Data Analysis with Python

Become an expert on how to analyze and visualize data using Python. This course covers in-depth knowledge of data analysis, including data cleaning, data manipulation, and data visualization. You will also learn SQL and Power BI/Tableau visualization to be an expert data analyst. Enroll now and start your journey to becoming a data analyst!

Upcoming Batch Details

Main Course: April 27, 2024

Trial Class: April 20, 2024

Course Overview

Python is a high-level, interpreted programming language that is widely used in data analysis. It is an open-source language that is easy to learn and has a large community of developers. Python is important to learn because it is a versatile language that can be used for a wide range of tasks, including data analysis. It is also a popular language in the field of data analysis, and learning Python can help you get a job in this field. Python is used in data analysis because it is easy to use, has a large number of libraries, and is fast. Data analysis is the process of examining data to extract useful information. It involves cleaning, transforming, and modeling data to discover useful insights. Data analysis is an important field because it helps organizations make better decisions. Data analysts are in high demand, and the job prospects for data analysts are good. According to the U.S. Bureau of Labor Statistics, employment of data scientists and mathematical science occupations will grow 31% through most of this decade. Data analysts are sought-after professionals who serve in high positions in renowned tech, eCommerce, pharmaceutical, and manufacturing industries. The career path for data analyst professionals depends on their skills and interests. The most significant number of analytics job ads in the US are in New York. SQL and Python are the most sought-after techniques for data analysts. 54% of employers list Excel as an essential skill for data analytics roles. The top three degrees for data analysts are computer science, statistics, and engineering. Python has many libraries and features that make it a powerful tool for data analysis. Some of the most popular libraries for data analysis in Python include NumPy, SciPy, and Pandas. NumPy is a library for scientific computing that provides support for arrays and matrices. SciPy is a library for scientific and technical computing that provides support for optimization, integration, and linear algebra. Pandas is a library for data manipulation and analysis that provides support for data structures and tools for data analysis. Python also has many features that make it a powerful tool for data analysis, including its ability to handle large datasets, its support for parallel processing, and its ability to integrate with other tools and languages. SQL is a programming language that is used to manage and manipulate data in relational databases. Power BI is a data visualization tool that allows users to create interactive and dynamic visualizations of data. SQL and Power BI are important tools for data analysis because they allow users to analyze and visualize data in a way that is easy to understand. SQL is used to extract data from databases, and Power BI is used to create visualizations of that data. Together, SQL and Power BI provide a powerful toolset for data analysis that can help organizations make better decisions.