Steps in the Data Science Process | IABAC
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The document outlines the steps in the data science process, emphasizing the importance of problem definition, project objectives, and critical inquiry as initial steps. It covers data collection, preparation, exploration, analysis, and model building, highlighting data quality and ethical considerations throughout the process. Ultimately, it focuses on aligning data science efforts with business goals and stakeholder needs.
Steps in the Data Science Process | IABAC
- 1. Steps in the Data Science Process www.iabac.org
- 2. Content 1. Understanding the Problem 2. Data Collection and Preparation 3. Data Exploration and Analysis www.iabac.org
- 3. 01 02 03 Problem Definition Importance of Clarity Focus on Business Goals Clearly defining the business goal or problem to be addressed is the initial step in the data science process. A well-defined problem ensures that the subsequent steps are aligned with the overarching objective. Emphasizing the relevance of the problem to real-world applications. Understanding the Problem www.iabac.org
- 4. Identifying Project Objectives Goal Setting Alignment with Business Goals Relevance to Stakeholders Establishing specific, measurable objectives for the data science project. Ensuring that the project objectives are in line with the organization's strategic aims. Addressing the needs and expectations of relevant stakeholders. www.iabac.org
- 5. Formulating Key Questions Critical Inquiry Role of Inquiry in Data Science Developing Analytical Thinking Encouraging students to ask pertinent questions related to the problem at hand. Highlighting the significance of questioning in driving the data science process. Fostering a mindset of critical analysis and inquiry. www.iabac.org
- 6. 01 02 03 Sourcing Relevant Data Data Acquisition Data Quality Considerations Ethical Data Collection Exploring methods for obtaining data, including internal and external sources. Emphasizing the importance of data accuracy, completeness, and relevance. Addressing the ethical implications of data sourcing and usage. Data Collection and Preparation www.iabac.org
- 7. Data Cleaning and Transformation Data Preprocessing Handling Missing Values Normalization and Standardization Discussing the need for data cleaning and transformation to ensure data quality. Strategies for dealing with missing data to maintain the integrity of the dataset. Explaining techniques to prepare the data for analysis and modeling. www.iabac.org
- 8. Exploratory Data Analysis Descriptive Statistics Data Visualization Hypothesis Testing Calculating basic statistics to gain insights into the dataset's characteristics. Utilizing visualizations to identify patterns, trends, and anomalies in the data. Introducing the concept of hypothesis testing to validate assumptions about the data. Data Exploration and Analysis www.iabac.org
- 9. Model Building and Evaluation Algorithm Selection Model Training and Validation Performance Metrics Discussing the process of choosing suitable algorithms based on the nature of the problem. Exploring the iterative process of training and evaluating predictive models. Introducing evaluation metrics to assess the effectiveness of the models. www.iabac.org