Difference between Data Scientist and Data Analyst

What are their skills?

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

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

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.

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.