📚 Data science is the field of study that involves extracting knowledge and insights from noisy data and turning them into actions for businesses or organizations.
🤝 Data science is the intersection of computer science, mathematics, and another important discipline.
📊 Data science is the intersection of business expertise, math, and programming.
🔍 There are different types of data science methods based on the complexity and value of the questions asked.
📈 Descriptive analytics focuses on understanding what is happening in a business through accurate data collection.
📈 Diagnostic analytics: understanding why sales go up or down.
🔮 Predictive analytics: using historical patterns to predict future sales performance.
📋 Prescriptive analytics: recommending actions to achieve specific outcomes.
🔍 Data science starts with business understanding and asking the right questions.
💻 Data mining is the process of procuring relevant data for analysis.
🧹 Data cleaning is necessary before further analysis and interpretation.
💡 Data preparation and cleaning are necessary before analysis.
🔍 Exploration involves using analytical tools to answer questions.
📈 Advanced analytics, such as machine learning, enable predictive and prescriptive actions.
📊 Data visualization is important for communicating insights.
🔍 There are different roles in the data science lifecycle, including business analysts, data engineers, and data scientists.
💡 Data scientists contribute to exploring and applying advanced machine learning techniques.
🔍 Collaboration is critical in data science as there is overlap between different roles.
🔄 The data science life cycle helps transform noisy data into actionable insights.
📊 Visualization is important in understanding and analyzing data.