Data Science vs Machine Learning- Career That is Right for You

What are the Benefits of Data Science?

For any business, Data Science helps highly to:

• Make any data better for processing
• Make products smarter & better
• Help make fact-based, fast decisions
• Helps in flowing better business information, which helps to hit better business opportunities like identifying new products in the market for increased expansion or introducing personalized customer experiences

Data Science & Its Limitations

Every process, the technique has some shortcomings, so does Data Science have:

• If data is missing a value or is too small, unorganized, incomplete, the whole outcome can be misleading.
• Not all data collected could be similar in quality or format, which might either lead to wastage of complete data or manpower to bring those data to a certain level.
• Data could be arbitrary, chosen on a random whim, which might not produce effective analysis as required.

What’s the solution to beat this shortcoming? To get the perfect analysis, it is always preferred to collect effective & measurable data in a similar format from credible sources only!

How is Machine Learning Beneficial?

Machine Learning helps to revolutionize the way things work, it helps data-driven enterprises:

• Helps in reviewing a large volume of data & discovering trends & patterns. 
• Since ML is all about giving machines the ability to self-learn & react, it automates the process of making predictions & improving algorithms per se.
• They improve systems or applications on their own, and each time brings amazing accuracy & efficiency which makes predictions better & faster.
• They can easily operate multi-variety & dimensional data in any certain or uncertain environment.
• Good for applications within any vertical be it healthcare, banking, or more, it helps businesses to give more targeted customer reach & personalized user experiences.

What are Machine Learning’s Limitations?

Before you drive into machine learning vs data science, learn about the limitations of machine learning. Machine learning, though, is beneficial in eliminating the intervention of data engineers or ML engineers in further procedures. However, such professionals would still be needed to make data models, systems, and algorithms that are enabled to solve new problems if they arise. 

Other than that:

• Labeling training data is a laborious task
• Batch training can be time taking
• Training data needs to be tagged
• Learning generally needs to be supervised
• It can be hard to debug the systems.

How are these Machine Learning challenges curbed? However, these limitations or complexities are nothing if very skilled Machine learning trainers are deployed and the data transformation isn’t very complex.  

Difference Between Data Science vs Machine Learning Based on Concepts

Factors	Data Science	Machine Learning
Purpose	Data Science helps to extract insights from data to improve decision-making & processes.	Machine learning helps in advancing the systems by letting it predict & analyze the outcome of new datasets, based on past or old datasets.
Requires Understanding of	Different business domains & verticals SQL, NoSQL systems Data Visualization ETL & Data profiling Standard Reporting	Data Wrangling Python & R Programming Mathematical & statistical knowledge SQL Model Visualization
Data Type	Input data is ready for human read & transformation	Input data requires transformation based on the algorithm type
System Type	Scalable systems needed to handle loads of data	GPUs are recommended to support intensive vector operations
Complexity	Requires handling unstructured, raw complex data, which leads to misleading interpretations.	Mathematical concepts & algorithms can be complex to handle.
Components or types	There are 4 components of data science: Data Strategy Data Engineering Data Modeling & Analysis Data Visualization and Operationalization	Machine Learning has 3 types: Supervised learning Unsupervised learning Reinforcement learning

Real Life Case Studies To Understand Data Science Vs Machine Learning!

To understand data science vs machine learning better let us check out some real life case studies of data science and machine learning and how they have been pioneer in success of many organizations. Data science has become integral to modern businesses and organizations, driving decision-making, optimizing operations, and improving customer experiences. 

Case Study 1: Predictive Maintenance in Manufacturing

• GE:General Electric (GE), a global industrial conglomerate, leverages data science to implement predictive maintenance solutions. By analyzing sensor data from their industrial equipment, such as jet engines and wind turbines, GE can predict the need for maintenance before a breakdown occurs. This proactive approach minimized downtime and reduced maintenance costs.

Case Study 2: Healthcare Diagnostics and Treatment Personalization

• IBM Watson Health: IBM Watson Health employs data science to enhance healthcare by providing personalized diagnostic and treatment recommendations. Watson's natural language processing capabilities enable it to sift through vast medical literature and patient records to assist doctors in making more informed decisions.

Case Study 3: Fraud Detection and Prevention in Finance

• PayPal:PayPal, a leader in online payments, employs advanced data science techniques to detect and prevent fraudulent transactions in real-time. They analyze transaction data, user behavior, and other relevant factors to identify suspicious activity.

