What does a data analyst do?

Did you know that 2.5 quintillion bytes of data are created every day? That’s equivalent to 250,000 Libraries of Congress, or 5 million laptops! In this ocean of data, how do we make sense of it all? That’s where data analysts come in. 

Data analysts are the detectives of the digital world, using their skills and tools to uncover patterns, trends, and insights from data. They help businesses and organizations make informed decisions, solve problems, and seize opportunities. Whether it’s predicting customer behavior, optimizing marketing campaigns, improving healthcare outcomes, or enhancing educational experiences, data analysts play a crucial role in shaping our world. In this article, we will explore what a data analyst does, what skills and tools they need, what types of data analysis they perform, and what roles and responsibilities they have. 

If you’re curious about data analysis, or considering a career in this field, read on to discover the fascinating world of data analysts.

What are the main tasks of a data analyst? 

A data analyst is responsible for turning raw data into meaningful information that can help businesses and organizations make better decisions. To achieve this, a data analyst typically performs the following tasks:

• Gathering data: This involves collecting data from various sources, such as databases, files, surveys, web pages, APIs, etc. A data analyst needs to identify the relevant data sources, and use appropriate methods and tools to access and extract the data. For example, a data analyst may use SQL to query a relational database, or Python to scrape a web page.
• Cleaning data: This involves preparing the data for analysis, by removing errors, inconsistencies, duplicates, outliers, missing values, etc. A data analyst needs to ensure the data quality, and use appropriate methods and tools to clean and transform the data. For example, a data analyst may use Excel to filter and sort the data, or R to impute missing values.
• Transforming & adapting data: This process involves modifying and adjusting the data to make it suitable and ready for analysis. It involves applying various techniques, such as filtering, sorting, grouping, aggregating, merging, splitting, pivoting, and reshaping the data, to change the structure, format, and quality of the data. Transforming and adapting data can help to reduce the complexity and redundancy of the data, and to enhance the clarity and accuracy of the data.
• Modeling data: This involves applying statistical or machine learning techniques to the data, to discover patterns, trends, relationships, or predictions. A data analyst needs to choose the right models and parameters, and use appropriate methods and tools to fit and evaluate the models. For example,a data analyst may use SPSS to perform a regression analysis, or TensorFlow to build a neural network.
• Interpreting data: This involves analyzing the results of the models, and deriving insights and conclusions from the data. A data analyst needs to use critical thinking and domain knowledge, and use appropriate methods and tools to interpret and validate the results. For example, a data analyst may use Tableau to create a dashboard, or Power BI to generate a report.
• Presenting insights: This involves communicating the findings and recommendations from the data analysis, to the stakeholders or clients. A data analyst needs to use effective storytelling and visualization skills, and use appropriate methods and tools to present and explain the insights. For example, a data analyst may use PowerPoint to create a slide deck, or Google Docs to write a document.

What are the main skills of a data analyst? 

A data analyst needs to have a combination of soft and hard skills to be successful in their job. Some of the key skills include:

• Soft skills: Some of the important soft skills for a data analyst include critical thinking, problem-solving, attention to detail, creativity, and teamwork. A data analyst also has to have strong written and verbal communication skills to be able to explain complex data concepts in a clear and concise manner.
• SQL: This is a programming language that is used for querying and manipulating data in relational databases. It is like a language that talks to databases and allows a data analyst to extract, filter, join, and aggregate data from different tables.
• Excel: This is a spreadsheet software that is used for organizing, analyzing, and visualizing data. It is like a Swiss army knife that has a wide range of functions and features for data analysis, such as formulas, charts, pivot tables, and macros.
• R or Python: These are programming languages that are used for data analysis, visualization, and machine learning. R is more focused on statistical analysis and has a rich ecosystem of packages and libraries for data analysis. Python is more general-purpose and has a wide range of libraries and frameworks for data analysis, such as Pandas, NumPy, and Scikit-learn.
• Tableau: This is a data visualization software that is used for creating interactive dashboards and reports. It allows a data analyst to explore and visualize data in a user-friendly and intuitive way, and to share the insights with others.

