Data Science vs Machine Learning

Data science and machine learning are driving forces behind helping businesses better understand current trends, gain insights about their customers, and their markets. In this article we’ll discuss the differences between these two fields.

For decades businesses both large and small have been collecting VAST amounts of data on their customers, products, markets, and more.

But until recently it’s been difficult to extract actionable information from this data.

Consider the case of the NFL. A score sheet used to contain one line for every play of the game. This gave coaches and teams roughly 600 data points to examine. Now teams collect 10 data points per player, PER SECOND!

This is where machine learning and data science enter the picture.

It would be impossible for a human to interpret these vast quantities of information. But data visualization and analytics algorithms allow us to quickly discover useful patterns.

Data Science vs Machine Learning Technologies

Although they might seem similar, it’s important to understand the differences in the two fields.

Data science generally deals with data extraction and computing the collected data through distributing and data processing. The analyzed data is then visualized to get a better understanding, as it helps in making automated decisions using machine learning algorithms. 

Machine learning on the other hand is a group of algorithms or techniques used by data scientists to get accurate and effective results. Basically, it understands the problem, explores the data, upon which a model is designed and trained to solve the issue. 

Usually, data scientists and machine learning engineers work closely together, since data scientists provide insight from the collected data and machine learning engineers offer accurate prediction analysis. Data is the key factor for the growth of an organization as businesses use customer data, financial data and such, more effectively to solve problems.

Here is everything you need to know to understand the differences between data science and machine learning based on careers, salary packages and skill sets.

What is Data Science?

In this data driven world, information is everywhere, and data science is all about deriving VALUE, knowledge, and insights from this data.

It is evolving at such a rapid pace that it is adapted by many industries. Data science is a blend of algorithms, statistics, business intelligence, machine learning, and programming. 

Simply put data science allows companies to gain insight and knowledge on a particular issue based on the extracted data. But without a sophisticated method to extract and analyze this data it’s basically worthless. Data science allows companies to gain usable knowledge from their data, helping them understand trends and better predict customer needs.

Having a huge amount of data helps in applying various algorithms to get an accurate result. Apart from that, industries use data science to design a recommendation engine and predicting user behavior. Leveraging data science technologies can help a business in many different ways.

By applying data science, the process of decision making in business operations becomes easier. Managing processes becomes more efficient, as calculated data-driven decisions are made by using analytical algorithms like clustering and classification.

Careers in Data Science

The influence of data science in business is growing exponentially. There is a plethora of careers available in data science with a good salary package according to industry standards.

Some of the popular career choices are:

  • Business Intelligence Developer
  • Data Analyst
  • Data Architect
  • Data Scientist
  • Application Architect
  • Data Engineer.

Data scientists are in high demand and their skills and knowledge is needed almost in most industries. With the rise in demand in notable companies like Facebook, HP, Dell, IBM, Oracle, Microsoft, Twitter, Walmart, Spotify and Verizon, the average salary package also ranges from $90,000 to $161,272.

How Businesses Apply This Technology

Businesses are employing data scientists to enhance their decision making with better strategic plans, finding the market trends, enhancing the overall efficiency of a business, selecting a target audience, make the recruitment procedure easier.

The first and foremost step in a data science project is data collection. Next is to store the collected data and clean it. Then data is analyzed, and to communicate your findings from the analysis effectively, visualization is done for better decision making.

The Limitations of Data Science

Providing solutions using data science is limitless, however there are certain limitations of data science. The data set available is not always structured.

So, using that data set may be a waste of time and quite misleading. Furthermore, the chances of a breach are also high as the companies exchange certain information with their parent companies for mutual benefits. The data analysis tools are also expensive, and it is difficult to choose the right tools to create accurate results from created models. 

Data Science Certifications & Courses

There are an abundance of data science certification courses available all over the web. It is necessary to hone your skills to land yourself a job in this competitive field. There are lots of both free and premium courses taught by experts on Udemy, Coursera, LinkedIn Learning or edX.

To pursue a career in data science, enroll in one of the certification courses and gain a clear understanding of each concept as well as all the needed knowledge to flourish in it.

You might also consider training on the R programming language or Python. These programming languages are crucial for data scientists. With the right course, learning data science might not be so difficult at all.

What is Machine Learning?

Machine learning has turned out to be one of the most fascinating methods applied in the IT space. People are learning about machine learning, while others are improving their skills. Organizations have also begun applying machine learning to make decisions with minimal human intervention. 

Still, what exactly is machine learning?

It is a subset of artificial intelligence (AI) that is applied to reduce the amount of mundane and repetitive work carried out by human hands. Machines are taught to do these tasks by providing them with a huge amount of data without explicitly programming them. It creates useful models and patterns to enhance the efficiency of the work in order to give accurate results. Thus, by applying machine learning algorithms problems can be solved and predictions can also be easily done.

Machine learning can help businesses reduce costs, and currently industries are using it to make predictive analysis and maintenance, avoid manual data entry tasks and to detect spam.

Furthermore, organizations can develop a product-based recommendation system in an eCommerce platform, matching the customers mostly viewed or last purchased products.

