Data Science Methodology Overview

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.
• It involves computer science (software development, machine learning), math/statistics (traditional research), and subject matter expertise.

Learning Objectives

• Students should grasp what data science is and its importance.
• They should become familiar with the Data Science Life Cycle (DSLC).
• Understanding key roles within a data science project is essential.
• Problem formulation in data science is critical.

Why Data Science is Important

• Businesses rely on data for informed decisions (data-driven decision making).
• Organizations with strong data science capabilities have a competitive advantage.
• Real-world examples include Netflix recommendations, predictive maintenance, and fraud detection.

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).
• AI is a broader concept encompassing machines executing tasks intelligently, often employing data science techniques.

The Data Science Life Cycle (DSLC)

• The DSLC involves several steps: problem definition, data collection, data cleaning/preprocessing, exploratory data analysis (EDA), model building, model evaluation, model deployment, and communication of insights.
• This cycle is iterative.

Data Science Life Cycle (Detailed View)

• Problem definition: Understanding the business problem & translating it into a data science problem.
• Data collection: Gathering data from various sources (internal/external, structured/unstructured).
• Data preprocessing: Cleaning and transforming data for analysis (removing noise, handling missing values).

Data Science Life Cycle (Continued)

• Exploratory Data Analysis (EDA): Discovering patterns, anomalies, and confirming existing assumptions from data.
• Model Building: Developing models to predict or classify data points using machine learning or statistical techniques.
• Model Evaluation: Validating models by using metrics like accuracy, precision, and recall.

Data Science Methodology (Alternative View)

• Data science methodology involves ten steps that are repeated in an iterative cycle.
• These steps can be grouped logically into five key sections: from problem to approach, from requirements to collection, from understanding to preparation, from modelling to evaluation and lastly from deployment to feedback.

10 Steps of Data Science Methodology

• A cyclical process encompassing steps like business understanding, analytic approach, data requirements, data collection, data understanding, data preparation, modeling, evaluation, deployment, and feedback.

Iteration in the Data Science Process

• Data science is not linear. Returning to earlier stages to reframe the business issue or to gather further information is common.
• Feedback loops are crucial to improve model performance.

The Role of a Data Scientist

• Technical skills encompass programming (Python, R, SQL), machine learning frameworks, databases, and cloud computing.
• Mathematics/statistical skills are required to understand statistical methods, hypothesis testing, etc.
• A solid domain understanding of the relevant industry or issue is important.
• Key roles in data science include data engineers, data analysts, and machine learning engineers.

Top Hard Skills for Data Scientists

• Essential skills include statistical analysis, machine learning, algorithms, data wrangling, big data processing frameworks (Python/R), data visualization, communication skills, database management, deep learning, neural networks, distributed/cloud computing, and natural language processing (NLP).

Problem Formulation in Data Science

• A clear problem definition is crucial to avoid wasted effort on irrelevant data or models.
• Steps include understanding the business objective, framing the problem in data science terms, and identifying key metrics for success.
• An example of this would be turning a simple business objective like 'how can sales be increased?', to a more specific data science question like identifying potential customer churn risks and targeting those individuals with relevant incentives.

Data Science Case Study - Predicting Customer Churn

• A telecom company wants to predict which customers are likely to churn.
• The process involves problem formulation, data collection (customer activity, complaints, demographics), model building using strategies like logistic regression and decision trees, and deployment for future churn predictions and targeted interventions.

Tools Used in Data Science

• Programming languages include Python and R, with SQL useful for certain tasks.
• Essential libraries for machine learning are Scikit-learn, TensorFlow, and Keras.
• Visualization tools include Tableau, Power BI, Matplotlib, and Seaborn.

Ethical Considerations in Data Science

• Historical data can contain biases affecting model training.
• Models and data handling must comply with privacy regulations (like GDPR and HIPAA).
• Transparency and interpretability are important.

Summary

• Data science is an interdisciplinary field, iterative and employing statistics and machine learning to uncover insights.
• Clear problem formulation is crucial for successful projects.

Discussion Questions

• Students should list additional real-world applications of data science.
• They should explain how to ensure that data science models are ethical and unbiased.
• They should discuss the most crucial tools for data scientists to master.

This quiz covers the fundamental concepts of data science methodology, including its importance, key roles, and the Data Science Life Cycle (DSLC). Students will explore how data science integrates statistics, computer science, and domain knowledge for various applications. Gain insights into the impact of data-driven decision making in real-world scenarios.