Introduction to Data Science

Relationships and Intersections in Artificial Intelligence, Machine Learning, Deep Learning, Mathematics, Statistics, and Data Science

• Artificial Intelligence (AI): AI encompasses the broader field of creating intelligent systems that can simulate human-like intelligence and behavior.
• Machine Learning (ML): ML involves the development of algorithms and models that enable computers to learn from data without being explicitly programmed.
• Deep Learning (DL): DL is a subset of ML that focuses on artificial neural networks and deep architectures to model complex patterns and learn representations from data.
• Data Science (DS): DS involves the use of scientific methods, algorithms, and systems to extract insights and knowledge from data.
• Mathematics and Statistics: Mathematics provides the theoretical foundation for algorithms, optimization, and modeling in AI, ML, DL, and DS. Statistics provides methods for data analysis, inference, hypothesis testing, and predictive modeling.

Data Science Components

• Machine Learning (ML): ML algorithms enable systems to learn from data, make predictions, and identify patterns without being explicitly programmed.
• Deep Learning (DL): DL is a subset of ML that focuses on neural networks and hierarchical feature learning, used for tasks like image recognition and natural language processing.
• Big Data Technologies: Big data tools and technologies such as Hadoop, Spark, and NoSQL databases are used to handle and analyze large volumes of data.
• Data Visualization: Data visualization tools and techniques help in representing data visually, making it easier to understand and interpret insights.
• Data Preprocessing: Data preprocessing involves cleaning, transforming, and preparing data for analysis, ensuring data quality and reliability.
• Statistical Analysis: Statistical methods and techniques are applied to analyze data, derive insights, and make data-driven decisions.
• Business Intelligence (BI): BI tools and dashboards provide interactive visualizations and reports for business users to monitor and analyze key metrics and KPIs.
• Predictive Analytics: Predictive models and algorithms are used to forecast future trends, outcomes, and behaviors based on historical data.
• Natural Language Processing (NLP): NLP techniques process and analyze human language data, enabling applications such as sentiment analysis and chatbots.
• Cloud Computing: Cloud platforms and services facilitate data storage, processing, and analysis, offering scalability and flexibility for data science projects.

Data Science Job Roles

• Data Scientist: Data scientists analyze complex data sets to extract meaningful insights and patterns. They develop machine learning models, algorithms, and predictive analytics to solve business problems and make data-driven decisions.
• Data Engineer: Data engineers design and build data pipelines, databases, and data infrastructure to support data analysis and machine learning projects. They ensure data quality, reliability, and scalability.
• Data Analyst: Data analysts interpret data, create reports, and visualize data trends to support business decision-making. They use statistical analysis and data visualization tools to identify patterns and trends in data.
• Data Architect: Data architects design and manage data systems, databases, and data warehouses. They develop data models, schemas, and architecture to ensure data integrity, security, and accessibility.
• Data Admin: Data administrators oversee the maintenance, security, and backup of databases and data systems. They manage user access, troubleshoot data issues, and ensure data governance and compliance.
• Business Analyst: Business analysts bridge the gap between technical data analysis and business objectives. They gather and analyze business requirements, translate data insights into actionable strategies, and collaborate with stakeholders to drive business decisions.
• Data Science Educator/Trainer: Data science educators or trainers teach and train individuals or teams in data science concepts, tools, and techniques through workshops, courses, and online training programs.

