This rise of Big Data and human limitations led to the birth of new technologies that we all might have heard about - Data Science and Machine Learning. Many people often get confused between these two terms and use them interchangeably because they both have the same underlying idea - using data to find trends and patterns and use these findings to make decisions. However, Machine Learning and Data Science both are very different. Machine Learning, Data Science, and Artificial Intelligence are overlapping fields to some extent. Data Science is built upon various pillars and Machine Learning is one of them.
In this article, we will look at Data Science vs Machine Learning. Although they are closely related, they both have different functionalities and end goals. We will compare these two concepts and the differences between them. If you are someone who is confused between which fields out of these two, to pursue a career in, you will get an idea of what work and expectations are entailed in both these fields. Hopefully, you will get a clear idea between Data Science and Machine Learning, which is better for you. Before we jump on to the differences, let us understand what these fields actually involve.
A Quick Glance at Data Science vs Machine Learning:
The below table summarizes the comparison of Data Science vs Machine Learning as we have seen in the previous sections.
|Factor||Data Science||Machine Learning|
- Widely used in different industries
- Helps in deriving valuable insights to make better data-driven business decisions.
- Better customer engagement, better company performance, and increased profitability.
- Interpret large volumes of data and automate Data Science tasks
- Predictive analysis can be easily done using Machine Learning with minimal human interference.
|Careers||Wide range of career options like Data Analyst, Data Scientist, BI Developer, Data Storyteller, BI Analyst, etc. requiring knowledge of both technical skills and business skills||Multiple career options are available, that require more technical expertise in Machine Learning and Computer Science, like Computer Vision Engineer, Machine Learning Scientist, NLP Engineer, etc.|
- Performance dependent on data quality
- Challenge of data privacy
- Domain awareness required
- Huge datasets required for accurate model training
- Data labeling is time-consuming
- Can potentially complicate simple problems
- Some human intervention may be required
Data Science Workflow
Data Science is a field that involves the use of tools, statistical models, and scientific approaches to find insights and meaning from vast volumes of raw data. The raw data is transformed into important business information that can power critical business decisions. The data can be either structured or unstructured and can exist in any form - text, audio, images, video, etc. Analyzing this data can help us understand the industry, consumer dynamics, or market patterns that will, in turn, enable the organization to form data-driven strategies and increase effectiveness. Large organizations like Netflix and Amazon rely on Data Science methodologies extensively.
The Data Science workflow includes data acquisition from different sources, cleaning, preparation and preprocessing, analysis and visualization. Data Science requires fluency in programming languages like Python/R and SQL, knowledge of statistics, and understanding of databases. To learn Data Science using Python, you can always enroll in the KnowledgeHut data science with python.
What is Machine Learning?
Machine Learning is a part of Artificial Intelligence and a subfield of Data Science. It is a set of technologies and algorithms that enable the machines to automatically learn from data, without being directly programmed. These algorithms are used by Data Scientists to analyze the data and make predictions with minimal or no human involvement. The recommendation systems used in Amazon, Netflix, and other similar platforms all leverage machine learning algorithms that make predictions, i.e. new recommendations, based on past data, i.e., user activity. Moreover, email spam detection is also built using Machine Learning methods.
Machine Learning uses statistical methods to improve the efficiency and performance of output prediction. Machine learning methods are divided into three categories - Supervised, Unsupervised, and Reinforcement Learning.
Supervised Learning is generally used in the case of predictive applications, that predict either a category or a numeric value. During the model training, the data fed to the model contains the input features as well as the expected output labels. The model tries to fit itself according to these input-output pairs, by adjusting its hyperparameters, also known as weights. The model does this adjustment by trying to minimize the error in its predictions, which is proportional to the difference between the actual output and the predicted output. Object Recognition, Image Classification, Stock Market Prediction are all examples of Supervised Learning systems.
Unlike Supervised Learning, the model is fed unlabeled data in the case of Unsupervised Learning. There is no guidance or prior training involved. The model tries to divide the dataset into groups on its own, according to the similarities and patterns in the input features. Each of these groups is dissimilar from each other, in terms of the input features, but every data point within a particular group has high similarity with every other data point in the same group. Recommender Systems and Anomaly Detection are examples of Unsupervised Learning.
Reinforcement Learning is used in cases where sequential decisions are required. Each of these decisions can result in either a reward or a punishment, and the goal of the model is to maximize the rewards. Unlike Supervised Learning, there is no correct output in the case of Reinforcement Learning. Instead, we have the desired output state, which may be reached from the input state by following various paths. AlphaGo, which is an Artificial Intelligence program that plays the Go board game, was trained using Reinforcement Learning and is the perfect example of this method.
All these methods create a model that best fits the data. These models can make predictions efficiently and reliably.
The below diagram shows how Data Science and Machine Learning are related, and how they overlap.
Differences between Data Science and Machine Learning
Now that we have seen what these two fields are, let us now compare them and look at the difference between data science and machine learning. We will compare these two fields based on the three parameters: Importance, Careers, and Limitations.
