5 concepts you have when learning Pythpon for Datascience

5 Concepts You May Have Forgotten When Learning Python for Data Science

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Table Of Content

1. Introduction

2. Python version 2 is not the same as Python version 3

3. NumPy is a requirement for most data science tasks

4. You don’t need to understand the math behind every machine-learning algorithm

5. Data preparation is almost always a part of any data science project

6. Regression algorithms are often more reliable than ML algorithms

7. Conclusion

INTRODUCTION

Data science is a fast-moving field, and keeping up with the latest developments can be challenging. New concepts, libraries, and methods are emerging almost every day. This means you have to keep tabs on what’s new and relevant, drop old habits when something better comes along, and constantly update your knowledge as new information becomes available. To succeed in data science, you need to be constantly learning new things and discarding outdated ones. Unfortunately, keeping track of all the information that you need can be a challenge in itself. In this blog post, we will explore eight concepts that you may have forgotten when learning Python for data science. These are things that might seem obvious now but will help you later on when working with data more extensively or tackling more complex problems.

So Let’s Get Started!!!

1) Python version 2 is not the same as Python version 3

Python is a great language for data science. It’s flexible, easy to learn, and has a huge community. Every data science student has likely heard about how important it is to use Python for data science, but it’s important to remember that there are two different versions. Version 2 is commonly referred to as Python Classic, and version 3 is referred to as Python Modern or Python 3. Many of the resources you come across might be written for either version, so it’s important to know what version of Python you are using. If you are using Python 2. X, it’s important to know that some libraries and modules have been removed in Python 3. For example, the built-in round() function has been replaced with a round() method on the numbers module in Python 3. This is important because it means you won’t be able to use them on Python 2.X. Fortunately, most libraries and modules have been updated to work with both Python versions. If you see that a particular library or module hasn’t been updated yet, you can always try to install a Python 2 version in a Python 3 environment.

2) NumPy is a requirement for most data science tasks

The core of many data science tasks is exploratory data analysis. In EDA, you take a problem, load the data that you want to analyze, and identify patterns and relationships. To do this, you will need to write code to read the data from a source, transform it into the format you want, and organize the data into a table. Many students start learning Python for data science by taking a class or reading a book. In these, they might skip the parts that cover the basics of programming with Python and focus on the specific techniques used in data science. This is a mistake. The Python code that you write to perform EDA tasks is the same code that you will use to prepare your data for modeling later on. For example, if you want to read data from a CSV file, you don’t need to use any special techniques other than the ones you would use to read data from an API or a website. You can use the code below to read in a CSV file and put the data into a table. The code uses the built-in Python library called NumPy to read the data.

3) You don’t need to understand the math behind every machine-learning algorithm

There are two main types of machine learning algorithms: supervised and unsupervised. Supervised algorithms require you to provide the labels for each data point, whereas unsupervised algorithms don’t. In supervised algorithms, the challenge is to find a function that maps the input data points to the correct labels. To do this, you will need to understand the math behind the different algorithms. For more complex supervised algorithms, such as deep neural networks, you will also need to understand how to implement them and how to scale them. This means that you will need an understanding of computer science, software engineering, and computer programming. For unsupervised algorithms, you don’t need to understand the mathematical function. You just have to know how to use the algorithm to find patterns in your data. This means that you don’t need to understand the math behind the algorithm, but you do need to know how to use it.

4) Data preparation is almost always a part of any data science project

Many students focus on the part of the data science process that comes after data analysis. This is a mistake. Data analysis is really important because it allows you to understand your dataset and get a good idea of what it looks like. Without data analysis, you can’t do anything else. It’s like building a house without a blueprint. You might come up with an idea of what you want, but without data analysis, you won’t know if it’s even possible. Many data science projects start with data analysis and end with modeling. This means that you will likely need to transform the data in some way before you can use it to build models or apply machine learning algorithms. It’s important to remember that there is no standard data format or structure in data science. The data might come from a variety of sources, it might be stored in different ways, and it might be formatted in different ways. You might have to convert the data from a source that your computer can read, to a format that you can use in your analysis. You might have to transform the data in other ways too, such as removing unnecessary information, changing the way the data is formatted, or applying a mathematical transformation to the data.

5)Regression algorithms are often more reliable than ML algorithms

Many machine learning algorithms that you can use in data science can be broken down into two categories: supervised and unsupervised. Supervised algorithms are used to predict labels or outcomes, such as the price of a house based on features such as the number of bedrooms and the number of bathrooms. Unsupervised algorithms are used to identify patterns in the data, such as grouping features based on their similarity. When choosing which algorithm to use, you should consider the reliability of the model concerning your data. If you’re building a model that predicts the price of a house, it may be more reliable to use a regression algorithm. This is because regression algorithms are more tolerant of errors in the data. When you use a regression algorithm, you can also typically use more of your data to train the model. This means that you can use all the data, even if there are errors in the data, and you can still use the model to make predictions.

Conclusion