Here’s another article to clear the often confused concepts from the world of Data. This time around we will be comparing Data Science, Data Analytics and Machine Learning.

Did you know Data Science, Data Analytics and Machine Learning are in some way related to each other?

Data Analytics and Machine Learning fall under the giant umbrella of Data Science. 

What are these three fields?

To start with here is a basic view of what are these three fields are and what is their underlying purpose.

Data Science

Data science is an interdisciplinary field that converts basic numbers to structured data and draws meaningful insights from it. 

Think of it as an umbrella that houses many disciplines such as Data Visualization, Deep Learning, Data Analytics and Machine Learning among others. Insights drawn from the data-sets are used to answer crucial business problems.

Because Data Science is a bit of both the worlds- art and science, a Data Science enthusiast like you needs to have a perfect blend of scientific aptitude and logical, analytical and reasoning skills.

Data Science isn’t just a specific skill but a practice on a whole.

When you think of searching something on the Internet, what is the first thing that comes to your mind?

Yes, Google is the first go-to search medium. It not just gives you search results but also tells you how many search results did your search term yield and the time it took to show you the results.

This is possible only with the help of the organized way of using data, i.e., Data Science. Internet Search is one of the basic examples of the use of Data Science. There are many others like predicting customer behavior, fraud detection, etc.

Data Analytics

With an aim to facilitate strategic decision making, Data Analytics is the process of extracting patterns and insights from real-time data and historical data to derive observations and inferences.

This is possible through the statistical analysis done on the organized data-sets. The especially crafted information systems empower Data Analytics and are built on machine learning programs.

Data Science makes use of Data Analytics to put forth actionable insights into the existing problems with the businesses. Thinking of immediate improvements for some processes? Have some questions related to this process enhancement? Data Analytics will come to your rescue and give you the answers to such questions.

Here is an illustration to clear what Data Analytics is.

Just like it did to many other sectors, Data Analytics has transformed the Healthcare arena. Both sides- doctors as well as the patients can benefit from the Data Analytics. Hospitals and doctors have the patients’ data- their history and preferences. 

Now, the patients too have access to a lot of information that has been analyzed, segregated and made into particular data-sets as per the patients’ preferences and other dimensions. This helps the patients make decisions about which medical policy to apply or which Healthcare to opt for.

Machine Learning

Think of machine learning to be that intermediary field that makes both- Data Analytics possible and paves the way for artificial intelligence.

How machine learning and artificial intelligence are related? That’s another story. You can check it out here.

The process to make machines learn and perform tasks with minimal human supervision is termed as Machine Learning. This is done through various algorithms. 

The world of machine learning is all about making predictions and learning from its mistakes. You feed the data to show the difference between a cat and dog; a happy face and sad face and see that the machine will recognize the pictures based on these inputs. Only if it doesn’t make any mistake! Program it not to do so and gradually it will give you perfect answers!

Here’s an illustration to make things easy for you.

Everyone has a Facebook account. You upload images and tag your friends. The next time you upload an image, you see an “auto tag” feature. The machine identifies an image based on the previous knowledge and identification. This is image recognition, which is possible because of machine learning algorithms. 

What is the workflow?

Now, that you went through the very basics of Data Science, Data Analytics and Machine Learning, it’s time to dive deep into them.

Data Science

The process of making numbers speak isn’t as easy as it seems; it involves a lot of procedures, processes and steps. 

The process starts from collecting the raw data, cleaning it to give it a form, performing analysis on it to make it say what the Data Scientist wants to, visualizing the findings and then reporting the answers found. 

Have a look at the processes in Data Science from a Data Analyst and the Data Scientist’s viewpoint.

The two statistical procedures used to analyze the data are:

1. Descriptive Statistics
2. Inferential Statistics

As discussed earlier, algorithms are used to make predictions; what will be the expected outcome, etc. Both the statistical procedures and algorithms are used to arrive at answers needed for the problems at hand. 

Just like there are different statistical procedures for different types of analysis, similarly, there are five algorithms for five different types of problems. 

