Introduction to Data Analysis with Python
Python is the preferred language for data analytics as to the Jupyter Notebook IDE, along with Pandas and NumPy library, helps manage the data and manipulate the same as per user’s requirements. Logical thinking and critical analysis of data are important to do data analysis, and Python helps to do all the modes of analysis with its various libraries. The data interpretation is not difficult, even if the datasets are large. This makes users to do any mode of analysis and visual interpretation of data using graphs or charts in the screen. Evaluations to any level can be done using Python language.
What is Data Analysis with Python?
- Python is an easy programming language that all can learn with basic coding skills, and it is known mainly for its flexibility as it can be used for any application.
- Also, with its vast libraries, Python is used mainly in data analysis and machine learning projects.
- All libraries are easy to use and can be handled for various data analytics jobs. For example, Pandas library is dedicated for data analysis alone, and we have a NumPy library that can do any numeric calculations easily.
Why Data Analytics using Python?
- The most important reason for using Python in data analytics is due to its simple syntax and easy application to any type of data. This helps anyone to use the language without prior knowledge in coding, and here engineering is not important to understand Python. We can do faster prototypes using Python than any other coding language.
- Python is open source as it is supported by communities. It can be downloaded and used in windows and Linux machines and is transferable to any environment. This flexibility makes python again a go to language for data analytics, even for beginners. In addition, the codes are simple and less lines of codes makes python’s storage requirements less.
- Python has good documentation, and we can get support from anyone as it has huge fans from anywhere, and the answers are readily available on the internet for any clarifications. Also, we need not write many code lines because libraries will take care of all the analytics work.
Data Analysis with Python Certification
We have several online courses available in this area for free and paid mode.
- NPTEL offers data analysis with Python for free, and faculties take the course from IIT. If a certificate is needed, we should register for the exam, and a nominal fee is included here. We get all the assignments and projects online, and the faculties make sure that the course is taught in an interesting manner.
- Simplilearn website offers the course in Data science with Python where the faculties are experienced in the industry level, and they take classes either on weekends or daily. So we will get a mail notification regarding the same, and we should submit the project to get the certification.
- Udemy and UpGrad also offers courses in data analytics, and all the faculties are experienced in data analytics. Moreover, they make the course interesting with several live examples, making it easy to understand for anyone enrolled.
Several MOOCs are available for data analytics with Python, which is worth looking for as the course always gets updated with new examples.
Guide to Data Analysis with Python
- Domain expertise is needed to understand the data and to predict the data from the collected information. This helps anyone to follow all the data models and do data forecasting based on the performance of the data.
- Basic programming skills are required to write codes and call necessary libraries to do data analysis faster. These libraries are based on statistics, and hence statistics knowledge will help in doing data analysis.
- Visualization and communication skills are important for a data analyst. With visualization, they can explain what the data is predicting to the stakeholders and answer their query regarding the charts drawn based on data analysis.
Prerequisites Data Analysis with Python
Given below are the prerequisites data analysis with python:
- SQL: It is important for all data analysts to know SQL as it simplifies the work, and we can do basic data analysis using SQL initially. When it comes to large datasets, we can use Python, and Pandas will help this analysis. SQL helps in all forms of data analysis and management.
- It is not necessary to be an Engineer to work in data analysis using Python, but logical thinking is important as it makes the prediction of data trends easier. We can understand the graphs easily, and this makes the visualization easy to follow. Also, basic statistic knowledge is important to use the libraries in Python. All libraries are not always needed, but few libraries will be used often, and statistics knowledge helps understand data analytics.
Application of Data Analytics
- Data analytics helps solve traffic problems by designing alternate routes to drivers, which helps reduce vehicle congestion on the main route.
- Data analytics helps logistic companies design the best routes to predict the delivery time and inform the user.
- When we search any information in web browsers, it picks up a key word and searches it using algorithms, and the fastest result is presented to us.
- In manufacturing companies, demands can be predicted using prediction analysis of data, and the products can be manufactured accordingly. This reduces the inventory of products in warehouses, thus saving money.
- Another important application is healthcare, where we can know the treatment for a particular disease within seconds and do necessary advancements.
- Data analytics is also used in the military for security purposes and also to collect information regarding any aspect from any part of the world.
Data analysis is a beautiful field of study for those who are interested in statistics and logical thinking. Python comes as a savior for those who do not need to spend a long time in doing statistical calculations. All the interpretations and visual graphs can be drawn, which gives instant answers.
This is a guide to Data Analysis with Python. Here we discuss the introduction, data analysis with Python certification, prerequisites, and application. You may also have a look at the following articles to learn more –