Steps in the Data Science Process | IABAC

The "Steps in the Data Science Process" certification covers essential phases like data collection, cleaning, exploration, modeling, and evaluation. It equips learners with practical skills to manage data-driven projects effectively, from raw data to actionable insights.

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 StakeholdersEstablishing 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 StandardizationDiscussing 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 MetricsDiscussing 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

10. Thank you
www.iabac.org