5 Hands-On Tools On Becoming An Expert At Data Analyst
A degree in MBA in Data Science and Data Analytics from a reputed institute will give you a strong foothold in this career.
Data analysis is emerging as one of the most preferred and top professions in India and across the world. A report highlights that India has witnessed a 45% jump in analytics job openings in 2018 as compared to its previous year. There are around 97,000 job openings in data analytics and science careers currently. Given that the Indian analytics market is expected to register 26% CAGR growth become USD 16 billion market by 2025 and the global market is projected to grow at 29.7% to reach USD 40.6 billion market by 2023, the demand for data analysts will surge significantly.
Today, there is hardly any sector or industry which has been left untouched by big data, artificial learning, machine learning and other sophisticated technologies. Sectors such as BFSI, telecommunication, energy, power, retail, e-commerce, pharma and healthcare, etc are generating big data in voluminous amounts. They need specialists who can dig into this data and generate useful information and insightful patterns. Data analysts fit the bill perfectly for this role.
A degree in MBA in Data Science and Data Analytics from a reputed institute will give you a strong foothold in this career. Additionally, you also need to know the following five hands-on tools that are prerequisite to 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 computing. You can easily find 10,000+ R packages in CRAN which is the global repository of open-source packages. Many other repositories with free public access also have several R packages. The wide and diverse availability of R enables to customize solutions according to client needs. R also offers a diverse array of techniques such as classical statistical tests, machine learning algorithms, linear and non-linear modelling, time-series analysis and others. Other features that work in its favour are that it an open-source tool, allows for free experimentation and 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 modelling, SAS is the most suitable option. Its special features such as intuitive analytics, seamless integration with MS Office tools, self-service ad-hoc reporting and visual data exploration make it more popular among data analysts. Irrespective of the source or volume of data, SAS can also analyse it with efficiency and accuracy. SAS also enables data analysts to prepare elaborate reports with fine details for business decision-makers.
Microsoft Excel offers highly sophisticated mathematical, statistical, pivot tables and formatting functions that can ease out the burden of data analysts significantly. It also comes with powerful capabilities for accounting, finance, engineering and any other branch that calls for analysing numbers. No matter how complex or large the data sets are, Microsoft Excel can sort, filter, organize, manoeuvre, visualize and analyse them within a matter of minutes. Microsoft Excel can give accurate and quick results when used in parallel with R, Python and other languages that have big data management features.
SQL (Structured Query Language) is highly useful, open-source tool to store, access and run 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 across different frameworks and applications because most RDBMS/RDSMS come with SQL integrations. It is simple to use due to which you can extract a large amount of information quickly and that can be a powerful tool when businesses need to make informed decisions in real-time.
Each of these above-mentioned five tools has their own merits and can be used as a standalone or in amalgamation, depending on the nature and complexity of data sets. The bottom line is that these tools are a must-have in any data analyst’s kitty of skills. They can take your career to new heights.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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