Difference between Data Scientist and Data Analyst

What are their skills?

Data Analyst

  • Data Mining
  • Data Warehousing
  • Math, Statistics
  • Tableau and data visualization
  • SQL
  • Business Intelligence
  • Advanced Excel skills

Data Scientist

  • Data Mining
  • Data Warehousing
  • Math, Statistics, Computer Science
  • Tableau and Data Visualization/Storytelling
  • Python, R, JAVA, Scala, SQL, Matlab, Pig
  • Economics
  • Big Data/Hadoop
  • Machine Learning

Educational requirements

Data Analyst

  • Foundational math, statistics
  • Basic fluency in R, Python, SQL
  • SAS, Excel, business intelligence software
  • Analytical thinking, data visualization

Data Scientist

  • Advanced statistics, predictive analytics
  • Advanced object-oriented programming
  • Hadoop, MySQL, TensorFlow, Spark
  • Machine learning, data modelling

What do they do?

Data Analyst

  • Collaborating with organizational leaders to identify informational needs
  • Acquiring data from primary and secondary sources
  • Cleaning and reorganizing data for analysis
  • Analyzing data sets to spot trends and patterns that can be translated into actionable insights
  • Presenting findings in an easy-to-understand way to inform data-driven decisions

Data Scientist

  • Gathering, cleaning, and processing raw data
  • Designing predictive models and machine learning algorithms to mine big data sets
  • Developing tools and processes to monitor and analyze data accuracy
  • Building data visualization tools, dashboards, and reports
  • Writing program to automate data collection and processing

Specific Roles

Data Analyst

  • Data querying using SQL
  • Data analysis and forecasting Excel
  • Creating dashboards using business intelligence software.
  • Performing various types of analytics including descriptive, diagnostic, predictive or prescription analytics.

Data Scientist

  • Spend up to 60% of their time scrubbing data.
  • Data mining using APIs or building ETL pipelines.
  • Data cleaning using programming language (e.g. Python or R).
  • Statistical analysis using machine learning algorithm such as, logistic regression, KNN, Random Forest or gradient boosting etc.
  • Creating programming and automation techniques, using tools like TensorFlow to develop and train machine learning models.
  • Developing big data infrastructures using Hadoop and Spark and tools such as Pig and Hive.

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