5 practical tools to become an expert at Data Analyst
An MBA degree in Data Science and Data Analytics from a reputable institute will give you a solid foundation in this career.
Data analytics is emerging as one of the most favorite and beloved professions in India and around the world. A report points out that India saw a 45% increase in analytics job vacancies in 2018 compared to the previous year. There are currently around 97,000 vacancies in data analytics and science careers. Since the India analytics market is expected to grow 26% CAGR to become a market of $ 16 billion by 2025 and the global market is expected to grow 29.7% to reach 40.6 billion dollars by 2023, the demand for data analysts will increase dramatically.
Today, there is hardly any sector or industry that has not been spared by big data, machine learning, machine learning, and other sophisticated technologies. Sectors such as BFSI, telecommunications, energy, power, retail, e-commerce, pharmacy and healthcare, etc. generate massive amounts of big data. They need specialists who can tap into that data and generate useful information and insightful models. Data analysts are ideal for this role.
An MBA degree in Data Science and Data Analytics from a reputable institute will give you a solid foundation in this career. In addition, you should also know the following five practical tools that are essential for any career or job profile in data analysis:
R programming draws its comparisons from SAS and Python, but data analysts prefer it the most when they want a powerful tool for graphical methods and statistical computation. You can easily find over 10,000 R packages in CRAN, the global open source package repository. Many other repositories with free public access also have several R packages. R’s wide and diverse availability allows solutions to be customized according to customer needs. R also offers a wide range of techniques such as classical statistical testing, machine learning algorithms, linear and nonlinear modeling, time series analysis and others. Other features that work in its favor are that it is an open source tool, that it allows free experimentation, and that it is compatible with Windows, UNIX, macOS and other platforms.
When data analysts need an easily accessible and manageable statistical software tool for business intelligence, data mining, data management, reporting and predictive modeling, SAS is the most appropriate option. Its special features like intuitive analysis, seamless integration with MS Office tools, ad hoc self-service reports and visual data mining make it more popular among data analysts. Regardless of the source or volume of data, SAS can also analyze it efficiently and accurately. SAS also allows data analysts to prepare elaborate reports with fine detail for business decision makers.
Microsoft Excel offers highly sophisticated mathematical, statistical, pivotal, and formatting functions that can significantly lighten the load on data analysts. It is also packed with powerful features for accounting, finance, engineering and any other branch that requires number analysis. Regardless of the complexity or size of the datasets, Microsoft Excel can sort, filter, organize, manipulate, visualize and analyze them in minutes. Microsoft Excel can give precise and fast results when used in conjunction with R, Python, and other languages with big data management capabilities.
SQL (Structured Query Language) is a very useful open source tool for storing, accessing and executing queries on data in RDBMS or RDSMS. It can integrate with R, Python, and other database management systems. This special-purpose programming language can help data analysts navigate and manipulate data in different frameworks and applications, as most RDBMS / RDSMS come with SQL integrations. It is simple to use whereby you can extract a large amount of information quickly and can be a powerful tool when businesses need to make informed decisions in real time.
Each of these five tools mentioned above has its own merits and can be used alone or in combination, depending on the nature and complexity of the data sets. The bottom line is that these tools are a must in any data analyst’s skill set. They can take your career to new heights.