Case Study 4: Urban planning and smart cities

• Singapore: Singapore is pioneering the smart city concept, using data science to optimize urban planning and public services. They gather data from various sources, including sensors and citizen feedback, to manage traffic flow, reduce energy consumption, and improve the overall quality of life in the city-state.

Case Study 5: E-commerce Personalization and Recommendation Systems

• Amazon: Amazon, the e-commerce giant, heavily relies on data science to personalize the shopping experience for its customers. They use algorithms to analyze customers' browsing and purchasing history, making product recommendations tailored to individual preferences. This approach has contributed significantly to Amazon's success and customer satisfaction by reducing customer service response times by 40%.

Case Study 6: Transportation and Route Optimization

• Uber: Uber revolutionized the transportation industry by using data science to optimize ride-sharing and delivery routes. Their algorithms consider real-time traffic conditions, driver availability, and passenger demand to provide efficient, cost-effective transportation services.

Case Study 7: Environmental Conservation and Data Analysis

• NASA: NASA collects and analyzes vast amounts of data to better understand Earth's environment and climate. Their satellite observations, climate models, and data science tools contribute to crucial insights about climate change, weather forecasting, and natural disaster monitoring. Machine learning, a subset of artificial intelligence, has received significant attention in recent years. It has revolutionized various industries by enabling computers to learn and make intelligent decisions without explicit programming.

To gain more information on machine learning vs data science, explore real-life use cases and success stories that demonstrate the practical applications and transformative impact of machine learning.

1. Netflix is a great example of a company using machine learning to improve the customer experience. The streaming giant has long been using machine learning algorithms to personalize its recommendations for viewers. By using data like a customer's viewing history, the viewing history of customers with similar entertainment interests, information about individual shows and movies, and even data about when a customer is likely to be most active on the platform, Netflix is able to recommend relevant content to individual viewers. This recommendation system is estimated to drive “80 percent of hours of content streamed on the platform.”
2. YouTube :YouTube is another company that has been using machine learning for business for a long time. The site uses a technique called deep learning to recommend videos to viewers, which is based on modeling massive amounts of historical data.
3. MIT: The MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) created a program called ICU Intervene that uses machine learning to predict possible treatments. It learns from massive amounts of intensive-care-unit (ICU) data, including symptom data, to make real-time predictions. It also explains the reasons behind the decisions it makes, which helps to improve patient care. This could ultimately make a huge difference in the quality of care that patients receive.
4. In April 2017, Wells Fargo launched a chatbot for Facebook Messenger that helps customers with their banking needs. Wells Fargo’s bot for Messenger focuses on incorporating financial services into third-party environments to meet customers where they are and into the moments they want to use them. Their goal is to deliver information ‘in the moment’ to help customers make better informed financial decisions using AI technology instead of requiring customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment. 
5. Facebook: Facebook is using machine learning for a variety of tasks, including facial recognition, content moderation, and ad targeting. In addition, they're using it to build chatbots and to improve the performance of their search engine.
6. Airbnb: Airbnb is another company that relies heavily on machine learning. They use it to personalize your search results, to match you with potential hosts, and to help you find the perfect listing.
7. Tinder: Modern dating wouldn't be the same without Tinder. This app has changed the game by using machine learning to create a smooth user experience and perfect matches. Tinder's algorithm looks at a variety of factors, such as your age, location, and interests, to find matches for you. This might have helped you understand data science and machine learning difference in a better way. If you seek a vibrant future in machine learning or data science, check out career the respective career path.

Difference Between Data Science Vs Machine Learning Engineer Roles & Responsibilities

Let’s look into the roles & duties of each technology, one by one.

Data Scientist Roles & Responsibilities

The main obligation of any Data Scientist is to:

• Pull out an information investigation, a process of accumulating information or datasets for further analysis & transformation.
• To collect, oversee, clean & prepare the 2 types of information for further processing. The two types of information or data sets are --- Organized information, where it is easy to sort & process, for instance, data like sales figures, ledgers, bills, etc. The other is unstructured information, such information isn’t often organized, varies, has no sync, does not have room for innovation, and requires quite a time to analyze, like the different data from customer surveys, metrics of social media performance, lead inquiry, etc.
• To act as a lead information strategist for utilizing new datasets & build groups to bring forth the improvement of information items.
• To perform logical trials efficiently to handle different issues & have a positive effect on different areas and ventures of business.
• To bring forth important information sources and place them for digging customer business needs, and from them gather structured and unstructured datasets & factors.
• To create and use calculations & intelligent models to mine massive information stores, perform information & blunder examination to improve models, further clean & approve information for exactness & consistency.
• To look into the information & analyze it for patterns or examples & decode that information from the point of view of the targets.
• To implement models & algorithms by teaming up with programming designers & Machine Learning engineers.
• To communicate scientific answers for business partners, stakeholders, organizations in totality and achieve enhancements as expected to operational frameworks.

