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.
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:
Multiple reasons can encourage one to become a data analyst. The five most important ones are:
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:
The data analyst position offers specific fields to work in. The different specializations to look forward to include:
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.
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.
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.
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.
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 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.
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.
The candidates looking for data analyst roles must have technical and professional skills.
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.
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.
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.
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.
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.
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.
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.
Data analysts have to work online. The different involved responsibilities require a variety of tools and proficiency in them. The important ones are:
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ans. If you are at the school level, select math and statistics subjects. Further, pursue a bachelor’s degree in statistics, computer science, math, or others. Gain experience through internships and polish your skills with professional certificates. People wishing to switch fields can also gain professional certifications and enter their careers.
Ans. The average data analyst salary in India is around 6 lakhs per year. The additional average compensation is around INR 75,000.
Ans. Yes, data analysis is part of the IT job. It requires mathematical and computer science skills, which are technical.
Ans. No, though the positions hold similarities, the job profiles and responsibilities for both are different. Data analysts interpret the data to derive meaningful information, but data scientists focus on complex statistical models and algorithms for pattern identification and predictions.
Ans. Data analysis is a good job with numerous opportunities. It is a demanding career profile with a good pay scale.
Ans. Four types of analysis are done on the data to serve the business. It includes descriptive, predictive, diagnostic, and prescriptive analysis.
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