We know that businesses are heavily reliant upon data analysis and new technologies in determining their organizational strategies and developing their products and services. As tech professionals, we hear terms like machine learning (ML), artificial intelligence (AI), automation, algorithms, and data on the daily. Understanding these technologies and learning how to harness the power of these tools allows organizations to make stronger data-driven decisions and secure a competitive edge in their industry.
Data analysis has been around for centuries, beginning with the development of Business Intelligence (BI) in the 1800s. However, data analysis has greatly transformed with the introduction of new technologies and ways of organizing, breaking down, analyzing, and securing actionable insights from said data. Machine learning has played a crucial role in those developments. Machine learning is used to automate the data analysis process and aids the workflow in order to arrive at deeper and more holistic insights. Let’s break down each area of work and from there we’ll gain a better understanding of how companies can utilize these technologies to make stronger decisions and become more competitive organizations.
Machine learning enhances data analysis by identifying patterns and making predictions based on large datasets. For example, retailers use machine learning algorithms to analyze customer behavior and improve inventory management. Financial institutions leverage machine learning for fraud detection by analyzing transaction patterns and flagging anomalies.
According to a report by McKinsey, businesses leveraging machine learning can enhance their marketing ROI by 15-20%. Additionally, Gartner predicts that by 2025, machine learning will be a critical component in 80% of data analytics solutions, up from 50% today.
What is Data Analysis?
Data analysis involves examining and interpreting large amounts of data with the intention of identifying meaningful patterns and trends. Through uncovering valuable insights from data, organizations are more capable of making strong, data-informed decisions. Data analysis ultimately can help sharpen business strategies, improve products or services, or expand an organization’s understanding of their clientele.
Data analysis today relies heavily on various tools and techniques to aid the data analysis and visualization process. These techniques may include statistical analysis, machine learning algorithms, and data visualization methods. Through these techniques, organizations can gain valuable insights that will help in optimizing their operations. This information can later inform business decisions, help cater marketing campaigns to specific consumer bases, enhance products and services, and lead to other actions that strengthen the organization.
What Responsibilities do Data Analysts have?
A data analyst’s role is to transform unstructured and unorganized data into valuable insights that inform a company’s data-driven decisions. A data analyst’s actual work may vary across organizations and fields of work, yet, the essence of utilizing a variety of tools and techniques in order to make sense of messy data remains the core task for these tech professionals. In other words, their core tasks include:
Data collection and organization: data analysts gather data from multiple sources and structure it in a format suitable for analysis.
Data cleaning: once data is collected and organized, analysts must “clean” the data to ensure that it’s accurate, consistent, and free of errors or missing values.
Data analysis: through the use of various data analysis tools and techniques, data analysts examine data to uncover patterns, trends, and other valuable insights. They often leverage machine learning algorithms to build predictive models that support in gaining a deeper understanding of the data and help data analysts make stronger recommendations.
Exploratory data analysis (EDA): is a standard data analysis approach that looks to uncover fundamental patterns, trends, relationships, and irregularities in the data. It relies upon an initial data exploration phase, weaves in statistical analysis, and often utilizes Python functions to manipulate data and conduct the exploratory analysis efficiently. EDA is often an initial step in the data analysis process.
Data visualization: is the process of transforming observations and findings into comprehensive visual reports and graphics to best communicate those insights to stakeholders who may not have a technical data analyst background. Effectively communicating findings is necessary in facilitating strong decision making–read our tips on how to effectively present data here.
Data security and privacy: is crucial preserving the integrity of the data and data analysis process. Data analysts must take measures to protect sensitive data, monitor and control access to authorized personnel, and comply with laws and other relevant data protection regulations.
Data analysts contribute to data-driven decision-making and offer valuable insights and recommendations to help companies secure a competitive advantage in their field. The tasks range from technical organization and analytical work to collaboration and communication to non-technical stakeholders.
To be successful in their role, analysts must be proficient in data analysis tools and techniques, be detail oriented, and possess strong soft skills to help communicate their findings to relevant stakeholders. Many data analysts today look to improve their skills through data analysis courses or verify their skill sets by securing a data analyst certificate.
Source: Turing
What is Machine Learning?
We hear the term all the time, but what is the definition of machine learning? Machine learning is a subset of artificial intelligence (AI) that utilizes algorithms to break down vast amounts of data. Through the development of these algorithms and models, computers can effectively “learn” and make predictions or decisions without being explicitly programmed. Machine learning essentially supports the design and development of systems that will automatically transform and be improved with the introduction of data or through experience.
Unlike traditional programming where a computer scientist writes specific directions for the computer to follow, machine learning is based on the computer’s “learned” conclusions. In other words, computers are trained in hand with large amounts of data and actually learn based on the patterns and relationships found within that data.
