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Data Science vs Machine Learning: 7 Differentiating points

Data Science and Machine Learning are two booming and fast-pacing technologies. Being the talk of the hour, they entice the attention from tech buffs and IT explorers from around the world. Organizations look for workers who sift through the data real quick and deliver insights to drive business decisions efficiently. People may often confuse the two technologies, for they are closely wired but have relatively different functions and goals.

Data Science and Machine Learning buzzwords are the most searched on the Internet nowadays. Hence, it is worth knowing why the two domains are exciting while looking for job potential as a fresher. And the skill sets one must own to gain a strong foothold in either of the fields in the long run. Here’s a draw out of the differentiating points between data science and machine learning.

Unbelievably, Google, Microsoft, Amazon, and Facebook store 1200 petabytes of information. Thus, there is a heavy dependence on quality data in the industrial sector, now more than ever. Therefore, it only makes sense to form this huge amount of data as quality information for business stakeholders and analysts to escalate businesses to unknown heights. These six points described below will help you explain how Data Science and Machine Learning help businesses to evolve together.

Also Read: Data Science vs Data Analytics


Definition

Data as information exists in textual, numerical, audio and video formats. Thus, data science deals with data extraction, sanctification, preparation and analysis to understand it from the business perspective.

Data science deals in gathering data from disparate sources in different structures and formats. The data engineers are then responsible for transforming, combining and processing the captured raw data into quality data readily available for further analysis. Data analysts and scientists pick up the processed data to extract critical information and significant patterns for analysis and predictive insights that impact invaluable industry decisions.

This stream handles the Big Data using pre-processing tools, predictive analysis and statistical models to derive regular patterns for enforcing reasonable acuities. For example, Netflix uses data science to study the user’s viewing interest patterns by mining his recent search results and viewing history.

Data Science is a field of study that approaches to find insights from raw data.

As a branch of computer science, Machine Learning is a study that enables the computer to solve problems without implementing explicit programs to solve them step-by-step. ML is implementable using different methods such as supervised, unsupervised and reinforcement learning methods. Every ML method has its pros and cons.

Using Machine Learning, your machine or system is learning by applying algorithms to the data set. And these algorithms act as the instructions for the ML method to perform a process. A machine determines the patterns itself from the given data and then learns from the approach to make its own decisions. One of the hyped machine learning techniques of the hour is Neural Networks, which require a machine to make decisions similar to a human brain.

In Neural Networks, machine learning applies the algorithms to process the data and train itself to deliver future forecasts without any human intervention. It enables the machine to learn from the past data and apply the resultant patterns to other given tasks automatically. For example, Google and Facebook use the inputs from ML as a set of instructions/ data/ observations for anticipating ads and notifications to users.

Machine Learning allows the computer to learn from past experiences on its owning using learning and pattern recognising algorithms. It uses statistical methods to improve the performance and predict output without being explicitly programmed.

Theoretical Knowledge and Skills Requirements

Data science is an umbrella that encircles data analytics, data mining, machine learning and other related disciplines. To develop a strong career in this domain, one must gain expertise in these three critical divisions: analysis, programming and domain knowledge.

Now, you can carve out a strong career as a data scientist with the following skill sets adorning your resume:

  • Strong algorithmic programming and knowledge of Python, R, Scala, SAS and Java.
  • Ability to work with unstructured data from different sources, such as videos and social media.
  • Theoretical understanding of analytic functions and statistical concepts.
  • Capability to perform data mining, cleansing and visualizing skills.
  • Expertise in writing SQL DB queries.
  • Fundamental knowledge of Big Data tools like Hadoop and Hive.

Furthermore, these subsequent crucial skills are just what you need to jumpstart your career in the machine learning domain:

  • Knowledge of applied mathematics, statistics and probability concepts.
  • Good programming knowledge of languages such as Python, R and Julia.
  • Practical knowledge and understanding of Machine Learning algorithms.
  • Natural Language Processing
  • Data modeling and evaluation understanding.

Also Read: What is Machine Learning? Is it different from Deep Learning and AI?


