Data Science vs. Machine Learning

We often see that machine learning and data science are used interchangeably. Interestingly, there are some significant differences between these two concepts. In this lesson, we’ll discuss their relationship, similarities, and differences. Let’s start by revisiting their definitions.

Defining data science and machine learning

Data science involves various tasks to discover important insights from large and complex datasets. This includes collecting, organizing, and analyzing data and using statistics to understand a specific problem. Data scientists apply their skills to define and solve real business problems, using tools like statistics, data modeling, and data analytics to make informed decisions based on data.

Machine learning, on the other hand, aims to make machines learn from data by creating algorithms and models. This helps machines become smarter, make predictions, and learn independently. Unlike data science, machine learning doesn’t need a lot of human help. It deals with big data to make predictions and improves through learning.

Machine learning is a subfield of AI that deals with the required preprocessing, training, and testing of a model on the given data. We can say that it’s one of the most important parts of the data science project. In summary, data science involves data collection, cleaning, modeling, analyzing, and decision-making, while machine learning is a specialized subset of data science that focuses on building models.

Key differences

Let’s look at the differences between data science and machine learning in the following illustration:

The illustration represents the data science and machine learning pipelines. Modeling and analysis is an important part of data science, where we can apply machine learning. If we’re using machine learning, this step is itself a pipeline that consists of feature engineering, model training and testing, and its performance evaluation. Based on the testing, we might need to tune our model according to our goals and objectives.

Data science pipeline

The process of data science has several important steps. It starts with defining the problem, where we figure out what we want to achieve, and what limitations we have. Then, we gather data from different sources and clean it so that it’s ready to use. After that, we explore the data to find patterns and help us make decisions.

Next, we create models and analyze the data, which includes choosing what information to use, training the models, checking how well they work, and understanding the results. If the models work well, we deploy them for real-world use. Insights are communicated to stakeholders using data visualization and storytelling, and feedback guides iterative improvements in the data, models, and analyses.

Machine learning pipeline

The first step of the machine learning pipeline is feature engineering. This is the process of selecting and transforming relevant input variables (features) on which we want our model to be trained. The next step is selecting an appropriate model and training and testing it. For this, we need to understand the nature of the given task and dataset to find the suitable learning type—supervised learning, unsupervised learning, or reinforcement learning. It provides us a base to find the appropriate models to be trained.

Then we use either train/test split or cross-validation to train and test the selected model. In the train/test split, we have a holdout reserved for testing. While in cross-validation, we create different folds for testing the entire dataset in different iterations. In the last step, we evaluate the performance of the model by various metrics and statistical techniques.

Goals and objectives

The primary goal of data science is to extract valuable insights from data for decision-making. It helps us achieve the following objectives:

• Getting a deeper understanding of the data and its context.

• Identifying trends, anomalies, and patterns within the data.

• Providing insights for decision-making to improve the products, services, and/or processes.

The main goal of machine learning is to develop models that can make predictions to automate tasks and assist in decision-making. It helps us achieve the following objectives:

• Building models to predict future outcomes.

• Automating tasks or processes to increase efficiency.

• Training models to learn from data patterns.

Overlapping areas

Machine learning and data science are bound to have several overlaps. The following are the areas where these concepts overlap the most:

• They both rely heavily on data. Data science involves the collection, cleaning, and exploration of data to derive valuable insights and make informed decisions. Machine learning often uses this data to build predictive models, identify patterns, and make predictions or classifications.

• Machine learning is a tool for predictive analysis, which data science can make use of whenever required.

• Both use statistics and probability. Data science uses statistical methods to make sense of data, while machine learning also uses statistics, especially for model evaluation. Probability is used for predictive analysis.

• Preprocessing is a part of both data science and machine learning. Before being trained, the data needs to be put in the right format.

Data science applications without machine learning

Many of the use cases of data science have machine learning implemented at some level. However, oftentimes, data science doesn’t require a machine learning model. For example:

• Statistical language models: These models may rely on bigram or trigram structures, where the prediction of the next word depends on the preceding words. This prediction is based on probabilities calculated from historical data.

• Detecting spelling mistakes: A spelling correction algorithm for a language can use naive Bayes and Levenshtein distance, or edit distance, and an ensemble model to correct the respective spellings.

• Social media sentiment analysis: Examines data from social media to grasp how people feel about a specific subject, brand, or product. It applies text analysis methods to classify these feelings as either positive, negative, or neutral.

Challenges and considerations

Even though machine learning and data science are closely related, data science is a much broader field that only utilizes machine learning techniques when needed. Data scientists are required to know a considerable amount of machine learning concepts. Given the inherent challenges associated with the dynamic nature of these fields, we need to address some considerations in data science and machine learning. It’s important to consider ethical and accuracy aspects, so let’s discuss a few examples of such concerns.

• Data privacy: One of the major concerns is data privacy. Handling sensitive and personal data in data science and machine learning projects raises significant privacy concerns. Ensuring the protection of individuals’ information is a significant challenge. We need to define and meet data privacy regulations considering international, national, and company-specific compliance.

• Data anonymization: Related to privacy, another major concern is data anonymization. We need to find a balance between data analysis and the protection of privacy, which can be quite challenging sometimes.

• Bias in models: Biases in training data can lead to biases in models, resulting in discriminatory outcomes based on factors such as race or gender. Such biases raise questions about the fairness of results. Ideally, models should be carefully designed to avoid reinforcing existing biases. However, identifying and mitigating bias in models is an ongoing challenge. It also involves human feedback, which poses challenges about whether people prefer the unbiased outcomes.

• Model interpretability: Many machine learning models and particularly deep learning-based models are considered opaque-box models, which means it’s hard to interpret how these models reach a specific outcome. Making such opaque-box models interpretable and explainable in way that is understandable to nontechnical stakeholders is a big challenge.

Test yourself!

Let’s test your knowledge of the concepts covered in this lesson.