Data Analyst: Scope & Responsibilities

If you are interested in a career as a data scientist, here are five things to think about.

  1. A bachelor’s degree is earned in a field that emphasizes statistical and analytical skills such as computer science or math.
  2. Learn important data analytics skills
  3. Take into consideration certification
  4. Get your first entry-level data analyst job
  5. A master’s degree is available in data analytics.

What does a Data Analyst do?

Data analysts collect, process and analyze large data sets. They help solve problems and answer questions with data. Data analysis has improved with the advancement of computers and a growing trend toward technological intertwinement. Data analysts were given a new breathing space with the development of relational databases. This allowed them to use SQL (pronounced “sequel”, or “s-ql”) to retrieve data from their databases.

Job Description for Data Analyst

Data analytics jobs involve the collection and analysis of data in order to discover trends and provide business insight. The job of a data analyst varies depending upon the industry, company, and the type of data analytics that you specialize in. Data analysts can be responsible for designing, maintaining, and creating dashboards for various departments within their organization with business intelligence software, tableau, or programming.

Data analysts often work in collaboration with IT teams, managers and/or data scientists to achieve organizational goals. They collect and clean data from primary and secondary sources, then analyze and interpret the results using standard statistical tools. They are able to identify trends, correlations, and patterns within complex data sets, and then find ways to improve the process. Data analysts need to create reports on their findings, and communicate the next steps to key stakeholders.

Data Analyst Qualifications

Data analysts require certain skills

  • Programming Languages: Data analysts need to be fluent in at least one language, and have a working knowledge of several others. Data analysts use programming languages like SAS and R to gather data, clean it, and visualize it.
  • Analytical and Creative Thinking: Creativity and curiosity are key characteristics of a data analyst. While it is important to be able to use statistical methods effectively, it is even more crucial to apply creative and analytical thinking to problems. This will allow the analyst to create interesting research questions that will improve a company’s understanding.
  • Effective and Strong Communication Data analysts need to clearly communicate their findings, whether it is to a large audience or a small group of executives making business decisions. Communication is key to success.
  • Data Visualization – Effective data visualization requires trial and error. Data analysts are skilled in understanding the types of graphs that can be used, how to scale them, and which charts to use depending upon their audience.
  • Data Warehouse: Some data analysts are responsible for the back-end. They use querying languages to search for and manage data and connect databases from different sources.
  • SQL Databases – SQL databases are relational databases that store structured data. Data is stored in tables. A data analyst pulls information out of different tables to perform analysis.
  • Database Querying Languages – SQL is the most commonly used querying language by data analysts. There are many versions of SQL, such as PostreSQL and T-SQL (Procedural Language/SQL).
  • Data Mining and Cleaning: If data isn’t stored in a well-organized database, data analysts will need to use other tools to collect unstructured data. Once they have enough data they can clean it and then process it through programming.
  • Advanced Microsoft Excel Data analysts need to be proficient in excel and should understand advanced modeling and analysis techniques.
  • Machine learning Data analysts with machine-learning skills are extremely valuable. However, machine learning is not a skill that is expected in most data analyst jobs.

Data Analyst Responsibilities

A day in the life of a data analyst

Data analysts’ day-to-day activities depend on their location and the tools they use. Some data analysts prefer Excel and statistical software over programming languages. Regression analysis and data visualizations are two examples of what analysts do depending on the problem they are solving. Sometimes, data analysts who are not experienced can be called “junior data scientist” or “data scientists-in-training.” They may also perform regression analysis or create data visualizations depending on the problem they are solving.

“Creating player projections for Fantasy Baseball is a big part of what I do. These are used to power our draft rooms’ default rankings and also inform my in-season and preseason rankings of players. Fantasy customers and readers rely on these projections. We have high levels of interaction with them, since we are responsible for answering questions about player performance and value. These recommendations are based on statistical analysis, regardless of whether they are made via social media, written and/or video content, podcasts, or written.

What tools are used by data analysts?

These are other tools that data analysts need to do their job well:

  • Google Analytics (GA).: GA allows analysts to gain insight into customer data. This includes trends and customer experience areas that require improvement on landing pages, calls to action (CTAs), or other customer data.
  • Tableau – Analysts use Tableau for data analysis and aggregation. They can share dashboards and create visualizations with other team members.
  • Jupyter Notebook System: Jupyter notebooks allow data analysts to quickly test code. Because of its markdown feature, non-technical people prefer the simplicity of jupyter Notebooks.
  • Github – Github is a tool for building and sharing technical projects. Data analysts who work with object-oriented programming should use this platform.
  • AWS S3 – AWS S3 is a cloud storage platform. It can be used by data analysts to store and retrieve large data sets

Outlook for Data Analyst Jobs

Data analysts today should be ready for change. Analyst roles are becoming increasingly complex. Analysts who are experienced use predictive analytics and modeling to produce useful insights and take action. They then have to share what they have discovered with confused laymen. They must transform themselves from data analysts to data scientists.

Market research analyst jobs are expected to increase by 22% while management analyst posts are all expected to rise by 14%. This is significantly faster than the average job growth, according to the Bureau of Labor Statistics. Data analysts can work in a variety of industries, including finance, healthcare, information and professional services. Data is being collected at every possible point. The implication of predictive analytics assists society in becoming better.

Data Analyst Salary

Salary numbers depend on job responsibilities. High salaries can be earned by a senior data analyst who has the skills of a data scientist.

Salary for Data Analysts

Entry-level salary for data analysts: $83,750

Data analysts earn an average salary of $100,250

The average salary for senior data analysts is $118,750 to $142,500

Are you interested in a new career? Learn more about bootcamps

Bootcamps are a great option if you are looking to make a career shift or improve your knowledge of data analytics. Tech bootcamps offer fast-paced training in particular programming languages like R, SQL, and Python. They are available in many formats. Online Coding Bootcamps provide intensive learning experiences that replicate the real world. Learn how to create projects from scratch.

A data analysis bootcamp could help you prepare for a new career. Most data analytics bootcamps cover statistical analysis and analyzing data to uncover insight. They also teach you how to use business intelligence software like Tableau, as well as other tools that data analysts might use.

You can also enroll in a bootcamp. Data science bootcamps usually cover advanced analytical concepts, machine learning, natural language processing, and neural networks. If you are unsure which bootcamp you should enroll in, think about your career goals and the things you would like to accomplish in your current or future role.

Bootcamps may last from a week up to several months, depending on whether you are enrolled in a full-time or part-time program. There are many options available. You can choose the one that suits your learning goals and schedule. Many boot camps offer workshops and prep courses to ensure student success.

FAQs for Data Analysts

What should I learn to become a data analyst?

Data analysts have many tools they use every day. Data analysts may use business intelligence software. Other data analysts may also use programming languages that include various visualization and statistical libraries, such as R, Excel, Tableau, and Python. You may also have the following skills:

  • Analytical and creative thinking
  • Communication
  • Database querying
  • Data mining
  • Data cleaning

Does a Data Analyst need to know how to code?

While some data analysts can program, others may be proficient in Excel or analytics software to analyze and give insights. The job and the employer will determine whether or not programming is necessary for data analysts. In job postings, employers may list programming as a requirement skill for data analysts. Before applying, it is important to review the job description carefully and reflect on your past.

What is the best career for a data analyst?

Both data analysts and data scientists are expected to grow at an average rate. According to O*NET Online (O*NET), Data analysts enjoy a bright job outlook. Between 2020 and 2030, is projected growth of 15% for data analysts. Salary for data analysts varies depending on their work location and industry. O*NET reported that data analysts made an average salary of $98,230 per year in 2020.

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