Data Analytics

A Comprehensive Guide To Data Analytics Vs Machine Learning

These are the times of rapid technological advancement witnessing exponential growth in computing power. We can harness this processing power for more complex tasks. This has led to the emergence and rapid growth of fields of data analytics, machine learning, and AI. Let’s understand the difference between these closely linked technologies and compare the disciplines of data analytics vs machine learning. 

Data analytics vs Machine Learning is one of the most talked about topics among data science aspirants. The focus of both fields is on data, and they are amongst one of the most in-demand sectors. While choosing for a career in data science it is quite normal to get confused about these trending domains.

What is Data Analytics?

Data analytics, at its heart, can be defined as the science of analyzing data sets to establish trends, answering questions, and drawing conclusions. Data analytics is a varied and complex field that relies on the use of specialized software, algorithms, and automation.

The basic principles and concepts of data analytics can be applied across any industry. Organizations today are employing data analysts to enable them to make well-informed decisions that are data-driven regarding different areas of their businesses.

Generally, for analysis, the existing data regarding past events is analyzed. Thus, the existing trends can be identified.

Data Analytics Can Be Classified as Below:

  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics

Data Science Vs Data Analytics

For better understanding and comparing data analytics vs machine learning, it is important to understand the difference between data analytics data science & machine learning, and AI.

The terms data science and data analytics are sometimes used interchangeably which is incorrect.  The focal point of Data analytics is an examination of data sets for identifying and explaining trends.

Whereas Data science broadly looks at the processes level for data modeling and production, creation of algorithms, and predictive modeling.

The two disciplines do have a certain degree of interchangeability. Data science relates more to the wider field focusing on the discovery of large sets of data. Data analytics, within that scope, is a more focused area offering insights using examining existing information.

Read here about the:

What is Artificial Intelligence?

The concept of Artificial intelligence (or AI) has been around for a while. AI means the ability to provide computers with the ability to replicate human intelligence.

Through the creation of such computers, which can learn, it is possible to teach them from experience. Artificial intelligence systems have three qualities.

  • Intentionality
  • Intelligence
  • Adaptability

What is Machine Learning?

Machine learning is a subset of artificial intelligence. Computers are programmed in such a way that they can learn automatically and act in a similar way to humans. This also improves their learning as they process additional data.

Machine learning mostly focuses on creating programs and software that are capable of learning to make predictions and decisions without direct programming to do so. Thus, the technology is useful for all purposes be it powering search engines or diagnosing any medical conditions.

Data Analytics Vs Machine Learning – Jobs on Offer

The most important factor for comparing Data analytics vs machine learning as a career option is the type of job offered. If these data-driven areas are of interest to you and you are considering a career path related to them the following are examples of jobs that would deeply explain data analytics vs machine learning.

Jobs Related to Data Analytics

  • Data analyst. A data analyst mainly focuses on processing raw data to create meaningful and useful insights. The most important part of the job is identifying and presenting trends in a meaningful and easy-to-understand way.
  • BI analyst. The work of Business intelligence (BI) analysts revolves around providing data insights that can be used to make well-informed business decisions. For this, a variety of techniques and technologies are used to enable organizations to make informed choices backed & supported by data.

Jobs Related to Machine Learning.

  • Machine learning engineer. This is a role with certain elements of software engineering and data science overlapping. Machine learning engineers are required for the creation of algorithms and programs that enable computers to learn automatically.
  • NLP scientist. Natural language processing (NLP) is the technology of creating algorithms that are helpful for computers to understand natural human language.

Are you looking for Data Analytics Jobs? First, must go through the most important Data Analyst Interview Questions

Data Analytics Vs. Machine Learning: Understanding the Jobs and the Associated Responsibilities.

A passionate aspirant of data analytics will be spending most of the time collecting and analyzing data. The role of a data analyst is extremely crucial in forecasting problems and also in providing effective solutions to existing problems. Thus, the importance of the role of a data analyst in an organization differs based on the type of business of the organization.

