Data Science
What is the work of a Data Scientist?
Work of Data Scientist
1. Goal of Data Scientist
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The data scientist works with business stakeholders to define goals and objectives for the analysis.
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These goals can be defined specifically, such as optimizing an advertising campaign, or broadly, such as improving overall production efficiency.
2. Data Collection
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Data collection is one of the most important responsibilities of a data scientist.
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It involves gathering relevant data from various sources for analysis and modeling.
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Effective data collection is essential for generating valuable insights and making informed decisions in data science projects.
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It requires careful planning, attention to detail, and a systematic approach to gathering, cleaning, and managing data effectively.
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When data collection and storing systems are not in place, the data scientists establish a systematic process for data collection.
Data Integration & Management
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The data scientist applies best data integration practices to transform raw data into clean information, that’s ready for analysis.
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Data integration and management process involves data replication, ingestion, and transformation to combine different types of data into standardized formats which are then stored in a repository such as a data lake or data warehouse.
4. Data Investigation & Exploration
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In this step, the data scientist performs an initial investigation of the data and exploratory data analysis.
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Data investigation and exploration involve understanding and analyzing a dataset to uncover patterns, relationships, and insights.
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It includes tasks like calculating summary statistics, visualizing data with charts and graphs, identifying outliers, and cleaning the data to prepare it for analysis.
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These steps help data scientists gain a deeper understanding of the data and inform further analysis and decision-making.
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This investigation and exploration is typically performed using a data analytics platform or business intelligence tool, such as Tableau or Power BI.
5. Model Development
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Based on the business objective and the data exploration, the data scientist chooses one or more potential analytical models and algorithms.
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Then build these models using languages such as SQL, R, or Python and apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence.
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Then the models are "trained" via iterative testing until they operate as required.
6. Model Deployment and Presentation
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Once the models have been selected and refined, they are run using the available data to produce insights.
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These insights are then shared with all stakeholders using sophisticated data visualization and dashboards.
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Based on feedback from stakeholders, the data scientist makes any necessary adjustments to the model.
Work of Data Scientist
1. Goal of Data Scientist
- The data scientist works with business stakeholders to define goals and objectives for the analysis.
- These goals can be defined specifically, such as optimizing an advertising campaign, or broadly, such as improving overall production efficiency.
2. Data Collection
- Data collection is one of the most important responsibilities of a data scientist.
- It involves gathering relevant data from various sources for analysis and modeling.
- Effective data collection is essential for generating valuable insights and making informed decisions in data science projects.
- It requires careful planning, attention to detail, and a systematic approach to gathering, cleaning, and managing data effectively.
- When data collection and storing systems are not in place, the data scientists establish a systematic process for data collection.
Data Integration & Management
- The data scientist applies best data integration practices to transform raw data into clean information, that’s ready for analysis.
- Data integration and management process involves data replication, ingestion, and transformation to combine different types of data into standardized formats which are then stored in a repository such as a data lake or data warehouse.
4. Data Investigation & Exploration
- In this step, the data scientist performs an initial investigation of the data and exploratory data analysis.
- Data investigation and exploration involve understanding and analyzing a dataset to uncover patterns, relationships, and insights.
- It includes tasks like calculating summary statistics, visualizing data with charts and graphs, identifying outliers, and cleaning the data to prepare it for analysis.
- These steps help data scientists gain a deeper understanding of the data and inform further analysis and decision-making.
- This investigation and exploration is typically performed using a data analytics platform or business intelligence tool, such as Tableau or Power BI.
5. Model Development
- Based on the business objective and the data exploration, the data scientist chooses one or more potential analytical models and algorithms.
- Then build these models using languages such as SQL, R, or Python and apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence.
- Then the models are "trained" via iterative testing until they operate as required.
6. Model Deployment and Presentation
- Once the models have been selected and refined, they are run using the available data to produce insights.
- These insights are then shared with all stakeholders using sophisticated data visualization and dashboards.
- Based on feedback from stakeholders, the data scientist makes any necessary adjustments to the model.