What is Data Science? | Free Data Science Course | Data Science for Beginners | codebasics
codebasics・2 minutes read
Data science involves using technology like Excel to analyze data and make business decisions, but as data volume grows, advanced tools like Python and Apache Spark are needed. The process includes defining a problem, collecting and cleaning data, building models with machine learning, and deploying them for applications like product recommendations and fraud detection.
Insights
- Excel and bar charts are traditional tools used in data science for making business decisions, but with the rise of big data, more advanced technologies like Python, R, Apache Hadoop, and Apache Spark are needed for effective analysis.
- Data science projects involve a structured process from defining a business problem to deploying models for predictive analysis, utilizing machine learning techniques like grid search cv2 and hyperparameter tuning to derive insights with real-world applications in industries such as e-commerce, finance, and logistics.
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Recent questions
What tools are used in data science?
Python, R, Apache Hadoop, Apache Spark
How does a data science project typically start?
Defining a business problem
What are some real-life applications of data science?
Product recommendations, fraud detection, route optimization
Why are traditional tools like Excel insufficient for big data?
Inadequate for handling large data volumes
How are data science models deployed for predictive analysis?
Deployed to production for business decisions
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Summary
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"Data Science: From Excel to Python"
- Data science involves deriving insights from data using technology like Excel files and bar charts to make informed business decisions, such as running special promotions based on sales data.
- With the increase in data volume from sources like the Internet and smart devices, traditional tools like Excel are insufficient for handling big data, necessitating advanced technologies like Python, R, Apache Hadoop, and Apache Spark for analysis.
- A typical data science project starts with defining a business problem, followed by data collection, cleaning, and exploration, leading to model building using machine learning techniques like grid search cv2 and hyperparameter tuning.
- Data scientists deploy models to production for predictive analysis, aiding in making business decisions, with real-life applications including product recommendations on Amazon, fraud detection in finance, and route optimization in the shipping industry.