Data Requirements

In Data Science, you can work with raw, structured or unstructured data to transform and present it in a meaningful format. Also, one can extract the data from various sources in different structures and formats. Some of the various data structures and formats supported are as below:

  • Textual
  • Audio
  • Video
  • Numerical
  • Images
  • Vectors

Furthermore. Machine Learning uses structured data by applying algorithms to it to study the recurring patterns and perform actions based on the learning.


Tools and Software Used

One must have a good command over the working of ML tools to work on structured and unstructured data. Some of the most used tools in data science are as listed below:

  • Use of Big Data tools such as Hadoop, Hive and Apache Stark.
  • Tableau – This is a data visualization software that focuses on industries working in the field of business intelligence. It is capable of interfacing with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, and can visualize geographical data.
  • BigML – This data science tool delivers a fully interactable, cloud-based GUI environment that one can use for processing Machine Learning Algorithms.
  • Excel – It acts as an analytical tool for data science. One can use the various formulae, tables, filters, and slicers. Also, you can create your custom functions and formula using Excel. It might not be good for performing calculations on a huge amount of data, but it’s still an ideal choice for creating powerful data visualizations and spreadsheets.

Machine learning engineers should be proficient in skills such as computer science fundamentals, programming knowledge of Python or R, ML algorithms, statistics and probability concepts. With the above skills, one must have a sound understanding and experienced in working on one of the tools to land a good job.

  • Scikit Learn – This software is free to use by engineers for machine learning development in python. It equips models and algorithms for Classification, Regression, Clustering, Dimensional reduction, Model selection, and Pre-processing.
  • PyTorch – It is a python machine learning library. The torch is a Lua-based computing framework, scripting language, and machine learning library. It helps in the building and optimization of neural networks.
  • TensorFlow – This javascript library helps in machine learning. APIs associated with it helps to build and train the ML models. It assists in neural networks and human pose estimation.
  • Colab – This is a cloud service which supports python. It helps build machine learning applications using the libraries of PyTorch, Keras, TensorFlow, and OpenCV.

Also Read: What is the difference between Big Data and Data Science?


Industrial Exposure and Opportunities

Data science is still a vast and growing field that offers endless opportunities for a tech aspirant. You can start your career in data science as a data engineer and gradually develop your interest to specialize in a specific subdomain such as statistics, or else pursue the route of growth to become a business analyst. 

The most popular job roles in the field of data science are as stated:

  • Data Analyst.
  • Data Engineers.
  • Database Administrator.
  • Data Scientist.
  • Data Architect.
  • Statistician.
  • Business Analyst.

Data scientists invest much of their time in handling, cleansing the data, and understanding its patterns.

Though ML is a sub-disciple of data science, it offers many fascinating opportunities to develop new and exciting technology and attracts many professionals to the industry. There are many positions to explore in ML and to get started with.

  • Machine Learning Engineer
  • Robotics Engineer
  • Natural Language Processing (NLP) Scientist
  • Software Developer
  • Cybersecurity Analyst
  • Artificial Intelligence (AI) Engineer

Usually, ML engineers spend most of the time managing the complexities that occur during the implementation of algorithms and the mathematical concepts behind them.


Goals and Outcomes

Within data science, you conduct operations over various data sources to prove or disprove a certain hypothesis. Data science goal evolves around understanding and discovering hidden patterns or meaningful insights from the data to take smarter business decisions. Consequently, data scientists and business analysts produce valuable reports based on inputs and insights derived from the significant data.

Using machine learning, developers can develop software that learns by itself by extracting meaning from the data. ML is a subfield of data science that facilitates the machine to learn from past data and experiences automatically. Therefore, the key objective of ML is to make predictions and classify the result for new data points. As an outcome, self-learning ML models that are capable enough of making their decisions based on past data and experiences.


Scope

Data Science is a vast term that includes various steps to create a model for a given problem and deploy the model. So, one must perform activities like data acquisition, cleaning, investigation, gathering insights and manipulation within the scope of data science to design a well-structured and future predictive model.

Using machine learning, developers can develop software that learns by itself by extracting meaning from the data. ML is a subfield of data science that facilitates the machine to learn from past data and experiences automatically. Therefore, the key objective of ML is to make predictions and classify the result for new data points. As an outcome, self-learning ML models that are capable enough of making their decisions based on past data and experiences.

Also Read: What is the difference between Big Data and Data Science?

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