Applications of Data Science

• Internet Search: Google search uses Data science technology to search for a specific result within a fraction of a second.
• Recommendation Systems: Data Science is used to create recommendation systems like “suggested friends” on Facebook or “suggested videos” on YouTube.
• Image & Speech Recognition: Speech recognition systems like Siri, Google Assistant, and Alexa, as well as image recognition on platforms like Facebook, are powered by Data Science.
• Gaming world: Gaming companies like EA Sports, Sony, and Nintendo use Data Science to enhance gaming experiences, develop games using Machine Learning techniques, and update games dynamically based on player interactions.
• Online Price Comparison: Platforms like PriceRunner, Junglee, and Shopzilla utilize Data Science to fetch data from relevant websites using APIs for online price comparison.
• Healthcare Analytics: Data Science is used in healthcare for analyzing medical records, predicting patient outcomes, personalized medicine, and optimizing healthcare operations.
• Fraud Detection: Banks and financial institutions use Data Science to detect fraudulent activities, identify suspicious transactions, and improve security measures.
• Social Media Analytics: Social media platforms leverage Data Science for sentiment analysis, user behavior analysis, targeted advertising, and content recommendation.
• Supply Chain Optimization: Data Science is applied in supply chain management for demand forecasting, inventory optimization, logistics planning, and supplier relationship management.
• Energy Management: Energy companies use Data Science for energy consumption analysis, renewable energy optimization, predictive maintenance of equipment, and grid management.

Challenges of Data Science Technology

• A high variety of information & data is required for accurate analysis: Data Science requires diverse and comprehensive data sets for meaningful insights, which can be challenging to gather and analyze.
• Not adequate data science talent pool available: There is a shortage of skilled data scientists and professionals with expertise in data analysis, machine learning, and statistical modeling.
• Management does not provide financial support for a data science team: Lack of investment and resources from management can hinder the development and implementation of data science projects and initiatives.
• Unavailability of/difficult access to data: Data accessibility issues, including data silos, limited data sources, and data privacy concerns, can impede data science efforts.
• Business decision-makers do not effectively use Data Science results: Data-driven decision-making requires understanding and trust in data science outcomes, which may be lacking among business leaders.
• Explaining data science to others is difficult: Communicating complex data science concepts and insights to non-technical stakeholders can be challenging, leading to misunderstandings and misinterpretations.
• Privacy issues: Data privacy regulations and concerns, such as GDPR and data security breaches, can impact data collection, storage, and analysis practices in Data Science projects.
• Lack of significant domain expert: Domain knowledge is crucial for interpreting data in context and deriving actionable insights, and the absence of domain experts can limit the effectiveness of data science solutions.
• If an organization is very small, it cannot have a Data Science team: Limited resources and capabilities in small organizations may prevent them from establishing dedicated data science teams or investing in data science technologies.

Data Science Subject Areas

Broadly speaking, data science comprises of the three main subject areas:

1. Computer Science and Programming: Computational science and programming refer to the study of computational tools like programming languages, software libraries, and other tools. The knowledge of programming is essential for anyone who wishes to apply data science to problems in their field.
2. Statistics and Machine Learning: Statistics and machine learning form the theoretical foundations of data science methods and algorithms. An understanding of the theoretical underpinnings of data science is required to know the limits of the methods being applied, as well as to interpret the results of the data science process properly.
3. Domain Knowledge: Domain knowledge is often referred to as a general discipline or field to which data science is applied to. An expert or specialist in a field such as biotech is said to possess domain knowledge of that industry.

The first two items in the list above are essential skills that are required by all practitioners of data science and are common to all applications of data science regardless of the domain.

On the other hand, domain knowledge is more specialized. The lack of domain knowledge makes it difficult to apply the right methods as well as to judge their performance properly. In fact, the application of domain knowledge must be pervasive throughout the data science process in order for it to be effective.

Data Science Process and Domain Knowledge

Here, we will discuss how domain knowledge applies to every part of the data science process. The data science process can be divided into four sub-processes as described below. The following figure summarizes the data science process:

1. Problem Definition: The first step in any data science project is defining the problem to be solved. It involves starting from a generic description of the problem and defining desired performance criteria. For example, in financial data analysis, problem definition could be predicting credit defaults based on past borrower data.
2. Data Cleaning and Feature Engineering: Most data collected in any field is seldom clean and ready for use. Data cleaning and feature engineering involve transforming the data and selecting relevant features. For instance, in healthcare data analysis, domain knowledge helps in identifying important health indicators as features for predictive modeling.
3. Model Building: The model-building step involves fitting a model to data to solve the defined problem. The choice of an appropriate model is crucial and is influenced by domain knowledge. For example, in marketing analytics, domain experts may use regression models to predict customer purchasing behavior.
4. Performance Measurement: Performance measurement is the final step that involves evaluating how well the model performs on new data. Domain knowledge is essential in choosing performance metrics and thresholds that align with the specific goals and requirements of the domain. For instance, in fraud detection, domain experts may focus on minimizing false negatives to catch potential fraudsters.

