Introduction

According to the Bureau of Labor Statistics (BLS), the employment of research analysts, including data analysts, is projected to increase by 23% from 2021 to 2031. This significant growth in data analysis careers presents promising prospects for aspiring candidates. It profoundly impacts the services and products provided to the public. As a data analyst, you must possess problem-solving and analytical skills and technical knowledge of computer science, statistics, and mathematics. This field offers ample opportunities for personal and professional growth, allowing you to work with cutting-edge technologies. But what exactly does this exciting career path entail? Let’s explore the expectations placed upon an ideal candidate providing data analysis services to a company.

What Does a Data Analyst Do?

Data analysis refers to gaining information from data or analyzing it to use it for business benefit. It provides quality insights with crucial points that direct the company’s decision-making process. The job’s roles and responsibilities include:

• Gathering data for analysis. It will involve discovering or collecting different types of data through various modes. Examples include surveys, polls, questionnaires, and tracking the visitor characteristics on the website. Alternatively, the datasets can be purchased depending on the requirement and availability.
• Programming languages perform the cleaning process on the data generated after the previous step, called raw data. The name implies the presence of unwanted information, including outliers, errors, and duplicates, that require processing. The cleaning process aims to enhance the quality of the data and make it usable.
• The data needs to be modeled now by providing it with a structure and representation in an organized manner. It will also involve categorizing data and other relevant processes to make it presentable.
• The data thus formed will serve multiple purposes. The usage will depend on the problem statement, which will also determine the method of interpretation. Data interpretation mainly involves finding trends or patterns in data.
• Presentation of data is an equally important task where the prime requirement is to let the information reach the viewers and involved parties in the same way as intended. It requires presentation and communication skills. Often data analysts take the aid of charts and graphs, followed by report writing and information presentation.

Reasons to Become a Data Analyst

Multiple reasons can encourage one to become a data analyst. The five most important ones are:

• High demand: The rise in data generation has led to tons of unprocessed data. It holds numerous secrets that the companies can use. The requirement for individuals who can carry out the task is growing exponentially, with the standard requirement of 3000 positions annually.
• Dynamic field: The data analyst job has loads to offer if you like to combat challenges and find joy in overcoming problems. With intriguing and new challenges each day, it is a place that requires analytical thinking and brainstorming to come up with solutions. You also get to learn a lot in the journey contributing to self-improvement.
• High pay: The pay scale for a data analyst position is high and worth pursuing the career. The pay rise varies according to the industry and promises higher incomes with bonuses in some fields.
• Universality: The requirement of a data analyst is not limited to a specific field. Every industry generates loads of data and requires logical decisions based on information. Thus, the job is open to every specialization, regardless of your background or interest.
• Lead the career choice: The skillful data analyst is set to bring value to the position and company. The possibility of growth, promotions, and additional benefits remains open everywhere. It positions you to get a change, lead the groups, teach them, become competitive, or shape the workforce culture.

Demand and Future Job Trends

The current demand for data analysts is high at a good pay scale. The requirement in the future is also expected to grow based on the current speed of data generation. With the generation of new technologies and ease of data collection, the future will surely provide new opportunities to the talents. Some of the expected new job roles for data analysts in the future include:

• Explain the functionality and suitability of AI. Quality analysis of the newly developed functions.
• Working on a combination of real-time analytics in business operations and data processing. It will guide toward planning based on logic and strategy.
• Generated data interpretation reports need to be self-explainable and easy to interpret. Data visualization is crucial, and the field holds good career scope.
• Expect the introduction of augmented analytics, where complex datasets can be handled via ML algorithms and NLP algorithms. It will be engaging and universally accessible.
• Development of Machine Learning and the Internet of Things to ensure the possibility of currently impossible things is also expected to occur.

