Data Science vs Machine Learning - What's the Difference?
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Overview
Although Data Science and Machine learning are the most popular buzzwords and are often used interchangeably when talking about generating valuable insights from the data, they should not be considered synonyms for each other.
Data Science implements Machine Learning techniques but both are different fields with different goals.
If you want to pursue a career in any of the above fields, it becomes important to understand each field and how they differ.
In this post, we will talk about Data Science vs Machine Learning.
What is Data Science?
Data Science is the field of study to process the data residing in the organization’s repositories by applying various scientific methods.
It is a discipline that brings together statistics, data analysis, Machine Learning, Computer Science, and their related methods to process the data and understand the underlying patterns in it.
It includes collection, cleaning, and preparation of the data and identifying the patterns to generate insights that can help organizations become data-driven in decision-making for the growth and success of the company.
Data Scientists are responsible for implementing various data science techniques for an organization. They collect and process structured and unstructured data from a business point of view and apply various methods such as statistics, machine learning, etc., for insights generation.
Skills Required to Become a Data Scientist
If you are looking to pursue a career in Data Science , below are the skills you will need to be proficient in regardless of your role -
- Strong programming knowledge of Python, R, Scala, etc.
- Experience in SQL database coding
- Knowledge of various data wrangling techniques
- Sound understanding of various Machine Learning algorithms
- Deep knowledge of Mathematics and Statistics concepts
- Ability to process structured and unstructured data
- Data mining, cleaning, and visualization skills
- Knowledge of Big Data processing frameworks such as Apache Spark, Hadoop, etc.
- Business acumen/Domain expertise
- Strong communication skills
Learn more about what skills are required to become a data scientist from here.
Limitations of Data Science
One of the biggest challenges in a Data Scientist’s life is finding the right data for business problems. The issues with data can be classified as either quantity or quality. Applying data science techniques to insufficient, messy, and noisy data can lead to arbitrary or misleading results.
What is Machine Learning?
Machine learning is a field in Computer Science that enables systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on developing computer programs that can access data and use it to learn for themselves. Movies recommendations by Netflix and Facebook/Instagram feeds are some of the examples that are powered by Machine Learning techniques.
Machine Learning Engineers focus on implementing various tools and techniques to automate the predictive models. A Machine Learning Engineer typically works as part of a larger data science team and will communicate with data scientists, administrators, data analysts, data engineers, and data architects.
Skills Required to Become a Machine Learning Engineer
Below skills are required to pursue a successful career in the Machine Learning field -
- In-depth knowledge of computer fundamentals and programming knowledge of Python, R, Scala, etc.
- Sound understanding of various Machine Learning algorithms
- Advanced knowledge of Mathematics and Statistics concepts
- Knowledge of data modeling and evaluation
Inherent Limitations of Machine Learning
Though Machine Learning algorithms let computers learn the underlying patterns in the data with minimal interventions, it still requires engineers to optimize and tune the algorithms each time to work on new business problems.
Aside, there are many problems that can’t be solved by applying Machine Learning. Also, these algorithms might add complexity to a business process if the problem can be solved using traditional statistical methods.
Difference Between Data Science and Machine Learning
To understand the difference between Data Science and Machine Learning, we need to refer to the Venn diagram shown below. Data Science can be considered as a combination of Computer Science, Mathematics, and Stats along with domain expertise, while Machine Learning mainly focuses on Computer Science and Applied Mathematics fundamentals.
So the main difference between these two techniques is understanding the business domain. If you wish to become a Data Scientist, then you need to acquire domain expertise to process the data in such a way that it can help companies grow and become profitable. If you can’t understand businesses and their problems, then you can’t use data science techniques in the best way for the organizations.
Data Science VS Machine Learning
| Factor | Data Science | Machine Learning |
|---|---|---|
| Scope | Data Science is a field that deals with processing data and identifying hidden patterns and useful insights by applying scientific methods. | Machine Learning is a group of techniques that allow computers to learn the patterns in the data without being explicitly programmed. |
| Components | Data Science is a combination of the entire analytics landscape, such as Business Analytics, Machine Learning, Data Wrangling, etc. | Machine Learning is a combination of Computer Science and Mathematics. |
| Lifecycle | Data science lifecycle includes six different steps starting from business requirements to solution deployment. | Machine Learning is used in data modeling steps in the data science lifecycle. |
| Type of Data | Data science deals with raw, structured, and unstructured data. | Machine Learning techniques mostly require structured data as an input. |
| Preferred Skill Set | Data Scientists must have relevant domain expertise along with a strong understanding of various Machine Learning algorithms, database management, maths, stats, big data framework (Spark, Hadoop, etc.) and programming knowledge of Python, SQL, Scala, etc. | Machine Learning engineers need to be proficient in computer science and applied maths fundamentals along with strong programming knowledge of Python, R, Scala, etc. |
| Time Spent | Data Scientists spend a lot of time cleaning, transforming, exploring the data, and understanding its patterns. | A Machine Learning engineer spends most of the time optimizing and evaluating the model performance during the implementation. |
| End Goal | The main goal of data science is to derive actionable insights and support decision-making. | The main goal of machine learning is to develop predictive or classification models based on historical data. |
| Tools Commonly Used | Data scientists often use tools like Jupyter Notebook, Pandas, Matplotlib, Tableau for data analysis. | Machine Learning engineers often use TensorFlow, Keras, Scikit-Learn, PyTorch for model development. |
| Interpretability | Data Scientists often need to explain their findings in a more business-friendly manner, emphasizing interpretability. | While Machine Learning can prioritize accuracy over interpretability, there’s an increasing emphasis on creating interpretable models, especially in regulated industries. |
Where is Machine Learning Used in Data Science?
We can understand the use of Machine Learning in Data Science by understanding its lifecycle. The Data Science lifecycle consists of 6 different steps, as shown in the below diagram.
- Business Requirements - This step includes an understanding of the business problems to which we want to apply data science tools and techniques. For e.g., building a recommender system to improve customer experience and engagement, predicting customer churn, etc.
- Data Acquisition - This step includes getting access to the right set of data for the given business problems. For e.g., getting access to items purchased by customers for building the product recommender system.
- Data Processing - In this step, raw data is transformed into a suitable format that can be processed and explored.
- Data Exploration- In this step, various statistical and visualization methods are applied to explore the patterns and trends in the data.
- Modeling- This is the step where Machine Learning algorithms are used to model the input data and learn the underlying patterns in it. This entire process includes cleaning & preparation of the data and training, testing & evaluating the Machine Learning model.
- Deployment - Once the Machine Learning model is trained, it is deployed in the business process to predict the outcomes.
Conclusion
- Data Science encompasses a broad range of data processes, while Machine Learning hones in on algorithm-driven prediction and learning.
- Data Scientists need a mix of domain expertise, analytics, and programming, whereas Machine Learning Engineers focus more on algorithm design and optimization.
- Data quality issues are central in Data Science, while Machine Learning grapples with algorithm tuning for diverse problems.
- Machine Learning is a subset within the broader Data Science lifecycle, emphasizing automated learning from data.
- While Data Science aims for holistic data insights, Machine Learning targets automated, predictive outcomes.
If you want to start a career in Data Science, check out Scaler’s Data Science Program.