Machine learning vs data analytics is one of the most talked-about topics among data science aspirants. Both of these fields focus on data and are among the most in-demand sectors. Thus, while choosing a data science career, it is quite natural to feel confused about these two trending domains.
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But worry not, for we’ve created the perfect guide to help you understand the difference between machine learning and data analytics.
Now, let’s get straight to the topic at hand – machine learning vs data analytics.
Machine Learning vs Data Analytics: Definition
To understand the difference between machine learning and data analytics, we must first look at their definitions. They will help you understand what makes these fields unique and different from each other.
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What is Machine Learning?
Machine learning refers to the study of algorithms that improve through experience. It is related to artificial intelligence. A machine learning algorithm learns from data automatically and applies the learning without requiring human intervention.
Machine learning has multiple branches and there are various methods to use them. Conventional machine learning solutions use predictive analysis and statistical analysis for finding patterns and catching hidden insights into the available data.
One of the best examples of machine learning at work is Netflix’s recommender system, which suggests movies and shows automatically based on collaborative and content-based filtering.
What is Data Analytics?
Data analytics, also known as data analysis, is the process of cleaning, inspecting, modelling, and transforming data for finding valuable information, informing conclusions and enhancing the decision-making process.
Data analytics focuses on generating valuable insights from the available data. Companies use data analytics to make better-informed decisions regarding various matters including marketing, production, etc. Data analytics helps you take raw data and extract helpful information from the same.
As you can see, a key difference between machine learning and data analytics is in how they use data. Data analytics focuses on using data to generate insights while machine learning focuses on creating and training algorithms through data so they can function independently.
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Machine Learning vs Data Analytics: Salary
In terms of pay, there’s a notable difference between machine learning and data analytics.
Machine Learning Salary in India
The average pay for a machine learning professional in India is INR 6.86 lakh per annum including shared profits and bonuses. Freshers in this field make around INR 3 lakh per annum on average. However, an experienced machine learning professional can get up to INR 20 lakh per year on average. It’s certainly one of the most lucrative sectors out there.
Machine learning professionals in Bangalore and Chennai earn considerably more than the national average while the machine learning professional in Delhi and Pune earn 25% and 10% less than the average.
Data Analytics Salary in India
In data analytics, the average pay for a professional is INR 4.24 lakh per annum. A fresher in this field with one to four years of professional experience would earn INR 3.9 lakh per year on average. On the other hand, a data analytics professional with five to nine years of experience earns around INR 6 lakh per annum on average. Similarly, data analysts with 10 to 19 years of experience earn an average pay of INR 9 lakh per annum.
You should keep in mind that with increasing experience and enriched skills, you can pursue senior roles in this profession such as Data Scientist or Business Analyst. Data analysts in Bangalore earn around 18% more than the national average while the data analysts in Delhi earn 6% less than the average.
Both of these professions offer numerous promising career opportunities with high pay packages. However, do remember that pay depends on multiple factors including your location, employer, experience, and skills.
Machine Learning vs Data Analytics: Skills
In terms of skills, there are some areas where both of these domains overlap while there are others where both differ completely.
Machine Learning Skills
As a machine learning professional, you’ll need to have the following skills in your arsenal:
Deep learning is an advanced branch of machine learning that focuses on making a machine function like a human brain and it helps in generating patterns and make smarter decisions. Deep learning has expanded substantially in the past few years and some of its notable applications include virtual assistants, self-driving cars, fraud detection, NLP (which we have discussed later), and news aggregation, to name a few.
Natural Language Processing (NLP)
Natural language processing (NLP) focuses on training machines to understand and interpret text or symbols. It is a branch of artificial intelligence and has many applications. One of the most popular examples of real-life NLP applications is Gmail’s auto-fill system for emails.
Computer vision is the field of artificial intelligence that focuses on training computers to understand visuals. It allows computers to interpret and understand images. We use computer vision to train machines to recognize objects and people in images. An excellent example of computer vision is facial recognition technology.
