Choosing a career path is often a big decision; choosing to switch career paths can be an even bigger one. Sometimes it means a leap of faith because after all, you want to make the right choice. Ideally, your career choice will be a rewarding one, and something that you’ll succeed at and genuinely enjoy. With all of the exciting roles and positions in the technology industry, it’s a vast field to work in with a lot of potentials. Specifically, working as a data analyst can be a fulfilling experience.

What Is a Data Analyst?

The goal of any data analysis project should be to provide helpful information for making informed business decisions. Typically, there are five loops involved in the data analyst job descriptions process:

  •  Identify the data you want to analyze
  • Acquire the information to be analyzed
  • Prepare the data for analysis by cleaning it
  • Analyze the data
  • Infer meaning from the analysis

The method used to analyze data will vary from one question to the next. You may read more about the sorts of data analysis here. Descriptive analytics informs us what happened; diagnostic analytics tells us why; predictive analytics produces future projections, and prescriptive analytics generates the next steps.

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Why Become a Data Analyst?

Job security is essential for most people, and a role in data is likely to be a secure one because these positions are in high demand. MorningFuture reports that the data analyst role will be the most in-demand position in the future. Knowing that your job is likely to be secure (or at the very least, it won’t be challenging to find a replacement), is a significant advantage.

Working as a data analyst can also be an exciting experience that gives you the chance to work on different projects in an array of industries. 

Salary is also another crucial deciding factor for many people when it comes to choosing a career path. And working as a data analyst is undoubtedly a lucrative option. Salaryexpert reports that in the United States, the average base pay for a data analyst is $92,038 annually.

What Exactly Does a Data Analyst Do?

A Data Analyst plays a crucial role in interpreting data to help organizations make informed decisions. Their work involves various tasks that span collecting, processing, analyzing, and presenting data. Here's a detailed look at what exactly a Data Analyst does:

1. Data Collection and Processing

  • Gathering Data: Data Analysts collect data from various sources, including internal databases, publicly available data, and third-party datasets. They need to ensure that the data is relevant and accurate.
  • Cleaning Data: This involves removing or correcting erroneous data, dealing with missing values, and ensuring consistency across the dataset. Clean data is crucial for accurate analysis.

2. Data Analysis

  • Exploratory Data Analysis (EDA): Before diving into complex analysis, Data Analysts perform EDA to understand the data's underlying patterns, outliers, and characteristics. This involves using statistical techniques and visualizations.
  • Statistical Analysis: They apply statistical methods to interpret data, identify trends, and make predictions. This includes using measures of central tendency, dispersion, hypothesis testing, and regression analysis.
  • Predictive Analysis: In some roles, Data Analysts use models to predict future trends based on historical data. This requires a good understanding of machine learning algorithms and statistical modeling.

3. Data Visualization

  • Creating Dashboards and Reports: Data Analysts use visualization tools (like Tableau, Power BI, or Excel) to create dashboards and reports. These visualizations make it easier for stakeholders to understand complex data insights.
  • Presenting Data: Beyond creating visualizations, Analysts must present their findings to stakeholders, explaining the insights clearly and promptly. This might involve storytelling with data to highlight key findings and recommendations.

4. Decision Support

  • Informing Strategy: Data Analysts help organizations in strategic planning by providing data-backed insights. They identify opportunities for improvement, efficiency gains, and areas of growth.
  • Supporting Various Departments: Their work supports various organizational functions, such as marketing, sales, finance, and operations, by providing them with the data they need to make informed decisions.

5. Technical and Soft Skills

  • Technical Proficiency: Data Analysts are proficient in programming languages like SQL for database management, Python or R for data analysis, and Excel for preliminary data examination and visualization.
  • Analytical Thinking: They possess strong analytical skills, allowing them to interpret complex datasets and draw meaningful conclusions.
  • Communication: Effective communication skills are essential for Data Analysts to translate technical findings into understandable insights for non-technical stakeholders.

