The main role of a Data analyst is to extract data and interpret the information obtained from the data for analyzing the outcome of a given problem in business. In this process, the analyst also discovers the various bottlenecks that are found in the results and provides possible solutions for the same. Extraction of information from given existing data is done using one or more standard methodologies such as data cleaning, data transformation, data visualization, and data modeling. Using these methodologies, a data analyst can make careful, data-driven decisions.
The major skills required to be a data analyst are Python and or R programming skills, Structured Query Language (SQL), Statistical Analysis Software (SAS), SAS Miner, Microsoft Excel, and or Tableau. The key areas and techniques which a data analyst should be well-versed with include the following:
Data preprocessing, which is an important step in data analysis, involves data cleaning, data integration, data transformation, and data reduction.
Data visualization, which is the graphical representation of data that can make information easy to analyze and understand.
Statistical modeling, which mainly involves two important kinds, descriptive or summary statistics and inferential statistics.
Programming skills, for which a data analyst may thoroughly practice and learn R and or Python programming that is mainly used in data analysis.
Communication and presentation skills, which are required for communicating with the team regarding the various reports and outcomes of results obtained after proper data analysis.
To summarize, a few of the major tasks that a data analyst is involved in are data acquisition, data management, data cleaning, and data filtering, data interpretation using statistical analysis, improving data quality and statistical efficiency, data visualization, and analytics reporting.
If you are interested in Data Science and would like to explore more out of interest or to apply it to certain real-life problems, then this book is for you — Data Science Fundamentals and Practical Approaches.
The content of the book describes the fundamentals of Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of the Python programming language.