Data Science Methodology - PDF

Data Science Methodology Prof. Dr. Mohamed Kholief ([email protected]) Fall 2024 Overview of Data Science and the Life Cycle Learning Objectives  Understand what data science is and why it's important.  Familiarize yourself with the...

Data Science Methodology Prof. Dr. Mohamed Kholief ([email protected]) Fall 2024 Overview of Data Science and the Life Cycle Learning Objectives  Understand what data science is and why it's important.  Familiarize yourself with the Data Science Life Cycle (DSLC).  Learn the key roles in a data science project.  Grasp the importance of problem formulation in data science. What is Data Science?  Definition: Data science combines statistics, computer science, and domain knowledge to extract insights from data.  Key Disciplines:  Data mining  Machine learning  Predictive analytics  Applications: Business, healthcare, social media, government, etc. Why Data Science is Important  Data-driven decision making  Businesses rely on data to drive insights and make informed decisions.  Competitive advantage  Organizations with strong data science capabilities outperform their competitors.  Real-world examples:  Netflix recommendations  Predictive maintenance in manufacturing  Fraud detection in finance Data Science vs. Related Fields  Data Science vs. Data Analytics:  Data analytics focuses on descriptive and diagnostic insights (what happened and why).  Data science focuses on predictive and prescriptive insights (what will happen and how to make it happen).  Data Science vs. Artificial Intelligence (AI):  AI is a broader concept of machines carrying out tasks in a smart way, often leveraging data science techniques. The Data Science Life Cycle (DSLC) Overview  Steps in the DSLC:  Problem Definition  Data Collection  Data Cleaning/Preprocessing  Exploratory Data Analysis (EDA)  Model Building  Model Evaluation  Model Deployment  Communication of Insights  Note: This is an iterative process! Data Science Life Cycle (Detailed View)  Problem Definition: Understand the business problem and translate it into a data science problem.  Data Collection: Collect data from various sources (internal/external, structured/unstructured).  Data Preprocessing: Clean and transform data for analysis (remove noise, handle missing data). Data Science Life Cycle (Continued)  Exploratory Data Analysis (EDA): Analyze data to discover patterns, spot anomalies, and check assumptions.  Model Building: Use machine learning or statistical techniques to create models that predict or classify outcomes.  Model Evaluation: Validate models using metrics like accuracy, precision, and recall. Data Science Methodology (An alternative view)  Data Science Methodology consists of ten steps that are repeated constantly for data scientists to arrive at the best solution.  These can be combined into five sections:  From Problem to Approach which includes the Business Understanding and Analytical Approach stages.  From Requirements to Collection under which the Data Requirements and Data collection stages are present.  From Understanding to Preparation which involves the Data Understanding and Data Preparation stages.  From Modeling to Evaluation which includes the Modeling and Evaluation stages.  And lastly, From Deployment to Feedback under which the Deployment and Feedback stages are included. 10 Steps of Data Science Methodology 1. Business Understanding 2. Analytic Approach 3. Data Requirements 4. Data Collection 5. Data Understanding 6. Data Preparation 7. Modeling 8. Evaluation 9. Deployment 10. Feedback Iteration in the Data Science Process  Data science is not linear:  After model evaluation, you may need to return to previous steps (e.g., reframe the problem or collect new data).  Feedback loops are critical for improving model performance. The iterative nature of data science (Yet another viewpoint!) The Role of a Data Scientist  Skills Required:  Technical Skills: Programming (Python, R), machine learning, databases, cloud computing.  Mathematics/Statistics: Understanding of statistical methods, hypothesis testing, etc.  Domain Knowledge: Understanding the industry or specific problem area.  Key Roles:  Data engineer  Data analyst  Machine learning engineer Problem Formulation in Data Science  Why it matters: Clear problem definition is crucial to avoid wasted effort on irrelevant data or models.  Steps to formulating a data science problem:  Understand the business objective.  Frame the problem in data science terms.  Identify key metrics for success.  Example: Turning a business problem (“How do we increase sales?”) into a data science problem (“Can we predict customer churn and target at-risk customers with incentives?”). Data Science Case Study - Predicting Customer Churn  Business Problem: A telecom company wants to reduce customer churn.  Data Science Problem: Build a model to predict which customers are likely to churn.  Steps:  Problem formulation  Data collection (customer activity, complaints, demographic info)  Model building (logistic regression, decision trees)  Deployment (predict churn and target at-risk customers) Tools Used in Data Science  Programming Languages: Python, R, SQL  Machine Learning Frameworks: Scikit-learn, TensorFlow, Keras  Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn  Data Handling: Pandas, NumPy, Spark Ethical Considerations in Data Science  Bias in Data & Models: Be aware of biases that can arise from historical data or biased model training.  Privacy & Data Security: Ensure that data is handled in compliance with legal regulations (GDPR, HIPAA).  Transparency: Models should be interpretable and transparent. Summary  Data science is an interdisciplinary field that applies statistics and machine learning to extract insights from data.  The data science life cycle is an iterative process with several key stages.  Clear problem formulation and understanding of the domain are critical for successful data science projects. Discussion Questions  What are some other examples of data science applications in the real world?  How can we ensure that data science models are ethical and unbiased?  What tools do you think are most important for a data scientist to master?