectives and required skills.

Understand the Exam Objectives

Review each section of the exam objectives carefully, as these will guide your study priorities.
Focus on the areas with the highest weight:
- Pre-Processing and Feature Engineering (33%)
- Exploratory Data Analysis (21%)
Make sure you’re comfortable with business problem evaluation, tool selection, and the basics of development tools and techniques.

Build a Strong Foundation in Data Science Basics

Python and R: Brush up on your programming skills, especially in Python, which is commonly used in data science and supported on IBM watsonx.
Statistics and Predictive Analytics: Strengthen your understanding of statistical concepts, as these are essential for analyzing data, evaluating models, and interpreting results.

Gain Hands-on Experience with IBM watsonx.ai

If you have access, practice using IBM watsonx.ai for data exploration, feature engineering, and model building.
Familiarize yourself with the platform’s interface, tools, and capabilities, especially any automated machine learning (AutoML) and AI workflow features that align with enterprise needs.

Practice Exploratory Data Analysis (EDA)

Practice using statistical summaries, visualizations, and insights to understand data patterns and potential issues like missing values and outliers.
Use tools like Pandas, NumPy, Matplotlib, and Seaborn in Python to perform these tasks efficiently.

Focus on Feature Engineering and Data Pre-processing Techniques

Study techniques for transforming data to make it suitable for machine learning, including scaling, encoding, handling missing values, and creating new features from raw data.
Understand the significance of different pre-processing methods, as this section carries the highest weight.

Review Model Training, Selection, and Evaluation Methods

Be familiar with basic machine learning models and know how to select, train, and tune them according to the data and business requirements.
Learn evaluation metrics (e.g., accuracy, precision, recall, F1 score) to assess model performance, particularly how to interpret these metrics in the context of business needs.

Use IBM’s Study Resources and Practice Exams

IBM offers a variety of resources, including tutorials, documentation, and case studies specific to IBM watsonx.ai. Leverage these for in-depth knowledge.
Take available C1000-177 practice exams to familiarize yourself with the exam format and question types.