Raise your hand if you’re totally confused with all those data-related terms you hear. I, for once, was very confused. Don’t worry, I’ll help you decode the enigma and analyze these terms bit by bit.
But first, let’s see how data is generated. Everytime you click a link, post a picture on Instagram, like Facebook pages, buy clothes online, tweet a message, or send nudges to your friends, data is generated and fed into the system’s database. Now, once you have data, you’ll need a data scientist to derive value from it.
Data science is an umbrella term for all things data related – data analytics, machine learning, data mining, big data, and others. Data science involves not only drawing insights and trends from the data collected over a certain span of time but also creating intelligent systems and developing predictive models, prototypes, and algorithms
Data analytics is the process of inspecting data, finding problem areas, making hypotheses, generating insights from the data, and eventually recommending solutions for the betterment of the product.
To put it simply, data analytics involves breaking a larger problem into smaller problems based on the data collected, whereas data science involves employing predictive modeling to solve a problem, i.e., predicting what’ll happen in the future based on the data analysis performed.
Big Data Analytics is the same as data analytics with the only difference being it involves working on data of humongous volume and velocity. Big data is categorized as structured data, i.e., the data collected by services, products, and electronic devices, and unstructured data, i.e., the data that comes from human input such as customer reviews.
Machine Learning is a type of artificial intelligence that teaches the system to learn and take decisions when exposed to a new set of data on the basis of the experience it gains while performing different actions. It uses pattern recognition, computational theories, and algorithms to provide computers with the ability to learn without being explicitly programmed.
Netflix movie recommendations and Amazon’s ‘You may also like’ are some fine examples of machine learning wherein the system recognizes patterns in the movies you watch or products you buy and presents you with related suggestions.
What Skills Do You Need to Become a Data Scientist?
You should have a balanced mix of left and right brain skills, i.e., you should be excellent with numbers and have a curiosity for any data-related job. There are certain technical skills too which are important for a career in data science. Let’s take a look at them.
- Programming languages – To start your journey as a data scientist, you need to have a sound knowledge of either of the three languages – Python, Java, or R.
a. Java: It is a high performance, general purpose, compiled language which makes it suitable for writing complex machine learning algorithms. It allows data science methods to be integrated directly into the existing codebase. It is fast and extremely scalable and is thus used by most startups for their product development.
b. Python: Python makes an excellent choice for data science and not just at an entry level. Even for advanced machine learning applications, Python leads the way with Pandas, Tensorflow, and Scikit-learn. Python is extremely powerful and easy to learn, thus recommended.
c. R: R is the lingua franca of data science! It allows you to carry out almost all quantitative and statistical applications. Neural networks, nonlinear regression, matrix algebra, advanced plotting – it handles them all! And this is what makes it the most preferred language to perform statistical analysis on large datasets.
d. SQL: To operate on data and drive the inputs in a manner so as to achieve the predicted outcome, you first need data. And what do you need to extract data? SQL (or NoSQL)! Organisations these days have huge databases to store all their data, so you need to be a master of this trade.
- Ms-Excel – Now that you’re only taking your first steps into data science and R seems too intimidating with the cocktail of features it offers, Excel is here to your rescue! For basic statistical modeling, Excel proves to be a great tool.
- Statistics and probability – Before you give me the eye, let’s recapitulate what data science is. You have a problem statement, you analyze the past data, build a hypothesis, predict the future results, and ensure that you do get the predicted results. Now, statistics involves analyzing the frequency of past data and probability involves predicting the likelihood of future events.
- Analytical rigor – To find innovative solutions, you need to know the ‘why’ of everything. Be inquisitive and ask a lot of questions. Some rate dropped – ask why. Some number increased – ask why. And start finding solutions!
- Structured thinking – The problem statements a data scientist gets are quite vague. To come up with concrete solutions, you first need to break the vague problem into smaller bits of concrete problems and then analyze the data. To do so, you’ll need to structure your analyses properly.
What Would be Your Career Options?
Data science, being the current hottest industry, offers various roles including data analyst, data engineer, machine learning engineer, business analyst, and data scientist, of course. With data analytics industry mushrooming all over the country, there is a rising demand for freshers in the data analytics domain.
Although students from the non-technical background are eligible for jobs in this field, the industry has a soft-corner for engineering students for they have an inherent knack for programming, statistics, and mathematics.
(Disclaimer: This is a guest post submitted on
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