Which python is used for data analysis?
The production and usage of data are on the rise and all companies irrespective of their size or turnover, are now dependent on data more than ever. This has led to a surge in the demand for Data Analytics and a plethora of professionals are drawn towards this field. These professionals turn to programming languages like R and Python to enhance their Data Analytical skills, but often fail to do so because of their wrong learning approach. Show Python is an object-oriented programming language and contains various libraries and tools that can streamline the Data Analysis work. This is the reason behind its increasing popularity amongst Data Analysts and Data Scientists. This article provides an introduction to Data Analytics with Python and explains why the Python Programming Language is so effective for this field. The blog will then explain the steps that you should follow if you wish to start from scratch and become efficient in Data Analytics using Python. Furthermore, it will discuss the common mistakes that you must avoid while on this learning journey. Read along to learn more about Data Analytics with Python! Table of Contents
Prerequisites
Introduction to Data Analytics with PythonImage SourceData Analytics involves collecting data from various sources and using Statistical Analysis and Machine Learning on that data to extract valuable insights from it. It is a popular concept, especially in the commercial sector as it allows organizations to make data-driven decisions based on the result of Data Analysis. Nowadays, Data Analytics and Python are two inseparable terms. The popularity that Python has witnessed in the field of Data Science and Data Analytics is because of its immense flexibility and functionality. Moreover, to implement Data Analytics with Python, you don’t have to learn everything about the programming language. Since you won’t be doing the development work, understanding certain libraries and functions offered by Python is sufficient. Also, you must develop your Data Science skills, otherwise, learning Python will be like having a tool and not knowing how to use it. Therefore, you need to develop some Statistics and Data Visualization skills and gain a certain degree of knowledge about the subject area to be extracted and analyzed. Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ data sources (including 30+ free data sources) to numerous Business Intelligence tools, Data Warehouses, or a destination of choice. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data. Get Started with Hevo for Free Let’s Look at Some Salient Features of Hevo:
Sign up here for a 14-Day Free Trial! Data Analytics can appear to be a complex process for a beginner, but you can easily understand the important aspects of implementing Data Analytics with Python by working along with the following steps:
Step 1: Set Up a Python EnvironmentImage SourceThe basic necessity to work in Data Analytics with Python is to have a platform where you can write your code and execute it. So, your first step is to set up an environment that is convenient to use and enables you to work in Python. There are multiple platforms available online for free that can provide you the required programming environment, the most popular being the Anaconda Python Platform. This one application will take care of most of your needs as it contains along with the core Python Programming Language, most of its important libraries such as Pandas, Numpy, Matplotlib, IPython, etc. You can download the Anaconda Package and install it on your system just like any other application. The package has various in-built programs one of which is the Jupyter Notebook. It will act as a well-developed environment for working in Python and will enable you to compile and run your code seamlessly. Jupyter Notebook will open in your browser and won’t require any internet connection to execute your code. Once this installation is complete, your environment is ready! To learn more about the installation of the Anaconda Package, visit here. Step 2: Learn the Basic Concepts of PythonIt is essential that you first understand the fundamental concepts of Python before jumping into any kind of Data Analytics with Python. You don’t need to become an expert in this programming language, just covering the following important topics will suffice:
Furthermore, you don’t need to enroll in any course to learn all of the above concepts. There are multiple resources like W3Schools, Tutorials Point, etc. available for free on the internet that provide detailed tutorials on Python fundamentals in the form of videos, notes, etc. Learning these concepts will provide you the required foundation to start your Data Analytics with Python. Step 3: Understand the Working of Python LibrariesA key feature of Python is that it has numerous librariesthat can simplify your work to a great extent. If you want to perform Data Analytics with Python, then you must familiarize yourself with some of the majorly used Python Libraries. The essential Python Libraries with regards to Data Science are:
These 4 Python Libraries are a must if you wish to work on Data Analytics with Python. Once you have understood these, you may try and explore other important libraries to further increase your knowledge of implementing Data Analytics with Python. These libraries and many more are pre-installed on your Jupyter Notebook. Still, if any library is absent, you can easily install it using the pip command. For more information on installing Python Libraries, visit here. Step 4: Practice Working with DatasetsImage SourceThe above 3 steps were aimed at learning certain tools and techniques that will facilitate your Data Analytics with Python. Now, it is time to implement this knowledge on actual Datasets. There are enough Datasets present in the StatsModels Libray in Python, and you can also download more from platforms like Kaggle for further practice. By applying basic Statistical and Analytical operations on these Datasets, your confidence in Data Analytics and Python will increase and you will realize the areas in which you need to improve. On these Datasets, you must practice the following 4 types of processes:
Mistakes to Avoid in Data Analytics with PythonMost beginners often commit the following mistakes while learning Data Analytics with Python:
Instead of falling into the trap of these mistakes, focus on learning at your own pace. Furthermore, focus on practical implementations of Python Libraries and simple Machine Learning algorithms. ConclusionThis article discussed the learning process of Data Analytics with Python. It explained the various steps that you should follow to succeed in your attempts to implement Data Analytics using Python. Moreover, it highlighted the common mistakes that you must avoid while starting your learning process. Although Data Analytics is a complex field, the inbuilt functions and libraries in Python can simplify it for you. All you need to do is follow the steps provided in this article and practice as much as possible. Visit our Website to Explore Hevo Data Analytics often involve data transfer from various sources to a Data Warehouse for further analysis. This work can be tiresome as it involves setting up an ETL process that will require a lot of your time and resources. Hevo Data helps you directly transfer data from a source of your choice to a Data Warehouse or desired destination in a fully automated and secure manner without having to write the code or export data repeatedly. It will make your life easier and make Data Migration hassle-free allowing you to focus on your Data Analysis work. It is User-Friendly, Reliable, and Secure.
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