Difference Between Data Science and Machine Learning
Data science is an evolutionary extension of statistics capable of dealing with the massive amounts of with the help of computer science technologies. Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Data science covers a wide range of data technologies including SQL, Python, R, and Hadoop, Spark, etc. Machine learning is seen as a process, it can be defined as the process by which a computer can work more accurately as it collects and learns from the data it is given.
Head to Head Comparison of Data Science and Machine Learning (Infographics)
Below is the top 5 comparison between Data Science and Machine Learning:
Key Difference between Data Science and Machine Learning
Below is the difference between Data Science and Machine Learning are as follows:
- Components – As mentioned earlier, Data Science systems covers entire data lifecycle and typically have components to cover following :
- Collection and profiling of data – ETL (Extract Transform Load) pipelines and profiling jobs
- Distributed computing – Horizontally scalable data distribution and processing
- Automating intelligence – Automated ML models for online responses(prediction,recommendations) and fraud detection.
- Data Visualization – Visually explore data to get a better intuition of data.The integral part of ML modeling.
- Dashboards and BI – Predefined dashboards with slice and dice capability for higher-level stakeholders.
- Data engineering – Making sure hot and cold data is always accessible. Covers data backup, security, disaster recovery
- Deployment in production mode – Migrate system into production with industry standard practices.
- Automated decisions – This includes running business logic on top of data or a complex mathematical model trained using any ML algorithm.
Machine Learning modeling starts with the data exist and typical components are as follows :
- Understand problem – Make sure an efficient way to solve the problem is ML. Note that not all problems solvable using ML.
- Explore Data – To get an intuition of features to be used in ML model. This might need more than one iteration. Data visualization plays a critical role here.
- Prepare data – This is an important stage with a high impact on the accuracy of ML model. It deals data issue like what to do with missing data for a feature? Replace with dummy value like zero, or mean of other values or drop the feature from model?. Scaling features, which make sure values of all features are in same range, is critical for many ML models. A lot of other techniques like polynomial feature generation is also used here to derive new features.
- Select a model and train – Model is selected based on a type of problem ( Prediction or classification etc. ) and type of feature set ( some algorithms works with a small number of instances with a large number of features and some others in other cases).
- Performance Measure – In Data Science, performance measures are not standardized, it will change case by case. Typically it will be an indication of Data Timeliness, Data Quality, Querying Capability, Concurrency limits in data access, Interactive visualization capability etc
In ML models, performance measures are crystal clear. Each algorithm will have a measure to indicate how well or bad the model describe the training data given. For example, RME(Root Mean Square Error) is used in Linear Regression as an indication of an error in model.
- Development methodology – Data Science projects are aligned more like an engineering project with clearly defined milestones. But ML projects are more of research like, which start with a hypothesis and trying to get it proved with data available.
- Visualization – Visualisation in general Data Science represents data directly using any popular graphs like bar, pie etc. But in ML, visualizations also used represents a mathematical model of training data. For example, visualizing confusion matrix of a multiclass classification helps to quickly identify false positives and negatives.
- Languages – SQL and SQL like syntax languages (HiveQL, Spark SQL etc) are the most used language in Data Science world. Popular data processing scripting languages like Perl, awk, sed are also in use.Framework-specific well-supported languages are another widely (Java for Hadoop, Scala for Spark etc) used category.
Python and R are the most used language in Machine Learning world. Nowadays Python is gaining more momentum as new deep learning researchers are mostly converted to python.SQL also plays an important role in data exploration phase of ML
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Data Science and Machine Learning Comparison Table
Below is the comparison table between Data Science and Machine Learning.
| Basis of Comparison | Data Science | Machine Learning |
| Scope | Create insights from data dealing with all real-world complexities. This includes tasks like understand the requirement, extracting data etc. | Accurately Classify or predict the outcome for new data point by learning patterns from historical data, using mathematical models. |
| Input Data | Most of the input data is generated as human consumable data which is to be read or analyzed by humans like tabular data or images. | Input data for ML will be transformed specifically for algorithms used. Feature scaling, Word embedding or adding polynomial features are some examples |
| System Complexity | ● Components for handling unstructured raw data coming.
● Lot of moving components typically scheduled by an orchestration layer to synchronize independent jobs |
● Major complexity is with algorithms and mathematical concepts behind that
● Ensemble models will have more than one ML model and each will have weighted contribution on final output |
| Preferred skill set | ● Domain expertise
● ETL and data profiling ● Strong SQL ● NoSQL systems ● Standard reporting/ visualization |
● Strong Maths understanding
● Python/R programming ● Data wrangling with SQL ● Model-specific visualisation |
| Hardware Specification | ● Horizontally scalable systems preferred to handle massive data
● High RAm and SSDs used to overcome I/O bottleneck |
● GPUs are preferred for intensive vector operations
● More powerful versions like TPUs(link) are on the way |
Conclusion
In both Data Science and Machine Learning, we are trying to extract information and insights from data. Machine learning trying to make algorithms learn on their own. Currently, advanced ML models are applied to Data Science to automatically detect and profile data. Google’s Cloud Dataprep is the best example of this.
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