Importance of Data Science
Data Science is widely being used in various industries including Finance, Healthcare, Tourism, Banking, and Marketing. With the help of Data Science, businesses can now efficiently understand and interpret vast amounts of data collected from different sources and derive valuable insights to make better data-driven business decisions. Companies can measure, track and record performance metrics and analyze trends to enhance the decision-making process. This has the potential to result in better customer engagement, better company performance, and increased profitability. Using Data Science, organizations can understand their customers and clients better using existing data and can even simulate user actions to come up with solutions to produce the best business outcomes.
Importance of Machine Learning
Machine learning is one of the many components of the Data Scientist toolbox which is being applied in many industries. Machine Learning is becoming popular because it can interpret large volumes of data and automate tasks involved in Data Science. It has transformed the way Data Extraction and Data Visualization operate. The predictions done using Machine Learning methods can direct smart decision-making in real-time with minimal human interference. Data-driven decisions determine if a business can keep up with the market and its competitors or will fall behind. Machine Learning is what can enable businesses to leverage market and consumer data to make choices to keep them ahead of their competitors. When you deploy machine learning models well, they learn features and patterns from the input data better, either known or previously unseen, in multiple iterations. New input data is then fed to test if the model works correctly. If the prediction is not accurate enough, the model is trained again. This lets the model continually learn on its own, gradually increasing in accuracy over time. This final model can now make predictions on new data based on its learnings.
Careers in Data Science
Data Science is needed wherever there is Big Data. As more and more businesses have begun to collect market and consumer data, the need for Data Science has increased, irrespective of the area of business. The field of Data Science provides multiple roles and career options, as a result, a lot of professionals are switching to data science. Some of the data science roles are listed below:
A Data Scientist understands business challenges and offers solutions to overcome these challenges by processing and analyzing huge datasets, either structured or unstructured. They investigate different data trends and assess the effect on the business. They provide actionable business insights based on their analysis of the data, which can help in making the best decisions to maintain sustainable and healthy growth in business.
Data Analysts acquire, visualize, process, and analyze data, typically structured, to determine industry trends. They prepare reports to present the trends and insights they have generated, that can be understood by non-expert users. They also perform testing to evaluate the performance of the Data Analysis model and decide whether the model needs to be enhanced based on the testing results.
Data Engineers prepare data for operational or analytical use cases. They create, design, and manage massive databases and data warehouses. They construct data pipelines and funnels to accumulate information from various sources and ensure an adequate flow of data. Data Engineers facilitate the ease of access of data as well as the improvement and maintenance of their company’s big data system.
Business Intelligence Analyst
Business Intelligence Analysts collect and extract data from warehouses using SQL queries, and analyze this data to find patterns. Based on their analysis, they create summary reports of the company's current standings for which they also use Data visualization and modeling. They also recommend suggestions to management regarding how to increase the efficiency of the business.
Business Intelligence Developer
Business Intelligence (BI) Developers have business-oriented work that involves designing and developing strategies to assist business users in finding the required information quickly and efficiently to make business decisions. They develop, deploy and maintain BI interfaces that are easy to understand for other people in the organization, and can provide quantifiable solutions to complex problems. They develop dashboards, reports, and Key Performance Indicator (KPI) scorecards.
The table below shows the average salary per year in USD for these roles in Data Science. The actual salary will depend on factors such as your years of experience, location, skills, education, etc.
|Role||Average Salary (per year in USD)|
|Business Intelligence Analyst||$81,306|
|Business Intelligence Developer||$108,661|
Other notable roles in Data Science include Business Analyst, Big Data Engineer, Statistician, Data and Analytics Manager, Data Storyteller, Database Administrator, etc. If you are unsure that Data Science is right for you, check out the application of data science and who can do data science course.
Careers in Machine Learning
As compared to Data Science, Machine Learning career options are more technical. Most of these roles are specialized roles that focus on a particular area in Artificial Intelligence and Machine Learning. Below are a few of the career roles in Machine Learning:
Machine Learning Engineer
Machine Learning Engineers leverage algorithms and statistical analysis techniques to build and enhance Machine Learning systems. They work with various machine learning algorithms like prediction, classification, clustering, and anomaly detection to tackle business challenges. They monitor the performance of the system to ensure reliability and fine-tune the systems based on the performance. They should have familiarity with building highly scalable and distributed systems as they deal with huge datasets.
Natural Language Processing (NLP) Scientist
A Natural Language Processing Scientist is a specialized role that requires the application of Machine Learning algorithms to textual information to design and build applications that can interpret human languages on their own, almost as accurately as humans do. These scientists require expertise in text representation techniques like Bag of Words, N-Grams, Semantic Extraction, and Modeling. Some of the application areas of Natural Language Processing are Grammar Correction like Grammarly, Smart Text Suggestions like that in Gmail and LinkedIn, Sentiment Analysis, Spam Filtering, etc.
