Preprocessing Data with NumPy

About this course

The problemMost data analyst, data science, and coding courses miss a crucial practical step. They don’t teach you how to work with raw data, how to clean and preprocess it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.The bootcamps we have seen online, and even live classes neglect this aspect and show you how to work with ‘clean’ data. But this isn’t doing you a favor. In reality, it will set you back both when you are applying for jobs, and when you’re on the job.The solutionOur goal is to provide you with complete preparation using the NumPy package. This course will turn you into capable data analyst with a fantastic understanding of one of the most prominent computing packages in the world. To take you there, we will cover the following topics extensively.· The ndarray class and why we use it· The type of data arrays usually contain· Slicing and squeezing datasets· Dimensions of arrays, and how to reduce them· Generating pseudo-random data· Importing data from external text files· Saving/Exporting data to external files· Computing the statistics of the dataset (max, min, mean, variance, etc.)· Data cleaning· Data preprocessing· Final practical exampleEach of these subjects builds on the previous ones. And this is precisely what makes our curriculum so valuable. Everything is shown in the right order and we guarantee that you are not going to get lost along the way, as we have provided all necessary steps in video (not a single one skipped). In other words, we are not going to teach you how to concatenate datasets before you know how to index or slice them.So, to prepare you for the long journey towards a data science pos

Course objectives

• to equip students with practical skills for handling raw data
• to build a strong foundation in using the NumPy package
• to prepare students for real-world data analysis tasks