Data Analyst

A data analyst collects, processes and performs statistical analyses on large dataset. They discover how data can be used to answer questions and solve problems. With the development of computers and an ever-increasing move toward technological intertwinement, data analysis has evolved. The development of the relational database gave a new breath to data analysts, which allowed analysts to use SQL (pronounced “sequel” or “s-q-l”) to retrieve data from databases.

Most data analysts work with IT teams, management and/or data scientists to determine organizational goals. They mine and clean data from primary and secondary sources then analyze and .Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theoriesinterpret results using standard statistical tools and techniques. In most cases, they pinpoint trends, correlations and patterns in complex data sets and identify new opportunities for process improvement. Data analysts must also create reports on their findings and communicate next steps to key stakeholders.

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains

Data Analysts are experienced data professionals in their organization who can query and process data, provide reports, summarize and visualize data. They have a strong understanding of how to leverage existing tools and methods to solve a problem, and help people from across the company understand specific queries with ad-hoc reports and charts.

Skills Required for Data Analysts

  1. Programming Languages (R/SAS): data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
  2. Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand.
  3. Strong and Effective Communication: Data analysts must clearly convey their findings — whether it’s to an audience of readers or a small team of executives making business decisions. Strong communication is the key to success.
  4. Data Visualization: Effective data visualization takes trial and error. A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience.
  5. Data Warehousing: Some data analysts work on the back-end. They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.
  6. SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables and a data analyst pulls information from different tables to perform analysis.
  7. Database Querying Languages: The most common querying language data analysts use is SQL and many variations of this language exist, including PostreSQL, T-SQL, PL/SQL (Procedural Language/SQL).
  8. Data Mining, Cleaning and Munging: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.
  9. Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.
  10. Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not expected skill of typical data analyst jobs.

Here are some other important tools data analysts use on the job:


  1. Google Analytics (GA): GA helps analysts gain an understanding of customer data, including trends and areas of customer experience that need improvement on landing pages or calls to action (CTAs)
  2. Tableau: Analysts use Tableau to aggregate and analyze data. They can create and share dashboards with different team members and create visualizations
  3. Jupyter Notebook system: Jupyter notebooks make it simple for data analysts to test code. Non-technical folks prefer the simple design of jupyter notebooks because of its markdown feature
  4. Github: Github is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming
  5. AWS S3: AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large datasets

However, they are not expected to deal with analyzing big data, nor are they typically expected to have the mathematical or research background to develop new algorithms for specific problems.

Skills: Data Analysts need to have a baseline understanding of some core skills: statistics, data munging, data visualization, exploratory data analysis,
Tools: Microsoft Excel, SPSS, SPSS Modeler, SAS, SAS Miner, SQL, Microsoft Access, Tableau, SSAS.

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