Each business has data to process and a Data Analyst collects that data, processes it, and provides some results like reducing the implementation cost. A Data Analyst performs statistical analysis on the data using his/her technical skills to help an organization make critical decisions. To do so, an analyst uses several tools like SQL, Tableau, Github, and so on to sort out the data.
How a Data Analyst is different from a Data Scientist?
You might know that both the jobs have some overlapping yet there are some differences. Based on the skills and job roles, given below are the points that differentiate a Data Analyst and a Data Scientist:
- Requires data visualization
- Hands-on experience in Excel, Python, R, SQL, or CQL
- Experience of active development methodologies
- Need to have adequate computer science and coding skills
- Should know how to utilize business intelligence and analytics tools
- Should be able to write code to assist in data analysis
- Should be able to identify trends with machine learning
- Need to have strong computer science and coding skills
- Need to have excellent mathematical and statistical skills
- Should be able to make speculations based on data trends
What are the types of data analytics?
In simple terms, data analytics is all about answering questions and making decisions based on the collected data. There are different types of data analytics based on the type of question asked. Each type helps to accomplish the goal of an organization and increase its value. The following are the types of data analytics:
- Descriptive analytics helps to investigate “what happened in the past” based on the available information like a monthly review, sales, website traffic, and so on. This type of information allows an organization to mark the business trends.
- Diagnostic analytics is used to consider “why did something happen” to describe the cause of positive and negative outcomes.
- Predictive analytics aims at what is likely to happen by observing the susceptibility in the descriptive and diagnostic analysis. It helps an organization to take discerning actions.
- Prescriptive analytics seeks to identify what action should be taken to avoid the problems and stand ahead of the other organizations. This is done using complex algorithms and advanced tools/technologies like machine learning.
What responsibilities would a Data Analyst have?
The responsibilities may vary depending upon the organization but some of the common ones include the following:
- Planning, developing and maintaining databases and data systems. It includes solving coding errors and other data-related issues.
- Mining data from primary and secondary sources, then rearranging the data in a readable way.
- Performing analysis and interpreting its results using statistical tools and techniques. It includes paying attention to patterns that are needed for diagnostic and predictive analytics efforts.
- Preparing data reports for management
- Cleaning and scrutinizing data for getting rid of irrelevant information
- Creating documentation for the data analysis process and replicate the analysis if necessary
What skills a Data Analyst should have?
A productive Data Analyst should have both technical and leadership skills.
Technical Skills: These include comprehension of database languages (SQL, R, or Python), spreadsheet tools (Microsoft Excel or Google Sheets), and software for data visualization (Tableau or Qlik).
Leadership Skills: These prepare a Data Analyst for decision-making and problem-solving tasks. These abilities allow analysts to plan the information for stakeholders to make data-driven business decisions and to converse the value of this information effectively.
Mathematical and statistical skills are also useful for gathering, measuring, arranging, and analyzing data.
What tools do Data Analysts use?
The analysts require software and applications to create and execute analytical processes that help organizations to make better business decisions to reduce costs and increase profits.
The following are some tools used by Data Analysts:
- R Programming: It’s the leading analytical tool that is widely used for statistics and data modeling.
- Tableau: Analysts use Tableau to accumulate and analyze data. It’s free software that connects any data source and helps to create visualizations.
- SAS: It’s a programming environment and language that is used for data manipulation. SAS can easily be used and managed, and it can analyze data from any sources.
- Apache Spark: Apache Spark is a fast-large-scale data processing engine that executes applications in Hadoop clusters. It is popular for machine learning models’ development.
- Github: Github is a platform that is used for sharing and developing technical projects. It is important for data analysts who use object-oriented programming.
- AWS S3: It’s a cloud storage system. Data analysts use AWS S3 to store and retrieve large datasets.
- Google Analytics (GA): It helps analysts to get an idea of customer data, including trends and areas of customer experience that require enhancement on landing pages or calls to action (CTAs).
- RapidMiner: It is a powerful incorporated data science platform. It can integrate any type of data sources like Access, Excel, Ingres, MySQL, IBM SPSS, Tera data, Oracle, Sybase, IBM DB2, Dbase, etc.
Why become a Data Analyst?
The increasing demand for data analytics in the market has generated many opportunities all over the world. A Data Analyst serves as a firewall for an organization’s data so that stakeholders can understand it and come up with strategic business decisions. Anyone with an undergraduate degree or master’s degree in analytics, science, computer modeling, or mathematics can apply for this technical job role.
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