Foundations of Data Science and Analytics

- Overview

Data science is about the systematic processes data scientists use to analyze, visualize, and model large amounts of data. 

Data science and data analytics are both fields that involve working with data to gain insights. Data science is an umbrella term for all aspects of data processing, including collection, modeling, and insights. Data analytics is a subset of data science that focuses on statistics, mathematics, and statistical analysis. 

Data science and data analytics can be considered different sides of the same coin, and their functions are highly interconnected. Here are some differences between data science and data analytics:

• Data science: Involves building, cleaning, and organizing datasets. Data scientists use data to understand the future, model data to make predictions, identify opportunities, and support strategy. Data science often involves using data to build models that can predict future outcomes.
• Data analytics: Involves understanding datasets and gleaning insights that can be turned into actions. Data analysts work with the data as a snapshot of what exists now, solving problems and spotting trends. Data analytics tends to focus more on analyzing past data to inform decisions in the present. Business users perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes.

Please refer to the following for more information:

• Wikipedia: Data Science

- Why Does Data Infrastructure Matter?

Data infrastructure refers to the various components - including hardware, software, networking, services, policies, and more - that enable data consumption, storage, and sharing. Having the right data infrastructure strategy is critical for organizations seeking to undertake data-driven digital transformation.

Organizations realize that data is a key competitive advantage, and they are increasingly looking to unlock the value of data. As the amount of data available within the enterprise explodes from the edge to the cloud, having a thoughtful data infrastructure strategy is critical to managing costs and meeting business needs.

One of the fundamental issues in any digital transformation project is ensuring that an organization's data infrastructure is correctly aligned with its required future state. Balancing storage and analysis needs with the cost of each possible solution is an important consideration. 

Infrastructure strategy errors can inhibit business agility, preventing organizations from taking advantage of emerging business opportunities and meeting new customer demands. 

If data is trapped in silos and inaccessible to the users or systems that need it, the ability to make effective decisions is hampered, increasing risk and cost. 

If the right security and governance controls are not applied consistently across the enterprise, organizations face potential regulatory action and damage to their corporate reputation.

- The Mathematical Foundations of Data Science

The mathematical foundations of data science include topics such as: linear algebra, calculus, statistics, probability, optimization, number theory, numerical linear algebra, scientific computing. 

Data scientists use these mathematical foundations to analyze large amounts of data and extract meaningful insights for business. 

Linear algebra is an essential part of coding and thus of data science and machine learning (ML). Calculus is key to understanding the linear algebra and statistics needed in ML and data science. 

Data science is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence (AI), and computer engineering.

According to UC Berkeley, the foundations of data science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. 

The four pillars of data science are: domain knowledge, math and statistics skills, computer science, and communication and visualization.

Mathematics is a core educational pillar for data scientists. It's crucial for statistical analysis, mathematical modeling, ML, and data visualization. 

Here are some roles of mathematics and key foundations in data science:

• Make sense of data: Mathematics helps uncover patterns, identify relationships, and draw conclusions from data. It also plays an important role in developing algorithms for ML and AI.
• Solve problems: Mathematics can help you solve problems, optimize model performance, and interpret complex data that answer business questions.
• Build accurate models: A strong foundation in mathematics is essential to building accurate models, making informed decisions, and communicating insights to non-technical stakeholders.
• Communicate complex ideas: Understanding the mathematical principles underlying data science and AI enables us to make better decisions, optimize processes, and effectively communicate complex ideas.

[University of Chicago]

- Characteristics of Data Science

As the name suggests, data science is a field of study that investigates large volumes of information using modern tools and techniques to discover unseen patterns, derive meaningful information, and make business decisions based on that information.

Predictive models are built using sophisticated machine learning (ML) algorithms in data science. Data for analysis can come from many different sources and be presented in a variety of formats.

- The Phases of the Data Science Lifecycle

The phases of the Data Science Lifecycle typically include: identifying a business problem, data collection, data preprocessing, exploratory data analysis, model building, model evaluation, and deployment; essentially, starting with understanding the problem, gathering relevant data, cleaning and preparing it, analyzing patterns, creating predictive models, assessing their accuracy, and finally putting the model into practical use within the organization.

The data science lifecycle consists of five distinct phases, each with its own tasks:

• Capture: data acquisition, data entry, signal reception, data extraction. This phase involves collecting raw structured and unstructured data.
• Maintenance: data warehouse, data cleansing, data staging, data processing, data architecture. This phase involves taking raw data and putting it into a usable form.
• Process: data mining, clustering/classification, data modeling, data aggregation. Data scientists take prepared data and examine it for patterns, range, and bias to determine its usefulness in predictive analytics.
• Analytics: Exploratory/confirmative, predictive analytics, regression, text mining, qualitative analysis. This is the real content of the life cycle. This phase involves performing various analyzes on the data.
• Communications: data reporting, data visualization, business intelligence, decision making. In this final step, the analyst prepares the analysis in an easy-to-read format such as charts, graphs, and reports.

- The Data Science Process

The data science process is a systematic approach to using data to solve problems and gain insights. It's a step-by-step framework that can help businesses make data-driven decisions, improve operations, and innovate.

The data science process helps data scientists use these tools to discover unseen patterns, extract data, and transform information into actionable insights that are meaningful to the company. This helps companies and businesses make decisions that contribute to customer retention and profits.

Furthermore, the data science process helps to discover hidden patterns in both structured and unstructured raw data. This process helps turn problems into solutions by viewing business problems as projects. 

The six steps of the data science process are as follows:

• Defining the problem
• Gather the raw data needed for the problem
• Process data for analysis
• Explore data
• Do an in-depth analysis
• Exchange Analysis Results

Since the data science process stages help in turning raw data into monetary gains and overall profits, any data scientist should have a good understanding of the process and its importance.

- The Life Cycle of Data Analytics

The data analytics lifecycle is a structure for doing data analytics that has business objectives at its core. it is a continuous process that can help businesses understand factors that affect success and failure. The data analytics lifecycle can also help businesses identify risks, improve efficiency, and enhance the customer experience.

The Data analytics life cycle has six phases:

1. Data discovery and formation
2. Data preparation and processing
3. Design a model
4. Model building
5. Result communication and publication
6. Measuring of effectiveness

The data analytics life cycle also has other life stages, including creation, testing, consumption, and reuse. Each stage has its own characteristics and significance. 

Here are some steps in the data analytics life cycle:

• Data exploration: An important first step where an analyst tries to understand an unfamiliar dataset.
• Data extraction: An essential step for many DA processes, where the necessary data is extracted from any data sources.
• Data modeling: A fundamental component of analytics that helps organizations collect and manage accurate data sources.
• Model evaluation: Involves evaluating the performance of the predictive model to ensure that it is accurate and reliable.
• Model deployment: The fourth stage in the model development life cycle, but is usually the most cumbersome for data scientists as it takes time and resources.

- Data Augmentation and Feature Engineering

Data augmentation and feature engineering are techniques that can improve deep learning models. Data augmentation involves creating new data samples from existing data by applying random transformations. Feature engineering involves extracting, creating, or selecting relevant features from raw data. 

Data augmentation can help: Increase the size and diversity of training data, Reduce overfitting, Enhance model robustness to different inputs, Improve model accuracy, and Reduce the cost of labeling and cleaning the raw dataset. 

Some data augmentation techniques include:

• Noise injection
• Shifting
• Changing the speed
• Changing the pitch
• Word or sentence shuffling
• Word replacement
• Syntax-tree manipulation

Some challenges of data augmentation include:

• Finding an optimal augmentation strategy for the data is non-trivial
• The inherent bias of original data persists in augmented data