The Following Are Typical Job Responsibilities of a Data Analyst:

  • The first and most important responsibility of a data analyst is the collection of data from internal and external sources. The way to collect the data can be through surveys, tracking customer transactions, gathering online behavior data, and monitoring social media. These methods are business-specific
  • There may be duplicity or repeated information in data collected through various sources. The data analyst should clean the data to prevent skewed data. Furthermore, the dispersed and random data must be aggregated into graphical or tabular form to simplify the analysis process. This is a very crucial role of a data analyst.
  • The key factor for enhancing the productivity of any organization depends upon the quality of the data collected. Thus, a data analyst should verify the importance and relevance of this information for the company. Further, in today’s world information & data have become extremely valuable assets to any organization, and securing them has become a critical role of a data analyst.
  • Lastly, it is important for data analysts to effectively communicate the findings in a meaningful way. So, the presentation skills of a data analyst should be strong. This is because the reports prepared by data analysts are very important in monitoring and forecasting the profitability of the organization.

The Following Are the Typical Roles and Responsibilities of a Machine Learning Engineer:

  • An aspirant choosing machine learning as a career shall have the primary responsibility of investigating and implementing the models that are developed by data analysts.
  • The aspirant needs to have strong collaborative & communication skills as the machine learning engineer works in association with data engineers, data analysts, etc for developing algorithms. The aspirant is required to create machine learning algorithms which shall be developed based on data collected from various sources and on models developed by the data analysts, capable of making predictions and learning.
  • Strong programming skills in Python and R with familiarity with API packages & libraries of other software companies. Integration skills are also of extreme importance.

The Different Skills for Data Analytics Vs Machine Learning

The fields of data analytics, machine learning, and artificial intelligence have certain similarities and many differences. It is important to know the skills needed to progress in each field. The comparison of data analytics vs machine learning can also be done based on the skills required or possessed by an individual.

Are you a fresher? Check here for the details about the Data Analytics Entry Level Jobs

Data Analytics vs Machine Learning – Common skills

Several skills are useful across machine learning, data analytics, and AI. Few of industry-specific skills along with universally useful skills are:

  • Programming. Any type of role requires you to have a basic understanding of programming languages like Python, C++, Java, etc.
  • Data analytics and data modeling. It is a must to have working knowledge of data analysis and creating data models.
  • Communication. Effective communication skills are vital for working across a diverse range of organizations and teams. Further, you may be required to explain reasoning and findings to non-experts.
  • Teamwork. Collaboration is very common for professionals in data science, ML and AI tend to produce good results. Teamwork and leadership attributes are highly desirable and useful qualities for this.

Data Analytics Skills

The most important and required skills for Data analytics are:

Statistical Analysis

It is the science of exploring, collecting, and presenting large quantities of data for identifying any underlying trends or patterns. In respect of data science and analytics, this means generating and analyzing the existing data for generating helpful insights.


SQL (Structured Query Language) is a programming language used for relational database management systems (RDMS) that allows access and manipulation of databases.

Knowledge of R and Python

Two of the most popular and important programming languages extremely useful for data science professionals are R and Python. R helps in the analysis of data while creating applications for statistical analysis. R is helpful in data modelling to data clustering. Python, on the other hand, has numerous libraries such as NumPy and SciPy making data analytics easier.

Machine Learning Skills

A machine learning professional must possess the following skills:

Deep Learning

It is an advanced branch of machine learning focusing mainly on making a machine function like a human brain and helps generate patterns to make smarter decisions. Some of its applications include virtual assistants, self-driving cars, fraud detection, NLP etc.

Natural Language Processing (NLP)

NLP has many applications and is mostly used for training machines to understand and interpret text or symbols. One of the most popular examples of NLP applications is the auto-fill system for emails.

Computer Vision

The field of artificial intelligence focuses on training computers to understand visuals. It enables computers to interpret and understand images. The perfect example of computer vision is facial recognition technology.

Thus, one may compare data analytics vs machine learning based on jobs offered, skills set required, or salary on offer in the respective disciplines before opting for making a career in the discipline.

Once you finalize the discipline you want to pursue by comparing data analytics vs machine learning, the following online courses will help you enhance your knowledge and skills in the discipline. These courses are affordable and offer certifications along with learning of various tools and programming languages which in itself can help in furthering your career.

Data Analytics Vs. Machine Learning: Tools Used.