Case Study: Predicting Credit Card Delinquency

In this example, we'll explore how data science can help predict whether a customer will be late in paying their credit card bill. This is important for credit card companies to manage risk and make smart decisions about issuing credit cards.

The data we have includes information about 100,000 customers and 10 different factors, such as their payment history and credit score. One of the factors tells us if a customer has been late in paying their bill in the past.

• Step 1: Defining the Problem

  Our first task is to define the problem clearly. In this case, it's simply predicting if a customer will be late in paying their credit card bill.

• Step 2: Cleaning and Organizing the Data

  We need to clean up the data because it's not evenly balanced between customers who are late payers and those who are on time. Most customers are on time with their payments, so we need to adjust for this imbalance in our analysis.

  A credit expert would know how to handle this imbalance, maybe by using a smaller but more balanced dataset for analysis.

• Step 3: Building the Prediction Model

  Next, we build a model to predict late payments. Based on past research, a common approach is to use logistic regression for this type of prediction.

• Step 4: Evaluating Model Performance

  We evaluate the model's performance to see how well it predicts late payments. It's tricky because a simple accuracy measure might not show the full picture. We need to consider the cost of misclassifying late payers and non-late payers.

  For example, mistakenly flagging a good payer as late is less costly than missing a late payer. A credit expert would help us choose the right evaluation criteria based on these considerations.

Structured Data

• The data, which is to the point, factual, and highly organized, is referred to as structured data.
• It is quantitative in nature, containing measurable numerical values like numbers, dates, and times.
• Structured data is easy to search and analyze.
• It exists in a predefined format, often in relational databases consisting of tables with rows and columns.
• Examples of structured data include data in Excel files and Google Docs spreadsheets.
• The programming language SQL (Structured Query Language) is used for managing structured data, developed by IBM in the 1970s primarily for relational databases and warehouses.
• Structured data is highly organized and understandable for machine language.
• Common applications of structured data include sales transactions, airline reservation systems, inventory control, and others.

Unstructured Data

• All unstructured data includes files like log files, audio files, and image files.
• Organizations often have a lot of unstructured data but struggle to derive value from it as it is raw and lacks a predefined model or format.
• Unstructured data requires significant storage space and is challenging to maintain security.
• It cannot be presented in a data model or schema, making it difficult to manage, analyze, or search.
• Unstructured data resides in various formats like text, images, audio, and video files.
• It is qualitative in nature and is sometimes stored in non-relational databases or NoSQL databases.
• Unstructured data is not stored in relational databases, making it challenging for computers and humans to interpret.
• Managing unstructured data requires data science experts and specialized tools due to its complexity.
• The amount of unstructured data is much larger than structured or semi-structured data.
• Examples of human-generated unstructured data include text files, emails, social media content, and mobile data.
• Machine-generated unstructured data includes satellite images, scientific data, sensor data, and digital surveillance.

Semi-structured Data

• Semi-structured data is information that does not reside in a relational database but has some organizational properties, making it easier to analyze.
• While some semi-structured data can be stored in relational databases with certain processes, it can be challenging for certain types of semi-structured data.
• Semi-structured data exists to save space and facilitate analysis.
• An example of semi-structured data is XML data, which has a defined structure but does not fit neatly into traditional relational database structures.

What Is the Data Analysis Process?

The process of data analysis, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights.

Data Analysis is a process of collecting, transforming, cleaning, and modeling data to discover the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making. Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modelling and Data Analysis are the same.

