One of the terms we hear often since the advent of the Digital Age is Data Science. However, we tend to overlook that this field has been around for a long time. Like Old wine in a new bottle, it has just gotten a makeover with the emergence of Computer Science and Information Technology.


In simple terms, Data Science is the Study of Data. From the Neanderthal Man who analyzed an existing problem of transportation and created the wheel to NASA sending the first men to the Moon, all of those achievements were possible due to careful analysis and extraction of information from obtainable data.

Data Science in the Modern World

In the present time, one of the trending fields to be working in is Data Science. Data scientists are the new standard for cool. Requiring a combination of knowledge in Computer Science, Statistics, and Mathematics, a data scientist’s job is to apply the information he/she has from the above fields to extract suitable data that can be used in the development of a product or service.

The data will have to be analyzed, cleaned, and stored in data sets, which will then be used in algorithms or models developed by the same scientist. The outputs of these algorithms will help in building a product or brand with higher efficiency.

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

The best and most relatable example to describe this process is the Netflix Algorithm. Their Model analyses the data from millions of viewers, which then helps to create personalized suggestions for further viewing. Now, the question might arise, is it possible to continuously sift through never-ending lines of data and still maintain your sanity? It is at this point, machine learning, a subset of the data science field comes in.

Data Science and Machine Learning

It has been understood now that to produce data in an operational format, algorithms or models developed by a data scientist will have to be used. These algorithms are then trained continuously in defined intervals by the data scientist or machine learning engineer until it is able to extract required data from the given data sets independently.  The knowledge required to train these algorithms is derived from the fields of artificial intelligence and machine learning. The diagram below will give a broader understanding of how all these domains interact.

Data Science fields


Fields of Data Science

Machine Learning from scratch

On the same note, one can simply define machine learning as the process by which machines learn and analyze data independently without human intervention. However, to reach that autonomous level of programming, it requires intensive human expertise in training the machines and programming them to utilize their artificial intelligence. And that effort can only be provided by certified individuals.

Could you be one of them? Definitely! Upskilling oneself in any given field has now become a pretty straightforward process.  Yet, choosing the exact portal that can propel you in your desired direction can be quite an uphill task. But before diving deeper, it is always preferable to straighten out the fundamental knowledge. Like everything related to the Computer Science field, machine learning to requires a basic familiarity with languages like Python, C++, and R.

Machine learning using Python

Python is by far the easiest and most popular language used by both machine learning beginners and experts. The advantage lies in the fact that it is an open-source programming language with a vast community of users. Also, the many library packages provided, ease the process of coding. Platforms like Github have a considerable amount of code provided by Python users worldwide that could easily help an aspiring beginner with his/her kick-off project. Once the basics of the language are clear, all that remains is to start training the data sets.

Machine Learning using Python

Neural Networks in Python

Neural Networks is the mechanical interpretation of the human body’s nervous system. Vast connections and interconnections of inputs that pass through different functions and assemblies to give a desired/fixed output. A simple example would be the binary OR table.

Input Input Output
0 0 0
0 1 1
1 0 1
1 1 1

This can be coded using python and after training the program for different levels and data sets of the given table, the system will be able to handle any binary OR table independently. This is just a tiny drop of what neural networks can achieve through python. Single programs can handle millions of bytes of data and produce outputs in exponentially fast time frames.

Python for Data Science

Python for Data Science is not much different from Machine Learning using Python. As machine learning is a field encompassed within the Data Science Industry, Python for data science deals with the methods of using the language for Machine learning, artificial intelligence and deep learning. Learning the programming fundamentals with respect to the Data Science industry will help in paving a smooth entry to careers as Data Scientists.

R for Data Science

R is a programming language extensively used for statistical research. An open source programming language like Python, it also has numerous specific libraries to help in the creation of code for machine learning, data analysis or statistical inference.

As per research, it is the language highly favored by the academic and healthcare industries due to its high-functioning capabilities of structuring any given random/varied data. In addition, companies like Twitter, Google, Facebook and Microsoft also utilize the language extensively to improve their user interactivity.

R for Data Science

Statistics for Data Science

“Mathematics is the music of reason”, said James Joseph Sylvester, An English Mathematician. Statistics, a branch of mathematics, is truly the backbone and underlying reason for the Data Science Industry. Like the famous phrase , “A chain is no stronger than its weakest link”, having substantial knowledge in the computer science domain but none in the statistics domain can make your chain one important link weaker.

A good knowledge in statistics can ensure whether a data scientist just survives or thrives in the industry. If you can code on command, but are unable to differentiate between the structured and unstructured data, or divide into data sets based on the statistical findings, your role and influence in the industry would be greatly limited.

