The role of analytics, statistics, and machine learning in data science life cycle

In the 21st century, data is like the new oil. Just as oil is extracted, refined, and used to fuel engines, data is collected, processed, and used to drive business decisions.

Data science is a branch of computer science that encompasses the fields of analytics, statistics, and machine learning. The combination of the three fields when used effectively can give businesses a serious competitive advantage, which is why its’ importance has grown rapidly in the last couple of decades.

However, the field of data science can be complex and confusing, especially for those without a technical background. This blog aims to clarify and simplify the core aspects of data science, how they work together in the data science lifecycle and how business can benefit from its practise.

If you are interested in learning about the various methodologies of implementing a data science project read our previous blog on ‘Navigating the world of data science’

Data Science methodologies

What is Analytics?

Analytics is a powerful tool that uses different techniques like data collection, cleaning, transformation, mining, and visualisation to extract insights from raw data that one can act on. This process of examining and visualising data to understand its main characteristics, identify patterns, and detect anomalies is sometimes referred to as exploratory data analytics. It helps researchers get an initial sense of the data and form hypotheses for further analysis. Analytics also pushes to ask the right questions in a data science project.

How data analytics helps businesses:

Analytics is the crux of data driven decision making in organisations. It takes raw data and transforms them into actionable insights.
It can analyse large amounts of data and present it a digestible format, accessibly by everyone including non-technical stakeholders.
It tracks and reveals information about your target audiences’ buying preferences and find out which services, products, or materials are most popular. This allows businesses to offer personalised recommendations and it informs their product innovation and inventory.
It supports automated data cleansing and correction leading to higher quality of data. The reduction of mistakes and unnecessary tasks.

However, it's important to understand the limits of analytics. While it provides useful insights, it doesn't always show clear cause-and-effect relationships. Also, the analytics process often needs manual work and takes time, relying on human skills, with few opportunities for automation.

What is Statistics?

Sometimes, analytics can be enough to solve a problem. However, there are times when the problem is complicated and needs a more advanced approach to find a solution, especially if there's a crucial decision to be made without concrete data to back it up. Statistics provides a structured and systematic method to answer questions, allowing analysts to draw conclusions with a certain level of confidence.

Analytics vs Statistics:

Analytics helps us form hypotheses & ideas and improves the quality of our questions. Statistics helps us test these ideas and improves the quality of our answers.
Analytics explores events and possible explanations. Statistics compares these explanations, giving more weight to some and doubting others.
Analysts focus on understanding data and business patterns, while statisticians focus on testing statistical hypotheses and revealing cause-and-effect relationships.

How statistics can help businesses:

Statistical methods enable businesses to develop effective policies that are informed by empirical evidence and insights derived from analysing quantitative data across production, sales, purchases, finance and other functionalities.
By applying time series analysis techniques, organisations can predict the impact of various factors within a business operation with good accuracy. For instance, in a retail context, factors could include things like consumer spending habits, promotions, weather patterns, or competitor activities.
They reduce uncertainty in operations providing evidence-based insights that guide decision-making, leading to smarter and more successful business strategies.
Statistical techniques like Bayesian Decision Theory will allow organisations tp determine the most optimal decisions by directly evaluating the advantages of each potential action.

Few limitations to be aware of: Statistics analyses quantitative data like production, income, and physical measurements, but can't effectively handle non-quantifiable qualities. It may not always be entirely accurate as analysis is often conducted within certain parameters rather than the entire population. Additionally, statistical methods are used to explore problems rather than invent solutions.

Wondering if your company is too small for data science? Don't worry, you're not alone! Regardless of scale, industry, longevity- you can benefit from its ability to assess the past and predict the future. Ei Square has decades of experiences helping businesses of all sizes. Talk with one of our experts to discover how data science can address your unique requirements.

How can data science help my business?

What is Machine Learning?

In the world of data science, machine learning is often seen as a branch of artificial intelligence that focuses on making decisions. Essentially, machine learning is all about making decisions on a large scale. It involves using computer algorithms to recognise patterns and extract information from data. The main goal of machine learning algorithms is to learn from data and generalise to carry out specific tasks.

Traditional programming vs machine learning

In traditional programming, rules are manually defined for the computer, while machine learning uses data to identify the best model by applying algorithms to input and output data. Machine learning models adjust parameters to enhance performance over time.