These are just some of the main skills of a data analyst, but there are many other skills and tools that can be useful, depending on the specific domain or industry. A data analyst needs to be constantly learning and adapting to new technologies and trends in the field of data analysis.

Types and stages of data analysis

• Descriptive analysis: This type of analysis is used to summarize and describe the data, and to understand the basic features and patterns of the data. It involves using measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and visualizations (such as histograms, box plots, and scatter plots). For example, a data analyst may use descriptive analysis to understand the distribution of customer ages, the average purchase amount, or the relationship between customer satisfaction and product features.
• Diagnostic analysis: This type of analysis is used to identify the causes and factors that are associated with the data, and to understand the underlying reasons and relationships of the data. It involves using correlation analysis, regression analysis, and hypothesis testing. For example, a data analyst may use diagnostic analysis to understand the factors that are associated with customer churn, the impact of marketing campaigns on sales, or the effect of pricing on demand.
• Predictive analysis: This type of analysis is used to forecast and predict the future values or outcomes of the data, and to understand the likelihood and uncertainty of the data. It involves using time series analysis, machine learning models, and simulation techniques. For example, a data analyst may use predictive analysis to forecast the future sales of a product, the probability of a customer buying a product, or the risk of a loan default.
• Prescriptive analysis: This type of analysis is used to recommend and optimize the best actions and decisions, and to understand the trade-offs and constraints of the data. It involves using optimization models, decision analysis, and scenario analysis. For example, a data analyst may use prescriptive analysis to recommend the best pricing strategy, the optimal allocation of resources, or the most effective marketing mix.

Job outlook 

Data analyst is one of the most in-demand and fastest-growing jobs in the world, as more and more organizations rely on data to make informed decisions and gain insights. According to the World Economic Forum, data analyst is among the top 10 emerging jobs in 2020, and according to LinkedIn, data analyst is among the top 15 most hired roles in 2020.

Data analyst is also one of the most lucrative and rewarding jobs in the world, as data analysts can work in various industries and domains, and have a high earning potential and career growth. According to Glassdoor, the average salary for a data analyst in the world is $62,453, and according to PayScale, the average salary for a data analyst in the US is $61,473. However, the salary can vary depending on the location, experience, education, and skills of the data analyst.

Data analyst is also one of the most future-proof and resilient jobs in the world, as data analysis skills are expected to remain relevant and valuable in the post-pandemic era. According to the US Bureau of Labor Statistics, the employment of data analysts is projected to grow 25% from 2019 to 2029, much faster than the average for all occupations. The demand for data analysts is driven by the increasing use of data in various sectors, such as health care, finance, e-commerce, education, and government.

How to become a data analyst? 

There are several paths you can choose from to become a data analyst: 

• Professional certificate: A professional certificate is a credential that validates the data analysis skills and knowledge of an individual, and can be obtained from various online platforms. A professional certificate can help an individual to learn the fundamentals and best practices of data analysis, and to demonstrate their proficiency and competency to potential employers.
• Degree: A degree is a formal qualification that indicates the completion of a course of study in a specific field, such as data science, statistics, computer science, or business analytics. A degree can help an individual to gain a comprehensive and in-depth understanding of the theoretical and practical aspects of data analysis, and to develop the critical thinking and problem-solving skills required for data analysis.
• Self-study: Self-study is a mode of learning that involves the individual’s own initiative and motivation to acquire the data analysis skills and knowledge, without the guidance or supervision of a teacher or instructor. Self-study can be done through various resources, such as books, blogs, podcasts, videos, and online communities. It can help an individual to learn at their own pace and style, and to explore the topics and tools that interest them the most.

All of these paths can lead to a successful career as a data analyst, as long as the individual is passionate, curious, and diligent about data analysis. Self-study can be as valuable as a degree in the long-term, as knowledge and capabilities are more valued than a degree in the field of data analysis. However, self-study requires more discipline, self-motivation, and self-evaluation, and may not provide the same level of recognition, feedback, and support as a professional certificate or a degree. Therefore, you should choose the path that suits your goals, preferences, and resources the best.