Data analysts collect, process and perform statistical data analysis. Their skills may not be as advanced as data scientists (ie they can not create new algorithms), but their goals are the same – to find out how data can be used to ‘answering questions and solving problems.
Data Analyst Responsibilities:
Depending on their level of expertise, data analysts can:
-Work with IT teams, management scientists and / or data to determine organizational goals
-Image data from primary and secondary sources
-Clean and correct the data to remove irrelevant information
-Analyze results with standard statistical tools
-Determine trends, correlations and patterns in complex data sets
-Identify new opportunities for improving the process
-Provide accurate data reports and clear visualization of management data
-Design, creation and maintenance of relational databases and data systems
-Triage code issues and data related issues
Data analysts are often referred to as a new data scientists or a data scientist in making. Rather than being free to create large-scale project data, they can be limited to addressing specific business tasks by using existing tools, systems, and data sets.
However, there are many companies that do not make a clear distinction between the two roles. In some cases, data analysts can write scientific research or address standard morning requirements and create or experiment with custom solutions with relational databases, Hadoop and NoSQL in the afternoon.
How to become a Data Analyst?
1. Make a Bachelor’s Degree in Math, Statistics, Computer science, Information Management, Finance or Economics
Most of the first level candidates will need a bachelor’s degree in maths, computer science statistics, information management, finance or economics. All these subjects put special emphasis on statistical and analytical skills.
To climb your career steps or transition to the role of a data scientist, you may need to acquire a master’s degree in computer science or information management or a postgraduate certificate in a similar field.
Note: We discuss self-training opportunities in the qualifications section of scientists.
2. Correct Your Technical, Analytical and Programming Skills
-Technical Skills for Data Analysts
-Statistical methods and packs (eg SPSS)
R and / or SAS
-Data warehouses and business intelligence platforms
-SQL databases and database languages
-Designing the database
-Data Cleansing and Missing
-Data visualization and reporting techniques
-Recognizing the work of Hadoop & MapReduce
-Learning Machine Technique
This is a sample list and thus may change.
The skills of a Data Analyst may not be as advanced as data scientists (i.e., they can not create new algorithms), but their goals are often the same – to find out how data can be used to ‘answering questions and solving problems.