Analytics vs Machine Learning:

Analytics is focused on understanding and interpreting data to gain insights and inform decision-making. Machine learning is focused on developing algorithms that can learn from data and make predictions or decisions without explicit programming.
Analytics often relies on human interpretation and expertise to derive insights from data. Machine learning, on the other hand, uses algorithms to identify patterns and relationships in data and make predictions or decisions.
Analytics typically requires predefined methodologies and often struggles with highly complex or nonlinear relationships within data. Machine learning can automatically learn and adjust their internal representations to capture intricate patterns, making them more suitable for tasks with high levels of complexity.

Statistics vs Machine Learning:

Statistics involves analysing data and drawing conclusions, while Machine Learning uses algorithms to find patterns and make predictions.
Statistical modelling is strict about uncertainty and relies heavily on confidence intervals and hypothesis tests. Machine Learning is more lenient towards uncertainty, as it doesn't depend on many assumptions.
Statistical models use parametric methods, relying on a fixed number of parameters and making assumptions based on them. Machine Learning models, however, are non-parametric and don't assume specific data distributions.

How Machine Learning can help businesses:

ML models can analyse historical data to make predictions about future outcomes. For businesses, this means they can anticipate customer behaviour, market trends, and demand for products or services.
ML can automate repetitive tasks and processes, freeing up human resources for more strategic activities. For example, ML-powered chatbots can handle customer inquiries and support requests, reducing response times and improving customer service efficiency.
ML systems can quickly identify suspicious behaviour and alert businesses to potential security threats. Additionally, ML can help businesses assess and mitigate various risks, such as credit risk, supply chain disruptions, and cybersecurity threats.

While understanding the individual components of data science is crucial, it's equally important to recognize how these elements come together in a structured process to solve real-world problems effectively. This is where the concept of the data science lifecycle comes into play.

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What is the Data Science Lifecycle?

The data science lifecycle model is a structured framework that guides data science projects from start to finish. Various data science process frameworks exist, CRISP-DM is the most popular data science process framework due to its flexibility and ability to customise. It’s also a proven method to guide data mining projects. It provides a systematic approach to solving data problems and consists of six phases. By following this lifecycle, organisations can make sure their data science initiatives align business goals, is implemented smoothly, and provide real benefits. Each phase builds on the previous one, creating a flexible and adaptive process that can handle changing needs or unexpected problems.

The six phases in the data process life cycle:

Business understanding – What does the business need?
Data understanding– What data do we have / need? Is it clean?
Data preparation – How do we organise the data for modelling?
Modelling – What modelling techniques should we apply?
Evaluation – Which model best meets the business objectives?
Deployment – How do stakeholders access the results?

Data Science Lifecycle: Roles of Analytics, Stats, and ML

Business Understanding: This phase involves analytics to understand the business problem, goals, and requirements.

Data Understanding: Analytics techniques like exploratory data analysis (EDA) are used to gain insights into the available data. Statistical methods may be employed to assess data quality and identify potential issues.

Data Preparation: Analytics is crucial for data cleaning, transformation, and integration tasks. Statistical techniques can be used for handling missing data or outlier treatment.

Modelling: This phase heavily relies on machine learning algorithms (supervised, unsupervised, or reinforcement learning) to build predictive models or find patterns in the data. Statistical methods like regression analysis, hypothesis testing, and sampling are also employed in model development and evaluation.

Evaluation: ML performance metrics and visualisation techniques are used to evaluate model performance and interpret results. Statistical metrics and hypothesis testing are employed to assess the significance and reliability of the models.

Deployment: ML monitors and maintains the deployed models. Statistical process control methods can be used to track model performance over time.

While all phases involve some level of analytics, the modelling phase is where machine learning techniques take centre stage. Statistics is heavily utilised throughout the lifecycle, from data understanding and preparation to model development, evaluation, and monitoring.

It's important to note that the data science lifecycle is an iterative process, and the application of these disciplines may overlap or occur in multiple phases. For example, exploratory data analysis (analytics) can happen during both the data understanding and modelling phases, while statistical methods may be used for data preparation as well as model evaluation.

Now if you’re thinking: That’s all great then, what’s the next step? How can I implement this? This blog might be the perfect read for you: 'Navigating the world of data science’.

Or in case you’re wondering “I understand it’s benefits and how to implement but where do I start? Who can help me do this? Book a call with us using the link below.

Bottom line

Data science is a roadmap for turning raw data into useful insights. Following a structured approach such as CRISP-DM helps us tackle tough business problems step by step. Each stage—like understanding the business, sorting out the data, getting it ready, building models, checking how well they work, and putting them into action—is crucial for success.

Throughout this journey, clear communication with everyone involved, keeping the data organised, and knowing our analytical tools well are vital. As data keeps growing, mastering these steps will help companies make smarter decisions, be more innovative, and stay competitive.