Data analysts are the data detectives who are changing the world. They use their skills to uncover valuable insights from vast datasets, helping organizations make better decisions, solve complex problems, and create a more sustainable future.
In today's data-driven world, the data analyst role is increasingly vital across various industries. From measuring compliance with the Sustainable Development Goals using Earth observation big data from satellite images to grouping business customers into different segments to understand their characteristics, data analysts use their skills to make a real difference in the world.
In this topic, you'll delve into the key hard skills required to become a data analyst, what a typical day on the job looks like, and the likely challenges a data analyst may face on the job.
Hard skills for a data analyst
Data analysts require technical expertise to develop meaningful insights from data. They are responsible for preprocessing data into machine-readable format, performing statistical and predictive analysis, and preparing visualization and reports to communicate their findings. All of these tasks require a well-developed set of technical or hard skills.
Data cleaning and preprocessing
Data cleaning and preprocessing is the essential first step in any data analysis project. It is like excavating a treasure trove of data, meticulously removing layers of dirt and debris to reveal the valuable insights hidden within. Data analysts must be skilled in a variety of techniques to clean and preprocess data, including:
Checking for typos, inconsistencies, and outliers.
Dealing with missing data by imputation, deletion, or weighting.
Transforming data by scaling, encoding, and binning.
Data cleaning and preprocessing can be time-consuming, but it is essential for ensuring the accuracy and reliability of your analysis results. After all, garbage in, garbage out.
Microsoft Excel
Microsoft Excel is a widely used spreadsheet software for data analysis. You can use it for data cleaning, manipulation, visualization, and analysis. In addition to these basic tasks, you can also use Excel for more advanced data analysis such as linear programming, optimization, forecasting, and Monte Carlo simulation with plugins installed.
Python in Excel is a new integration that allows users to access the power of Python within the familiar Excel environment. This opens up a new world of data analysis possibilities for Excel users.
With Python in Excel, users can:
Perform complex statistical analyses with popular Python packages such as pandas and statsmodels.
Create sophisticated visualizations using Matplotlib and Seaborn.
Train and evaluate machine learning models with the Scikit-Learn library.
Integrating Python into Excel is an effective new tool for data analysts. It combines the best of both worlds, providing users with the power of Python and the accessibility of Excel.
Data visualization
Data visualization is an essential skill for data analysts. It allows them to communicate their findings effectively to various audiences, from other data analysts to non-technical stakeholders.
Data analysts have a variety of data visualization tools to choose from, including Tableau, Power BI, Looker, and Python libraries such as Matplotlib, Seaborn, and HoloViews, as well as the popular ggplot2 package in R. Data analysts should choose the tool that best meets their needs and those of their audience.
Data visualization is used to:
Explore and understand data by identifying patterns and trends that otherwise would be difficult to see.
Communicate findings to others in a clear, concise, and engaging way.
Shiny and Streamlit are two popular tools that allow data analysts to create interactive web applications that can be used to visualize and explore data. These applications are typically deployed on the web, and a much wider audience can access them through a web browser.
Probability and statistics
Data analysts use probability to measure the likelihood of events happening. This allows them to make better decisions. A data analyst might use probability to calculate the chances of a customer clicking on an ad or the chances of a product being defective.
Data analysts use statistics to draw meaningful conclusions from data samples. This allows them to make inferences about populations, even when they don't have data for the entire population. A data analyst might use statistics to estimate the average height of all adults in a country based on a sample of adults from that country.
Data analysts use probability and statistics to:
Test hypotheses and draw meaningful conclusions from data.
Identify patterns and trends to make predictions and improve decision-making.
Build and interpret predictive models to forecast future outcomes.
Relational and non-relational databases
Databases are the backbone of data analysis. They store, retrieve, and manage the data that analysts use to gain insights and make informed decisions.
Relational (or SQL) databases are the most common type of database. They store data in tables, which are made up of rows and columns, and are very efficient for storing and querying structured data such as customer information or product inventory. Examples of relational databases include SQLite, MySQL, and PostgreSQL.
Non-relational (or NoSQL) databases are newer databases designed to handle unstructured data, such as social media posts or audio data. Examples of non-relational databases include MongoDB, Redis, and DynamoDB.
Data analysts need to be familiar with both SQL and NoSQL databases. SQL databases are still the most widely used type of database, but NoSQL databases are becoming more popular as the amount of unstructured data grows.
