What are your favorite python tools
Data Visualization: The Tools You Must Know About
The correct presentation of the data, designed to be understandable and well thought out, depending on who is looking at it: this is an important and often underestimated part of the data analysis process in the study of the reference market and the process of user acquisition.
Data visualization is the discipline that deals with identifying the best ways to graphically represent data, using tools that make the data usable and accessible at all times. In this article, we are going to discover a number of extremely simple, economical, and fast data visualization tools that you can use by highlighting the key issues related to data visualization.
„See the dates. Show the visual.
Tell the story. Inspire the audience "
The vast amount of data available today requires tools that can transform the data into information to aid the analysis and decision-making of businesses of all kinds. The only way to turn the large amount of data into understandable communication. The visual content certainly has a different attraction than textual or numerical content. Indeed, the graphical representation enables a quick and effective assimilation of data, the understanding of data also through a color outside the sector and finally it concretely shapes the information obtained from the data.
Data is coming from more and more sources and the most important thing is to be able to select and aggregate it in such a way that you can get useful insights and quick answers to the daily questions any company may have.
Create clear and efficient reports and dashboards
The dashboards fit this scenario perfectly and show all identified and selected KPIs at a glance. Specific tools allow you to have continuously updated dashboards coming in real time from various data sources.
What steps are being taken to create a clear and effective dashboard?
The basis of every dashboard is certainly a careful and precise analysis of the available data sources. As a growth hacker, you need to ask yourself the following questions at this stage:
What data are available to you?
Which key figures do you want to display?
Which key figures can best represent the development and progress of the company?
Which graphs best show the trend of the selected KPIs?
Who Should Read the Dashboard?
Which device is the dashboard being viewed from?
It is imperative that every chart, dashboard, and display in general be designed for those who need to read it. You shouldn't forget that the ultimate goal is to make the data understandable for those who will use it. The same data can be presented in many different ways and among these you need to choose the one that is correct and clear to the end user. Once you have a clear idea of what to visualize and how to visualize it, you can move on to preparing the data and developing the connectors that are essential for connecting the data to various software data visualizations.
The often underestimated design phase is undoubtedly the most important phase in this case.
What are the software for the development and sharing of reports and dashboards?
Google Data Studio
Google Data Studio is that completely free Google Data Visualization Tool. Many changes and additions have been made since its inception that make it an instant and easy-to-use tool.
The user interface is simple and intuitive and consists of a dashboard on which you can insert the various elements: Graphics, indicators, images, filters and texts.Each element is customizable by font and color: creating reports and dashboards becomes a real game. Thanks to the possibility of creating tailor-made key figures from the key figures in the data record, you can display your own KPIs that go beyond the metrics extracted from each source.
The ports currently available are as follows:
2. Attribution 360
3. Big Query
4. Cloud SQL
6. Google Analytics
7. Google Sheets
9. Search Console
10. Youtube Analytics
One of the weak points of this tool is the connectors: the inability to connect to platforms outside of Google and the inability to merge data from different connectors. Problems that can be easily resolved by importing data into BigQuery or other data sets such as Google Sheets. With the Supermetrics tool, it is possible to automatically import data from many other sources, such as Facebook Insight, Facebook ADS, Twitter and many others, into Google's spreadsheets. Google Sheets have almost all the functionality of a classic spreadsheet and therefore allow you to perform complex operations on imported data, which can then be viewed in custom metrics in the Data Studio.
The Google Data Studio dashboards can be shared with anyone as well as with any Google Drive document with different levels of access.
Klipfolio is a platform that enables you to get a real-time overview of data and KPIs. Klipfolio is also available as an application, downloadable and available from any smartphone to read the various dashboards in real time anywhere, anytime of the day. For each available connector, the Klipfolio developers have provided a large number of preset clips that can be used with can be configured with just a few clicks.
Unlike Data Studio, Klipfolio allows you to use multiple data sets to create a single Klip. In fact, it is possible to link two or more different data sets with the help of functions such as Look Up. Data can be combined to create custom metrics and views.
The data visualization platforms are really diverse; Additional solutions to those already mentioned are possible, such as:
- Power BI, from Microsoft. Economical, with a high graphic impact and a large number of connections.
- Tableau, professional platform for data visualization and business intelligence; the costs increase, but the functionalities are almost unlimited and allow you to enter the data extremely deeply.
Development and programming for data visualization
There are really numerous means of displaying and analyzing the data. There are two worth mentioning here: R and Python.
These two tools are two different interpreted programming languages that allow you to analyze and display data about libraries and functions that have already been created.
R. is a development environment for statistical data analysis. The benefit of this tool lies in the possibility of querying the data set and carrying out statistical analyzes which, based on this, provide significantly more complex diagrams and a high analysis value.
python is an object-oriented programming language suitable for developing applications, especially used for numerical solving of complex problems (which is why Python is widely used in science and research thanks to its excellent visualization libraries and a number of interesting calculation modules). Numpy, Scipy, and Pandas are just a few of the libraries that allow you to develop a quality job!
Libraries for Python and R
R. is a programming language and development environment designed for data analysis; by R. it is possible to take data from many different sources and process the different sets of data to obtain clustering of data, regressions of different types, and an in-depth study of the correlation between different data. The environment is extremely flexible and also allows you to work with multiple data sources simultaneously, giving you reports and views of data that are very different from what is available through the classic proprietary tools from Google, Facebook and other advertising platforms.
If you want to develop more complex applications, possibly web-based (e.g. thanks to the Django framework), you can opt for the Python programming language, which integrates a good number of libraries of various kinds, both for data analysis and for other activities that are extremely are useful.
Among the libraries to be underlined, there is certainly Scrapy, which enables you to collect data from web pages and a whole host of other data that is useful for a lot of analysis on the web. A library with scrapy will be particularly useful in search engine optimization, for example, where it can be successfully used for competitive analysis and for developing scripts and analytics applications that integrate data from APIs and data from scraping web pages.
With dashboards like this, everything will be simpler and easier to set up so you can focus on strategy and continuous improvement.
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