Python is one of the most popular languages for programmers today, but what do you actually need to know to call yourself a master of it? Do you really need to know logistic regression, linear regression, NumPy arrays, python libraries, Pandas dataframe, functions, algorithms, datasets, confusion matrix, and a whole host of other technical terms? Depending on what you want to do, absolutely. However, here a few terms on how to get started.
Logistic Regression and Your Progression
Machine learning is one of the biggest trends right now and learning logistic regression in python will allow you to build logistic regression models that will enable algorithms to manage datasets. In your career as a specialist, you are going to encounter machine learning classification problems, such as the confusion matrix. The ability to create logistical models based on a deep understanding of logistic regression (and some understanding of linear regression) will be necessary, in order to predict some of these machine learning classification problems in advance so that you may better find an innovative solution.
This is not just the case for developers, but also for data analysts, data architects, and data scientists as well. This is true, regardless of whether you are using a training set or a test set, and regardless of your data type, training data or train dataset. Therefore, you need to create a good logistic regression model in order to predict classification problems so that you may be able to create a good machine learning model in order to predict machine learning problems.
Moreover, it is advisable to have a similar level of understanding of your ratio between categories should you desire to model classification problems in order to predict in similar ways to the above example. This will help regarding missing data points, inappropriate null values and false positives, such as those you might find within a Pandas dataframe. You can also use a function through regression to assist in the imputation of missing variables, regardless of whether they are dependent variables or independent variables. This can be done by creating a DataFrame with boolean values, provided that the cell contains “true” if it is a null value.
NumPy Arrange and Hamlet
What is NumPy arrange used for in Python? It may not sound as profound as Hamlet’s “To be or not be?,” but it is the question. NumPy is the main library for numerical computation and is vital for data manipulation within datasets. You cannot be an expert in machine learning without being able to manage huge amounts of data, especially numerical data, within datasets. When you want to use machine learning techniques to solve data classification problems, especially for common functions and the functions that rely on numerical ranges, NumPy is a good choice because other libraries like Pandas and SpiCy need them.
Value stream mapping is also a hot topic in the manufacturing process nowadays, and it is vital for any manager to learn about it in order to improve supply chains, logistics, product development, customer service, etc. A humungous amount of data regarding customers and products need to be analyzed for value stream mapping to be effective, and those behemoth data systems are usually developed using np.arange.
Jupiter, the King of the Gods
If you want to use open source web applications with python, familiarization with Jupyter Notebook is recommended. In addition to Python, Jupyter also supports Julia and R, (thus the name Jupyter). Unless you are using Anaconda, Jupyter Notebook needs to be installed separately but that can be done using pip. Once your notebook is installed, you will be able to run cells and clear their output and manipulate Kernel cells. The Heading Cell, however, is no longer supported. In addition to your notebook, Jupyter allows you to make a text, file, folder or terminal in your browser. This means that you can run Bash, Powershell, etc., and any other commands that you might need to, in your browser.
For all of the reasons above, and the fact that it is free, Jupyter is the computational notebook of choice for data scientists. For data scientists, it is more than just a notebook, it is a computational narrative that allows for hypothesis, analysis, and conjecture. Professionals from a variety of disciplines, from data science to astronomy to medicine, are now able to use their code in an entirely different way.
The Pythoneer’s Odyssey
In conclusion, as we embark on our odyssey as aspiring pythoneers, we know that we will need to learn a variety of terms and skills. Certainly, techniques like linear regression and resources like NumPy arrays and Jupyter Notebook will expedite your exciting journey into the high-tech world. The best part is, these tricks are really simple.