Computer Vision Engineer
A Computer Vision engineer works on applying Computer Vision, Deep Learning, and Machine Learning techniques on images and videos so that machines can perceive and understand these images or videos. They automate the extraction, analysis, and interpretation of features and contexts from images. Some of the application areas of Computer Vision include Image and Object Recognition and Segmentation, Scene Understanding, etc.
Machine Learning Scientist
Machine Learning Scientists are more involved in the research industry or academia. They work on researching and developing new and improved algorithms that can be applied for Data Science use cases. Most of the work results in publications. Becoming a Machine Learning Scientist usually requires an advanced degree - Masters's or a Ph.D. in Machine Learning or related fields.
The table below shows the average salary per year in USD for these Machine Learning roles. Compare the same with top data science job roles and salaries. Note that these figures vary depending on your skillset, experience level, education, and location.
|Role||Average Salary (per year in USD)|
|Machine Learning Engineer||$122,123|
|Natural Language Processing Scientist||$88,055|
|Computer Vision Engineer||$127,289|
|Machine Learning Scientist||$128,723|
Limitations of Data Science
While Data Science is a lucrative and in-demand career path, it does not come without limitations. Let us look at some of the drawbacks and limitations that Data Science presents.
Performance of Data Science depends on data quality
Data is the main component of Data Science. You cannot be an effective Data Scientist if there is no data available. One major limitation of Data Science is that the results depend on the quality of data available. If the dataset size is small, or if the data available is incorrect or messy, the analysis models will produce meaningless or misleading results. Poor quality data has the potential of failing the entire Data Science workflow.
The challenge of data privacy
As businesses collect consumer-related data by tracking user activities, they have to take utmost precautions to ensure users’ privacy. This data might contain sensitive information - about the users or the organization itself - that could lead to severe repercussions, including lawsuits, in case of a data breach. One solution to mitigate the risk associated with such datasets is to generate synthetic datasets.
Domain awareness required
Another limitation of Data Science is that it relies on domain knowledge. People working in Data Science roles require knowledge of various fields including Mathematics, Statistics, Computer Science, Machine Learning, and the business as well. Lack of knowledge in any one of these fields would make it challenging for professionals to solve Data Science problems. Moreover, it becomes extremely important to understand the background of the business and challenges faced by them before trying to find solutions to these challenges using Data Science.
Limitations of Machine Learning
While Machine Learning has proven to be a revolutionary field, it is not all-powerful. Let us now look at the limitations and drawbacks of Machine Learning:
Requirement of a huge dataset for accurate training
Training Machine Learning models effectively require a large volume of data. It is difficult to acquire huge datasets of good quality for specific business use cases, even though data is being generated rapidly. If you use less data during training, the model accuracy will suffer.
Labeling of training data is time-consuming
This limitation is especially present in the case of Supervised Machine Learning methods. These methods require datasets that are “labeled” - which means human expertise is required to mark the correct output for the training data. This step is necessary for the working of supervised algorithms. Although labeling is not difficult to do, it is extremely time-consuming.
Potentially complicate simpler problems
Machine Learning can potentially complicate problems that are simple enough to be solved using traditional programs and equations. Moreover, Machine Learning models are prone to “overfitting” during training. Overfitting happens when the model focuses on noise, random fluctuations, and insignificant details present in the training data, considers them as features, and learns them. This negatively impacts the model performance.
Require human intervention to work on new problems
Machine Learning algorithms require minimal human intervention. However, expertise and programming might be required in certain cases to constrain and optimize these algorithms to work on new problems.
Learning without Limitations
This is not an in-depth comparison of Data Science vs. Machine Learning, but a superficial one, based on the factors such as career options, limitations, and importance. While Data Science is an interdisciplinary field using large amounts of data to obtain insights, Machine Learning can be considered as a part of Data Science and is one of the ways how Data Science goals can be achieved. Both these fields have multiple career options that are well-paying and in huge demand today with the rise of Big Data.
Frequently Asked Questions(FAQs)
1. Which is better between Data Science and Machine Learning?
There is no concrete answer to this as both these fields provide great career options. Data Science is a broader field whereas Machine Learning is a purely technical and specialized career field. Machine Learning careers will have limited responsibilities while Data Science roles will require you to take up varied and broad sets of responsibilities, both technical and non-technical.
2. Which field out of Data Science and Machine Learning pays more?
Both the career options are equally well-paying and in high demand. Depending on your experience level, skills, industry, and location, the average salary might differ for both fields. According to Payscale and the US Bureau of Labor Statistics, the average Data Scientist’s salary was $97K and $98K respectively. For entry-level careers in Data Science, this figure is estimated to be $85K and it can go to $197K for candidates with higher experience. The average salary of a Machine Learning Engineer is $112K. As you can observe, these figures are all in a similar range.
3. Is Machine Learning a must for Data Science?
Yes, it is mandatory to have Machine Learning skills for Data Science. The ability to evaluate Machine Learning is one of the most relevant Data Science skills. Understanding Machine Learning for quality predictions and estimations will help machines to take real-time decisions and actions with almost no human intervention.