Programming languages
Programming languages are essential tools for data analysts. They allow data analysts to automate tasks, write custom scripts, build machine learning models, and perform advanced data analysis tasks. Python, R, and Julia are the top programming languages for data analysis:
Python is a versatile and easy-to-learn language. It has many libraries and frameworks for data analysis tasks, such as data preprocessing, visualization, statistical modeling, and machine learning. It also has a good online community, which makes it very popular in the industry.
R is a statistical programming language that is well-suited for data exploration, hypothesis testing, and statistical modeling. It is also very good at data visualization. R is popular in academia but is also used in the industry.
Julia is a high-performance programming language that is well-suited for data science. It is known for its speed and performance, making it a good choice for data-heavy tasks and complex computations. Julia is also relatively easy to learn and has a growing community of users and developers.
Your choice of programming language may depend on the organization, the project, or the dominant language in the industry where you reside.
Machine learning
Machine learning is an important tool that data analysts can use to build predictive models and enhance their analytical capabilities. Machine learning algorithms can be used to identify patterns in data and make predictions about the future. This can be useful for various tasks, such as forecasting customer churn, detecting fraud, or recommending products.
While data analysts are not typically expected to be machine learning experts, having a basic understanding of machine learning concepts and algorithms can be a valuable asset. This can help data analysts choose the right tools for the job, interpret the results of machine learning models, and communicate their findings to others.
Domain knowledge
Domain knowledge is understanding the specific industry or field in which the data analyst works. It helps data analysts better understand the data they are analyzing and identify insights relevant to their business.
Data analysts with domain knowledge are more valuable to employers because they can better understand the specific needs of their organizations or clients and develop solutions that are tailored to those needs.
A data analyst with domain knowledge in the healthcare industry would be better able to understand the data collected by a hospital and identify insights that can help the hospital improve its patient care.
Domain knowledge can help data analysts:
Ask and answer the right questions.
Interpreting the data correctly.
Communicating their findings effectively.
A typical day for a data analyst
Data analysts play a crucial role in helping organizations make informed decisions. Their day begins with engaging with stakeholders to understand their needs and objectives, ensuring their work aligns with the organization's goals.
Data analysts then develop a comprehensive data collection plan, outlining the sources and methods to be used for data gathering and cleaning. Data can be collected from various sources, including databases, surveys, web scraping, and APIs. Once the data is clean, analysts explore it and formulate hypotheses based on their understanding of the data, business objectives, and knowledge of best practices.
Suppose a data analyst enhances a company's customer retention rate. Their journey commences with meticulous data collection from diverse sources such as customer surveys, website analytics, and CRM systems. They then use statistical or machine learning techniques to conduct rigorous data analysis, identifying factors linked to customer retention, such as loyalty, service quality, value, and NPS. They may perform several analyses to understand how combining the different factors affects customer retention rate.
Finally, they collate their findings and craft insightful recommendations presented to management, who may implement all, some, or none of the recommendations as part of their customer retention strategy.
Challenges
One of the biggest challenges is poor data quality. Data analysts often have to deal with incomplete, inaccurate, or inconsistent data, which can lead to misleading conclusions. Data analysts must develop strong data collection, cleaning, and preprocessing skills to overcome this challenge.
Another challenge is data privacy concerns. Data analysts must handle sensitive data carefully and ensure that it is used responsibly and ethically. This means following the best data security and privacy practices and being transparent about how data is collected and used.
Ethical considerations are a major challenge in data analysis, as data analysts' recommendations can significantly impact people's lives. To address this challenge, data analysts must be aware of and mitigate potential biases in their data and algorithms and be transparent and accountable in their work.
Handling big data is another challenge. Analyzing and processing large datasets requires significant computational resources and expertise. Data analysts must be proficient in big data processing tools and techniques to overcome this challenge.
Data analysts must also be able to communicate their findings effectively to both technical and non-technical audiences. This can be challenging, as data analysis often involves complex technical concepts. Domain knowledge and good presentation skills can help data analysts effectively communicate the results of their work.
Conclusion
In summary:
Data analysts play a pivotal role, leveraging data to inform decisions and drive positive change in diverse industries.
Their technical proficiency turns raw data into actionable insights.
Challenges encompass data quality, privacy, handling big data, and staying current with evolving tech.
Mitigating biases in data and upholding ethical standards are additional hurdles.
Effective communication bridges technical analysis and non-technical stakeholders.
Adapting to evolving technologies is vital for staying relevant in this dynamic field.
Data analysts should embrace ethics, combat biases, and adapt to changes, ensuring their insights foster positive, ethical, and impactful data-driven decisions.