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In recent years, data analytics has evolved thanks to the development of technology and the ever-increasing networking through it. Nowadays, organizations across all industries increasingly rely on data to make critical and well-informed decisions. That’s where data analysts come in—they analyze and make sense of said data.

Further on, we go more in-depth and cover what a data analysts’ job description is, their responsibilities, skills, career path opportunities, and ultimately, the qualifications to become one.

Data Analyst Job Description

A data analyst’s job is to add value to a corporation, be it the corporation of their own employer or their clients. Most data analytics jobs involve gathering and cleaning data to uncover trends, correlations, patterns, and business insights. A data analyst gathers, processes, and carries out statistical analyses on large datasets. As a result of their findings, they discover how data can be used to answer questions, solve problems and improve processes.

Depending on the industry and who they work for, data analysts usually work with IT teams, management, or data scientists to establish organizational objectives. They mine and clean information from primary and secondary sources then analyze and translate their results using standard statistical tools and techniques.

 

Data analysts tend to work across a range of areas, including business intelligence, data assurance, finance, higher education, marketing, sales, consulting, among others.

 

Data analyst responsibilities

So, exactly what does a data analyst do on a daily basis? Well, their tasks will vary depending on the type of organization they work for and the extent to which it has adopted data-driven decision-making practices. Generally speaking, data analysts are tasked with:

  • Identifying areas to increase efficiency and process automation.
  • Providing sector and competitor benchmarking.
  • Identifying, evaluating, and implementing external services and tools to support data validation and cleansing.
  • Analyzing, interpreting, and manipulating complex data sets relating to the employer’s business.
  • Producing and tracking key performance indicators.
  • Analyzing and mining large datasets to draw valid inferences and presenting them successfully to management.
  • Developing and supporting reporting processes.
  • Monitoring and auditing data quality.
  • Cooperating with internal and external clients to fully understand data content.
  • Gathering and documenting detailed business requirements using appropriate tools and techniques.
  • Designing and carrying out surveys and analyzing survey data.
  • Creating data dashboards, graphs, and visualizations.
  • Preparing reports for internal and external audiences using business analytics reporting tools.
  • Setting up and maintaining automated data processes.

 

Data analyst skills

Effective data analysts acquire a combination of technical skills and soft skills. The ability to pay attention to detail, communicate and be highly organized are essential skills these professionals need to possess to be successful. Besides understanding the data, data analysts should be able to provide insight and analysis through clear visual, written, and verbal communication.

 

As for technical skills, they need to have:

  • Preeminent numerical and analytical skills.
  • Knowledge of data analysis tools such as advanced skills in Excel.
  • A grasp of relational databases (e.g., MS Access).
  • Comprehension of data modeling, data cleansing, and data enrichment techniques.
  • Proficiency in programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
  • Proficiency in Hadoop open-source data analytics.
  • Knowledge of the common data analysts’ querying language–SQL and the variations of this language, including PostgreSQL, T-SQL, PL/SQL (Procedural Language/SQL).
  • Aptitude for Google Analytics, SEO, keyword analysis, and web analytics (for marketing analyst roles).
  • Expertise in statistical methodologies and data analysis techniques.
  • The ability to develop and document procedures and workflows.
  • The capacity to carry out data quality control, validation, and linkage.
  • An understanding of data protection issues.
  • Awareness and knowledge of industry-specific databases and data sets.

What tools do data analysts use?

A data analyst relies on various tools to collect and make sense of the data they work with. They use specialized tools to efficiently gather data from different sources such as social media, news pages, and publications, as well as tools to sort and categorize data to visualize that data for reports and presentations.

These are other important tools data analysts use on the job:

 

  • Google Analytics (GA) helps data analysts gain an understanding of customer data, including trends, areas where customer experience can improve (eg. On landing pages or calls to action (CTAs).
  • Tableau is a free software to aggregate and analyze data. Through this software, data analysts can create and share dashboards with different team members and create visualizations.
  • Jupyter notebooks to make it simple for them to test code.
  • AWS S3 (a cloud storage system) that they can use to store and retrieve large datasets.
  • Python is able to handle text data really well. It can be assembled on any platform like an SQL server, a MongoDB database, or JSON. 
  • Excel is a widely used analytical tool in almost all industries. It features the advanced business analytics option, which helps in modeling capabilities like automatic relationship detection, the creation of DAX measures, and time grouping.

Data Analyst Career Paths

Data analytics is a fast-growing field, and skilled analysts are in high demand across all industries and domain types, including education, healthcare, construction, media, retail, and even real estate. The great thing about data analytics is that it relies on a broad range of skills that are often transferable. 

Since their expertise is required across multiple industries, a particular data analyst professional can follow a career path that combines their analytical skills with a particular area of interest. Depending on the industry of interest, one could get job positions such as financial analyst, healthcare analyst, machine learning analyst, digital marketing analyst, among others.

Further down their career path, many data analysts tend to go on to become data analytics consultants. These analysts usually work for consulting firms, but many opt for self-employment. Finally, skilled analysts can also find roles in academic research or government advisory bodies.

These options just go on to prove that the data analytics profession leads to a more flexible career path, depending on interests, experience, and ones’ aspirations. Thus, it’s important to gain knowledge, gain hands-on experience, and hone your skills. 

 

Data Analyst Qualifications: How To Become a Data Analyst?

Regardless of your specific interests in the data analytics world, you’re going to need a degree to get started in the field. If you’re someone interested in building a career in analytics, you need to obtain a degree in a relevant discipline such as data science, information technology, finance, and economics.

In addition to education come specific skills needed for these professionals. You’ll need to develop database querying skills with SQL, learn the fundamentals of Python (the go-to language used by analysts), and grasp key concepts such as data mining and ethics.

Our Master of Science in Big Data Analytics prepares students for a career as a data analyst by delivering rigorous training in computational techniques and by providing mastery of data analysis and the frameworks for critically looking at data, interpreting, and visualizing data. In addition, a master’s degree in analytics equips students with knowledge about analytical tools, testing skills, basic statistical skills, machine learning.

 

To conclude, those who wish to pursue a career in data analytics are those who have a passion for technology and data, are eager to learn new things, and are driven by constant development in their field of work.

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