Data Analysis Process consists of the following phases that are iterative in nature:

• Data Requirements Specification: The data required for analysis is based on a question or an experiment. For example, if a company wants to analyze customer satisfaction, the data requirements might include customer ratings and feedback.
• Data Collection: Gathering information on targeted variables ensuring accuracy and honesty. In our example, data collection involves collecting customer ratings and feedback from surveys and online platforms.
• Data Processing: Organizing collected data for analysis, which may involve structuring it in rows and columns or creating data models. For instance, organizing customer ratings and feedback into a spreadsheet or database format.
• Data Cleaning: Preventing and correcting errors in processed data, such as incomplete or duplicate entries. This step ensures that the data is accurate and reliable for analysis.
• Data Analysis: Applying various techniques to understand and interpret data, including statistical models like correlation and regression analysis. In our example, using regression analysis to identify factors influencing customer satisfaction.
• Communication/Visualization: Reporting analysis results in user-friendly formats using data visualization techniques like charts and tables to convey insights clearly. For example, creating a dashboard with charts showing customer satisfaction trends over time.

After completing the data analysis process, the company can use the insights gained to make informed decisions and take actions to improve customer satisfaction. The data-driven approach helps identify areas of improvement and track progress over time, leading to better business outcomes.

What is Data Analytics?

Data analytics refers to the process of examining datasets to draw conclusions about the information they contain. It involves the use of statistical techniques, algorithms, and software tools to analyze raw data and uncover patterns, trends, and relationships.
Data analytics and data science are two closely related fields that involve extracting insights and knowledge from data. They are essential for making informed decisions in various industries.

Data Analytics Life Cycle

1. Discovery: Acquire data from various sources such as web servers, social media, census datasets, and online APIs to answer business questions.
2. Preparation: Cleanse data by addressing inconsistencies like missing values, blank columns, and incorrect formats to improve predictions.
3. Model Planning: Plan methods and techniques to establish relationships between input variables using statistical formulas and visualization tools like SQL analysis services, R, and SAS/access.
4. Model Building: Split datasets for training and testing, apply techniques like association, classification, and clustering to the training set, and test the model against a separate "testing" dataset.
5. Operationalize: Deliver the finalized model with reports, code, and technical documents, deploy it into a real-time production environment after thorough testing.
6. Communicate Results: Share key findings with stakeholders to assess project success or failure based on the model's outputs.

Example:

Imagine a retail company that wants to improve its sales forecasting using data analytics. They follow the Data Analytics Life Cycle as follows:

1. Discovery: The company acquires sales data from its stores, including transaction history, customer demographics, and product details. They also gather external data like economic indicators and weather forecasts to understand factors influencing sales.
2. Preparation: The data is cleaned to remove inconsistencies and errors. Missing sales records are filled in using historical trends, and data formats are standardized for analysis. Additionally, they segment customers based on purchasing behavior and demographics.
3. Model Planning: Statistical analysis is used to identify correlations between sales and various factors such as promotions, seasonality, customer segments, and external factors like holidays or events. Visualization tools help visualize trends and patterns in the data.
4. Model Building: Machine learning algorithms, such as time series forecasting models or regression models, are applied to predict future sales based on historical data and identified variables. The model is trained and tested using a portion of the data.
5. Operationalize: The finalized sales forecasting model is integrated into the company's systems. Reports and dashboards are created to monitor sales predictions in real-time. The model is regularly updated with new data to improve accuracy.
6. Communicate Results: Key findings from the sales forecasting model, including predicted sales trends and insights, are communicated to stakeholders such as sales teams, marketing departments, and executives. This information guides decision-making, inventory planning, and marketing strategies.

Significant Advantages of using Data Analytics Technology:

• Improved Decision Making: Data analytics helps businesses make better decisions by providing insights based on data analysis.
• Enhanced Efficiency: By automating data processing and analysis, organizations can streamline operations and save time.
• Cost Savings: Data analytics can identify cost-saving opportunities and optimize resource allocation.
• Competitive Advantage: Utilizing data analytics gives companies a competitive edge by enabling them to adapt quickly to market trends and customer preferences.
• Enhanced Customer Experience: Personalized recommendations and targeted marketing campaigns based on data analysis can improve customer satisfaction.