When it comes to choice, we generally have questions, right? It’s normal. The same goes for data analytics tools. Among so many data analytics, which one’s better and why? Which one suits my needs? Which one would be the best choice for me to study?
Even though this is quite an ordinary subject, it is extremely significant, and we have been striving to seek after the response to this extreme issue. If you go online to look for the answers right now, it is hard to see a reasonable perspective. This might be due to experts who assess a specific data analytics tool evaluate it from a different point of view, some reviews may be even paid. So what to do then? Well, stick to our blog because we have your back!
Those days are gone when a Data Analyst knew or took a shot at only one tool. Any individual who works with data nowadays is knowledgeable with different software tools. You must be thinking are there any tools that are fundamental for any data analyst? Without any question, there are! There are so many tools but we are only going to cover the top 5 in this blog. A data analyst needs to know to make work and life that a lot simpler and productive. The selection of tools should be contingent upon the sort of analysis performed, and the kind of data worked.
Top 5 Tools For Data Analysts
By the time, we become familiar with a tool, it eventually makes its place in our hearts but today, let us set aside these emotions. So brace yourself, data hoarders, and the top 5 data analytics tools are as follows:
1. Excel
With a wide range of incredible features, like, index-match, PivotTable, VBA, and so on, Excel's framework is so versatile to the point that no analytics tool can outperform it, guaranteeing that analysts can examine data as indicated by their needs. However, a few people may scorn to use Excel as a tool because Excel can't deal with huge data. If you’re not fully aware of Excel’s features, then it is just an ordinary tool to generate spreadsheets. Be that as it may, if we pay close attention to the data we use in our daily life it does not surpass the limit of big data. Excel is an amazing tool, it works best for small data, and with modules, and it can deal with a large number of data as well. If you need to learn data analysis, Excel is unquestionably the first choice.
2. R and Python
Both these languages are tailor-made for data analysis – adaptable and powerful. R is best at statistical analysis, for example, cluster classification algorithms, regression analysis, and normal distribution. While Python can handle text-based data with a lot of ease. Python has pre-built libraries Tensorflow, viz. Scikitlearn, Theano, and Keras. Both R and Python can be used for professional as well as individual predictive analysis.
3. Tableau Public
On number three, we have Tableau Public. It associates any data source whether it is Microsoft Excel, corporate Data Warehouse, or electronic data, and cater data visualization features, for example, maps, dashboards. They can likewise be shared through online networking or with the customer. It permits the access to download the record in various formats and configurations. The power of Tableau lies in the data source, more diverse and excellent data source means powerful results. Tableau’s features for Big data makes them significant and one of the best data visualization tool in the market.
4. SAS
Developed by SAS Institute for advanced analytics, SAS is a software suite used for business intelligence, predictive analysis, and data management. SAS offers various data management and data analysis tasks and is a great analytics tool for power clients. SAS can deal with basically any sort of statistical modeling and large amount of data without a flinch.
5. QlikView
QlikView has numerous interesting features like patented technology and has in-memory data processing, which executes the outcome quickly to the end clients and stores the data in the report itself. Data association in QlikView is consequently kept up and can be packed to practically 10% from its unique size. Data association is envisioned utilizing colors – a particular shading color is given to related data and another shading color for non-related data.
How To Ace Data Analytics Tools?
If we talk about Excel, the learning short keys will help you a lot. But if we talk about Python, then using pre-built libraries can make your life simpler. The tips and tricks to master the tools vary so instead of explicitly describing for each, why not have a look at some general tips? You can use the tips to master any new skill in your life, sounds cool? Let’s keep it going then:
Shortlist Relevant Courses
Unquestionably, a great deal of courses through instructional classes and eLearning would be an offer. Pick the courses, and find what is pertinent to your calling. Make a timetable of what courses you can take without getting overwhelmed. When you have a reasonable calendar and the necessary resources, get yourself registered for these courses.
Practice What You Learn
This is one of the most impressive techniques to ace any new expertise. Practice what you learn. Any information, ability or skill without training or practice will in general go wasted and loses significance over some time. Given the fast pace of development these days, any aptitude you gain can become obsolete except if you practice it daily.
Join a community forum
This is the most feasible option to go for but mentioning it in the last shows that it will not help you out unless you pump up your game with the above 2 tips. We have a huge community for Python and Excel. You can join these forums and participate in QA sessions.
As a data analyst, if you want to make your life simpler—and perhaps impress everybody in your office—you should try your hands on these top 5 tools. If you’re a novice in this field, then you can enroll in a data analytics training to become an expert data analyst.
All the very best with your future endeavors.