Machine learning is reliant upon algorithms to analyze data, identify patterns, and build mathematical models based on those patterns. The models created can be used to make predictions or decisions, test hypotheses, or secure comprehensive insights on unseen or future data. Thus, machine learning is proving crucial in expanding the terrain for data analysis and making even stronger organizational decisions.
There are three standard machine learning algorithms:
Supervised learning: is the process of training a model using labeled data, where the desired output or conclusion is known. The algorithm learns from clear examples in order to make predictions surrounding new, unknown, or unlabeled data.
Unsupervised learning: is essentially the opposite of supervised learning. Instead of training a model with labeled examples, the algorithm learns alongside unlabeled data. Its work is to find patterns, similarities, or groupings without a predetermined or predefined outcome.
Reinforcement learning: essentially trains an agent to engage a new environment and learn alongside the feedback it receives. The algorithm slowly develops alongside this feedback and adapts its decision-making strategy accordingly, improving its performance over time.
Machine learning is quickly becoming a staple in many organization’s data analysis processes and continues to advance and improve company’s ability to test hypotheses and make data-driven decisions.
Key Roles of Machine Learning in Data Analysis
In the end, data analysts and machine learning engineers work closely together as their work is both concerned with understanding and exploiting data in order to enhance company decisions. However, the two greatly differ in their objectives and approaches to processing and utilizing data. Data analysis is primarily concerned with interpreting and understanding data with the intention of securing actionable insights, while machine learning focuses on the development of algorithms and models through data so that they can function without human intervention.
Photo by Drew Dizzy Graham on Unsplash
How Can Machine Learning Help Enhance Data Analysis?
In many ways, data analysts and machine learning engineers rely on one another in order to gain a deeper understanding of data. Data analysts carry out the first step of conducting statistical analysis, and from those insights, a machine learning engineer creates models and machine learning systems that scale data, test hypotheses, and ultimately extract deeper insights from data.
Through these advanced techniques and capabilities, machine learning complements and enhances the data analysis process in the following ways:
Recognizing patterns: through data exploration, data visualization, and data mining, data analysts can identify patterns and generate hypotheses. Machine learning aids data analysts in the face of increasingly large and complex data sets. Through the application of machine learning algorithms, data analysts ensure a more comprehensive understanding of the underlying patterns and trends in their data.
Predictive analytics: machine learning models can be trained to more accurate predictions based on historical data. Through the models created, data analysis can offer a sharper analysis on what the future holds, supporting businesses to better mitigate risk, forecast trends and outcomes, and make more proactive decisions.
Algorithms and automation: machine learning algorithms help automate the most repetitive data analysis tasks like data cleaning, data preprocessing, and manual data manipulation. Machine learning makes the data analysis process more time efficient and thus gives tech professionals more time to interpret and strengthen their understanding of the data.
Detecting anomalies: the first step of data analysis after one obtains data is preparing and cleaning that data such that it’s free of anomalies, errors, or outliers. Machine learning can support detecting and correcting errors, finding and removing outliers, adding missing values, and merging distinct data sets. This is particularly useful in fraud detection, catching faulty machinery, or in identifying abnormal consumer patterns.
Communicating findings: machine learning aids data analysts provide enhanced data visualization. Machine learning techniques can be integrated with data visualization tools in order to create more dynamic and interactive representations of data.
Data segmentation: machine learning is often used to segment data into specific groups based on similarities and patterns identified. From these segments, whether they be customer segments, market segments, or other categories, companies can offer a more personalized experience and optimize everything from marketing campaigns to product design.
In many ways, data analysis is the precursor or the complementary step to machine learning. Through integrating machine learning techniques, data analysts can automate repetitive tasks, deepen their understanding of data, use algorithms to test hypotheses that strengthen predictions and help mitigate risk, and finally, lead to stronger recommendations and company decisions.
Steps to Implement Machine Learning in Data Analysis
Understand the Basics: Learn the fundamentals of machine learning through online courses or tutorials.
Choose the Right Tools: Use tools like TensorFlow, Scikit-Learn, and Pandas for data analysis and model building.
Prepare Your Data: Clean and preprocess your data to ensure accuracy in model training.
Build and Train Models: Develop machine learning models using your prepared data and tools.
Evaluate and Iterate: Continuously evaluate your models for accuracy and make necessary adjustments.
If you’re interested in a job in data analysis or working as a machine learning engineer, then look no further. Ironhack offers bootcamps which will arm you with the basics to kickstart your career, or help you dive deeper into these areas to strengthen and expand your opportunities as a tech professional.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
