These are the essential skills needed by data science graduates
Data science is one of the most essential pillars of modern society. Every day it becomes clearer that processing and analyzing data has immense value – and that’s where a data scientist comes in. Due to the tremendous explosion of data that has resulted in the convergence of current technology and smarter goods in the age of artificial intelligence, the goal of the data scientist is to gain deeper insight into how it can derive useful information from the data.
Dr. Abhijit Dasgupta, Director, Bachelor of Data Science (BDS) Program, SP Jain School of Global Management
Knowledge of Python
Python is a dynamic, object-oriented programming language that is used for creating many types of software. Its main advantage would be to facilitate connections with other programming languages and software development tools. This makes it easier to create better program code. Several companies, like Google, YouTube, Quora, Pinterest, and many more, are now adopting the Python programming language for development.
The essence of data science is problem solving. Programmers must first understand how humans solve problems, then translate that “algorithm” into something a machine can execute, and finally “write” the exact syntax to complete the task. To solve any given problem, we must first understand the basics of NumPy, Pandas data structures (stack, queue, linked list) and algorithms. NumPy is a Python library that supports massive, multidimensional arrays, as well as a wide variety of high-level mathematical functions for working with those 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 the input into the desired output.
Statistical understanding aids in the collection of data, the application of reliable analyses, and the effective presentation of results. It removes unnecessary data and catalogs important data in an easy and efficient way as it helps in prediction and classification, helps in creating probability distribution and estimation, pattern detection and grouping, test of hypotheses, etc.
A database is a structured collection of information that can be simply collected and maintained. To make it easier to find important information, you can organize data into tables, rows, and columns and search for them. SQL stands for Structured Query Language, it allows you to connect and manipulate databases. NoSQL databases are non-tabular databases and accumulate data differently than relational tables. NoSQL databases are classified into several categories based on the structure of the data.
Data can sometimes be stressful. There is too much data, little time to digest it, or you simply cannot perceive the data you have. If so, visual data analytics, which combines data analysis and data visualization approaches, can help you figure it all out. We can use different external libraries such as sklearn, Matplotlib, Keras and TensorFlow. Scikit Learn is a generic machine learning package based on NumPy. It includes a variety of functions for pre- and post-processing of genetic information. 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 and easy-to-use TensorFlow API, which allows you to quickly design and test a neural network with a few lines of code. Tableau is a visual analytics platform that changes the way we approach and process information to analyze problems by empowering individuals and organizations to get more out of their data.
Cloud computing allows businesses to use the Internet to access various computing services such as databases, servers, software, artificial intelligence, and data analytics. When a Data Scientist wanted to undertake data analysis or extract insights from data, they first had to 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 the essence of data engineering. Without data engineering, the data obtained would be inconsistent and the insights they provide would be useless. With the huge explosion of Big Data and the increasing speed of computing power, Data Scientists will realize that tools like Apache Spark and other Big Data Analytics engines are essential, and they will quickly become the industry standard for perform Big Data Analytics and solve complex business problems. on a large scale in real time. Hadoop’s MapReduce architecture is used to build applications capable of handling huge volumes of data on large clusters. It is also a programming approach that allows us to process large sets of data across many computer clusters.
Data Science is indeed evolving, and one thing is certain in this profession, learning never stops. One day you master the tool, the next day it’s overwritten by a more complex tool. A data scientist must be curious and eager to learn new skills. Being a data scientist in this decade is exhilarating and there will be plenty of advancements in the future.
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