Preprocessing Data with NumPy (Udemy.com)

The problem

Most 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 solution

Our 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 example

Each 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 position, we created a course that will show you all the tools for the job: The Preprocessing Data with NumPy course [MG1] .

We believe that this resource will significantly boost your chances of landing a job, as it will prepare you for practical tasks and concepts that are frequently included in interviews.

NumPy is Python’s fundamental package for scientific computing. It has established itself as the go-to tool when you need to compute mathematical and statical operations.

Why learn it?

A large portion of a data analyst’s work is dedicated to preprocessing datasets. Unquestionably, this involves tons of mathematical and statistical techniques that NumPy is renowned for. What’s more, the package introduces multi-dimensional array structures and provides a plethora of built-in functions and methods to use while working with them. In other words, NumPy can be described as a computationally stable state-of-the-art Python instrument that provides great flexibility and can take your analysis to the next level.

Some of the topics we will cover:

1. Fundamentals of NumPy

2. Random Generators

3. Working with text files

4. Statistics with NumPy

5. Data preprocessing

6. Final practical example

1. Fundamentals of NumPy

To fully grasp the capabilities of NumPy, we need to start from the fundamentals. In this part of the course, we’ll examine the ndarray class, discuss why it’s so popular and get familiar with terms like “indexing”, “slicing”, “dimensions” and “reducing”.

Why learn it?

As stated above, NumPy is the quintessential package for scientific computing, and to understand its true value, we need to start from its very core – the ndarray class. The better we comprehend the basics, the easier it’s going to be to grasp the more difficult concepts. That’s why it’s fundamental to lay a good foundation on which to build our NumPy skills.

2. Random Generators

After we’ve learned the basics, we’ll move on to pseudo-random data and random generators. These generators will help construct a set of arbitrary variables from a given probability distribution, or a fixed set of viable options.

Why learn it?

Working in a data-driven field, we sometimes need to construct partially arbitrary tests to see if our code works as intended. And here lies the value of random generators, as they allow us to construct datasets of pseudo-random data. The added benefit of random generators is that we can set a seed if we wish to replicate a particular randomization, but we’ll go into all the details in the course itself.

3. Working with text files

Exchanging information with text files is practically how we exchange information today. In this part of the course, we will use the Python, pandas, and NumPy tools covered earlier to give you the essentials you need when importing or saving data.

Why learn it?

In many courses, you are just given a dataset to practice your analytical and programming skills. However, we don’t want to close our eyes to reality, where converting a raw dataset from an external file into a workable Python format can be a massive challenge.

4. Statistics with NumPy

Once we’ve learned how to import large sets of information from external text files, we’ll finally be ready to explore one of NumPy’s strengths – statistics. Since the package is extremely computationally durable, we often rely on its functions and methods to calculate the statistics of a sample dataset. These include the likes of the mean, the standard deviation, and much more.

Why learn it?

To become a data scientist, you not only need to be able to preprocess a dataset, but also to extract valuable insights. One way to learn more about a dataset is by examining its statistics. So, we’ll use the package to understand more about the data and how to convert this knowledge into crucial information we can use for forecasting.

5. Data preprocessing

Even when your dataset is in clean and comprehensible shape, it isn’t quite ready to be processed for visualizations and analysis just yet. There is a crucial step in between, and that’s data preprocessing.

Why learn it?

Data preprocessing is where a data analyst can demonstrate how good or great they are at their job. This stage of the work requires the ability to choose the right statistical tool that will improve the quality of your dataset and the knowledge to implement it with advanced pandas and NumPy techniques. Only when you’ve completed this step can you say that your dataset is preprocessed and ready for the next part, which is data visualization.

6. Practical example