What is Data Science?
TLDRThe script defines data science as the intersection of computer science, math, and business expertise used to extract insights from noisy data to guide business decisions. It outlines different types of analytics from descriptive to prescriptive based on complexity and value. It then walks through the data science lifecycle from formulating the right business questions to data mining, cleaning, analysis, and visualization. It also describes key roles like business analysts, data engineers, and data scientists and how collaboration across them is critical.
Takeaways
- 😀 Data science involves extracting insights from noisy data to guide business actions
- 😊 It encompasses computer science, math and business expertise
- 📊 There are four levels of analytics: descriptive, diagnostic, predictive and prescriptive
- 🔎 Descriptive analytics focuses on what is happening
- 🔍 Diagnostic analytics identifies why something happened
- 🔮 Predictive analytics forecasts what will happen next
- 💡 Prescriptive analytics recommends the best course of action
- 📈 The data science lifecycle moves from business needs to data mining, cleaning, analysis and visualization
- 👥 Collaboration between business analysts, data engineers and data scientists is key
- 🎯 The goal is to turn noisy data into meaningful insights and actions
Q & A
What are the three main disciplines that intersect to form data science?
-The three main disciplines that intersect to form data science are computer science, mathematics, and business expertise.
What are the four main types of data science methods discussed, in order of increasing complexity and value?
-The four main types of data science methods discussed are: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
What is the first step in the data science lifecycle?
-The first step in the data science lifecycle is business understanding, which involves clearly defining the business problem to solve.
What happens during the data cleaning phase of the data science lifecycle?
-During data cleaning, issues like missing values, duplicate rows, and other data quality problems are addressed to prepare the data for analysis.
What types of advanced analytical tools can be used during the exploration phase?
-During exploration, advanced analytical tools like machine learning can be used to uncover deeper insights and make predictions.
Why is collaboration important between different roles like business analysts, data engineers, and data scientists?
-Collaboration between these roles is critical because there is overlap in their responsibilities, so they need to work together across the data science lifecycle.
How can properly visualized insights from data science impact a business?
-Effective data visualizations allow the key insights uncovered to be clearly communicated to business stakeholders so they can take meaningful actions.
What value does diagnostic analytics provide over descriptive analytics?
-While descriptive analytics answers what is happening, diagnostic analytics provides more value by answering why something occurred.
How could machine learning be leveraged for prescriptive analytics?
-Machine learning models could be trained to recommend optimal decisions and actions for a business to take to achieve a desired future outcome.
Why is domain expertise important when formulating questions to analyze?
-Domain knowledge of the business ensures data science efforts start by asking the right questions that will provide the most valuable insights.
Outlines
📊 What is Data Science and its Different Types
This paragraph defines data science as the field involving extracting insights from noisy data and turning them into business actions. It covers how data science combines computer science, math, and business expertise. The paragraph also classifies different types of data science methods based on complexity and value - from descriptive (what happened) to predictive (what will happen) to prescriptive analytics (what actions should be taken).
🚀 The Data Science Lifecycle and Roles Involved
This paragraph explains the key stages in the data science lifecycle - from formulating the right business questions, data mining, cleaning, analysis and visualization of insights. It describes the different roles involved - business analysts, data engineers and data scientists - and how they collaborate across stages, with some overlap in responsibilities.
Mindmap
Keywords
💡Data science
💡Descriptive analytics
💡Diagnostic analytics
💡Predictive analytics
💡Prescriptive analytics
💡Data science lifecycle
💡Machine learning
💡Business analyst
💡Data engineer
💡Data scientist
Highlights
Data science involves extracting insights from noisy data to drive business actions
Data science combines computer science, math, and business expertise
Descriptive analytics answers what is happening in the business
Diagnostic analytics answers why something happened
Predictive analytics forecasts what will happen next
Prescriptive analytics recommends the best action to achieve a goal
Start data science projects by understanding the business problem
Data mining retrieves relevant data for analysis
Data cleaning prepares noisy data for reliable analysis
Data exploration applies analytical tools to extract insights
Machine learning enables advanced predictive and prescriptive analytics
Data visualization communicates insights to stakeholders
Business analysts frame questions and visualize insights
Data engineers access, clean, and explore data
Data scientists perform advanced analytics and machine learning
Transcripts
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