Most people seem to use these terms interchangeably, simply because they all seem to deal with the same domain: Data. However, if you take a closer look, you’ll understand that this is not true. In this article, we look into all three, list the skillset required by each and round up the difference between each of them.
Data science is a multidisciplinary group of study, directed towards using data to the best of its ability and derive smart business decisions without having to expend human resources. Data analytics provides the answers to the specific questions raised by Data Science and tries to focus on the marketing aspect of businesses. Machine Learning, on the other hand, is the process of teaching computer systems how to make sense of all the raw data that has been fed into them. Both machine learning and data analytics fall into the broader umbrella of Data Science.
Data science and the required skillset
The study of data is called as Data Science in simple terms. A data scientist typically uses a combination of tools and algorithms to make sense of raw data and recognise the underlying patterns. In order to mine out sense from all the available raw data, data scientists need to perform exploration techniques. With this level of investigation, they are able to detect and uncover patterns and characteristics in the data. Data scientists build algorithms to apply on data, perform tests, refine the data and are instrumental in the technical deployment.
To become a data scientist, you must possess the following abilities:
- You need to have mathematical expertise because you are going to have to view massive amounts of data under a quantitative lens. You are going to have to learn statistics and also algebra because most machine learning algorithms, as well as inferential techniques, rely on it.
- Next, you’re going to understand technology. You must have proper technical skills to be able to use the tools that you are definitely going to need to make sense of the data you have on your hands. And, Excel is not going to be enough; you must understand how to be able to use different tools in a way that serves your purposes best.
- Because you are going to be working so closely with data and using it to churn out informative decisions, you will have to have strong business acumen. This will help you resolve business problems with smart data.
Data Analytics and the required skillset
By applying Data Analytics techniques to sets of data, businesses make informed decisions on how they can serve their customers better. Businesses use these techniques to understand the customer psyche better, create a target audience, generate personalised ads and many more. Data Analytics ensures that decision making is improved, marketing campaigns are customised and targeted, provide better customer service and smoother internal operations.
All in all, an excellent data analytics team will help you reduce cost and boost your revenue by giving your customers exactly what they want, all thanks to the miracle performed by Data Analytics.
A lot of companies are constantly on the search for good data analysts. A good analytical team implies that they have a constant supply of intelligent insights into the data they have collected.
In order to become a member of such an organisation’s data analytics team, you may want to enrich your skillset with:
- Advanced mathematical skills
- Analysing, modelling and logically interpreting raw sets of data
- Comfortably working with programming languages such as SQL and Python
- A sound understanding of Machine Learning
- An ability to visualise data and present it in a way that your audience can understand it
Learn the difference between Data Analytics and Data Science
Considered by most as one and the same, probably because of the common first word in their names, Data Science and Data Analytics are actually two different schools of study. Yes, they are both interconnected, but they focus on two very different areas in the same universe of data. Data Science is an umbrella of several disciplines intended on finding insights and making sense of raw data.
Data Analytics falls under this umbrella but focuses on treating this data with statistics and analysis. While Data Science deals with data in the broader sense of the term, the main difference between these two lies in the fact that Data Analytics is much narrower, concentrating on finding the answers to specific questions. In a way, you can say that Data Analytics provides answers to questions that are risen by Data Science.
Machine Learning and the required skillset
Nearly all industries require Machine Learning techniques that can save long, laborious work hours by taking quick and informative decisions. With it becoming more and more powerful, the algorithms deliver quality results with minimal interventions. Machine Learning requires tons and tons of data and also the knowledge of how to treat this data.
The algorithms created with Machine Learning techniques are based on data that has been created by humans, data scientists in particular, who can properly organise the copious amounts of data apply the required knowledge to make sense of the data.
If you’re looking for a job in Machine Learning, ensure that you have the following in your skillset:
- Understand the basic principles of computer science and also a bit of programming
- Learn the characterisation of probability and the techniques that can be derived from it
- Understand statistics, including measures, distributions and different analytical methods
- Be able to detect useful data patterns and predict unknown properties with data modelling
- Apply Machine Learning Algorithms to data
- Understand the advantages and disadvantages of taking different approaches
- Understand software engineering and careful system design
Difference between Machine Learning and Data Science
Data science, at its core, is a mix of information technology, data modelling and business management. It is a much broader, interdisciplinary field that encompasses several technologies, one of which is Machine Learning. Machine Learning fits into Data Science as a technique that helps computers understand and learn from data.
While these two intersect, the skillset required for both varies by a lot. As a data scientist, you must understand Statistics, Data Mining, Cleaning and Data Visualisation and have sound experience with programming languages such as R and Python, among other things. A career in Machine Learning will require you to understand how to apply algorithms and perform data evaluation and modelling.
At Coding Ninjas, we teach you how to extract meaningful insights from raw data by using concepts such as Data Cleaning, Data Analysis, Machine Learning and Python. To begin your journey with us in Data Science, you need to have a sound understanding of basic programming and the implementation of Data Structures.
A Data Science and Machine Learning course with Coding Ninjas will enrich your skillset by teaching you how to work with analytical tools and libraries such as Tableau, Pandas and NumPy. Our institute also enables students to work for and secure jobs as Business Intelligence Experts, Data Analysts, Data Architects and more. Taught by Ankush Singla, alumni of IIT Delhi & Stanford and Nidhi Agarwal, alumni of IIT Delhi, this course is great for students who are eager to make it big in the world of Data Science.
If you are looking to make a career in Data Science & Machine Learning, the Data Science and Machine Learning or Machine Learning course separately offered by Coding Ninjas will be a good starting point. With a preliminary look at the specifics, you can make your decision about which field you wish to pursue by weighing your strengths and skillset.
Read more about Data Science here.