Statistics for Data Science

Artificial Intelligence and Machine Learning

Similar to how Machine learning is a field within the Data Science Industry, Machine learning is a subset of the Artificial Engineering Field. Wherein machine learning trains machines to parse through data and interpret it in meaningful ways, Artificial Intelligence aims to emulate the complete human psyche in a machine.

Yesteryear science fiction ideas have been implemented and are running thanks to the improvements in the AI field. Even some of the more traditional fields like the Automobile Industry seem to be benefiting from the AI revolution.

Driver less cars, personalized AI operators etc. have taken the industry by storm. Many Automotive OEMs (Original equipment manufacturers) like Ford, GM etc. have dedicated teams to work towards the development of Autonomous Drive Vehicles. These are vehicles that can function perfectly and follow all rules without any human intervention. So, the need for data scientists even in these seemingly traditional fields will skyrocket in the coming years.

Artificial Intelligence in Autonomous Drive Vehicles


AI in Autonomous Drive Vehicles

Deep Learning with Python

Deep learning, in simple terms, is a machine learning technique. Deep learning uses neural networks to help the machine analyze the data and predict the outcomes much like a human brain would. From a visual standpoint, it could be imagined as a bridge between the AI and machine learning fields.

Introduction to Data Science

Now that you have a clear and fundamental idea of the Data Science Industry, you can put a foot forward and upskill yourself. The process of choosing a course can be very daunting and tedious, without careful research you could end up with a course that goes right above your heads or a course that makes you re-learn your ABCs.

Your first step would be to prioritize your requirements, what do you aim to achieve from the course? Is your end goal to become a data scientist? Or are you doing it for the love of knowledge and keeping up with the latest technological trends? Make a list and answer all these questions, after which you can use your answers to skim through courses and decide what fits your interests best.

Learn Machine Learning


Research, Analyze and Compute

Henry Harvin provides courses on Machine Learning with R and Machine Learning with Python and also Data Science with R and Python. Known for starting from scratch and building up to a substantial amount of knowledge, Henry Harvin’s Data Science course could be the perfect fit for any beginner. Don’t just take our word for it, research and analyze all the data, and be your own data scientist. After which, approach us for the professional touch.

Recommended Read:

Also, check this video:


Will one course give me sufficient knowledge to gain a foothold in the Industry?
A course will give you the proper introduction to the Industry, but climbing the ranks and gaining a foothold lies completely on each individual’s efforts

Won’t buying the necessary books suffice to learn about the field?
If you noticed, this article contained images of various books you could refer to. Yes, books will be an important part of the journey, but they alone won’t be sufficient. If that were the case, the school wouldn’t be required and everyone could study individually.
Exposure to peers interested in the same field and experienced tutors are extremely essential to build the working knowledge, as theory knowledge alone is not sufficient

If I don’t have a background in engineering, will I be able to cope with the pace of the industry?
Research suggests that 32% of data scientists are math majors and only 16% are engineers. The degree doesn’t matter as long as you have the interest to learn.

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  1. Navira Menon Reply

    The article is a valuable resource for anyone looking to understand the differences between data science and machine learning. It provides a comprehensive comparison of both fields, highlighting their unique features and applications. The article is well-written and accessible, making it a great resource for beginners and professionals alike.

  2. The article goes on to explain the key differences between data science and machine learning, including the fact that data science is a more comprehensive field that includes many different techniques beyond machine learning. It can be a useful resource for individuals interested in pursuing a career in data science or machine learning.

  3. Kenita Dmello Reply

    The article is a useful resource for individuals who are looking to understand the differences between data science and machine learning. However, it should be supplemented with additional research and readings to gain a more comprehensive understanding of these complex fields.

  4. Great comparison of Data Science and Machine Learning. It clearly explains the differences and similarities between the two fields. Thank you for the informative read. Both are important fields, but understanding the differences is crucial for professionals seeking a career in these areas. Thank you for this helpful and information.

  5. Great article clearly outlines the differences between data science and machine learning. Both are important for businesses in today’s data-driven world.

  6. Great article on the difference between data science and machine learning. It’s important to understand the distinction between the two as they are often used interchangeably, but they have unique roles in the field of data analysis.

  7. Very satisfactory and very helpful Article on data science vs machine learning, you compare very well, I find your blog really interesting.

  8. Very good introduction course. Clear and understandable explanations for beginners, good structure and helpful examples. Also the provided slides are beneficial for later reference and repetition. Thanks for sharing such a helpful content with us. Candidates who are willing to pursue their career in data science must visit this blog Information provided in this blog is totally detailed and can be very useful to the new candidates. Your blog is worth appreciat This helped a lot!

  9. Very well explained how anybody can start this course from scratch which includes data science, Machine Learnings and Artificial Intelligence.

  10. The FAQs have been explained very clearly and also clearly defines data science, Artificial intelligence and Machine learnings.

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