Preparing for a Data Science Career: Tools to Master 

It is evident that a career in data science and data analytics requires lifelong learning if one wants to foster continuous progress.

Most often, the dilemma of data enthusiasts is not how to progress along their chosen journey but rather how to take the first step. In one of our articles, The Successful Data Scientist, we explained that programming languages, data visualization, data cleaning, machine learning, and statistical and mathematical skills are some of the skills a data scientist needs to equip themselves with in order to provide solutions to valuable business problems and satisfy the thirst for a successful career.

Similarly, in this article, we will answer one frequently asked question by aspiring data practitioners, which is “What tools do I need to start a data analytics career?”. For aspiring data practitioners or professionals transitioning to data analytics or data science careers, the existence of numerous data analytics tools can be overwhelming, and keeping up with trends in the industry can make them feel like they are stuck in a throng of technologies. Upskilling in the tools below will help aspiring data practitioners find their feet and prepare them for a successful career.


Microsoft Excel: Microsoft Excel is one of the most popular applications among data practitioners today. Microsoft Excel is an all-in-one data management software that allows data analysts to import, clean, analyze, and visualize data. Knowledge of Microsoft Excel can be a great addition to a curriculum vitae and boost the chances of getting a data entry job.


Tableau: Tableau is another tool that helps data analysts obtain detailed analysis of business reports. This is an end-to-end data analytics platform that allows data professionals to prep, analyze, collaborate, and share big data insights. Both data visualization and statistical skills can be best explored on Tableau.


SQL: SQL, or Structured Query Language, is a programming language used for storing and processing information in a relational database management system (RDBMS). SQL allows data analysts to communicate with a database as well as store, manipulate, and retrieve data.


Microsoft Power Business Intelligence  (BI): Microsoft Power BI is a suite of software services, apps, and connectors that work together to turn unrelated sources of data into coherent, visually immersive, and interactive insights. Microsoft Power BI can also be used to find insights within an organization’s data as well as help create charts or graphs to provide visuals of the data.


Though it may seem that these tools perform the same function to some extent, there are subtle differences between them that an aspiring data practitioner should be aware of.


For example, Microsoft Power BI has a faster processing speed and can handle large sets of data as compared to Microsoft Excel, which is not capable of handling big data.


Tableau, like Microsoft Power BI, allows for the creation of customized dashboards that consist of different types of charts, maps, and diagrams. The relational database management system, SQL, is more flexible and can handle over one million fields of data as compared to Microsoft Excel. Upskill yourself with these tools and become that data practitioner you aspire to be.


Using the appropriate methods to advertise oneself in the field of data science is essential, and as a result, creating a strong CV is of the utmost importance. A surefire path to becoming the desired data scientist is to have the appropriate resume format, such as  SQL Server Skills and Python Developer Resume from Enhancv.


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