The world is now intensely driven by data, thus making terms like Data Science and Machine Learning more and more popular. More often than not, these terms are used interchangeably in the context of data. Knowing some technical concepts like Machine Learning, Modeling, Statistics, Programming, and Databases would do you good. Although Data Science and Machine Learning are used interchangeably, the two are different fields altogether having different meanings. Hence, it is important to know what is Data Science vs Machine Learning. To know more about Data Science, look for a complete Data Science course in India.
Data Science vs Machine Learning: Head-to-Head Comparison
The below table summarizes the comparison of Data Science vs Machine Learning as we have seen in the previous sections.
|Parameters ||Data Science ||Machine Learning |
Performing operations on data to solve a business problem, or to prove/disprove a certain hypothesis.
Developing or applying algorithms that learn by themselves by extracting meaningful patterns from data.
Involves data gathering, data cleaning, data exploration, data analysis, data visualization
Involves data modeling, Supervised, Unsupervised or Semi-supervised algorithms, regression, classification
Report (generally containing visuals) based on the key data
Machine Learning Model
An end-to-end Data Science project on predicting the sales of a company in the next quarter.
A supervised machine learning model (regression model) that gives sales predictions.
Raw Unstructured data. Data can be in the form of text, images, audio or videos.
Structured and clean data. Especially transformed and scaled for algorithm use.
Computation power to support complex algorithms and mathematical computations on large data
GPUs for complex operations.
- Fraud and Risk Detection
- Image Recognition
Self Driving cars
- 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.
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 engineering, etc.
- Huge datasets are required for accurate model training
Data labeling is time-consuming
Can potentially complicate simple problems
Some human intervention may be required
Key Differences Between Data Science and Machine Learning
The main difference between Data Science and Machine Learning is that Data Science is the complete study of Data, whereas Machine Learning corresponds to just the modeling part of it.
If you are looking forward to becoming a Data scientist, it Is important to know and learn each and every step of Data Science workflow, starting from understanding the problem, to the stage where you present your output to the stakeholders.
On the other hand, if you are willing to become a Machine Learning Engineer, your focus should be on learning various Machine Learning algorithms, their mathematical understanding, and their applications. You should also be familiar with various data structures and space-time complexities of using various methods.
Data Science vs Machine Learning example mainly lies in the objective of the business problem we are trying to solve. ML Engineer vs Data Scientist works particularly on featured data to train suitable models that can predict outcomes’ likelihood based on the previous trends. Is Data Science and Machine Learning the same? Let's find out.
Data Science vs Machine Learning: Overview
What is Data Science?
Data Science can be considered an extension of statistics which is capable of handling humongous amounts of data with the help of advanced computational technologies. It is the field that shows how to study data, which includes formulating research questions, collecting data, processing it, storing it, and then finally building reports to present the findings.
Data can be present not just in textual form but also in the form of images, audio, and videos. Therefore, Data Science is a diversified field having vast areas of applications. Data Science has become an integral part of many industries. Processing and analyzing data help companies to better understand their consumers, optimize business processes, make effective decisions, and offer better products. They rely heavily on data and numbers.
Data Science Workflow
Here is what a simple Data Science workflow looks like:
- Asking the Right Questions: This step involves being interrogative in order to find out what it is that we need to solve and understand the problem in hand.
- Getting the Data: Once we are clear with the problem, it is time to start gathering the data. Looking out for the right data is crucial to the whole process.
- Exploring the Data: This step is all about studying the data that has been gathered. Doing Exploratory Data Analysis, plotting data, and finding anomalies are some of the key aspects of data exploration.
- Modeling the Data: In this step, models are trained and tested for various reasons such as predictive analysis and descriptive analysis.
- Communicating and Visualizing Results: This is the final step of the Data Science workflow, where we present our work and findings to the stakeholders.
What is Machine Learning?
Now that we know what Data Science is let us understand what Machine Learning is. Machine Learning is a branch of Computer Science that teaches how to make computers solve problems without explicitly being programmed to solve them step-wise.
Machine Learning can be divided into various learning methods - supervised, unsupervised, and reinforcement learning. Each of these types of ML has its applications, pros, and cons. Each of these ML learning methods uses different algorithms. Algorithms in Machine Learning determine how a model will learn from data.
Data Science vs Machine Learning: Skills
Skills Required to Become Data Scientist
Now that we have understood what is Data Science and its workflow, let us talk about the skills that are needed for one to become a Data Scientist:
- Programming: It is the most obvious skill to have if you want to succeed as a Data Scientist. You should be well versed in one of the programming languages; it is better if it is Python or R. All the processes like data cleaning, analysis, and modeling rely on your programming skills.
- Mathematics: It is one of the fundamental skills that many people ignore. Data Science is about data and numbers, and mathematics is at the very core of it. It is also important to know the underlying math in order to understand various ML algorithms. Linear Algebra and Probability are two of the most important Math topics you should focus on. Basic Calculus can also come in handy if you work with advanced Machine Learning and Deep Learning methods.
- Statistics: Statistics is yet another important skill to have if you are willing to be a Data Scientist. You should know topics like Central tendencies, Probability, PDF, CDF, etc.
- Data Wrangling: This refers to the process of data cleaning, also known as data munging, which means cleaning and transforming the data into a more readable format.
- Data Visualization: Visualizing data has become even more important for a better understanding of outputs and emerging patterns inside the data. It is one of the skills every Data Scientist should have so that she can tell the story through visuals that the data is trying to show.
