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Welcome to the "Data Visualization with Python Masterclass | Python A-Z" course.
Learn python and how to use it for data analysis and visualization, present data. Includes codes of data visualization.

Because data can mean an endless number of things, it’s important to choose the right visualization tools for the job. Whether you’re interested in learning Tableau, D3.js, After Effects, or Python, OAK Academy has a course for you.
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In this course, we will learn what is data visualization and how does it work with python.

Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.

This course has suitable for everybody who interested in data visualisation concept.

First of all, in this course, we will learn some fundamentals of pyhton, and object oriented programming ( OOP ). These are our first steps in our Data Visualisation journey. After then we take our journey to the Data Science world. Here we will take a look at data literacy and data science concepts. Then we will arrive at our next stop. Numpy library. Here we learn what is numpy and how we can use it. After then we arrive at our next stop. Pandas library. And now our journey becomes an adventure. In this adventure we'll enter the Matplotlib world then we exit the Seaborn world. Then we'll try to understand how we can visualize our data, data viz. But our journey won’t be over. Then we will arrive our final destination. Geographical drawing or best known as Geoplotlib in tableau data visualization.

Learn python and how to use it to python data analysis and visualization, present data. Includes tons of code data vizualisation.

Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.

In this course, you will learn data analysis and visualization in detail.

Also during the course, you will learn:

1. Fundamental stuff of pyhton and OOP, Overview of Jupyter Notebook and Google Colab.

2. What is the Data Science and Data Literacy

3. Fundamental stuff of Numpy and Pandas library in data analysis.

4. What is Data Visualization

5. Python data analysis and visualization

6. Python data analysis

7. Data visualization

8. Advanced excel for data analysis

9. The Logic of Matplotlib

   • What is Matplotlib

   • Using Matplotlib

   • Pyplot – Pylab - Matplotlib - Excel

   • Figure, Subplot, Multiplot, Axes,

   • Figure Customization

   • Plot Customization

   • Grid, Spines, Ticks

   • Basic Plots in Matplotlib

   • Overview of Jupyter Notebook and Google Colab

10. Seaborn library with these topics

    • What is Seaborn

    • Controlling Figure Aesthetics

    • Color Palettes

    • Basic Plots in Seaborn

    • Multi-Plots in Seaborn

    • Regression Plots and Squarify

11. Geoplotlib with these topics

    • What is Geoplotlib

    • Tile Providers and Custom Layers

And of course, we enhanced all of it lots of examples with different concept and level. I bet you will like it.

Why would you want to take this course?

Our answer is simple: The quality of teaching.

What is data visualization?
Data visualization is the graphical representation of information and data. It is a storytelling tool that provides a way to communicate the meaning behind a data set. Simply put, data visualization helps users — the individuals or teams who generate the data, and in many cases, their audience — make sense of data and make the best data-driven decisions. Good visualizations can magically transform complex data analysis into appealing and easily understood representations that, in turn, inform smarter, more calculated business moves. Using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

What are the most common data visualization tools?
There are a variety of popular data visualization tools used by professionals in a variety of settings and at all levels. Some of the most widely utilized platforms include Microsoft Excel, Tableau, Python, and R; Udemy offers courses that can get you up to speed on all of these platforms. It’s important to note that dozens of data visualization tools are free and/or open-source, which means that the software’s original source code is freely available and can be distributed by anyone. Some tools, such as Power BI and Tableau, are free but not open-source, offering a free license but limited functionality. In addition, there are also modules or packages for open-source programming languages such as Matplotlib for Python and D3.jf, Plotly, and Chart.js for JavaScript. Lastly, there are niche tools such as Leaflet and OpenLayers for interactive mapping.

What careers use data visualization?
While there are specific careers that require data visualization skills, such as data scientist, data engineer, and business intelligence analyst, many industries require these skills to succeed and drive profit. In today’s data-driven world, it’s wise for professionals from all walks of life to have basic data visualization skills. For example, in the financial services sector, data visualization skills are critical when it comes to understanding finance data. Today’s journalists can also make great use of data visualization tools for quickly processing raw data, interpreting statistics, and improving their storytelling capabilities. Across all business sectors, more and more companies are figuring out how important it is to be able to converse with data and the role it plays in their success.

What are the most common types of data visualization?
There are many ways to interpret data and tell a story visually. Some of the most common examples include charts (area, bar, and pie), tables (highlight and text), graphs (bullet and wedge stack), and maps (dot distribution and heat), as well as dashboards, histograms, and other infographics. You can create all of these using software such as Excel and Tableau. Selecting the correct visualization depends on the type of data you need to interpret: categorical, which describes categories or groups, or numerical, representing numbers. Udemy offers a variety of courses that teach you how to create impactful data visualizations and drive action with data-driven decisions.

What are the limitations of Python?

Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant making Python even more popular.

How is Python used?

Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library, although there are currently some drawbacks Python needs to overcome when it comes to mobile development.

What jobs use Python?

Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems.

How do I learn Python on my own?

Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own.

What is data analysis?

Data analysis is the process of studying or manipulating a dataset to gain some sort of insight. Usually, the insight can be leveraged to inform a decision or action. It includes everything from simple math operations to complex statistical calculations. Most people conduct data analysis on a daily basis. For example, you may sort your credit card charges by the highest amount to uncover the three most expensive costs in the previous month. Or, you may calculate the average number of points your favorite athlete scored in a game to predict their performance in a future game.

What skills do I need to be a data analyst?

To be a data analyst, you’ll need technical skills to analyze data and report insights successfully. Technical skills may include data analysis, statistical knowledge, data storytelling, communication, and problem-solving. Business intuition and strategic thinking are also useful for data analysts that often partner with business stakeholders. Data analysis involves taking a business question or need and turning it into a data question. Then you'll transform and analyze the data to extract an answer to that question. Data storytelling includes both graphing and communication skills, which means that you'll need to create graphs and charts that help communicate your data and findings visually. You also need to communicate clearly in multiple formats, which may include strong writing, speaking, explaining, and listening skills. Problem-solving skills are useful because they help you do things like create innovative approaches to overcome challenges and resolve issues with data gaps.

What jobs use data analysis?

Data analysts are in every industry, and their job titles can vary. Typical sectors include (but are not limited to) retail, healthcare, banking and finance, transportation, education, construction, and technology. Types of jobs that require knowledge of data analytics include Data Scientists, Business Intelligence Analyst, Data Engineer, Quantitative Analyst, Data Analytics Consultant, Operations Analyst, Marketing Analyst, Project Manager, IT Systems Analyst, and Transportation Logistics Specialist. Data Scientist roles typically earn higher salaries. Specific data scientist jobs include Machine Learning Engineer, Machine Learning Scientist, Applications Architect, Enterprise Architect, Data Architect, Infrastructure Architect, Data Engineer, and Statistician.

What is data science?

We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

What does a data scientist do?

Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production.

What are the most popular coding languages for data science?

Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up Python very quickly.

OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise.Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.

Fresh Content

It’s no secret how technology is advancing at a rapid rate. New tools are released every day, and it’s crucial to stay on top of the latest knowledge for being a better Python developer. You will always have up-to-date content for this course at no extra charge.

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All our content is created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

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• Lifetime Access to The Course

• Fast & Friendly Support in the Q&A section

• Udemy Certificate of Completion Ready for Download

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