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

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:

• Formulate and test a hypothesis with an analytical mindset.
• Design an experimental project, apply data science workflows, and curate data relevant to business outcomes.
• Analyze data for insights using visualizations.
• 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:

• Write code to read data, access packages, and apply logic.
• Debug, optimize, organize, and comment on your code.
• Clean data using statistical approaches.
• Create, read, update, and delete data on databases.
• 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.

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

Checklist for Data Engineer

This checklist will help data engineers use the right tools to develop a high-performance infrastructure that efficiently consumes and understands data.

Business Analysis, Approach, and Management

Tools Required: Notebooks and pens.

Expected work:

• Think analytically and work with stakeholders to identify data sources.
• Apply inferential and descriptive statistics to understand the characteristics of a population and identify trends.

Programming, Database Concepts, and Big Data

Tools Required: Python, R, Matlab, IDEs, notebooks, databases, data stores, Talend, Hadoop, and Spark ecosystems.

Expected work:

• Write code to read data, access packages, and apply logic.
• Debug, optimize, organize, and comment on your code.
• Manage database schema, extract, transform, and load data..
• Optimize the performance of database queries and collect data from various sources.
• Scale data science projects with architectural components.
• Architect high-performance frameworks to process a variety of data.
• Ensure that data is easily accessible for analysis.

Data Analyst Checklist

This checklist will enable organizations to communicate insights that use exploratory analysis to deliver business value.

Business Analysis and Data Visualization

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

Expected work:

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

Programming, Data Modelling, and Database Concepts

Tools Required: R Studio, Pandas, SAS, Excel, A/B testing, data mining, Python, R, Matlab, IDEs, and MySQL.

Expected work:

• Write code to read data, access packages, and apply logic.
• Debug, optimize, organize, and comment on your code
• Create, read, update, and delete data on databases.
• Extract, transform and load your data.
• Identify outliers and clean data using statistical approaches.

Use this checklist to find the right fit in your team. In case you need help or would like to connect with a team that can take care of all your data science needs, connects with us today.