Believe me, this is just the right amount of knowledge to set the pace for you. Wish to know more? Want to read in detail? Go through our Data Science beginner’s guide.

Data Analytics

Informed business decisions is the gift of Data Analytics. Businesses can leverage this gift to benefit their market analysis, product/services, predictions of their actions, etc. 

The scientific community also tests and validates their scientific models and hypotheses with the help of Data Analytics. 

There are two facets of Data Analytics:

1. Quantitative Data Analytics
2. Qualitative Data Analytics

To conclude, Data Analytics and Data Science are more like the two different sides of the same coin. Data Science asks questions while Data Analytics answers them; Data Analytics is a part of the whole process of Data Science.

Machine Learning

Predictions are made easy in the world of machine learning.

Algorithms ease the prediction task and the machine learns, improves and puts forth a much efficient solution to a problem.

Right from recognizing human emotions to predicting human reaction to a particular tone or music piece is a wonder from machine learning for you. 

Getting to the core of the massive data, speedily and with complete accuracy is not possible.

To top this situation, more than 80% of this data is unstructured. Machine learning comes to your rescue and simplifies the process; it finds patterns in data and solves the problem at hand. 

You save time and arrive at the best possible and accurate solutions, thus paving the way to make machines intelligent. You will find machine learning applications in almost all sectors. Want to know what is the scope of machine learning? Here it is:

– Data collection
– Data filtering
– Data analyzing
– Training algorithms
– Testing algorithms
– Making use of algorithms for future predictions

Before moving to Applications of Data Science, Data Analytics and Machine Learning, let’s revisit!

Data Science is a broader concept that houses many disciplines, including Data Analytics and Machine Learning. On the other hand, Machine Learning is a narrower concept. It aims at building machines that will find out patterns with the help of algorithms. Data Analytics is extracting insights from different data sources. 

Applications of Data Science, Data Analytics And Machine Learning

There is hardly any field or sector that is not benefited from these three interlinked processes. As a result, there are many applications that make our lives easier, while helping businesses scale heights. 

1. Refining Search Engine Results

Search engines make use of machine learning to refine the search results.

Did you click the top results or ignored them and moved to the few specific ones; whether you clicked the middle links or the ones at the last; did you stay for long on the web page you opened or quickly drifted away and did you go to the page 2 at all?

These are some of the points that the backend algorithms focus on after you searched for a particular term. This not only helps in refining the search results according to the user but also collecting the data related to a specific search term.

2. Product Recommendation

Whether with an intention to buy products or just browse through the collections, you visit a website. After some time, you leave the site and go about your usual business, browsing through other sites, checking out your Facebook account, etc.

On these websites, you see the products you browsed through or clicked. This is because Google tracks your browsing history and makes recommendations, based on the data collected, even when you are on other sites.

These product recommendation ads are one of the most useful applications of data science and machine learning.

3. Product and Service R&D

Quantitative data like sales figures, projected sales, etc. and qualitative data like analyzing images, posts, etc. on social media to gauge customer’s preferences is useful in coming up with new products and services. 

Remember that feedback you gave for a particular product and asked the manufacturers to come up with something different; posts and feedback like these help the businesses make decisions on which products or services to introduce or which ones to amend.

What’s the Future?

With enormous amounts of data flowing in every second, the future of all of these fields is bright.

Machines are getting ‘intelligent’ with continuous advancements in the field. Businesses are taking targeted and customer-focused decisions and seeing an upsurge in the businesses. 

Both the historical data and real-time data are opening up avenues for organizations to recognize the scope of these three fields. The demand for Data Analytics is set to increase in the future with more and more sectors turning towards this analytics giant.

This brings us to the end of the post. I hope the distinction between Data Science, Data Analytics and Machine Learning is clear. 

Data is, was and will be at the core of all businesses’ operations. As data grows, operations and applications of these three fields will grow simultaneously.

In case, you have something to share or have some doubts, do let us know in the comments section below.