Specializations in Data Analyst Field

The data analyst position offers specific fields to work in. The different specializations to look forward to include:

Risk Analyst

It includes working for money-based companies such as financial institutions and insurance companies. Their work is mainly focused on predictions based on the data. The risk analyst must go through economic conditions, financial documents, and other things as per the requirement.

Budget Analyst

They are generally found to work in businesses or industries to assist in analyzing downward and upward trends. Typical examples of this sector include private businesses, educational institutions, and government-based institutions. The work here also is dependent on financial situations and documents.

Operation Analyst

These job profiles are concerned with finding solutions to the problems in business. The specific focus here is on operations which can include different projects, manufacturing, or any other operation problem in the company.

Research Analyst

They are concerned with research and deep insight into the available data. The research analyst has to deal with market information and extract information for investment, selling, and designing future strategies. The financial, investment, and equity expertise are of focus here.

Marketing Analyst

It involves a sole focus on market conditions with an emphasis on trends, requirements, and needs of customers. It includes products and services, the target audience, and the ideal price for the company’s offerings.

Business Intelligence Analyst

Business Intelligence Analyst analyzes complex data sets to provide insights and make data-driven recommendations for business improvement. They develop and maintain dashboards, reports, and data models, ensuring accurate and timely information. Their role involves translating data into actionable insights to support strategic decision-making and drive business success.

Healthcare Analyst

The field is basically for a healthcare system that includes hospitals and pharmaceutical companies. The work here broadly focuses on public health, clinical information, pharmaceuticals, claims, costs, patient behavior, and satisfaction. The ultimate aim is to improve the process.

8 Must Have Skills to Become a Data Analyst

The candidates looking for data analyst roles must have technical and professional skills.

Technical Skills

1. Knowledge of Database Tools Such as Microsoft

Microsoft Excel helps summarize and simplify the data through pivot tables and provides fascinating representation methods, commands, and add-ins or additional features to handle the data. SQL helps manage data in relational databases, interpretation, reading, and manipulation.

2. Ability to Work with Programming Languages Such as R or Python

Knowledge of programming languages is required for statistical analysis, machine learning, web development, data manipulation, and integration with web applications to simplify complex mathematical problems and data processing.

3. Good Presentation Skills to Present the Interpreted Data

It involves using software like Jupyter Notebook and Tableau. They aid by providing interactive and dynamic visualization and helping curate dashboards and reports. Helping with data exploration, analysis, and iterative development allows independent running of code cells and debugging.

4. Information and Application Ability of Statistics and Mathematics

Statistics serve to summarize and describe the data through variability, correlation, central tendency, and identification of patterns. Mathematics contributes to algorithm development and data modeling, providing concepts of linear regression, probability, and multiple significant theories.

Professional Skills

6. Ability to Have a Passion for Solving the Problem and Overcoming Challenges

Dealing with a wide variety and high amount of data will pose a challenge. The demands and problems will vary with no solution. The analytical and problem-solving mindset will help you overcome them without looking for existing answers. It will help you create the solution.

7. Communicate Clearly and Precisely

Data visualization is crucial in data analysis. However, it must be communicated effectively to the team members, seniors, management, and other involved authorities for easier and clearer interpretation. The inability to clarify them will decrease the value of your work leading to your efforts in vain.

8. Core Knowledge of the Industry

Data analysis is applicable in a wide array of industries. Working in a specific industry, for instance, healthcare will make you come across medical terms. Familiarity with them will ease the workflow and increase the efficiency of results and work.

Tools Used by Data Analysts

Data analysts have to work online. The different involved responsibilities require a variety of tools and proficiency in them. The important ones are:

SAS

SAS or Statistical Analysis Software is used for statistical modeling. It allows data processing and manipulation by providing comprehensive procedures, functions, and data programming technology. SAS allows description, inferential statistics and regression, time series, and survival analysis which serves in the analysis procedure. It also contributes to visualization and data mining through information charts, plots, and graphs with customization options. Data analysts also use SAS to handle large-scale datasheets by efficiently subsetting, sorting, and merging the data.

