What tools are required to become a data analyst?

Initiation of a career in the data analytics discipline would require anyone to utilize a set of differential tools so as to formulate an effective operational competency basis on which such a career could be expanded. Since the data industry is changing at an alarming pace, it has become imperative to remain updated regarding the latest data analysis tools through which such tasks could be performed.

Data Analytics Tools

SQL

Initially, it is required by any aspiring data analyst to become proficient in the SQL which is the most commonly utilized database language in the data management industry. It is necessary to know about database languages which could permit any person to analyse the metadata content since the current data analysis generally deals with Petabyte levels of data. Most of the large business organisations generally utilize the SQL data analysis language or the multiple variations which could be found in the market in the forms of Oracle, MySQL, SQL Server and so on so that effective storage of such data could be ensured.

Tableau

The vital tasks of communication and visualization could be performed by data analysts through utilization of the Tableau tool.  The significance of this tool is that it facilitates the researcher/data analyst to select appropriate graphs and charts concerning the different situations. Dashboards could be formulated under this tool and Tableau also assists in terms of the multiplicity of data source management including SQL & MS Excel.

Python

This could be considered to be a general purpose programming language which has gained increasing credence in the data analysis discipline. This could be utilized as a definite substitute for MS Excel in terms of organizing and evaluation of data sets which could be large in extent. For the data analysts, this tool could be of greater effectiveness than various other programming languages since it enables the analyst to perform multiple computational tasks simultaneously and with online data libraries such as MatPlotLib, Pandas, NumPy and so on. Furthermore, the syntax of Python is also mostly user-friendly in configuration.

Tools for Analysis

For the data analysts, including the junior and the senior ones, achievement of proper expertise in the Excel would be an imperative requirement. The formulas and Pivot Table based operations would be necessitated to be ingrained in the professional competency profile of the data analyst so as to ensure accuracy and speed. This could be stated regarding the VBA as well. Furthermore, SPSS, SAS and R could be required to be mastered by the senior data analysts completely so as to become proficient in their professional responsibility scenario.

SPSS

This tool is considered to be an efficient general purpose statistical analytical software based tool for the beginners. Any data analyst with rudimentary training could effectively operate this software package. SPSS is also effective in terms of implementation of standardization of analysis and data outcomes. The current version of the SPSS software service package is 18. The name of this tool has been transformed also from SPSS to PASW Statistics. This version could be further acknowledged as predictive analytical software which now holds sway over the other similar tools in terms of business analysis.

SAS

SPSS could be substituted by SAS since this tool is of greater effectiveness than SPSS.  However, SAS is of greater difficulty in terms of learning and mastering. The benefits of mastering of SAS could be understood to be the development of competency to apply discrete selection based models, orthogonal experiments based designs and probability based sampling processes as well.

R

R could be identified as a programming language developed for the purpose of statistical computing services. This tool could effectively develop data demonstration graphics as well. This programming language could be considered to be comparatively of greater efficiency in terms of the development of data mining related surveys, polls, statistical scholarly analysis and conductance of various secondary research procedures which involve extensive online information repository analysis and search activities.

This tool is significant in terms of writing codes. Thus, R is preferred by the data mining experts/ metadata engineers. The measure of popularity of R amongst the entire community of data analyst experts/data mining professionals could be acknowledged from the 16^th rank of this programming language within the TIOBE index. R is primarily a complementary tool to Python programming language. The add-on service packages, which, are associated with that of R, are greater than 4400 in number. Due to such extensive capabilities, R could be utilized by data mining experts to store substantial volume of data as well as complicated data analysis procedures. R is a difficult data programming software package to master at the initial points.

Data Visualization as a process

The process of data visualization could be alternatively termed as a data exhibition process which could be assisted by a plethora of different programming and analytical tools including the ones which have been so far mentioned about. In this context, the most frequently utilized tool could be identified as Business Intelligence (BI). This involves a complete operational solution package for the traditional data demonstration functions. BI could efficaciously integrate the complete set of data which could be put into it and could provide reports for the purpose of the analyst to come to specific decisions. The core components of the BI involve ETL, OLAP, Data Warehousing and Access Control mechanisms. For demonstrative purposes, the instance of FineReport could be highlighted as one of the most frequently utilized BI tool. The applications of this tool are dual fold. The first one is that it could generate reports in the automated manner. Sorting and summarizing the   enormous volumes of data which data analysts/ engineers are regularly confronted with are essential tasks which are also required to be completed with a definite period of time. This workload can be effectively managed through FineReport since this application brings into effect the services of data downloading, modeling and shaping.

The second one is the utilization of visualization functions to perform effective analysis. In this functionality, the FineReport has greater service application in comparison to Excel since it provides greater visualization functionalities which are not much difficult to operate effectively even for the beginners in data analysis disciplines.