data analytics 1

Data Analytics

involves examining, cleaning, transforming, and interpreting data to extract meaningful insights and support decision-making.

Education, Government, Healthcare, Weather Patterns, Banking, Agriculture, Fast Food, Airline, E-Commerce

top big data applications

Descriptive, Predictive, Prescriptive

types of data analytics

Descriptive Analytics

what happened

Predictive Analytics

what is likely to happen in future

Prescriptive Analytics

what is best course of action

Descriptive Analytics

it simply describes the answer to what happened and it alters raw information from

numerous data sources to give important knowledge into the past. Though, these outcomes barely signal that something is wrong or right, without clarifying why.

Predictive Analytics

it is giving hints that it is something related to future prediction. It uses the discoveries of

descriptive and diagnostic analytics to identify bunches and special cases and to predict future trends, which makes it a significant device for estimating.

Prescriptive Analytics

it tell you what steps to take to avoid a future problem or capitalize on a potential trend.

Prescriptive analytics employs complex tools and technologies such as machine learning, business rules, and algorithms, making it easy to implement and administer.

More efficient operations

More effective marketing

Improved decision making

Better customer service

benefits of data analytics

Microsoft Excel, Python and R, SQL, Data Visualization Tools

tools in data analytics

Microsoft Excel

A versatile spreadsheet tool for basic data analysis.

Python and R

Programming languages commonly used for more advanced analysis.

SQL

Structured Query Language for managing and querying databases.

Data Visualization Tools

Such as Tableau, Power BI, or Matplotlib for creating visual representations.

Define the problem

Clearly outline what you want to analyze and achieve.

Collect Data

Gather relevant data from reliable sources.

Clean and Prepare

Organize and clean the data to remove errors or inconsistencies.

Explore and Analyze

Use visualizations and statistical tools to explore patterns and trends.

Interpret and Draw Conclusions

Analyze the results and draw meaningful insights.

Communicate Results

Present your findings in a clear and understandable manner.