If you thought spreadsheets were all that you needed to be an expert data analyst, you are in for a heart-breaking surprise. While spreadsheets might be a part of data analysts’ arsenal, it is not the only thing keeping their boat afloat. To be a successful data analyst, diversity in terms of the skillset needs to be there. These tools bring flexibility, expand your domain of effectiveness, and reduce reliance and wastage of manual hours
1. Spark
It’s not just you that needs upscaling, but you do too. When handling major chunks of data, Python and SQL will hit their limits. In times like these, it is important to bring in the big guns to handle bigger datasets. One such tool is Spark, allowing you to handle clusters of data by splitting it up across nodes, and working on the same. It is easier to split the data and work on it in chunks, rather than handling all of it at once.
2. Python
Python is one of the most versatile and easy-to-learn programming languages out there. If you are entering this field, make sure you are well-versed with Python. The use case and wide range of applications that come attached to it make it even more relevant. There are endless possibilities to work with Python, be it any field, or any work area you choose.
3. SQL
SQL is an easy to work with database of information, making not just our work easier, but simplifying a truckload of the same. SQL is that best friend that you cannot make if you are looking to enter the field of data analysis. With an easy-to-learn syntax and lots of upside when it comes to working, there is a lot of use that can be drawn out of SQL, especially in terms of data segregation, extraction, and sorting.
4. Julia
It’s not just a fancy name given to a tool, the young-generation language might be pretty new-age based, but it has captured a fair share of the market on its own, due to the competency it offers. One of the best picks for scientific computing, machine learning, and data mining tasks, Julia is already taking the analysis industry by storm. In some areas, the popularity of the same has risen so much, that it is giving Python a run for its money.
5. R Programming
Next up on the list is R language. Though R might not be the first pick for a lot of people, only well-established analysts know the impact it can create well effectively clubbed up with Python. Be it tasks revolving around data science or advanced computing, with the help of R programming, it can be done in the blink of an eye.
6. Excel
One of the most basic skills, the use of Excel is spread across all sectors, and all sorts of work-cases can be sorted out with this one single tool. Excel has swiftly made its place known among professionals because of its easy-to-use interface, greater applicability, and effectiveness. Even with the rise in those fancy tools for analysis, no replacement for Excel has been found so far.
7. Power BI
Bored of working and dragging yourself across those default cells of Excel, and need a little bit of spice in your data analysis workspace, it is time to make the switch to Power BI. Bring in data from multiple sources, put them in place, analyse them, draw valuable insights, and present them through simple, yet clear visualisations, all with the help of Power BI.
8. Tableau
The list doesn’t end here, at least not without the mention of Tableau. As if the tools listed above weren’t enough, Tableau can surely be the one-stop solution for you. Offering a similar application and capacity to Power BI, it pulls through when it comes to beautiful, impactful, and powerful visualisations. Tableau comes packed with a lot of unique and powerful features unique to this tool alone.