In the last few years, the demand for Data Analytics jobs has risen an impressive 29% per year. And, it will keep growing over time because of the increasing importance of Big Data in various industries. When Data Science is gaining so much popularity, it’s certainly the perfect time to learn this skill and advance your career.
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What is Data Analytics?
Before we start discussing the tools, let’s first get a little familiar with data analytics itself. It refers to organizing, describing, interpreting and modeling the data for analysis.
To put it simply, just like you analyze things in real life, data analytics helps you analyze complex data for a particular purpose.
When you go to a restaurant, you look at the past experience you had with the various dishes, right? Like which dish did you enjoy the most, and which one was unbearable?
Data analytics lets businesses do a similar thing with their decision making. They can look back and understand what went wrong and how they can avoid the same.
There are many methods of data analysis, such as Prescriptive Analysis and Diagnostic Analysis, but that’s a topic for a whole other article.
Recent Trends in Data Analytics
The field of data analytics is ever-evolving as new trends keep arriving. For example, the popularity of open-source tools and software has risen considerably this year. Recently, we’ve seen a rise in the demand for CDOs (Chief Data Officer) in companies. Clearly, companies are more eager to use the data available to them to enhance their progress. Besides CDOs, Predictive Analysis, Dark Data, and Quantum Computing are some of the recent trends in the data analytics domain.
Now that you have a fair idea of what data analytics entails, we can start discussing the best tools for it as well.
10 Best Tools for Data Analysis
Every data analyst has a bunch of tools he or she uses to perform data analysis. Without them, it’d be impossible to deliver quality results in the current market.
Different analysts have different preferences. Some tools are common among everyone, while some tools have a small user base. In any case, here are our top ten picks for the best Data Analysis tools:
- Tableau for Data Visualization
Tableau Public helps you create charts, dashboards, and other analytics products as well as share them. You get layouts for phones and tablets, which you can customize according to your needs. It also provides you with highlighters, filters, and parameters.
You can connect with cloud databases through Tableau Online. You can use this tool for free. But, if you have higher requirements, you can go for Tableau Creator.
2. R Programming for Data Science
It’s not software, it’s a programming language. But, it is so beneficial and important that this list would be incomplete without it. A lot of standard functions of data analysis are written in the R language. It lets you link FORTRAN, C and C++ code as well. Many of its features are perfect for data science.
3. Python for Data Analytics
Python is also a programming language. In its few years of existence, it has become one of the most popular languages among coders because of its versatility. You can learn it easily and use its amazing machine learning libraries, including Theano and Keras. You can use it to take care of text data too.
4. Rapid Miner for Data Analytics
This platform can use real-life data for generating analytics, and that is why it deserves a place on this list. You can use it with any data source type, including Microsoft SQL, Sybase, Access, and Ingres.
You can use this tool without any need for programming, which enhances its flexibility further. It also enables you to control the formats for its predictive analysis. All in all, a must-have for a data analyst.
5. Excel for Business Analytics
Microsoft Excel doesn’t need an introduction. When you’re using your client’s internal data, you’ll need to use Excel. It is a great tool for data summarization. You can filter your client’s data to save everyone’s time and increase the efficiency of the process. It comes with an advanced business analytics option, which helps you with data modeling.
There are plenty of functions in Excel a data analyst can use, including Pivot Tables and IF Formulas, which all make the analysis much simpler and efficient.
6. Orange for Data Visualization
It’s a toolkit for data visualization. Its great scatter plot makes the task of visualization easier while its platform simplifies plenty of complex tasks. You can create a node in a tree, branch in the dendrogram and select data points from the scatter plot through its platform. You can use it on Windows, Mac, and Linux.
7. Apache Spark for Data Processing
It’s a data processing engine built on data science. Its purpose was to make data science easier, and it does its job very well. It comes with a library called MLIib, which has a set of machine algorithms for many data science techniques such as Regression and Clustering.
8. SAS for Data Analytics
SAS is popularly used for profiling customers. It helps you in predicting their behavior and optimizing your communication accordingly. It has been in the market for many decades, and that has enabled this platform to grow substantially.
Currently, SAS is a leader in the data analytics field, so you should definitely get familiar with it.
9. Looker for Data Visualization
Looker uses the Look Machine Learning language for helping you create small applications. Although it helps you with data management, analytics, and visualization, its main feature is the level of data security it offers.
Looker queries your data, finds its answer and stores the same in a cache. You can keep the cache saved for 30 days or less, depending on your requirements.
You can use JavaScript in Looker to create visualizations as well, which we think is a great feature.
10. KNIME for Data Analytics
With a drag-and-drop interface, KNIME simplifies your task considerably. It was created by a bunch of engineers in 2004, and it has come a long way since then.
Its server automates your workflow and enables collaborations on projects. You can script in Python as well as R, which increases its flexibility further.
The best thing about KNIME, though, is you don’t need any coding skills to use it. KNIME gives you many solutions, including KNIME integrations and the KNIME Analytics platform. The Analytics platform is free to use, but you’ll need to pay $8500 to use the server.
Conclusion
Data analytics is a very broad concept. Don’t worry if you got overwhelmed. If you want to find out which tool you to employ for which task, we suggest taking up a data analytics course. Just as these tools simplify data analysis, the course simplifies the learning experience. Which data analytics tool did you find the most impressive? Let us know.
