The Learning Python for Data Analysis and Visualization course will kickstart your learning of using Python programming language to analyze and visualize data, as well as a full understanding of Python programming & data science in general. This Learning Python for Data Analysis and Visualization course will help you learn Python for analyzing and visualizing from the ground up. If you would like to dive into Python and data analysis, this course will be a good boot camp.
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About The Python Data Analysis Course
Python is now one of the most popular and in-demand programming language for data analysis and visualization. Because of the simplicity and high readability features, increasingly more developers prefer switching to Python. The Learning Python for Data Analysis and Visualization course will teach you more about Python for data science, including data analysis, data visualization, present data, machine learning and more. Once bought, you will get 21 hours of on-demand video, 3 articles, 4 downloadable resources, and 110 lectures. This assumes learners’ a strong willingness to finish the course. So, if you want to get the most out of the Learning Python for Data Analysis and Visualization course, learn with the industry’s expert at your own schedule.
What you will learn:
- Learn to master Python programming language or advance your current skill level to the better
- Learn what the numpy library is and how to use it for manipulating arrays
- Learn how to utilize the Jupyter notebook environment
- Learn what the types of data formats are in Python
- Learn how to be better in using data visualizations
- Learn how to practise in data analysis projects
- Learn more about machine learning such as linear regression, logistic regression multiple class classification, support vector machine, natural language processing, and more
- Learn statistics
- Learn SQL and Python
- Learn how to utilize Python for web scraping
- … much more
To learn this Learning Python for Data Analysis and Visualization course suggest learners have the basic knowledge of math and Python. An intermediate Python skill will be more helpful, but not necessary. Besides that, to finish his course still requires learners a strong desire to learn.
This Learning Python for Data Analysis and Visualization course is designed for anyone who want to dive deep into Python for data science, not matter who you are, data analyst, developer, data scientist, or complete newcomers. So, if you are interested in learning deeply about Python for data analysis and visualization, don’t miss out the opportunity today. Why wait? It is the real time to arm yourself and build up your skills.
Some details of the course:
- Course name: Learning Python for Data Analysis and Visualization
- Instructor: Jose Portilla
- Rating: As of 10/2019, the rating is 4.3 (11,650 ratings)
- Students: As of 10/2019, there have been over 127,708 students enrolled and keep increasing
- Language: English, Indonesian, Japanese, German, Italian, Polish, Portuguese and Spanish
- Video: 21 hours of video guide
- Resources: 3 articles and 4 downloadable resources
- Lectures: 110 lectures
- Price: $11.99
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- Device required: Either Mac, Windows, or Linux
What each lecture will teach you:
- 2 lectures simply introduce the course and Python
- 3 lectures teach how to get started with the course
- 8 lectures will help you learn Numpy
- 11 lectures will give you an introduction to Pandas
- 22 lectures in total focus on teaching how to work with data
- 7 lectures will introduce data visualization
- 17 lectures offer you example data projects
- 20 lectures will help you learn more about machine learning
- 11 lectures will give you an overview of statistics
- 3 lectures will teach something you need to know about SQL and Python
- 2 lectures will teach about web scraping with Python
- 3 lectures will offer Python special offers
- 1 lecture is about a bonus lecture
More About Python for Data Analysis and Visualization
In this Learning Python for Data Analysis and Visualization course, you will learn Python for data science. Is Python important for data analysis & visualization? Of course yes. Python is a programming language that is used in the development of the web as well as desktop applications. Scientific applications and delicate numeric analysis can also be developed by using this general-purpose programming language. These numerous applications make python the fastest growing programming language among all the other programming languages.
The importance of Python in the analysis of results as well as understanding the crude data with the use of statistical techniques. It provides deliberate and ongoing results.it is being used to optimize the statistical efficiency and quality of the raw data. This is why it is very important for the analysis of data and also the visualization. To know more about Python for data analysis and visualization, this Learning Python for Data Analysis and Visualization course can help serve as your guide.
