data analysis
1. Definition of Data and Data Analyst: The speaker emphasizes that there is no simple one-sentence answer to what data is, due to its broad nature, and the same applies to data analysis.
2. Data Definition: Data is described as factual information, such as measurements or statistics, used for reasoning, discussion, or calculation, and as information in digital form that can be processed or transmitted.
3. Analysis Definition: Analysis is not just about statistical or mathematical methods. It is a detailed examination to understand complex elements and their relationships. It applies to everyday work in the data field.
4. Role of a Data Analyst: A data analyst is someone who studies and determines the nature and relationships of factual information (in digital form), doing a thorough examination to understand its features or make calculations based on it.
Discovering if you’re an analyst
The main points of the summary are:
1. Everyday Data Use: The speaker explains that by using tools like fitness trackers or calorie counters, people are already gathering and analyzing data for decision-making, such as adjusting activities based on past data.
2. Wider Role of Data Analysts: Many people, even those not officially titled “data analysts,” are performing data-related tasks in their jobs, such as collecting, storing, managing, and analyzing data, even if their job titles don’t explicitly mention data analysis.
3. Data Tasks in Daily Work: Common tasks like exporting data to Excel, cleaning and transforming it, building calculations, joining datasets, and visualizing data (e.g., with charts or graphs) are all examples of work data analysts do.
4. Core Functions of a Data Analyst: Data analysts typically find, collect, inspect, clean, transform, and model data, seeking answers to questions to provide insights and support informed decision-
Organizational roles in data
1. Organizational Roles in Data Analysis: The roles in data analysis vary by organization based on size, workforce, and the industry they serve. A small company serving 1,000 people has a different approach than a large, highly-regulated organization serving millions.
2. Research: This role helps define the questions to be answered, sets data parameters, and involves business specialists focused on methods and statistical outcomes.
3. Data Governance: This ensures data ownership and accountability are clearly defined, and it ensures that those who need access to data can get it. Data governance should cross all departments, not just be isolated in IT.
4. Technology’s Role: Technology in organizations supports data infrastructure, security, and access control. It’s involved in data governance and plays a significant role across all organizational levels.
5. Dedicated Data Departments: Larger organizations with big data may have specialized departments with data scientists, engineers, and analysts. Smaller organizations might integrate these roles into their technology departments.
6. Overall Purpose of Data Roles: The goal of these roles is to improve the product, service, and bottom line of the company, while also contributing to solving larger global issues.
Here are the key points from the summary:
1. Tech Roles in IT: IT professionals, such as server and database administrators, are often required to handle data tasks like querying and providing data, even if their focus is on technology rather than data visualization or analysis.
2. Specialization in Technology: Technology skills are highly specialized, and no one role can cover all areas. Different roles require different expertise, such as server management versus data analysis or visualization.
3. Multidisciplinary Research Teams: In a research team, roles vary depending on the project. For example, the speaker’s role involves providing structured, cleaned datasets to a team of economists, public health experts, and education experts. The speaker’s role may shift between data architect, data engineer, and data analyst, depending on the needs.
4. Data Architect: A senior role responsible for defining how data is stored, managed, and integrated at the organizational level. Data architects translate business requirements into technology requirements and set data standards.
5. Data Scientist: While highly sought after, data scientists need more than just technical skills in data, coding, and statistics. If an organization lacks data architects and engineers, data scientists may need to take on those roles before doing their primary work.
6. Data Engineer: This role focuses on building databases, data warehouses, and systems that transform and prepare data for analysis. Data engineers are crucial in making data meaningful for research, statistics, and data science work. They typically don’t focus on statistics but on creating systems for data consumption.
7. Role Placement in Organizations: Traditionally, roles like database administrators, architects, engineers, and data security are under the technology department. However, as data grows in importance, many data roles are now found in business departments and back-office operations.
8. Business Analysts and System Analysts: These roles involve dealing with data within business processes and systems. Business analysts typically work in business units, while system analysts are aligned with IT.
9. Business Intelligence Roles: Roles such as business intelligence analysts or specialists focus on visualizing and reporting data to improve business operations.
10. Continuous Learning: In the data field, it’s essential to continuously learn and explore different data-related courses, as the field is constantly evolving.
Here are the key points from the summary:
1. Analogy of a Carpenter: Just as a carpenter uses different tools based on the project, a data analyst uses various tools and experiences to solve problems. A seasoned professional has a broad set of tools and knowledge, while an apprentice may be limited in both.
2. Types of Skills: Data analysts need both technical skills (tools) and soft skills (communication and understanding). These are fundamental skills that can be applied across different data tools.
3. Creating and Understanding Questions: One key skill is being able to formulate and understand the questions that need to be answered with data. For example, determining if targets for shipping orders are being met and what the key metrics are.
4. Finding and Gathering Data: Analysts need to know where to find the relevant data, how to access it, and how much data is needed to answer specific questions.
5. Data Quality: Understanding and assessing the quality of data is crucial. It’s important to know where the data comes from and whether it’s reliable—whether it’s entered directly by a customer, tracked by a system, or recorded on a spreadsheet.
6. Identifying Relevant Data: Analysts must determine what data is important to answer the question. Often, there’s more data available than necessary, and this will help in deciding how much cleaning or transformation is needed.
7. Creating Valid Data: Sometimes, data isn’t available in the exact form needed. Analysts may need to create new data, like calculating the number of days between two dates, to answer the question at hand.
8. Data Presentation: One of the most critical skills for data analysts is presenting data in a clear and actionable way. It’s not about explaining every technical detail, but ensuring the answer is clear, understandable, and actionable through visuals or reports.
9. Pivot Tables and Core Skills: Starting with basic tools, like pivot tables, is essential. As analysts become more experienced, they may learn more advanced tools and even coding. But a solid foundation in basic data skills is crucial.
10. Continuous Learning: As data grows and new tools like Power BI and Tableau emerge, analysts need to continually grow their skills to keep up with the evolving landscape of data. The demand for data skills will only increase as innovation continues.