Data science is one of the most essential pillars of modern society. Every day, it becomes clearer that data processing and analysis have immense value—and this is where a data scientist plays its role. Because of the tremendous data explosion that has resulted in the convergence of current technology and smarter goods in the era of Artificial Intelligence, the purpose of the data scientist is to gain a deeper knowledge of how he or she can make useful insights from the data.
Dr. Abhijit Dasgupta, Director, Bachelor of Data Science (BDS) program, SP Jain School of Global Management
Python is a dynamic and object-oriented programming language that is used for the creation of many types of software. Its major benefit would be that it makes connections with other programming languages and software development tools easier. This makes it easier to create better program code. Several firms, like Google, YouTube, Quora, Pinterest, and so many more, are presently embracing the Python programming language for development.
The essence of Data science is problem-solving. Programmers must first understand how humans solve the problems, then translate this "algorithm" into something that a machine can carry out, and lastly "write" the exact syntax to finish the task. To solve any given problem, we first need to understand the basics of NumPy, Pandas Data structures (stack, queue, linked list) and algorithms. NumPy is a Python library that provides support for massive, multidimensional arrays, as well as a vast variety of high-level mathematical functions for working with these arrays. Pandas is a high-level data manipulation tool based on the NumPy library. In a virtual system, a data structure is a way of positioning data. An algorithm is a series of instructions that a computer follows to change input into the desired output.
Statistical understanding helps in the collection of data, the application of reliable analyses, and the efficient presentation of results. It removes unnecessary data and catalogs the important data in a simple and efficient manner as it helps in Prediction and Classification, helps to create Probability Distribution and Estimation, Pattern Detection and Grouping, Hypothesis Testing, etc.
A database is a structured collection of information that can be collected and maintained simply. To make it simpler to discover important information, you may arrange data into tables, rows, and columns and search it. SQL stands for Structured Query Language, it allows you to connect to and manipulate databases. NoSQL databases are non-tabular databases and accumulate data differently than relational tables. NoSQL databases are classified into several categories depending upon the data structure.
Data may be stressful at times. There's too much data, limited time to digest it, or you just can't perceive the data you have at your disposal. If so, visual data analysis, which combines data analytics and data visualization approaches, can help you make sense of it all. We can use different external libraries such as sklearn, Matplotlib, Keras, and TensorFlow. Scikit Learn is a NumPy-based generic machine learning package. It includes a variety of functions for genetic information pre- and post-processing. It is a Python package used to build conventional models. Matplotlib is a data visualization and visual plotting package for Python and its numerical extension NumPy which is cross-platform. Keras is a high-level TensorFlow API and is simple to use, which allows you to quickly design and test a neural network with few lines of code. Tableau is a visual analytics platform that changes how we approach and handle the information to analyze issues by allowing individuals and organisations to make the most of their data.
Cloud Computing
Cloud computing enables businesses to use the internet to access various computing services such as databases, servers, software, artificial intelligence, and data analytics. When a Data Scientist wished to undertake data analysis or extract information from the data, they had to first move the data from the central servers to their system and then perform the analysis. Data Science in combination with Cloud Computing has become so popular that it has launched Data as a Service (DaaS).
Creating and maintaining the underlying systems that collect and report data is what data engineering is all about. Without data engineering, the obtained data would be inconsistent, and the knowledge it provides would be useless. With the huge explosion of Big Data and the increasing speed of computational power, Data Scientists will realize tools like Apache Spark and other Big Data Analytics engines are essential, and they will quickly become the industry standard for performing Big Data Analytics and solving complex business problems at scale in real-time. The MapReduce architecture of Hadoop is used to build applications that can handle massive volumes of data on big clusters. It is also referred to as a programming approach that enables us to process large datasets across numerous computer clusters.
Data Science is indeed evolving, and one thing is for sure in this profession, learning never ends. One day you master the tool, the following day it is squashed by a more complex tool. A data scientist must be inquisitive and eager to learn new skills. Being a data scientist in this decade is exhilarating and there will be many advancements in the future.
