Introduction to Data Science Overview

Data science started as a field to analyze large amounts of data. It evolved from statistical methods and was popularized by technologies like Python.

In 1962, John Tukey, a mathematician, described a concept similar to modern data science, called 'data analysis'.

In 1974, Peter Naur, a Danish computer engineer, suggested 'data science' as an alternative name to computer science.

In 1997, C.F. Jeff Wu first used the term "data science" to describe statistics.

The rise of the internet and big data in the 2000's significantly fueled the development and popularity of data science.

The rise of AI, machine learning, and deep learning increased the speed and scale of data analysis in the 2000's and 2010's, fostering data science's evolution and wider adoption.

Data for data science comes from many places, including sensors, surveys, social media, business, and scientific experiments.

Data science became increasingly applied in fields like medicine, engineering, and business as data became more accessible and its value became apparent.

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from data. It combines domain expertise, programming skills, and statistical knowledge to understand patterns, make predictions, and drive better decisions.

Data science involves gathering, analyzing, and interpreting data to discover meaningful patterns and trends. It uses these insights to predict future outcomes and guide decision-making.

Data science is applied in various industries like banking, healthcare, and manufacturing to improve efficiency, personalize experiences, and make better predictions.

Data science helps predict flight delays by analyzing historical data and identifying patterns that contribute to delays. This allows airlines to proactively inform passengers and manage resources effectively.

Data science can analyze customer data to personalize promotional offers, tailoring them based on individual preferences and purchasing history to increase customer engagement and sales.

Data science helps companies forecast revenue by analyzing historical data and market trends. This allows businesses to plan for future growth and make informed financial decisions.

Data science can analyze the results of training programs to assess their impact on health outcomes. This allows for optimizing training plans and developing evidence-based health strategies.

Data science can analyze voter data, social media trends, and historical voting patterns to predict election outcomes. It can provide insights into voter behavior and inform political campaign strategies.

Due to massive amounts of data, it's impossible for humans to analyze it efficiently. Data comes from diverse sources, often unstructured, incomplete, inaccurate, or at different scales making it difficult to compare.

A data scientist analyzes messy data to extract valuable insights and patterns. They use machine learning, statistics, programming, mathematics, and databases to achieve this.

Facebook collects location data to connect friends, but also to identify migration patterns and analyze fanbases. Data is used for multiple purposes.

Target uses customer purchase and interaction data to predict pregnancy and target related products more effectively.

A data scientist needs expertise in various areas: machine learning, statistics, programming (like Python or R), mathematics, and databases.

While statisticians, analysts, and programmers existed before, data scientists combine these skills into a single profession.

Before finding patterns, data needs to be organized into a standard format. Like unboxing and organizing new purchases.

Data scientists ask the right questions to understand the business problem, explore and collect data from various sources, extract and clean it for analysis.

Scaling data values to a consistent range (e.g., 0 to 1) to make them comparable and avoid bias due to different measurement units.

A company where decisions are based on data insights and analysis, influencing both strategic planning and day-to-day operations.

Improved customer understanding, better market knowledge, and more effective decision-making leading to increased efficiency and profitability.

A leadership role responsible for overseeing data strategy, governance, and utilization within an organization.

A leader who uses data science expertise to support strategic decision-making, research, and innovation within an organization.

The ability to effectively convey complex data analysis results and insights to non-technical audiences in a story-driven and actionable way.

Data-driven findings that directly translate into practical steps and actions for improving business processes or decision-making.

The process of ensuring data is accurate, organized, consistent, and error-free. It involves removing inconsistencies and inaccuracies.

Data analysis focuses on structured data to answer specific questions, while data science uses various techniques to explore patterns in large, often complex datasets.

Examining smaller, structured datasets to answer specific questions or solve problems. It involves cleaning, visualizing, and exploring data to gain insights.

Clean and accurate data is crucial for reliable insights. Poor data quality can lead to incorrect conclusions and decisions.

An organization where the importance of data quality and its application is understood and valued by all members.

Data science is a broader field that uses various techniques to extract knowledge and insights from data. Data analysis is a subset that focuses on structured data and specific questions.

Data science has revolutionized various industries by enabling better decision-making, personalization, and prediction through the analysis of vast amounts of data.

Walmart used data on product sales, placement impact on sales, seasonal trends, and regional customer preferences. They analyzed this data to manage their inventory and predict future demand.

Walmart became the first company to use barcode scanners at cash registers in the 1980s. This allowed them to capture more accurate and detailed data on product sales.

GE uses data collected from its airline engines in real time to monitor performance, identify potential issues, and improve engine efficiency. They analyze a massive amount of data generated during flights.

Data-driven decisions enable better operational efficiency, improved customer satisfaction, enhanced product development, and more effective risk management.

Radio-frequency identification (RFID) technology is used to track inventory and manage supply chains efficiently in data-driven organizations.

Companies like Google, Amazon, Facebook, and LinkedIn use data to personalize user experiences, target advertising, and improve product recommendations.

Data-driven strategies are employed across diverse industries like retail, healthcare, finance, and manufacturing to improve efficiency, customer service, and operations.