6. Continuous Learning

  • Keeping Up with Trends: The field of data analysis is constantly evolving, with new tools, techniques, and best practices emerging. Data Analysts must stay updated to remain effective in their roles.

The Role of a Data Analyst

Just about everything is data-driven these days, from market research and sales figures to expenses and logistics. To most people, this information can be overwhelming and daunting. It can be difficult and time-consuming to sort through it all and know what’s important, what isn’t, and what it all means.

This is where data analysts come into the picture: they take this data and turn it into useful information for businesses. This allows them to make more informed decisions in the future. 

Data Analyst Roles and Responsibilities

  • Collect and integrate data from various sources for analysis.
  • Clean and preprocess data to ensure accuracy and reliability.
  • Conduct exploratory data analysis to identify patterns, trends, and anomalies.
  • Apply statistical methods and data modeling techniques to interpret data and make predictions.
  • Develop and implement databases and data collection systems for optimal data quality and efficiency.
  • Create visualizations and dashboards to present findings in an accessible way to stakeholders.
  • Communicate complex data insights in a clear and understandable manner to non-technical audiences.
  • Support decision-making by providing actionable insights based on data analysis.
  • Collaborate with cross-functional teams to understand data needs and deliver relevant data-driven solutions.
  • Ensure data privacy and security compliance in all data handling and processing activities.
  • Stay updated with the latest data analysis tools, techniques, and industry trends for continuous improvement.
  • Optimize and automate data analysis processes where possible to enhance efficiency.

What Tools Do Data Analysts Use?

Data Analysts use many tools to collect, process, analyze, and visualize data. Some of the most common tools include:

  1. SQL (Structured Query Language): Essential for querying and manipulating databases.
  2. Python: A programming language with libraries such as Pandas, NumPy, and Matplotlib for data manipulation and visualization.
  3. R: A programming language and environment focused on statistical computing and graphics.
  4. Microsoft Excel: Widely used for data analysis, especially for smaller and preliminary datasets.
  5. Tableau: A leading tool for creating interactive and shareable dashboards.
  6. Power BI: Microsoft's analytics and visualization tool, integrated with other Microsoft products.
  7. SAS (Statistical Analysis System): Software suite used for advanced analytics, multivariate analysis, business intelligence, and data management.
  8. Apache Spark: An engine for large-scale data processing, particularly useful for big data and machine learning projects.
  9. Google Analytics: For analyzing website traffic and the effectiveness of online marketing strategies.
  10. Looker: A business intelligence and data analytics platform for exploring, analyzing, and sharing business insights.
  11. Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
  12. Git: For version control, analysts can track and manage code changes.
  13. SPSS (Statistical Package for the Social Sciences): A software package used for interactive, or batched, statistical analysis.
  14. QlikView/Qlik Sense: Business intelligence and visualization software for creating guided analytics applications and dashboards.
  15. Alteryx: Provides an end-to-end platform for data preparation, data blending, and the creation of analytics applications.

Types of Data Analysts

Knowledge of data collection, organisation, and analysis is now an integral aspect of virtually every field. New technologies have vastly increased the kind and volume of data we collect. Data analysts are in high demand in many areas, including law enforcement, the fashion industry, the food industry, the IT industry, businesses, the environmental sector, and the government.

Data analysts may go by a variety of other names, including but not limited to:

  • Medical and health care analyst
  • Expertise in Market Analysis
  • Business analyst
  • Business intelligence analyst
  • Operations research analyst
  • Intelligence analyst

Data Analyst vs. Data Scientist

You've already done some reading about data science and the data analyst job descriptions. Although these two phrases are frequently used interchangeably, they refer to distinct professional paths that accomplish different goals and necessitate different skill sets.

As we've established, data analysts utilise a firm's data and interpret it for decision-makers within the company. They analyze data trends and create dashboards and visualisations for widespread use to provide answers to questions and build solutions.

