Data science has emerged as one of the greatest innovations of the century. It brings together probability, statistics, Machine Learning, and algorithms to leverage data so that it can be utilized and even monetized. At a time when data is as precious as gold and when every business needs a data-driven strategy to become successful, data science has turned out to be massively vital. It is so important that all the tech giants are hiring staff based on their data science skills and individuals with data science training in Bangalore and other top-tier cities of India are making it big. As a result, data science has become the best career option and experts advise students and professionals to upskill with data science to reap the benefits which data science currently offers. Whether you are a working professional looking for a salary hike or whether you are a student seeking a career that offers great job security and stability, Data Science is your best option.
However, before getting enrolled in a data science course you must consider the data science tools and techniques which you must master so that you crack a job interview and get hired by the best!
There are certain mathematical and statistical techniques you need to master before you can move on to more advanced data science tools. Basic concepts of these techniques will help you better understand data science and apply the same to become a successful data scientist. Some of those techniques are:
- Probability and Statistics
Probability deals with the chances of an event taking place. An extremely important chapter of mathematics, it lets you understand how to identify and predict occurrences of events and how certain events are more likely to happen compared to others. Grasping the concepts of probability will, later on, help you apply the same when creating an algorithm based on data so that the system accurately predicts whether a particular event is going to take place or not.
Statistics is another important chapter of mathematics that deals with data. Arranging and sorting relevant data from a huge data pool is something you will need to do on an everyday basis as a data scientist and thus important concepts of statistics are your best friend. Important concepts within the domain of statistics are descriptive statistics, inferential statistics, non-parametric statistics, etc.
- Linear Regression and Logistic Regression
Linear Regression is an algorithm used for the prediction of continuous values. It is a very useful technique used in Machine Learning for creating supervised learning. It takes help from independent variables to predict continuous values. It allows collinearity between independent variables and is used massively in forecasting stocks. Nowadays you will find various applications which makes stock prediction for you so that you can invest based on that information and such applications usually utilize linear regression.
Logistic regression is another supervised learning technique that is primarily used for solving classification problems. When you are needed to classify or separate dependent values through independent variables then you will need to use logistic regression. Logistic regression finds usage in image processing, natural language processing, etc.
- Neural Networks
Neural networks or artificial neural networks try to mimic the inner working of a human brain and is at the heart of machine learning or deep learning. It lets you train machines with available data so that the machine can make meaning of that data without further human supervision. The concept of neural networks will help you immensely as a data science as you might be needed to create a system capable of automating important tasks efficiently. Neural Networks find massive usage in image recognition, text recognition, and speech recognition!
- Data Collection and Storage tools
As a data scientist, you are required to possess the ability to collect humongous data pools so that they can be utilized later. Although data is easily available and there exist several relevant data sources collection and storage of relevant data is not easy and requires sophisticated tools such as —
Semantria: A cloud-based data extraction tool that can classify data sets based on sentiments and language!
Trackur: A very useful tool that is utilized to collect data from various social media platforms. Marketing companies use this tool for targeted marketing which helps to increase efficiency.
Apache Hadoop: As a data scientist you will be dealing with Big Data regularly. Thus, knowledge of Hadoop, a tool to store and compute Big Data is extremely important.
Hence, if you intend to become a successful data scientist then you should aim to master the above-listed tools and techniques!