Data Science and Machine Learning (ML) are two of the most widely popular concepts used in the field of modern technology. While Data Science deals with the extraction, refining, and analysis of data from a large volume of data, Machine Learning is essentially a subfield of Artificial Intelligence (AI) and an integrated part of data science. These two buzzwords are often confused with one another, therefore, it is important to know the difference between the two in the context of Artificial Intelligence and deep learning as well as the relation between the two concepts used in modern technology.
Data Science and Machine Learning correspond with career paths that have high demand and value across several industries due to their usability and reliability. Nearly every industry is adopting data science and Machine Learning to make use of data like never before.
A career in either field has a high return value. Those with knowledge in the fields of programming languages and statistics can benefit from both data science and Machine Learning which they can use to further their career ahead. This article focuses on Data Science and Machine Learning along with their differences and their relations.
Data Science vs. Machine LearningDespite their close correlation, Data Science, and Machine Learning have different functionalities and goals. It is said to be the accumulation of Business Management, Modeling, and IT. Data Science allows data scientists to collect raw data and refine it using various statistical tools and techniques to analyze and find insights from the data. This is where Machine Learning comes to help. Data scientists use machines to collect and analyze past data. In short, Machine Learning is a branch of AI that helps data scientists to understand and devise methods for utilizing data in order to improve performance and make informed predictions.
Machine Learning, when integrated with Data Science, has automated the process of data analysis by collecting (mining) data from a large population of data and making predictions based on the algorithms and models used in data analysis. Netflix is a good example of such data analysis where a user's viewing patterns are recorded and analyzed to understand their preferences and provide them with relevant suggestions to increase user engagement.
What do We Understand by Machine Learning?Machine Learning is the part of AI that uses statistical models and algorithms for extracting data and using them for predicting future trends. It is a fast-growing field of technology that enables machines to use various algorithms to learn from past data. For doing so, the software is programmed by engineers using various statistical models and algorithms that conduct analysis of data and in turn present patterns from the given data.
Every social media application today, like Facebook, Instagram, Twitter, YouTube, and TikTok, uses the power of Machine Learning to predict a user's viewing interests and preferences by gathering information about the user's viewing patterns and tweaking advertisement viewing content by showing them targeted products, services, and/or articles. Other popular applications of Machine Learning include online fraud/spam detection, the spam filtering in the mailbox, etc.
Machine Learning is used in data science as a set of tools and concepts to help data scientists gather relevant information from a pool of big data. Apart from data science, Machine Learning has various uses in the field of modern technology as well.
Skills Required to Learn Machine LearningIn order to become well versed in Machine Learning, you need to learn the following skills:
You need to grow your expertise in the field of computer science, including concepts of data structures, algorithms, and architecture.
You need to have a strong understanding of the concepts of statistics and probability.
You need to gain knowledge of Natural Language Processing.
You need to develop your knowledge of programming languages including Python, R, and more such programming languages.
You need to grow the ability to conduct data modeling and analysis using Machine Learning algorithms.
Machine Learning - LimitationsMachine Learning algorithms may seem to work like magic for producing optimal outcomes with minimum intervention, but there is still a lot of work left to be done. Machine Learning algorithms still require to be optimized in order to be functional for newer problems. In fact, Machine Learning is not applicable everywhere and may complicate a traditional equation or program.
Machine Learning - ImportanceMachine Learning makes decision-making and cost-cutting easier for businesses which is a lucrative offer for them. Many big-name companies have already adopted Machine Learning, and many more are joining the hype crowd too. Machine Learning is one of the many tools used in the field of data science. Machine Learning is incomplete without a skilled data scientist mining and organizing data, and applying the appropriate tools to fully make use of the numbers.
Careers Options in the Field of Machine LearningIf you decide to build a career in the field of Machine Learning and artificial intelligence, there are several options to choose from. Some of the many job roles in the field of Machine Learning are listed as follows:
Machine Learning Engineer:
A Machine Learning Engineer is responsible for researching, building, and designing the artificial intelligence (AI) model responsible for Machine Learning, and maintaining or improving AI systems.
An AI Engineer is responsible for the development and production of AI infrastructure, as well as its implementation.
A Cloud Engineer helps in building and maintaining cloud infrastructure.
A Computational Linguist develops and designs computers that deal with the working of the human languages.
Human-centered AI systems designer:
The job description of an AI Systems Designer is to create, deploy, and develop systems (robots) that can learn & adapt along with us for the betterment of society and internal systems.
