Data Scientist vs Data Analyst

A data analyst may spend more time on routine analysis, providing reports regularly. A data scientist may design the way data is stored, manipulated and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist works on new ways of capturing and analyzing data to be used by the analysts.

If you love numbers and statistics as well as computer programming, either path could be a good fit for your career goals.

Data Analyst

  • Data Mining
  • Data Warehousing 
  • Math, Statistics
  • Tableau and Data 
  • Visualization
  • SQL
  • Business Intelligence
  • SAS
  • Advanced Excel Skills

Data Scientist

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

Their Skills

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 programs to automate data collection and processing

Specific Roles

Data Analyst

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

Data Scientist

  • Spend up to 60% of their time scrubbing data
  • Data mining using APIs or building ETL pipelines 
  • Data cleaning using programming languages 
  • Statistical Analysis using machine learning algorithms 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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