On this special day, we celebrate the invaluable contributions of data scientists everywhere. Happy Workers' Day to all who labor tirelessly to unlock the potential of data and drive progress in our interconnected world.
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Lastly, Effective communication and collaboration with domain experts, stakeholders, and team members are essential for a successful data science workflow.
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Throughout these stages, it's important to note that data science is an iterative process
You might find yourself going back and forth between stages as you uncover new insights, encounter challenges, and refine your approach.
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6. Model Deployment
After developing a successful model, the next step is to deploy it for real-world use
This involves integrating the model into a production environment, creating APIs for interaction, and ensuring the model can handle incoming data reliably and efficiently.
5 Model Building
This stage, you design, train, and evaluate machine learning models using preprocessed data & engineered features
You select appropriate algorithms based on the problem you're solving (classification,regression) tune their hyperparameters assess their performance
4 Feature Engineering This involves selecting, transforming &creating relevant features from your data to improve the performance of #ML models This stage requires domain knowledge to extract meaningful information &create features that capture the underlying patterns in the data
3. Data Visualization
This is a powerful tool for communicating findings& insights from your data
You can present complex information in a more understandable way by using graphs, charts etc
Data viz helps decision-makers &stakeholders understand the significance of your analysis
2 EDA
EDA is the process of summarizing & visualizing the main characteristics, patterns &relationships within a dataset
EDA assists you in understanding the data structure,identifying missing values,outliers & anomalies &gaining insights that will guide subsequent workflow steps
1. Programming
Programming is the foundation of any data science project.
It involves writing code to manipulate, clean, & preprocess data and to implement algorithms & models.
#Python & #R are popular programming languages for data science due to their rich libraries and tools.
Machine learning is a subset of AI that deals with algorithms, statistical models, and analytics. Machines have historically been programmed to obey certain instructions given to them, and have not had the opportunity to make decisions.
datainsightmag.com/introduction-t…
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