What do We Understand by Data Science?Data Science is the field of deep study of big data that includes extraction and analysis of data to gain useful insights from the data. In doing so, data scientists take help from various statistical tools, models, and Machine Learning algorithms. Data Science combines the knowledge of different programming languages such as Python and R, and knowledge of various statistical methods. Data scientists require a profound understanding of database architecture along with experience to apply these skills and techniques to solve real-world problems.
Data Science includes extraction of raw data from big data, and data cleaning, preparation, analysis as well as visualization of data. Data scientists achieve useful insights from the data by using various Machine Learning algorithms and models on structured and unstructured data. The data thus derived is used by businesses, governments, and other organizations to motivate profits, build better systems and services, innovate and update products and services, and do a lot more.
Skills Required to Learn Data ScienceTo become a data scientist, you need to gain some useful programming and data analytics skills which are an indispensable part of data science.
You need to acquire a strong knowledge of programming languages such as Python, R, Scala, SAS, and more such programming languages.
You need to be comfortable working with a huge amount of structured and unstructured data and have experience working with SQL database coding.
You have to be familiar with the processing and analyzing of big data for various needs of businesses.
You have to acquire a profound understanding of concepts of mathematics, statistics, and probability.
You have to acquire skills in data mining, cleaning, visualization, and data wrangling.
You have to gain knowledge of the various Machine Learning algorithms and models used in data science.
You need to refine your skills in using big data tools such as Hadoop.
You need to develop good communication and teamwork skills.
Data Science - LimitationsThe unparalleled growth of data science is essentially attributed to the accessibility to cheap computing power & big datasets. The power of data science can be fully enjoyed with the use of only these two factors. Messy or misleading data coupled with the presence of small datasets lead to misleading results by forming models which give unnecessary results and are a complete waste of resources. If by using the given data, the real reason for variations is not found, data science fails to work.
Careers in the Field of Data ScienceData Scientist:
A Data Scientist uses data for understanding and explaining the trends around us, and for helping, businesses, governments, and other organizations make better decisions.
A Data Analyst, much like a data scientist, accumulates, organizes, and studies datasets for helping to solve problems in businesses.
A Data Engineer is responsible for building systems that collect, manage, and transform big data into information for business analysts and data scientists.
A Data Architect is responsible for reviewing and analyzing the data infrastructure of an organization or business in order to plan databases and implement solutions for storing and managing data.
Business intelligence analyst:
A Business Intelligence Analyst is responsible for gathering, cleaning, and analyzing sales and customer data, as well as interpreting it, and sharing the findings of data analysis with the business teams.
How is Machine Learning Integrated into Data Science?Machine Learning is an indispensable part of Data Science and it can be easily understood from the development process of data science. The steps of the life cycle of data science include:
1. Business Understanding
2. Data Mining
3. Data Cleaning
4. Data Exploration
5. Feature Engineering
6. Predictive Modeling
7. Data Visualization
In the first step, the requirements of a business problem are chalked out which is achieved by going through the cycle starting from the next step, i.e., data mining. In the next step, raw data is collected from big data and then the raw data is stored in a suitable format for easier access during the next steps. Then the refined data is used for finding patterns to gain useful insights from the collected data. Then comes the role of Machine Learning in the development process during modeling where Machine Learning algorithms are used for importing, cleaning, and building data for training, testing, and improving the efficiency of the model. Statistical tools and concepts are used in the last phase of the data science development process for the visualization of refined data.
Online Courses for Data Science and Machine LearningNo matter whichever field you decide to pursue, you will most definitely need to gain technical skills in programming languages, statistics, and probability in order to land a job. You need to learn programming languages such as Python and SQL, along with data analysis and visualization as well as building Machine Learning models. Several online courses for data science and Machine Learning are available that you can put to use and start your journey towards data science. Choose one that suits you according to your time and comfort, and bag a high-paying job role in the field of Data Science and Machine Learning.
ConclusionData Science is a broad, interdisciplinary field that harnesses the power of data, along with the tools of Machine Learning, to gain valuable insights and information. Machine Learning empowers computers to automatically use the huge resource of data and put it to good use. The applications of both these technologies in the modern world are vast, but not limited.
Although data science coupled with Machine Learning is a powerful tool, it is incomplete without the knowledge of how to use them together. You can enroll yourself in an online course for learning Data Science with Machine Learning, and avail yourself of the many exclusive benefits and job offers that come with the tag of a data scientist or a Machine Learning engineer.