Unveiling Modern Technical Education: Data Analytics Training

For ages, Data has always been the buzzword. As there is a humongous amount of data available in the market it is of no use because it does not benefit the businesses. Whether the data is generated from an individual or generated from large-scale enterprises, in each aspect data needs to be analyzed to benefit individuals or businesses from it. Data Analytics refers to the techniques used to analyze data to enhance productivity and business gain. In the Indian landscape, a data analyst course in Bangalore helps young data aspirants hone their skills, identify their potential, and seek out lucrative employment opportunities after graduation.

A Data Analyst is a professional who can analyze data by applying various tools and techniques and gathering the required insights. The techniques and the tools used vary according to the organization or individual. In India, a data analyst course in Bangalore provides world-class education on data leveraging techniques.

• Gather Hidden Insights: Get all the hidden insights from the data that are gathered, and then analyzed according to the business requirements.

• Generate Reports: Generated Reports from the data are passed on to the respective teams and individuals to deal with further actions for a high rise in business & have a competitive edge.

• Perform Market Analysis: To understand the market sentiments, strengths, and weaknesses of competitors, etc. Market Analysis must need to be performed.

• Improve Business Requirement: Data analysis allows for improving Business to the consumer’s expectations, requirements, and experience.

A data analyst trained from a data analyst course in Bangalore should be able to take a specific question or topic, discuss what the data looks like, and represent that data to relevant stakeholders in the company.

• Knowledge of mathematical statistics
• Fluent understanding of R and Python
• Data wrangling
• Understand PIG/ HIVE
• Should possess skills such as Statistics, Data
• Cleaning, Exploratory Data Analysis, and
• Data Visualization.
• Know about Machine Learning to stand out from the crowd.
• Should understand Structured Query Language (SQL).
• Statistical visualization & critical thinking.
• Extensive knowledge of Microsoft Excel

The different types of data analytics for a company depend on its stage of development. Most companies are likely already using some sort of analytics, but it typically only affords insights to make reactive, not proactive, business decisions.  More and more, businesses are adopting sophisticated data analytics solutions with machine learning capabilities to make better business decisions and help determine market trends and opportunities.  Organizations that do not start to use data analytics with proactive, future-casting capabilities may find business performance lacking because they cannot uncover hidden patterns and gain other insights.  Typically, there are four main types of data used in Data Analytics. These are:

• Predictive Data Analytics

Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling; however, it is important to know that the two go hand in hand. For example, an advertising campaign for t-shirts on Facebook could apply predictive analytics to determine how closely the conversion rate correlates with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two (or more) target audiences and provide possible revenue values for each demographic.

• Prescriptive Data Analytics

Prescriptive analytics is where AI and big data combine to help predict outcomes and identify

what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in ML, prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.

• Descriptive Data Analytics

Descriptive analytics is the backbone of reporting, it is impossible to have business intelligence (BI) tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.” Descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports.

• Canned Reports: A canned report has been designed previously and contains information about a given subject. An example of this is a monthly report sent by your ad agency or ad team that details performance metrics on your latest ad efforts.

• Ad Hoc Reports: These are designed by you and usually are not scheduled. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query.