Data science is a field that incorporates statistics, logical strategies, artificial intelligence, and data analysis. It is focused to derive actionable insights and extracting values for data. Individuals who follow and pursue data science are called data scientists. Data science requires a range of skills for analysing data processed from the web, smartphones, sensors, etc.
What is Data Science?
Data science comprises composing data for analysis, thereby shriving, aggregating, and manipulating the data. The data is then ready to carry out advanced data analysis. Data scientists along with analytic applications evaluate the results to display patterns and enable business owners to depict informed insights.
Skills required to become a Data science
For becoming a data scientist, you need to have certain technical and soft skills. Here are some basic technology skills limited to this subject:
- Fundamentals
Know the fundamentals deeply as it is the basics if you aspire to become a data scientist. By fundamentals, we mean having knowledge about common terminologies such as Database Basics, Relational Algebra, Matrices Functions, Linear Algebra Functions, Extract Transform Load, Hash Functions & Binary Tree.
- Programming Skills
Programming skills is also one of the primary skills you need to have for becoming a data scientist. You must be well acquainted with the process of creating data models and analytical models. Major of functions in data science necessitates programming, hence it is best if you know one or more programming languages.
- Data Manipulation
Data manipulation is another key skill required to become a data scientist. You must be well aware of the art of organising, arranging, and changing data to make it readable. It is an important skill as you as a data scientist will receive raw data which needs to be manipulated.
- Machine Learning & AI
Machine Learning (ML) and Artificial Intelligence (AI) are important considerations of data science. Hence, if you have skills that go along with these two subjects, becoming a data scientist will be an easy task.
Also Read: Advanced Certification in Applied Data Science
What is Machine Learning?
Machine learning can be defined as a parameter of artificial intelligence (AI). ML focuses on teaching computers the idea of mimicking data and improving with experience. In machine learning, algorithms are instructed and upskilled to detect patterns and correlations in large data sets. After detecting the data pattern, it’s trained to make the best decisions and predictions based on the analysis.
Skills required to learn Machine Learning
Some of the major skills required by a candidate to learn machine learning as mentioned below:
1) Statistics and Probability: You must have a proper & clear understanding of statics and probability theories. These two theories help in the process of learning algorithms. Theories such as Naïve Bayes, Hidden Markov Models, etc are some ML algorithms that can be mastered by understanding statistics and probability.
2) Programming Languages: You must have a strong base of programming language to become a machine learner. Building a career in machine learning calls for having a working knowledge of all the programming languages. Some examples are Python, R, Hadoop C++, etc.
3) Data Modelling: For learning machine learning, you must have proper knowledge about data modelling. It is because a career in machine learning requires you to analyse unstructured data which is palliated on the science of data modelling. Try to gain a conceptual understanding of data modelling as it helps you create effective and efficient algorithms.
4) Effective Management of large datasets: If you are among someone who is planning to build a career in machine learning, learn the art to manage large datasets. Why? Because ML uses a distributed computing approach where the data is distributed across an entire cluster.
To learn more about Machine Learning, read our blog on – What is Machine Learning?
Difference between Data Science and Machine Learning
| S.No. | Data Science | Machine Learning |
| 1. | Data Science is all about processing and extracting data from structured and semi-structured data. | Machine Learning is all about studying and examining data that enables computers with the capability to learn. Computer learning is executed without the need for any programming. |
| 2. | Data science requires the entire analytics universe. | Machine learning (ML) is a combination of Machine and Data Science. |
| 3. | This is a branch that is purveyed with data. | ML is a branch where machines utilize data science ability to learn about the data. |
| 4. | Data in Data Science might be or might not be extended from a machine or mechanical process. | ML utilises several unique techniques such as regression and supervised clustering. |
| 5. | Data Science is focused on algorithms and statistics. It also looks after the data processing. | Machine learning is only focused on algorithm statistics. |
| 6. | Data science can be viewed as a broad term for multiple disciplines. | ML mitts within data science. |
| 8. | An example of Data Science is Netflix using Data Science technology. | An example of Machine learning is Facebook using Machine Learning technology. |
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