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Data Scientist

A Data Scientist's primary goal is to analyze large datasets to extract valuable insights and inform business decisions.

Capabilities​

Data Scientists should have the following capabilities:

  • Communication and Storytelling
  • Business Acumen and Domain Knowledge
  • Statistical Analysis and Probability
  • Machine Learning and Predictive Modelling
  • Data Visualization

Activities​

Core activities of a Data Scientist include:

  • Data Analysis
  • Model Development
  • Insight Communication
  • Business Understanding

Data Analysis​

  • Exploring and visualizing data to identify patterns, trends, and relationships
  • Conducting statistical analysis to draw meaningful insights from data
  • Applying machine learning techniques to detect patterns and make predictions

Model Development​

  • Selecting appropriate algorithms and techniques to create predictive models
  • Training, testing, and validating models to ensure accuracy and reliability
  • Refining models based on feedback and new data

Insight Communication​

  • Presenting findings and recommendations to stakeholders through reports and visualizations
  • Translating technical insights into clear, actionable business language
  • Collaborating with cross-functional teams to implement data-driven solutions

Business Understanding​

  • Researching the industry and company to identify opportunities for data-driven improvements
  • Defining relevant datasets and metrics to track based on business objectives
  • Providing guidance on data strategy and best practices

Relationships​

Data Scientists focus on extracting insights and building predictive models from data and more analytical with a business focus.

Data Engineers build and maintain the underlying data infrastructure to enable this analysis, and are more technical and systems focused.

Both roles collaborate closely to drive data-driven decision making in organizations.

Tech Stack​

While Data Engineers focus more on building and maintaining the data infrastructure, pipelines, and storage systems, Data Scientists primarily work with the data itself, performing analysis, building machine learning models, and creating visualizations.

  • Analysis
  • Modelling
  • Visualization
  • Big Data Processing
  • Cloud Computing

Analysis​

Data Analysis and Manipulation

  • SQL: Querying and manipulating relational databases
  • Python: General-purpose programming language with extensive data science libraries (Pandas, NumPy)
  • R: Statistical programming language for data analysis and visualization

Modelling​

Machine Learning and Predictive Modelling:

  • Scikit-learn: Python library for machine learning algorithms and models
  • TensorFlow: Open-source library for machine learning and deep learning
  • PyTorch: Open-source machine learning library based on Torch

Visualization​

Data Visualization and Reporting:

  • Tableau: Interactive data visualization and business intelligence platform
  • Power BI: Business analytics service by Microsoft for interactive visualizations
  • Matplotlib: Python plotting library for creating static, animated, and interactive visualizations

Big Data Processing​

  • Apache Spark: Used for large-scale data processing and machine learning
  • Apache Hadoop: Framework for distributed storage and processing of big data

Cloud Computing​

  • Amazon Web Services (AWS): Cloud computing platform with various data storage and analytics services
  • Google Cloud Platform (GCP): Suite of cloud computing services, including BigQuery and AI/ML tools
  • Microsoft Azure: Cloud computing service for building and deploying data-driven applications