Apart from the above advantage, below are the reasons which depict the importance of python for the data analysis as well as data visualization.
- Flexibility at its top
Python provides maximum flexibility to programmers. You can try new things with this programming language. If you are a novice developer and want to get a grip on this programming language, then it is all yours. It serves developers well to script applications and different websites.
- Simple learning
Python provides simplicity and readability which are the two basic considerations for Data analysis and data visualization. The simple and easy leaning procedure of python enables rookie programmers to learn well. It also provides a way to perform a big task by using fewer codes which is not possible through any old programming language.
The open-source reach of python makes it adaptable to work on any operating system, whether it is Windows or Linux. Also, it is free which is the most important benefit. It enables every programmer to opt easily.
Due to the above prevalent reasons, Python plays an important role in data analysis and visualization. It provides immense support for analysis. There is no doubt that the increasing popularity of Python is enormous because of numerous benefits.Enroll in the Learning Python for Data Analysis and Visualization course today, dive deep into Python for data science by learning from the industry’s expert.
Types of Data Formats in Python
Python is a simple programming language which is being widely used for various purposes in the world of software development. There are different types of file format are present in this particular programming language.
The file format is a way of storing data in files. If you have seen various variety of documents, then you must have gone through the format of given data. In this Learning Python for Data Analysis and Visualization course, you are going to learn the varying data format in python.
What is meant by file or data format?
The file format is a way of organizing data or records. Record organization is a standard manner by which data is encoded for its usage in a document. To begin with, the document organization determines whether the record is a twofold or ASCII document. Second, it indicates how the data is sorted out.
- Python CSV
CSV is not a term, it is an extension through which you can understand the type of data is stored in that particular file. CSV stands for Comma Separated values. As its full form identifies, the values which are separated using a comma is known as CSV file. You can know the type of file format by observing the extension mentioned in the name of the file.
- Python JSON
- Python XLS
XLS is also a kind of data format that we have seen in excel files. XLSX is a file format of Microsoft Excel open XML file. It comes in the format of the spreadsheet format of the file.
The XLS or XLSX is a format of representing data which is which a sheet will contain data in rows and columns in the sheet. The number of sheets can vary accordingly.
These were some of the formats of data stored in Python language. Alternatively, there are many other file formats that also present with the Python language, such as raw text format, zip archive files and many more.
Do you like to interpret any information in a graphical representation? If yes then the other name for that is Data visualization. The need for graphical representation of data is the next big thing. Though it is prevailing in this competitive time also. Let’s learn something about Data visualization. Or you can start a depth learning of data visualization from learning this Learning Python for Data Analysis and Visualization course on Udemy.
The graphical representation of quantitative information or raw numeric data is known as Data visualization. The human brain understands critical datasets in a virtual presentational way much easier. You can understand complicated information in a simpler way through Data visualization. There are numerous benefits of Data visualization. Some of them are briefly described below.
Benefits of data visualization
- It enables to take wise and better decisions
To make better decisions and simultaneously generating productive questions, organizations use it widely. Although technological improvement makes it simpler. The ever easy software programming languages enables it to learn easily and effectively.
- It enables to frame a good storyline
One of the most important uses of Data visualizations and informatics is in the field of media. Data journalism is the next big thing. Also, data journalism totally depends upon the effective way of data visualization. Many well established and reputed institutions use them to get through an interesting storyline with this. In this Learning Python for Data Analysis and Visualization course, you will know how important the data visualization technique is.
- It provides awareness about data literacy
One of the biggest requirement of this 21st century is the understanding and interpretation of Data visualization. It enables to gather interesting and viable information readily. With the correct usage of data and its visualization will help productively
So, at last, the question arises that why use data visualization.
Below are the things which we are able to perform by using data visualization:
- It represents data in an utmost easier way which is simple to understand and remember.
- You can discover different unknown trends and the fact
Due to these, the weight of Data visualization increases a lot. Let’s use it productively and give yourself the treat to understand anything.
