Data Scientist vs Data Engineer vs Data Analyst

Data Science jobs are eating job boards. Most companies continue to work on building their data teams with engineers, scientists, and analysts. So what exactly is the difference between these roles. Who does what? Read on to find more.

The Complete Checklist For Your Data Science Team

1. Checklist For Data Scientist

This checklist will enable data scientists to combine their data skills with analytical techniques to develop robust analytical models.

👉 Business Analysis and Data Visualization

Tools Required: Pen, paper, presentations, entity-relationship diagrams, and project management software such as Trello or Jira.

Expected work:

1. 1. 1. Formulate and test a hypothesis with an analytical mindset.
      2. Design an experimental project, apply data science workflows, and curate data relevant to business outcomes.
      3. Analyze data for insights using visualizations.
      4. Present data science projects to stakeholders using visuals that aid their understanding.

👉 Programming and Database Concepts

Tools Required: Python, R, Matlab, IDEs, and Notebooks

Expected work:

1. 1. 1. Write code to read data, access packages, and apply logic.
      2. Debug, optimize, organize, and comment on your code.
      3. Clean data using statistical approaches.
      4. Create, read, update, and delete data on databases.
      5. Apply data normalization and collect data from sources such as APIs and the web.

👉 Statistics, Big Data, and Machine Learning Algorithms

Tools Required: R Studio, Pandas, SAS, Excel, A/B testing, data mining, Hadoop, MongoDB, Tensorflow, and Amazon Machine Learning.

Expected work:

1. 1. 1. Build and measure the quality of statistical models over time.
      2. Apply inferential and descriptive statistics to understand the characteristics of a population and identify trends.
      3. Recommend appropriate searching and indexing methods.
      4. Use big data tools and platforms to access data and run models.
      5. Apply clustering, classification, and Natural Language Processing algorithms.