A wide range of tools are used by data analysts and machine learning to perform their jobs. No single tool is self-sufficient in solving every business case. Therefore, the best option is to use a combination of tools that work well together.

Explore here the basic to advanced Data Analytics Tools

A Few Important Tools Used by the Data Analyst:

Microsoft Excel

This is the most common tool used by almost all organizations and the tool is extremely useful in meaningful sorting of data. Microsoft Excel not only provides for the real-time collaboration of the data but can also be used to improve data insights but offers real-time collaboration. It also provides for data uploading options.


Python is another tool that is commonly and frequently used by data analysts & scientists. This is attributed to the fact that Python has a simple syntax and ease of learning. It provides an extensive library of tools for numerical computation, graphics, and data visualization.


R, an open-source programming language, is widely used in statistical computing and graphics and is very useful in building practical models & identifying patterns. It has a large library offering options for easier data interpretation using good interactive visualizations.

Jupyter Notebook

Jupyter Notebook is a very user-friendly code editor widely used by data analysts.  It supports languages like Python and R  & can be hosted on a local computer.

Apache Spark

It is an open-source distributed processing engine that is used for processing big data workloads. Thanks to its in-memory caching and improved query execution it is possible to run quick analytical queries against any size of data.


This Statistical Analytics Software tool is used by large-scale organizations as it is highly reliable for computing complex statistical operations.

Microsoft Power BI

This is a comprehensive data analytics tool that aids in comparing past and present data. It provides insights regarding percentage change in revenue, customer experience, and success of the organization.


This data analytics platform allows to preparation, analysis, and sharing of the insights collected through big data. With this tool one can work on live data sets saving time on data wrangling, allowing more focus on data analysis.


The Konstanz Information Miner is a free and open-source data analytics, reporting, and integration platform and works on a GUI-based workflow. No prior programming knowledge is required for implementation.

Explore here the best and highly recommended:

Following Are Some Tools Used by Machine Learning Engineers:


This is an open-source platform for machine learning. This tool is extremely popular amongst machine learning experts due to its extensive libraries which simplify the numerical computation specifically for deep learning and machine learning projects.


PyTorch tool is built on Python and Torch library and is an open-source machine learning program. This tool is most popularly used for deep learning research as its framework design enables the rapid transition from research simulation to implementation.

PyTorch Lightning

PyTorch Lightning tool with its ease of installation and the ability to integrate with other machine-learning tools can trigger error alerts for any mistakes made by the developer during coding.

Scikit Learn

It is a free software in Python providing a wide range of machine learning algorithms. Basic knowledge of Python along with knowledge of supervised & unsupervised models with commonly used libraries is a must for the developer.


The tool is an additional PyTorch framework. The design is such that it specifically helps in deep learning applications, supporting research and enabling quick experimentation.


XGBoost is mainly used for high speed of execution on large data sets along with outstanding model performance.

Light GBM

Light GBM converts continuous values into discrete bins for efficient usage of memory. Also, it can handle large data with faster training. It also supports parallel and GPU learning.


CatBoost uses Gradient-boosted decision trees. A series of decision trees are built sequentially during training leading to faster training and accurate models.


The advantage of is that deep learning is used across several operating systems, languages, and small datasets thus eliminating excessive engineering efforts.

PyTorch Ignite

It provides a simple user-friendly interface that simplifies the research capabilities and saves unnecessary coding. PyTorch Ignite possesses a standard version and integrates well with the machine learning ecosystem.

Data Analytics Vs. Machine Learning: Booming Sectors

Several industries, nowadays, rely on data analysts for gathering and interpreting the data and on machine learning engineers for automation of the decision-making process. In this section, let’s expand our perspective and understanding of various sectors employing data analysts and utilizing machine learning.

Data Analyst Expert: Flourishing sectors

In today’s scenario data analytics acts as a critical tool for the retail and wholesale sectors. These tools help in refining the pricing strategies and investigating the causes behind erratic demand fluctuations. Apart from this, data analytics is further helpful in facilitating these sectors for anticipating the actions of the competitors in the market. Similarly, this is helpful for banking and financial institutions to leverage the data gathered for personalizing services according to individual customer needs rather than offering typical standardized banking options for all.