SQL with Python
In this Learning Python for Data Analysis and Visualization course, you will get an introduction to SQL with Python. SQL is a language that stands for a structured query language that is most likely associated with the database. Connecting it with Python language can transform things in a simple way of understanding.
The discussion about using SQL in terms of Python will continue in this article. It is being utilized to establish a relation between the back-end database with the front-end of any application. With the usage of Python predefined functions transform this tough task into an easier one.
How to connect SQL with Python?
To connect Python with the database, we need a connector. Even before making usage of MySql to connect python with a database, we need a connector to install. The connector can be installed with a programming language.
This connector will work as an interface with a database and Python programming language. Let us understand how to establish a connection between python and SQL. Here we are going to use SQLite3 with Python. To complete master SQL with Python, the Learning Python for Data Analysis and Visualization course will help you.
Python has a predefined library for SQLite. The working is mentioned below-
- We must import the file of ssqllite3 to make use of it.
- Then creation of a connection using connect() function. We will have to insert the name of a particular database that is to be accessed. As usual cases, the file existing will lead to open that file.
- Then, an object named cursor is called and that is capable of directing the commands to the SQL. The cursor is a control structure used to navigate and bring the records of the database. The cursor has a significant job in working with Python. The cursor command is used by every command for execution.
- We can create an object while writing the command in SQL for the creation of a table in the database.
- Execution of the commands or order of any function is exceptionally simple. The cursor method can be called to execute and forward the name of the SQL direction in it as a parameter. As we all know afterward, we need to save the file by practicing the performed changes in it for its execution.
There are many succeeding methods that can be called and used after the connection established. We have tried to make people understand how SQL can get connected with Python with the help of this article.
This Learning Python for Data Analysis and Visualization course will teach you the Pandas software library for Python. There are numerous features of Python. Some of them are front end and super-efficient. Pandas is also one of them. Let’s know about this new Panda.
The software library which is written for data manipulation and analysis specifically for a python programming language is known as pandas. Particularly, it enables to manipulate numerical tables and time series for data structures and operations. It is also an open-source software encroaching the three clauses of the BSD license.
It provides numerous features that are very helpful for the manipulation of data structures especially time series. Getting to know about some of the various features is beneficial. Let’s get going.
Features of Pandas
- It enables you to insert and delete columns in data structures
- You can merge and join data sets
- You can reshape and pivot the datasets
- Aligning the data in a scaled manner and also enables you to deal with missing data
- By the use of integrated indexing, one can manipulate data for data frame
- One can easily execute split-apply –combine on datasets with the use of the group by engine format
- You can layout the complex data to lower dimension with the use of format i.e. hierarchical axis index
- You can easily convert frequency, shift date, lagging and other functions of time series
- This will enable you to read different files type formats such as CSV, XLSX, TXT, and other formats
- You can arrange your data both ways either ascending or descending
- If you are given a condition then also you can filter data
- You can easily analyze the time series
With the usage of these numerous features, you can make data visualization through python effective. Ultimately, Pandas acts as a subsidiary but an important addition to Python. There are some specific rules to install Pandas. If you want an efficient manipulation of datasets, then use Pandas in Python from now on. To master in using Pandas, the Learning Python for Data Analysis and Visualization course will give you a good bootcamp.
Discover more Python courses here:
- Python for Data Science and Machine Learning Bootcamp
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- Complete Python Bootcamp: Go from zero to hero in Python 3
No matter the current skill level or professional level, you can all benefit from learning how to analyze, visualize, and present data in Python. Whether a seasoned developer, a data scientist, or a analyst, this Learning Python for Data Analysis and Visualization course will help you enrich your knowledge and start a career in data science by honing your Python programming skills and data science techniques. Through the Udemy top-rated instructor’s support and rich resources, this Learning Python for Data Analysis and Visualization course will be a good boot camp for build up your skillsets for success in data analysis. Is it the real time to learn about Python, data science and data visualizations? Don’t miss out on the Udemy best-selling course at the most affordable price here.
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