Data Analytics Skills
Data analytics focuses on gathering insights from large amounts of data. So the most important skills for this field are:
Statistical analysis is the science of exploring, collecting and presenting large quantities of data to find underlying trends and patterns. In data science and analytics, it focuses on generating statistics from stored data and analysing the same to generate helpful insights. It is a fundamental skill for data analytics.
SQL is a programming language used for relational database management systems (RDMS). SQL stands for Structured Query Language and it allows you to access and manipulate databases. As a data analyst, you’d be working with many databases which is why having this skill will help you a lot in growing as a professional.
Knowledge of R and Python
R and Python are the most popular programming languages for data science professionals. R helps you analyse data and create applications for performing statistical analysis. From data modelling to data clustering, R helps you with tons of data analytics functions. Python, on the other hand, has numerous libraries such as NumPy and SciPy that make data analytics easier for you.
Also, you should know that some skills are intrinsic to both ML and data analytics, such as programming, calculus & linear algebra, etc. Similarly, both of them use similar tools for working with data such as Apache Spark.
Machine Learning vs Data Analytics: How to Enter?
No matter how different these fields are, in the end, they are both highly technical. This means to enter any one of them, you’ll need to acquire domain-specific skills and also obtain the necessary qualifications. Recruiters look for certified professionals in both of these fields as certification ensures the candidate would be familiar with the basic and advanced concepts of the field.
At upGrad, we offer multiple courses for both of these domains. In our courses, you study directly from industry experts through videos, live sessions, and assignments. Besides, you will get personalized mentorship, dedicated career assistance, career counseling, and resume feedback facilities to ensure you start your tech career easily.
All our courses are offered online, so you can start learning from anywhere at any time without interrupting your professional or personal commitments.
Machine Learning Courses
Here are some of the machine learning courses available at upGrad:
- Executive PG Programme in Machine Learning and AI
- Master of Science in Machine Learning & AI
- Master of Science in Machine Learning and Artificial Intelligence
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By now, we hope that you have a much clearer idea of the difference between machine learning and data analytics. While the former uses data to train and test algorithms, the latter focuses on finding patterns and insights from the data. However, both of them are technical disciplines and are largely dependent on each other.
Following are some more resources covering machine learning and data analytics. You can find more of such resources on the upGrad blog:
- Machine Learning with R: Everything You Need to Know
- Top 5 Online Machine Learning Courses to Improve your Career
- Top Data Analytics Tools Every Data Scientist Should Know About
- Career in Data Analytics: Ultimate Guide 
What are your thoughts on our article on machine learning vs data analytics? Let us know by dropping a comment below.
Is Data Analytics different from machine learning?
Yes, they are different. Data Analytics is the process of deriving meaningful insight from the data through various analytical methods and tools. Machine learning and analytics are two important processes of data science which are generally used together to transform data into useful information. In machine learning, computer programs are developed to make decisions/learn by themselves using algorithms programmed into them. On the contrary, data analytics is the process of deriving insights from the data.
Is data analytics necessary for machine learning?
Data analytics is a critical component of machine learning. When machine learning is used for the first time for a new situation, it is usually the data analytics part that makes the prediction. In a machine learning system, the raw input data is first passed through a cleaning and transformation process, which results in high-quality data that is suitable for machine learning. Then a machine learning model is applied to the data in order to produce the output. It is the job of the data analytics part to make sure that the input data is of the highest quality possible. This is because machine learning models tend to become unstable with low-quality input data.
Why are data analytical skills important?
Analytical skills are required for data analytics, data mining, and data science. Data analytical skills are required for visualizing, analyzing, and interpreting data. You can build analytical skills by taking online courses or a college degree. Analytical skills are important for data scientists because the job focuses on extracting knowledge from the data. Data analytics skills are important for data scientists because the job focuses on complex data manipulation. A programmer who wants to become a data scientist can start by taking an online course.