Data Science vs Machine Learning

Data Science vs Machine Learning

 Data Science

Data Science involves many different disciplines, such as mathematical and statistical modeling, extracting data from its origin and applying data visualization techniques. Generally, it also involves the management of large data technologies to gather structured and unstructured data. The main objective is to extract necessary or valuable information that can be used for various purposes, such as decision making, product development, trend analysis, and forecasting. A data scientist is an individual who practices data science.

Data science techniques include data mining, big data analysis, data extraction, and data recovery. In addition, the concepts and processes of data science are derived from data engineering, statistics, programming, social engineering, data warehousing, machine learning and natural language processing, among others.

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Applications Of Data Science

Some of the main applications of data science are the following:

  • Internet research
  • Custom recommendation systems
  • image recognition
  •  Fraud detection
  • Optimization techniques
  • Market analysis of actions
  • Pathological diagnosis

Machine Learning

Data science, machine learning and artificial intelligence are some of the main trending topics in the world of technology today. Data mining and Bayesian analysis are trends and this is adding to the demand for machine learning. Machine learning is a discipline that deals with the programming of systems, to make them learn and improve automatically with experience.

Here, learning involves recognizing and understanding input data and making informed decisions based on the data provided. It is very difficult to consider all decisions based on all possible entries. To solve this problem, algorithms are developed that build knowledge from specific data and past experiences, applying the principles of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory.

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Applications Of Machine Learning

The machine learning algorithms developed are used in several applications, such as

  • vision processing
  • language processing
  • Forecasting things such as stock market trends, climate
  • Pattern recognition
  • Games
  • Data mining
  • Specialized systems
  • Robotics

Data Science vs Machine learning

The learning of words in machine learning means that the algorithms depend on some data, used as a training set, to adjust some parameters of the model or algorithm. This encompasses many techniques, such as regression, naive Bayes or supervised clustering. But not all techniques fit into that category. A human being is needed to label the found clusters. Some techniques are hybrid, such as semi-supervised classification.

Some techniques of pattern detection or density estimation are adjusted in this category. The science of data is much more than the learning of the machine. The data, in data science, may or may not come from a machine or mechanical process (the research data can be collected manually, clinical trials involve a specific type of small data) and may have nothing to do with the learning. But the main difference is the fact that data science covers the whole spectrum of data processing, not just algorithmic or statistical aspects. In particular, data science also covers

  • data integration,
  • distributed architecture,
  • automating machine learning,
  • data visualization,
  • panels and BI,
  • data engineering,
  • implementation in production mode,
  • automated decisions based on data

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5 Best Comparisons Between Data Science and Machine Learning

Basis Of ComparisonData ScienceMachine Learning




Create data stories that address all the complexities of the real world. This includes tasks such as understanding the requirement, extracting data, etc.Classify or predict accurately the result for new data, learning patterns from historical data, using mathematical models.

Input Data

Most of the input data is generated as human consumption data that must be read or analyzed by humans, such as data or tabular images.The input data for the ML will be transformed specifically for the algorithms used. The escalation of resources, the incorporation of Word or the addition of polynomial resources are some examples


System Complexity

● Components for handling unstructured raw data

● Lot of mobile components normally programmed by an orchestration layer to synchronize independent jobs.

● Greater complexity is with algorithms and mathematical concepts behind it

● Ensemble models will have more than one ML model and each one will have a weighted contribution in the final output


Preferred skill set

● Expertise in the domain

● ETL and data profile ● Strong SQL

● NoSQL systems

● Reports / standard views

● Strong mathematical comprehension

● Programming in Python / R

● Data contention with SQL

● Model-specific visualization


Hardware specification

● Horizontally scalable systems prefer to handle massive data

● RAM and high SSD used to overcome the I/O neck

● GPUs are preferred for intensive vector operations

● More powerful versions, such as TPU, are on the way


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