Intro to Analytics Chapter 1.1

• Definition of Data Mining: A business process for exploring large amounts of data to discover meaningful patterns and rules.

• Learning Relationships: Small businesses form learning relationships with customers over time, enhancing customer loyalty and satisfaction.

• Larger Companies: Must leverage vast amounts of data generated from customer interactions to form learning relationships, unlike smaller businesses.

• Business Process: Data mining is an ongoing process that combines data collection, analysis, and actions that generate more data.

• Importance of Data: Data mining is most effective when large volumes of data are available; more data leads to better model training and meaningful insights.

• Meaningful Patterns: The primary goal of data mining is not just to find patterns but to find patterns that provide actionable insights that can improve business operations.

• Customer Relationship Management (CRM): Data mining plays a crucial role in improving CRM by fostering individual customer relationships and tailoring marketing strategies accordingly.

• Current Trends: Increased production and warehousing of data, affordable computing power, and strong interest in CRM are driving the growth of data mining since the 1990s.

• Skills for Data Miners: Proficiency in statistics, familiarity with tools like Excel, and communication skills to convey complex results are essential.

• The Virtuous Cycle of Data Mining: The process involves turning data into information, information into action, and action into value, emphasizing that the outcomes of data mining should lead to informed business decisions.

Key Terms

• data analytics: a coming together to solve a business problem through the creative use of data and statistical modeling to tell a compelling story that drives strategic action and results in business value

• data science: a field of study that involves using computational and statistical techniques to extract insights and knowledge from data

• variables: a container or storage location that holds a value; variables can be manipulated throughout the data analysis process to achieve the necessary results

Key Takeaways

1. Analytics encompasses so much more than “just” technical skills, and analytics exists in every facet of a business, not just IT.

2. Analytics is an umbrella discipline that represents many roles, and it is growing. While analytics started off with data scientists as the focus role (circa 2007), it has grown to include data analysts, data engineers, machine learning engineers, analytics product managers, decision scientists, visualization engineers/scientists, and more!

3. While analytics, as a whole, requires both hard and soft skills to drive business value and effect change, the roles under the analytics umbrella require different combinations of skill domains. So, if someone wants to go deep into coding in their professional job, there is a role for them. Conversely, if someone wants to spend more time with business stakeholders, there is a role for them, too! The portfolio of potential analytics roles will (and does) appeal to many people—making the field very accessible.

Key Takeaways- What is data Science

• Hacking Skills (Computer Programming): Essential for handling novel data sources like social media, images, and streaming data. Programming languages such as Python, R, C, C++, Java, and SQL are crucial for data manipulation and modeling.

• Mathematical Elements: Important for choosing appropriate procedures and diagnosing problems. Key areas include probability, linear algebra, calculus, and regression.

• Substantive Expertise: Each domain has unique goals and methods. Understanding the specific domain helps in implementing insights and making data science action-oriented.

• Hacking Skills (Computer Programming): Essential for handling novel data sources like social media, images, and streaming data. Programming languages such as Python, R, C, C++, Java, and SQL are crucial for data manipulation and modeling.

• Mathematical Elements: Important for choosing appropriate procedures and diagnosing problems. Key areas include probability, linear algebra, calculus, and regression.

• Substantive Expertise: Each domain has unique goals and methods. Understanding the specific domain helps in implementing insights and making data science action-oriented.

• Hacking Skills (Computer Programming): Essential for handling novel data sources like social media, images, and streaming data. Programming languages such as Python, R, C, C++, Java, and SQL are crucial for data manipulation and modeling.

• Mathematical Elements: Important for choosing appropriate procedures and diagnosing problems. Key areas include probability, linear algebra, calculus, and regression.

• Substantive Expertise: Each domain has unique goals and methods. Understanding the specific domain helps in implementing insights and making data science action-oriented.