Data Analytics

To become a data analyst, there are five main steps you will need to take:
1- Learn the essential data analysis skills
2- Gain a qualification that demonstrates these skills
3- Practice your data analyst skills on a variety of projects
4- Create a portfolio of your work
5- Apply for entry-level data analyst jobs
Eligibility Criteria for UG courses:
Duration: 3 to 4 Years
Eligibility: 10+2 from a recognised board with a minimum aggregate score. Admission Process: Entrance Exams or Merit-based. Fees: INR 3,00,000 to INR 10,00,000
Career After data analyst: Data Scientist, Data Architect, Data Engineer, Data Analyst, Statistician. Average Salary: INR 4 LPA to INR 6 LPA. For more details, you may visit the Shikhsa data analyst overview page. (https://www.shiksha.com/it-software/data-analytics-courses-chp). I hope this will help you.

The Data Analyst course will train you on Structured Query Language (SQL) basics, Statistics essentials, data Science, and data visualization using R and Python, Business Analytics with Excel, Power business intelligence, and Data Visualization with Tableau Desktop 10. A data analyst needs to deal with data throughout the Data Analysis pipeline. To become data analytics, then applicants can pursue a Bachelor of Technology in Computer Science and Engineering or a Master of Science in Computer Science with data analytics and data Science as major specializations at the undergraduate level. At the postgraduate level, students can take a Master of Science in Data Analytics. There are numerous online courses are also available these days. Students can learn all about data analytics courses online offered by Udemy, Coursera, CareerFoundry, etc. For more details, you may visit the Shikhsa Data Analytics page. (https://www.shiksha.com/it-software/data-analytics-courses-chp). I hope this will help you.

1- According to the current scenario, business intelligence is more demanding as compared to data analysis. Because Business intelligence analysts can get their careers started in analytical roles like data analysts.
2- Business intelligence analysts focus on translating raw operational data into meaningful financial dashboards and reports whereas, Data analysts focus more on combing data to find new patterns relevant to the business or other stakeholders.
3- In Business intelligence analysts there are two types of analytics that are Descriptive and diagnostic analytics, In Data analysts there are also two types of analytics Predictive and Prescriptive analytics.
4- Business intelligence analysts are better at making sense of what has happened and doing so at scale whereas, Data analysts are better at looking for patterns that state what might happen.
5- Tools used by Business intelligence analysts are SQL, Excel, Power Business intelligence, and Tableau whereas, Tools used by Data analysts are R, Python, Power Business intelligence, and Tableau.
6- The average starting salary of Business intelligence analysts is 86,972 dollars per annum whereas, the average starting salary of data analysts is 65,494 dollars per annum. I hope this will help you.

Business intelligence is a set of methodologies, architectures, processes, and technologies that transform raw data into meaningful and useful information used to enable more effective tactical, strategic, and operational insights and decision-making. Business intelligence includes tactics and tools such as:
1- Data Analytics
2- Benchmarking
3- Real-time monitoring
4- Data and text mining
5- Performance management
6- Dashboard development and reporting
7- Implementation of BI software, like Power BI
It is primarily used by leadership teams and non-technical personnel, such as financial directors, chief executives, or chief information officers. Data analytics is the process of inspecting, transforming, collecting, cleaning, storing, modeling, and querying data. Data analytics' goal is to produce insights that inform decision-making in business and in some other domains, too, such as the government, sciences, or education. Data analytics is divided into 4 parts. These are;
1- Descriptive analytics.
2- Diagnostic analytics.
3- Predictive analytics.
4- Prescriptive analytics. It is usually the data scientists, preserve of analysts and computer programmers who have a more technical focus. I hope this will help you.