- Machine Learning: It is expected from Data Scientists that they know what Machine Learning is and what the various algorithms and ML libraries can be used based on the kind of data and the problems to be solved. You can learn how to acquire these Data Science skills by joining a complete Data Science Bootcamp.
Skills Required to Become ML Engineer
To be a successful ML Engineer, you need to have the following skills:
- Statistics: Understanding Statistics is necessary if you want to be very good at Machine Learning. It is one thing to know the algorithms, but it is another to know how these algorithms work and what are the underlying statistical assumptions.
- Probability: Machine Learning is incomplete without the knowledge of probability. You need to understand the concepts of probability, especially conditional probability in order to be a good ML Engineer.
- Data Modeling: This is an obvious skill for an ML Engineer. If you want to be a successful ML engineer, you should be able to identify the right ML methods for your data problems.
- Programming: You cannot escape programming if you are Data Savvy. Today’s world is about big data, and it depends on your programming skills and how you leverage that data.
- Using ML Libraries and Algorithms: Knowing what all libraries and algorithms are there at your disposal is your biggest skill as an ML Engineer.
Data Science vs Machine Learning: Job Description
A Data scientist is mainly responsible for developing solutions using exploratory data analysis, building models and also reporting. Job description for the role of Data scientist generally includes keywords like EDA, Machine Learning, Regression, Classification, etc. It may or may not include research-intensive tasks like creating novel algorithms, as in most cases it is sufficient to use pre-existing algorithms or pre-trained models to do the job.
On the other hand, Machine Learning engineering is a more focused role to play where the main goal is to optimize the models built by Data Scientists and take them to production. The job description for this role includes roles like model optimization, making models compatible with custom deployment constraints, A/B testing, deployment, MLOps infrastructure for experimentation, monitoring the model performance post-deployment, etc.
Careers in Machine Learning demand more technical knowledge. It’s advantageous to have a computer science background in order to understand the Data Structures better and formulate optimized algorithms.
Data Science vs Machine Learning: Salary
Let's talk about what salaries Data Scientists draw based on their experience and location.
- A data scientist who is a beginner with up to 1 year of experience can easily earn somewhere around $80,000 a year in the USA. In India, the same experience can make you earn around Rs.6,00,000 per year. A Machine Learning Engineer with some experience in the USA earns around $85,000 a year and around Rs.6,50,500 in India.
- If you have up to 5 years of experience as a Data Scientist, you can earn anywhere between $95,000 to $120,000 in the States. In India, a similar experience can pay you between 9 LPA to 20 LPA depending on your skill set and the hiring company. For a Machine Learning Engineer, the numbers are similar.
- For people having more than 5 years of experience, the salary range starts from $125,000 in the States and 20 LPA in India for both profiles.
Data Science vs Machine Learning: Certifications
There are certifications available for both Data Science and Machine Learning. You can find both online and offline courses, University programs, and other certifications that provide extensive applied knowledge for Data Science and Machine Learning.
Many people wonder, “What should I learn first, Data Science or Machine Learning?”. My suggestion would be to opt for a certification that covers the whole Data Science, including Machine Learning, so that you get to cover the whole breadth of Data Science. That would give you the confidence and the edge to apply for Data Science jobs.
Once you are equipped with the knowledge, you can always go deep into Machine Learning in order to become a Machine Learning Engineer.
Data Science vs Machine Learning: Importance
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 better features and patterns from the input data, 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 has 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.
Data Science vs Machine Learning: Career
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:
1. Data Scientist
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.
2. Data Analyst
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.
3. Data Engineer
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.
4. 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.
5. 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 Indicators (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.
Average Salary (per year in USD)
Business Intelligence Analyst
Business Intelligence Developer
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 courses.
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:
1. 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.
2. 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.
3. 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.
4. 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 skill set, experience level, education, and location.
Average Salary (per year in USD)
Machine Learning Engineer
Natural Language Processing Scientist
Computer Vision Engineer
Machine Learning Scientist
Data Science vs Machine Learning: Limitations
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.
1. 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.
2. 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.
3. 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 business as well. A 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 the 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:
1. 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.
2. 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.
3. 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.
4. 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.
Data Science or Machine Learning: Which is Better?
Machine Learning or Data Science, which has a better future? While Data Science is a complete study of Data right from its collection to its cleaning, analyzing and modeling, Machine Learning is a narrow field that focuses mainly on building self-learning programs that can recognize patterns in data.
If you are interested in understanding and working with all stages of Data, Data Science is a more suitable field. On the other hand, if you are more interested in algorithms, data structures, programming dynamics, and mathematics, you would thrive better as an ML Engineer. Niching down is never a bad idea. To learn more, I suggest you check applied Data Science with Python specialization.
I hope you are clear about how Data Science and Machine Learning are two different yet related fields. It’s easy for beginners to get confused between the two terms. But now you know how they differ in meaning and their applications. But which is better, Data Science or Machine Learning? That is something you need to answer for yourself. The relation between Data Science and Machine Learning is such that they overlap in a lot of real-world spaces.
If you want to be a complete Data Scientist who deals with end to end journey of Data, you should be looking into the Data Science learning path. On the other hand, if you are only interested in modeling data and knowing how various Machine Learning Algorithms work, you should be looking into Machine Learning Engineer’s career path. We have already seen how and where these two fields overlap while being different. Whether it is the salary or the skill set required or the kind of projects, there are, Data Science and Machine Learning are both interesting and great subjects to learn and grow in.