Machine Learning Engineer Roles & Responsibilities

Machine Learning Engineer & Data Scientist roles & responsibilities are quite similar & both require impeccable data management & transformation skills. The role of Data Science, we just saw is to generate insights from data to drive decisions, processes & other tasks. While the role of Machine Learning Engineers is to analyze data & create self-running software that could learn & innovate on its own for predictive automation models.

Machine Learning engineers & Data Scientists work alongside each other. Machine Learning engineers ensure that the models used by Data Scientists are accurate & enough to analyze & process any type, kind, or volume of datasets. With each section the idea of data science vs machine learning will become clear.

Here are the common duties of Machine Learning Engineers with any firm:

• To analyze and convert data science prototypes.
• To develop smart, real-time Machine Learning systems & schemes.
• To conduct statistical analysis & enhance the efficacy of models using test results.
• To discover effective & reasonable datasets online for batch training purposes.
• To train & re-train ML systems or models whenever required or applicable.
• To expand & improve the ML frameworks and libraries.
• To design & develop Machine Learning apps according to business customer/client specifications & user experiences.
• To conduct colossal research, experiment, and implement effective ML algorithms and tools.
• To measure the problem-solving capacity, capabilities & use-cases of ML algorithms & put them in rank based on their success probability.
• To explore, transform, and visualize data for better understanding and identify major differences in data distribution that could affect model performance while deploying against real-time business scenarios.

Difference Between Data Science vs Machine Learning Skills/Prerequisites

If you ask machine learning vs data science which is easy, both Data Science and Machine Learning careers are attractive in terms of job opportunities & career expansion. Whether you choose machine learning or data science as a career option, the skills, prerequisites, qualifications each profile demands are very achievebale & possible for anyone.

To be a Data Scientist, you can pursue a diploma or degree in disciplines like computer science, mathematics, statistics, engineering, or any technology-related subject. However, it won’t be an issue, even if you are from a non-technical background, with little upskilling courses online, you can still make a profound career in Data Science. Further, going for a master's degree or Ph.D. degree in technical disciplines is purely subjective to your preference.

Along with degrees, you would be required to address the following technical skills in your resume:

• Proficiency in any programming language Python, R, SQL, or any other that’s in demand or is your preference.
• Understanding of Data mining methods like network analysis, linear models or regressions, boosting, and statistical analysis skills. 
• Ability to work around machine learning techniques such as AI neural networks, clustering, statistical modeling, datasets manipulation.
• Understanding of visualization & presentation of data.
• Knowledge of data & computing tools such as MYSQL, Spark, Hadoop, or more.

To be a Machine Learning Engineer, you can go for graduation or a master’s degree in any technical domain. Since this field is quite lacking in terms of demand & supply, you can easily make it into this career. However, it won’t be an issue, even if you are from a non-technical background, with little upskilling courses online, you can still make a profound career in Machine Learning!

Provided, you need to have the following skills or specialization at hand:

• Understanding of machine learning algorithms, APIs, packages, libraries.
• Knowledge of deep neural networks, visual processing, supervised learning, unsupervised learning & reinforcement learning.
• Proficiency in programming languages like R, Java, Python, Javascript, C++, Scale, etc.
• Understanding disciplines like mathematics, statistics, the probability to help systems understand machine learning algorithms.
• Practical knowledge of the use of machine learning-related tools like zookeeper, ETCD, MATLAB, etc.
• Strong data management, analytical skills, and knowledge of machine learning evaluation metrics.

Pursuing a master's in data science and machine learning-related subjects is purely subjective & personal preference. After finishing up the basic degree and enrolling in the formal training program, you can choose any one of these as a long-haul career option.

Difference Between Data Science Vs Machine Learning Job Titles

By completing the Data Science and Machine Learning course online, you can stand eligible for the following job titles:

Job Roles Under Data Science	Job Titles Under Machine Learning
Data Scientists Data Analyst Data Engineer Data Architect Data Storyteller NLP data scientists Machine Learning Engineer Machine Learning Scientist	Machine Learning Engineers Machine Learning Researchers Data mining and analysis specialist Data Scientist Data Analyst BI - Business Intelligence Developer Software Engineer/developer Designer in Human-centered machine learning Computational

Difference Data Science Vs Machine Learning Salary

The Data Scientist’s salary on average can be between $90,000 to $156,331 per year. However, this range can vary based on programming language, experience, locations, employer, industry, skills, certifications, interview performance & more such factors.

