Data wrangling is no less than lubrication in the IT machinery that helps in the process of collecting, gathering, and transforming raw data into another format for better understanding, decision-making, accessing, and analysis in a specific time period. In this era of data consumption and production, it is crucial to maintain it in a proper format, far away from any distortion. Nowadays, data shares a sensitive and complicated nature which requires getting sorted in the simplest manner for extracting the core information.

uCertify introduces Data Wrangling with Python course and lab in well-equipped format, which provides an understanding of the processes used along with the knowledge of the most popular tools and techniques in the domain. This course and lab also demonstrate how to use these interactive tools, teaches the skills of file handling Python back-end, and extracting/transforming data from an array of sources including the internet. The course has broad hands-on activities preliminary focusing on web scraping and data gathering, RDBMS and SQL, and it will assert you with the capability of application of data wrangling in real life.

The course also has a predominant presence of exam objectives, covering all the basic and advanced data structures, introduction to NumPy, Pandas, and Matplotlib, and file handling. This course dives deep into Data Wrangling with Python, makes you comfortable with different kinds of data sources, learning the hidden secrets of Data Wrangling, advanced web scraping, and data Gathering RDBMS and SQL. The course helps you in seeking knowledge and skills about data exploration, reshaping data, dealing with missing values, and filtering data which enables an efficient data set, to be used for different purposes like data analyzing, machine learning, data visualization, model training, etc.  So pull your socks up with Data Wrangling with Python course to master in the field of data handling.

Leave a reply

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>