Introduction to Data Science Methodology

Data Science Methodology

• Data science combines statistics, computer science, and domain knowledge to extract insights from data.
• Key disciplines include data mining, machine learning, and predictive analytics.
• Applications span business, healthcare, social media, and government.
• Data science involves computer science (software development, machine learning), mathematics/statistics (traditional research), and subject matter expertise.

Learning Objectives

• Understand what data science is and why it's important.
• Familiarize oneself with the Data Science Life Cycle (DSLC).
• Learn the key roles in a data science project.
• Appreciate the importance of problem formulation.

Why Data Science is Important

• Businesses rely on data to drive insights and make informed decisions.
• Organizations with strong data science capabilities outperform competitors.
• Real-world examples include Netflix recommendations, predictive maintenance in manufacturing, and fraud detection in finance.

Data Science vs. Related Fields

• 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).
• Artificial Intelligence (AI) is a broader concept encompassing machines that perform tasks in a smart way, often leveraging data science techniques.

The Data Science Life Cycle (DSLC)

• The DSLC is an iterative process.
• Steps include problem definition, data collection, data cleaning/preprocessing, exploratory data analysis (EDA), model building, model evaluation, model deployment, and communication of insights.

Data Science Life Cycle (Detailed View)

• Problem Definition: Understand the business problem and translate it into a data science problem.
• Data Collection: Gather data from various internal and external sources (structured or unstructured).
• Data Preprocessing: Clean and transform data for analysis (remove noise and handle missing values).

Data Science Life Cycle (Continued)

• Exploratory Data Analysis (EDA): Analyze data to reveal patterns, anomalies, and assumptions.
• Model Building: Develop models using machine learning or statistical techniques to predict or classify outcomes.
• Model Evaluation: Validate models using metrics like accuracy, precision, and recall.

Data Science Methodology (Alternative View)

• Data science methodology involves ten steps constantly repeated to reach the best solution.
• Steps are grouped into five main sections:
  • From Problem to Approach: Business understanding and analytic approach.
  • From Requirements to Collection: Data requirements and data collection.
  • From Understanding to Preparation: Data understanding and preparation.
  • From Modeling to Evaluation: Modeling and evaluation.
  • From Deployment to Feedback: Deployment and feedback

10 Steps of Data Science Methodology

• A detailed breakdown of the ten steps in the data science process (see diagram).

Iteration in the Data Science Process

• Data science is not linear.
• After evaluation, you may need to go back to previous stages (e.g., reframe the problem or collect new data).
• Feedback loops are vital for improving model performance.

The Iterative Nature of Data Science

• The data science lifecycle is presented as a cyclical process.

The Role of a Data Scientist

• Required skills include programming (Python, R, SQL), machine learning frameworks, databases, cloud computing, mathematics/statistics, and domain knowledge.
• Key roles include data engineer, data analyst, and machine learning engineer.

Top Hard Skills for Data Scientists

• Essential skills include statistical analysis, machine learning algorithms, data wrangling, big data processing frameworks, programming proficiency, data visualization, database management, deep learning, cloud computing, and natural language processing.

Problem Formulation in Data Science

• Clear problem definition prevents wasted effort on irrelevant data or models.
• Steps to formulate a data science problem include understanding the business objective, framing the problem in data science terms, and identifying key metrics.
• Example: Turning a business problem (increase sales) into a data science problem (predict customer churn and target at-risk customers).

Data Science Case Study - Predicting Customer Churn

• A telecom company seeks to reduce customer churn.
• Data science aims to build a model predicting likely churners.
• Steps involve: problem formulation, data collection, model building (e.g., logistic regression, decision trees), and deployment.

Tools Used in Data Science

• Essential tools include Python, R, SQL (programming), frameworks (scikit-learn, TensorFlow, Keras), visualization tools (Tableau, Power BI, Matplotlib, Seaborn), and data handling tools (Pandas, NumPy, Spark).

Ethical Considerations in Data Science

• Bias in data and models arising from historical data or biased training must be addressed.
• Data privacy and security must comply with regulations (GDPR, HIPAA).
• Models need to be interpretable and transparent.

Summary

• Data science combines statistics and machine learning to extract insights from data.
• The data science life cycle is iterative and involves key stages.
• Clear problem formulation and domain understanding are crucial for success.

Discussion Questions

• Examples of real-world data science applications.
• Ensuring ethical and unbiased data science models.
• Key tools for data scientists to master.