On the other hand, a data scientist will dig deeper into the data, employing data mining and machine learning to find patterns. They will conduct experiments, develop models, and conduct tests to validate or refute their hypotheses. Then, they'll provide recommendations for how a business might move forward in light of their results.

Who Should Be a Data Analyst?

If you want to be challenged and avoid the mundane, a data analyst role may be ideally suited for you. Good data analysts are problem solvers, have an interest and curiosity when it comes to data, are good with numbers, and of course, are analytical. If you possess these qualities, a data analyst may be your perfect career match.

It also takes a willingness to learn as a data analyst. Technology is always evolving, and people in these positions must be willing to adapt to changes and learn about new processes, programs, and techniques. As a data analyst, it’s also crucial to have specific technical skills as well. It is preferable to know particular programming languages, such as Python and R. Therefore, enrolling in an accredited Data Analyst online course can teach you the skills needed to succeed. Remember, the willingness to learn is what’s most important.

Note: As a data analyst, it would add an advantage to your skill if you know the types too!

Is Data Analytics a Good Career?

What Does a Data Analyst Do? Gaining experience as a data analyst may also lead to opportunities in related fields. Numerous entrant-level data analysts progress to more senior positions as data scientists. Data scientists, like analysts, conduct their work using quantitative and computational methods. On the other hand, a scientist might employ cutting-edge techniques to construct models and other instruments to shed light on upcoming tendencies.

Choose the Right Course

Simplilearn's Data Analytics courses provide a comprehensive understanding of key data analytics concepts, tools, and techniques. With industry-recognized certification, hands-on projects, and expert-led training, our courses help learners gain the skills needed to succeed in the data-driven world. Upgrade your career with Simplilearn today!

Program Name

Data Analyst

Post Graduate Program In Data Analytics

Data Analytics Bootcamp

Geo All Geos All Geos US
University Simplilearn Purdue Caltech
Course Duration 11 Months 8 Months 6 Months
Coding Experience Required No Basic No
Skills You Will Learn 10+ skills including Python, MySQL, Tableau, NumPy and more
Data Analytics, Statistical Analysis using Excel, Data Analysis Python and R, and more
Data Visualization with Tableau, Linear and Logistic Regression, Data Manipulation and more
Additional Benefits Applied Learning via Capstone and 20+ industry-relevant Data Analytics projects Purdue Alumni Association Membership
Free IIMJobs Pro-Membership of 6 months
Access to Integrated Practical Labs Caltech CTME Circle Membership
Cost $$ $$$$ $$$$
Explore Program Explore Program Explore Program

Data Analytics Courses

Once you’ve decided to become a data analyst, the next step is earning your data analytics certification. Enrolling in the right Data Analytics program will not only help you master data analytics but also prepare you for your certification exam. Earning your Data Analytics certification is an essential part of pursuing a career as a data analyst, as this can be a highly competitive position. In a competitive job market, it’s crucial to stand out in the best way possible by having the right credentials to include on your resume. 

Simplilearn is excited to announce that we’ve recently unrolled a Data Analyst Certification Course, which was created for budding data analysts and co-developed with IBM. This thorough program is perfect for all levels, whether you have some data analytics knowledge or you’re starting from scratch. Learn the fundamentals, as well as advanced skills, in our latest data analytics program and move one step closer to landing on your dream job as a data analyst.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Data Analytics Bootcamp

Cohort Starts: 24 Jun, 2024

6 Months$ 8,500
Post Graduate Program in Data Analytics

Cohort Starts: 8 Jul, 2024

8 Months$ 3,500
Post Graduate Program in Data Science

Cohort Starts: 11 Jul, 2024

11 Months$ 3,800
Post Graduate Program in Data Engineering

Cohort Starts: 16 Jul, 2024

8 Months$ 3,850
Caltech Post Graduate Program in Data Science

Cohort Starts: 22 Aug, 2024

11 Months$ 4,500
Applied AI & Data Science3 Months$ 2,624
Data Scientist11 Months$ 1,449
Data Analyst11 Months$ 1,449