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Beginners with little or no experience in data science can also become data analysts – there is no doubt about it. But, what’s the best way to become one, and how much money and time would it take to meet the expectations from the industry? According to the latest online research on how students from elite colleges search for data science topics, it was found that 95% of the online queries focused on beginners, and the time taken to become a certified data analyst. Despite the emergence of a large number of data analytics courses, the penetration of knowledge in these areas is fairly average. Only a handful of courses actually provide holistic training and academic support to students. There are thousands of data concepts in the field and it is nearly impossible for everyone to master these terms in a short period of time. However, if you have a focused research area where you want to explore the product innovation and services side of the business, starting with top data analytics courses is the way to go.
In this article, we have highlighted the top types of data analytics that are used widely in different types of business processes. Your competency in one or more of these fields could help you pursue a lucrative career in the data science market. For beginners, these are most suitable to start in this industry.
Exploratory Data Analytics
From summarizing data sets to building mainframe data visualization models — Exploratory Data Analytics is a must have skill for any analyst. And, when you learn it in the first six months of your career, you are set for a flourishing career in the industry.
Exploratory Data Analytics or EDA is one of the most popular branches of modern data science. Analysts often take up EDA to analyze and report Big Data information. EDA is used everywhere – market research, statistical analytics, business intelligence, sales forecasting, customer sentiment analysis, and healthcare patient experience management, financial health data analysis, and much more. In fact, all the modern business intelligence tools find their existence in EDA approaches.
But why learn EDA? It’s important to understand the importance of using EDA along with Artificial Intelligence and machine learning algorithms.
Advanced AI machine learning tools are used with EDA to test the veracity of a hypothesis and for anomaly detection that could affect the outcome of any analytical process. Starting with EDA would take you to learn Python and R, and MATLAB applications. A bit of solid coding with Python and R, and voila! You are on your way to becoming a pro in business analytics using EDA techniques.
Tableau Data Analytics
Tableau is a leading SaaS-based business analytics tool for enterprise users, but it also provides platforms for academic training and mentoring. Tableau data analytics was acquired by Salesforce, a leading CRM making company. So, you can understand how important Tableau has become for the whole commerce industry where Marketing, Sales, and Customer experience are valued immensely. Alongside MySQL and Excel, Tableau features among the top 5 data analytics skills in the global BI industry. It would take extensive training with Big Data and embedded analytics to master Tableau, but it is also possible for beginners to join the movement. For example, Tableau trainers provide extensive online training and mentoring on how to use various BI features for incremental and transformative techniques in Big Data and Embedded analytics. One thorough research and technical review of Tableau’s product suite for enterprises and students would help you understand how powerful any piece of data could become if trained and analyzed with the right set of tools and techniques.
Why Tableau?
Well, within 3 weeks of training with the best Tableau trainers in data analytics courses, you would realize the true potential of having an unhindered access to trained and semi-trained data. With Python and R programming, you can extrapolate on these techniques to build a very useful model that runs in real time on any device or cloud environment.
Knowledge is power, and you have to train yourself and your data set to behave “smartly.” Like your data models, data analysts should be intuitive and addictive. Training with the top data analytics mentors gets you there.
Whether you are an individual programmer, a big data analyst, or a senior data engineer, starting early is the best chance. In short, it’s a beginner’s best shot at having a magnificent career in data science with analytics and BI.