The average machine learning engineer salary can range between $110,000 to $163,000 per year. This range can vary in reality based on skills, certifications, location, seniority level, employer, industry, interview & resume, and more such factors.

An entry-level machine learning engineer can earn $96,090 on average, per year if they have skilled-up with professional online boot camps. However, an entry-level data scientist can earn around an average of $95,000 per year, fulfilling the demands for employers with their skill-up and professional candidature. And with the right certifications and skill and experience in the filed, the data scientist can earn upto $210k per annum and a senior machine learning engineer can earn $180- $200k per annum. Now you are also aware of the salary difference between data science and machine learning. 

Data Science Vs Machine Learning Certifications

Getting certified in both technologies can add a lot to your candidature. Securing a data scientist certification or machine learning certification will state that you have validated knowledge & you are full-proof in working on real-time challenges of business. 

However, both disciplines don’t have a particular body that conducts the certification exam and you will find a lot of variety of certification authorities listed on the internet. To help you realize & clear the right ones, we are jotting down your options of machine learning and data scientist course.

Is Machine Learning Easy to Learn?

To excel in machine learning, you don’t need hard & fast mathematical skills, on applying a bit of creativity, tenacity, and experimentation, you can skill up in Machine Learning with ease (from any career or educational background).

Machine learning is very practical and no rocket science to learn. It just depends on what kind of machine learning training you opt for. Choose the one taught with real use cases, ML tools & projects.

Is Data Science Easy to Learn?

Yes, similar to machine learning, data science is another very possible & easy technology to explore & have expertise at. However, this one requires a lot of determination, time & patience to learn but don’t worry, you will get there. A quick formal training, after completing the basic education would be helpful for you to skill up in this technology.

You can choose machine learning or data science according to your liking and understanding. Both the fields have equal opportunities to offer. 

Which is Better for Career Option - Data Science or Machine Learning?

Both Data Science and Machine Learning are growing tech profiles. The job demand, pay scale are very high in both fields. However, if you are confused between Data Science or Machine Learning, see which one’s skills interest you the most, or which one has a job opportunity at your dream company.

Whichever you choose between the two, you are anyway going to be benefitted. If still confused, try a free demo class of each field with us. We will help you underline the best option.

How Do You Know if Data Science Is the Career for You?

If you’re considering a career in data science, it’s essential to determine whether it’s the right fit for you. Here are some top tips to help you determine if data science is the career path you should pursue.

1. Learn What the Role Entails
2. Does Coding Make You Happy?
3. Think About Your Current Soft Skills
4. Talk to Junior and Experienced Data Scientists
5. Dip Your Toes Into Data Science With an Online Course
6. Consider Whether You Like Experimentation and Testing
7. Are You a Numbers Person?
8. Consider Your Ideal Career Trajectory

Before you begin applying for jobs, make sure you have a solid grasp on the following, which pop up frequently as requirements for machine learning engineer roles:

• Researching, designing and implementing ML models and systems
• Implementing machine learning algorithms and tools
• Scaling data science prototypes
• Selecting appropriate data sets, verifying data quality, cleaning and organizing data (in collaboration with data engineers)
• Performing statistical analysis
• Executing tests and optimizing machine learning models and algorithms
• Monitoring systems in production and retraining them to improve performance
• Utilizing machine learning libraries

What is Recommended Learning Routine for Data Science and Machine Learning Career?

To go about either Data Science or Machine Learning.

• Evaluate & research the field in terms of prerequisites, skills, duties, future scope, compensation, employers & more.
• Get formal training or invest in some Data Science and Machine Learning courses online.
• Talk to experts already working in the industry, expand your network, and take career counseling.
• Secure a credential, certification as discussed above.
• Start applying for summer internships, short-term projects, jobs to gain real-field experience.
• You can learn both of them one by one as both the fields are intertwined.
• Other than taking training, do invest in watching data scince tutorial videos, reading whitepapers, practicing sample questions yourself, and indulging in other relevant learning aspects on your own.

Is Data Science and Machine Learning Same?

Data Science and Machine Learning may seem similar as somewhat their nature & components involve similar action, but in reality, they can’t be interchanged. Both processes are needed in conjunction with each other, like without each other, the other’s task is incomplete. 

Data Science is a broad field that involves Machine Learning as a subset. Data Science is a complete process of extracting, analyzing & drawing insights from the data with the help of algorithms & statistics made possible by Machine Learning.