Machine learning can also be used in analyzing finance to get accurate results. With data breaches happening so often, machine learning can improve the security of an organization’s network. Machine learning continues to evolve, and as it does, its uses expand.

Deep Learning

Deep learning is a subset of machine learning and artificial intelligence that focuses on enhancing machine learning capabilities. It is often math-based in nature and uses data sets to teach an algorithm how to achieve more accurate results.

You can read more about deep learning and it’s uses in artificial intelligence in one of our upcoming articles.

Careers in Machine Learning

Nowadays machine learning is becoming a popular career choice. There is a wide array of opportunities present as it decreases human efforts. Career paths in machine learning are high in demand and well paid, like Machine Learning Engineer, Natural Language Processing (NLP) Scientist, Data Scientist and many more.

Generally, you start your career as a machine learning engineer and develop applications automating certain functions and move further. The average salary of a machine learning engineer ranges from $77,000 to $155,000 per year.

Machine learning is still evolving, and the career path looks bright and exciting with big salary figures. Improve your skills and land a job in the machine learning discipline. 

How Businesses Apply This Technology

To avoid human errors and make things easier, businesses are leaning towards automation and implementing machine learning and neural networks across their company. Machine learning can solve complex problems and offer accurate predictions from the given data sets.

The addition of deep learning into this mix further optimizes algorithms to more accurately perform specific complex tasks.

No matter what type of business, acquiring customers is the most vital part of an organization. Organizations build bots using machine learning, which helps in improving the customer support experience. This is very effective and offers 24*7 communication, and most of the eCommerce platforms have already opted for it. 

Also, organizations have started using forecasting models to predict sales performance. Since data is more valuable than oil nowadays, the chances of data breaches are also higher. Machine learning is implemented which helps in monitoring the network for security threats and secure sensitive data. 

The Limitations of Machine Learning

Machine learning is booming in the industry and yet it still isn’t perfect. Yes, machine learning has its own limitations. Machine learning requires a huge amount of data set to train on, which involves learning and relearning the patterns to offer effective reliable results. This makes it challenging and expensive for businesses that can’t allocate enough financial resources.

Also, it needs a lot of time and good quality resources. Algorithms are built to decrease human intervention and improve the results, but with a lack of data or quality data, the results can be biased. Also, there is no one algorithm to solve the problem, developers and engineers need to optimize these algorithms to provide solutions to new issues.

Machine Learning Certifications & Courses

To build your career in machine learning you must be pretty skilled in programming and familiar with computer science concepts. Also, you must have a strong mathematical background. To learn and improve your skills or get started, there are free and paid courses offered by experts in the machine learning and artificial intelligence field. 

Enroll in a course depending upon your skill level and improve your machine learning skills. Some of the best courses are available on Coursera, edX, LinkedIn Learning, Udacity, Udemy, and Plural Sight. There are many programs to start learning from, so, pick out the right course and get certified in machine learning.

Data Scientist vs Machine Learning Engineer

Skills Needed for Data Scientists

Irrespective of the type of company, there are some basic skills required in order to function as a data scientist, which is curated into three domains namely: programming, analytics and domain knowledge. They are expected to know more languages, frameworks, statistics, and tools, etc. Below are the necessary skills to carve out your career in this competitive field.

  • Clear understanding of programming languages like Python, SAS, R and Scala.
  • Hands-on experience in SQL databases.
  • Familiar with unstructured data management techniques.
  • Use big data tools like Hadoop, Hive and Pig
  • Strong mathematical foundation including statistics. 
  • Data mining, data extraction, data cleaning and data visualization
  • Visualization tools like Tableau 

Those looking to enter this competitive job market should expect to update their skills on a regular basis as technologies and systems within this field are constantly changing as the technology evolves.

Skills Needed for Machine Learning Engineers

In this rapid technological advancement, for you to become a successful machine learning engineer, you need to know and are required to have the following skills to begin your career. 

  • Deep understanding of programming languages like Python, R, JAVA, C++
  • Expert in fundamentals of computer science
  • Experience in statistics & statistical modeling
  • Machine learning libraries and algorithms
  • Data evaluation and modeling
  • Natural language processing
  • Familiarity with neural networks
  • Text representation techniques

Every day, machine learning is evolving to new heights and it is a must to stay updated with skills needed for machine learning engineers. So, stay tuned and keep yourself updated learning through reading lots of articles, blogs and watching numerous videos, as currently the career opportunities are limitless. 

Final Thoughts

The impact of data science and machine learning is growing significantly. By looking at the difference between these skill sets, the most important question is not which is better, but which is the right career choice for you. 

It is recommended to have a degree since many employers state that as a base requirement. Some employers prefer an advanced degree, but no matter the base or foundational requirement, it is also required for you to have all the necessary skills. Currently, compared to data scientists, machine learning engineers make more, but data scientists have a much broader role. 

Data science is a fast-growing field that helps in the understanding of business logic and gaining insight from data; while machine learning allows us to learn from the stored data. It doesn’t matter whether you become a machine learning engineer or a data scientist, you are working on cutting edge technologies shaping the future of businesses to the next level. 

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