In this new fast-paced world, where information is treated as a commodity, the mode of communication only seems to get better with the advent of technology. Enterprises which have a prevalent presence in the market are seeking professionals when it comes to learning or processing this information to benefit them, and stay ahead of the curve when it comes to competition.
Your intake of information can be through any medium, be it through social media, TV, radio or social gatherings. But have you considered that the decisions you end up taking are often based on hearsay and not on hard facts? Think about it – not everything you read or hear is true unless it is documented.
This is exactly where Data Science comes into play. It stops people from making decisions that aren’t based on evidenced reality.
What is Data Science?
In layman’s terms, it’s a pretty straightforward thing. It’s a blend of data inference, algorithm development, and technology in a multidisciplinary fashion to solve complex problems analytically.
A storehouse of raw information comes in and it is stored in Data Warehouse where it is learnt by mining it. The basic agenda behind Data Science is that it is used in creative ways to have better business value for your organisation. Data Scientists are taught how to discover hidden patterns in this raw data with the help of machine learning principles.
A lot of times people get confused between Data Scientists and Data Analyst. The difference between the two is pretty significant, as a Data Analyst can only tell what is going by processing the history of the data. On the other hand, a Data Scientist will not only do the same but will also use advanced machine learning algorithms to identify a particular event which should take place in the future.
To make things easier to understand, here are examples of three companies who use Data Science in terms of serving you, as a customer, better.
- Netflix: It reads and understands your behaviour on its website or app, and suggests you movies and TV Shows that you may like.
- Amazon: It deploys the same tactic, and by analysing the pattern of you checking out certain items, it helps you navigate your way through and get exactly what you want.
- Spotify: Based on your taste of music and genres, it helps you listen to other artists as well, and find new songs that you probably haven’t heard of.
What are the Top Data Science Algorithms?
Before explaining the Data Science Algorithms, we should delve into what is known as Machine Learning. It learns information from data and improves with experience, with NO human intervention. Tasks can vary from being functions such as mapping out input and output or learning the hidden structure in data which is unlabeled.
There are three types of Machine Learning Algorithms:
- Supervised Learning Algorithms
The data in this model has labels which are previously known. It has some target variables with values which are specific.
- Unsupervised Learning Algorithms
This model can classify or correct the data which has no predefined labels. It looks for commonality in the features and predicts the classes on new data.
- Reinforced Learning
It is the type of dynamic programming that trains algorithms to make a sequence of decisions. It learns to achieve a goal in an uncertain or potentially complex environment.
There are many different Machine Learning Algorithms when it comes to Data Science, but we focus primarily on six.
Top Machine Learning Algorithms for Data Science:
- Linear Regression
It is a model approximation of a casual relationship between two or more variables. They are extremely valuable as it is the most common way to make inferences and predictions. The fundamental idea is to obtain the line that best fits the data, where the total prediction error of all data points is as small as possible.
- Decision Tree
This belongs to the family of supervised machine learning algorithms. It is quite adaptable and can be used in almost every problem that is faced. Decision Tree is a versatile method which is capable of performing both regression and classifications tasks. Since most of the real-world problems are non-linear, the decision tree helps the scientist get rid of the non-linearity of the data and making it simpler to understand.
- Clustering
Unlike Decision Tree, this falls in the unsupervised machine learning algorithm. Its basic objective is to find different groups or structures within the data. By doing this, the elements of one cluster that are similar to each other get classified in one group, while the remaining get classified in another group. It will be able to tell that there are two different types of data by clustering it in two different classes.
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- Visualization
This is probably the most colloquial way of inferring data, as it can be easily guessed, by its name itself, through visualization. It clarifies key aspects of the analysis by clearly communicating the results to the general audience. It can be done through Histograms, Bar/Pie Charts, and Time Series, etc.
- Random Forests
This model consists of a large number of individual Decision Trees that operate as a committee. Every single individual tree in the random forest gives its own class predictions and the class with the most votes becomes this model’s prediction. In other words, it is quite as simple and powerful as the wisdom of the crowds.
- Principal Component Analysis
It is a method used to reduce the number of variables that can be found in the data. You can extract important ones from a large pool and reduce the dimensions of the data. It combines variables which are correlated together to form a smaller number set of variables and this is referred to as its principal components.
Top Essential Data Science Skills to Learn
Where can you learn these revolutionizing tools?
As you would’ve gone through the aforementioned information, a realisation could’ve come on that traditional education provided in universities might not be enough in the current work environment. After all, there is a huge difference between studying something in theory and witnessing its practical applications in front of you. Companies are readily looking for Data Scientists as they add an unparalleled value to an enterprise with their expertise and efficiency.
At upGrad, we offer you an opportunity to master these courses and be ahead of the pack in the coming future, and that too from an online portal.
In collaboration with IIIT Bangalore, we have launched a Data Science program, and here are all the details you need to consider taking your career to the next level:
- Course Length: 11 Months
- Minimum Eligibility: Bachelor’s degree (No Coding Experience Required)
- Program For: Engineers, Software & IT Professionals, Marketing and Sales Professionals
- Programming Tools and Languages Covered: Python, Tableau, Apache Spark, Hadoop, My SQL, Hive and Microsoft Excel
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Conclusion
Our Instructors are leading Data Scientists as well as prominent industry leaders, and it is an honour for us to have them in our faculty. If any of this seems like something you’re interested in, then check out PG Diploma in Data Science course and get an even more in-depth understanding of what we offer.
What are the limitations of using decision trees in ML?
If you are using a decision tree in machine learning, be ready to face complex calculations. When it comes to time, decision trees generally take a lot of time for the training of models. If a minor change occurs in the given data, the structure of the decision tree is changed to a great extent, thus causing instability. Overfitting of the data often occurs when you are using a decision tree.
How is a random forest different from a decision tree?
The random forest technique is primarily used to solve regression and classification problems. It contains many decision trees. So we can say that the random forest technique is a long process, but it is slow when compared to the decision tree technique. It is easy to operate a decision tree, but using a random forest technique is quite a task as rigorous training is required.
Are there any assumptions in PCA?
Yes, Principal Component Analysis makes the assumption that there is no single, unique variance and that the common variance and total variance are equal. It also assumes that the variables are on a metric or nominal scale, the features are two-dimensional in nature and that the nature of independent variables is numeric.