The techniques of data analytics are widely employed in the investment sector of the banking/financial industry and help in monitoring the financial market. An example of the same can be seen in stock exchange markets which employ data analytics for identification of unethical trade practices. As data analytics helps in quick decision-making, it is used in the healthcare industry.

In such conditions, where quick and successful decision-making is required, data analytics plays a critical role. This is because it aids in providing well-informed suggestions and recommendations for the decision-makers based on patient data and eventually increases the effectiveness of healthcare.

Data analytics is very useful in weather forecasting as it helps in forecasting weather through forecasting models, thereby helping in the prevention of natural disasters like flash floods. Data analytics provides options for short-range, medium-range, and long-range forecasting.

Machine Learning Engineers: Flourishing sectors

Machine learning plays a crucial role in the regulation of several processes of software development. The rapid growth of the software industry has also led to a significant increase in the demand for machine learning engineers. Also, the need for human intervention in decision-making and taking suitable actions can be eliminated by the use of machine learning.

The algorithms prepared by machine learning are extremely powerful and can be used to detect patterns from the data that is being entered into the system. A large quantum of data can be dealt with easily by machine learning as compared to humans. One such field having huge and overflowing data is marketing. Therefore, machine learning makes it easy and convenient to identify patterns in customer behavior thereby helping create advertisement campaigns accordingly.

For the manufacturing industry machine learning can offer workable solutions that are based on and in line with the demand. This results in cost reduction and easy & early detection of errors. Machine learning offers the added advantage of supply chain management along with tracking of assets for the manufacturing industry.

In today’s cyber-dependent world, cybersecurity plays a very crucial and critical role in safeguarding the data of organizations. Machine learning plays an important role in cybersecurity as it can help in identifying patterns and suggesting the preventive steps to be taken in the situation of any cyberattacks. It can provide real-time solutions to respond to active cyberattacks. Thus, machine learning proves to be a very powerful technology.

The finance sector can reap myriads of benefits from machine learning. This is because machine learning is capable of analyzing millions of data points in a short duration of time and can offer important and logical insights into patterns of trading. Thus, it creates a platform that is very safe and secure to carry out digital transactions.

The healthcare sector is another sector that can benefit a lot from machine learning. Machine learning has resulted in the development and advancement of wearable health-tracking devices such as heartbeat monitors, step counters, blood pressure monitors, and several other aids that help patients and doctors by monitoring real-time health parameters.

FAQs: Data Analytics vs Machine Learning

Q. Data analytics vs Machine learning – which is a better degree?

Both disciplines offer great career options, and it depends upon the learner what they would want or like to do. Data analytics is a better career choice for people who have an aptitude to work on raw data to identify patterns. Machine learning is a better career choice for aspirants who have an aptitude for creating advanced machine learning models and algorithms enabling computers to learn from experience.

Q. How does a data analyst become a data scientist?

Data analysts can become data scientists by upskilling and developing strong programming, mathematical, and analytical skills.

Q. Is knowledge of Data Science a must for Machine Learning?

Data Scientists must possess an understanding of Machine Learning for quality predictions and estimations.

Q. What is the Future of Data Science?

As several automated data analytics platforms have forayed into this field, the job scenario of data science jobs is bound to improve as organizations require data scientists to focus on more complex problems while solving simpler problems with data science tools.

Data Analytics vs Machine Learning – Final thoughts

This article serves as a guide for understanding data analytics vs machine learning while highlighting the differences between the two. We have explored how these topics overlap and what makes them unique, along with the career options on offer. After examining the main differences between them, the skill sets required for each one have also been mentioned.

Data analytics vs Machine learning is becoming a hot topic of interest for aspirants in the field of data science. The data science industry is set to become the next most sought-after field with an expected turnover of billions by 2025. As data is the new oil, data scientists, data analysts, and machine learning professionals are some of the most sought-after professionals.

Organizations need professionals from these 2 disciplines and thus data analytics vs machine learning becomes more relevant for them to shortlist the most suitable candidates from these disciplines to suit their needs. This article has been framed to provide a basic understanding to the aspirants regarding these disciplines and to help them in making well-informed decisions before selecting a discipline from data analytics vs machine learning.

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