Microsoft Excel

It is spreadsheet software that allows data organization and manipulation through categorization and the presence of numerous functions. ToolPal allows statistical analysis, while simple functions help in basic statistical analysis, such as mean, standard deviation, mode, and percentiles. It also provides numerous data representation methods. Excel provides What-If analysis for scenario modeling. The tools included in the category are scenario manager, goal seek, and data tables, including sensitivity analysis.

SQL

The software is primarily used by Data Analysts while dealing with relational databases. They need it to retrieve, manipulate and filter the data for aggregations, joining multiple tables and calculations. The SQL allows data transformation into different data types and the creation of new derived columns. It also provides basic functions similar to Excel. One of the characteristic features here is the WHAT clause to find the data based on search criteria like inclusion or exclusion of specific ranges or values and logical conditions. Besides, data analysts use SQL for defining indexes, modification of database structures, security and permission management, and optimizing query performance.

Jupyter Notebooks

It is a web-based application highly beneficial and efficient for data analysts in their tasks like creating and sharing documents comprising live code and narrative texts. Jupyter Notebook provides an interactive environment through the possibility of writing and executing code in different programming languages. It is integrated with significant analytical libraries, such as NumPy, pandas, and scikit-learn in Python useful for statistical and machine learning. Some visualization-based libraries include Seaborn, Matolotlib, and Plotly. It has the characteristic feature of reproducibility, a flexible learning environment, and integration services such as APIs and cloud platforms.

Google Sheets

People commonly choose Google Sheets as web-based spreadsheets because they offer real-time editing and sharing options. Allowing multiple users to work or monitor the results anytime provides all the operations, such as formatting, sorting, and filtering columns. The ability to communicate through comments and share the file in different formats like CSV and Excel is accompanied by the capability to import the data. Besides basic functions, Data analysts benefit from google sheets through add-ons and integrations with platforms like Google Analytics.

R or Python

The programming languages R and Python serve different purposes: R aids in statistical computing and analysis, while Python is widely employed for general-purpose programming. Python is preferable for integrating external sources and its vast library, resources, and tools specifically designed for data analysis. Both machine learning and data analysts use R and Python through their packages like xgboost, caret, scikit-learn, and TensorFlow. Data analysts utilize them for data manipulation, visualization, analysis, and transformation.

Tableau

People primarily use it for data visualization and business intelligence to analyze, visualize, and appealingly share data. Tableau provides a drag-and-drop interface that aids in creating interactive visualizations without the use of coding. It connects external sources like spreadsheets and databases, data exploration, and analytical features. These include slicing and dicing the data, filter application, and drilling to find trends and patterns. It is also the most sought option due to data governance and security features that allows access control and permissions. It also helps in complying with privacy regulations.

Microsoft Power BI

It is a business intelligence tool that helps in processing the raw data. Microsoft Power BI provides a wide variety of features. It allows data exploration and analysis. Advanced analytics options such as Azure Machine Learning and Cognitive Services help with data analysts in sentiment analysis and incorporation of machine learning models. It also aids natural language processing, interactive dashboards creation, and custom visuals. The tools come coupled with mobile-friendly access to the program. The responsive designs are flexible and adjustable according to different devices and screen sizes, allowing non-stop working on the go. It also supports row-level security and role-based access control.

Conclusion

No career is made in a day. It requires discipline and consistency to set your foot strong in any field. The driving factor or purpose of going into the career is the key to remain consistent and continue working hard. Be it any phase of life, gaining knowledge is easy now. Regardless of background, you can choose the right career path to reach the goal of becoming a data analyst. You might consider being a data analyst a good job. The answer to this lies in your passion and will to make a career out of it. If you love different aspects of this job, you will never have to ask yourself if being a data analyst is good. Head on to learn, through Certified AI and ML Blackbelt program offered by Analytics Vidhya to know if data analytics is a good career.

Frequently Asked Questions