Data Analytics

 

Data Analytics is the process of collecting, cleaning, and analyzing data to uncover useful insights, patterns, and trends that help in making better decisions. It combines statistical methods, programming, and visualization tools to turn raw data into meaningful information. Businesses use data analytics to improve performance, predict future outcomes, and make data-driven decisions across various fields like marketing, finance, healthcare, and more.

Data Analytics

Data Analytics is the process of collecting, cleaning, and analyzing data to uncover useful insights, patterns, and trends that help in making better decisions. It combines statistical methods, programming, and visualization tools to turn raw data into meaningful information. Businesses use data analytics to improve performance, predict future outcomes, and make data-driven decisions across various fields like marketing, finance, healthcare, and more.

course Features

ISO Certification

Duration : 6 Month
Job Guarantee
Online & Offline Sessions
Live Projects
Learn from industry experts
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Course Objective

Full Stack Development refers to the ability to design and build both the front end (client side) and the back end (server side) of web applications.A Full Stack Developer is someone who can work across all layers of a web application from user interfaces to databases and servers.

Course content

  • What is Data Analytics?

  • Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive

  • Data Analytics Lifecycle

  • Applications in various domains (Finance, Marketing, Healthcare, etc.)

  • Roles: Data Analyst vs Data Scientist vs Data Engineer

  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation

  • Probability Basics & Distributions (Normal, Binomial, Poisson)

  • Hypothesis Testing & Confidence Intervals

  • Correlation & Regression

  • ANOVA and Chi-Square Tests

Tools: Excel, R, Python (NumPy, SciPy)

  • Data Types and Structures

  • Relational Databases and ER Models

  • SQL Queries: SELECT, WHERE, GROUP BY, HAVING, ORDER BY

  • Joins, Subqueries, and Window Functions

  • Data Cleaning and Preparation using SQL

Tools: MySQL / PostgreSQL / MS SQL Server

  • Principles of Effective Data Visualization

  • Creating Charts and Dashboards

  • Storytelling with Data

  • KPI Design and Reporting

Tools: Tableau, Power BI, Excel, or Python (Matplotlib, Seaborn, Plotly)

Python:

  • Libraries: Pandas, NumPy, Matplotlib, Seaborn

  • Data Wrangling and Cleaning

  • Exploratory Data Analysis (EDA)

  • Introduction to Machine Learning with scikit-learn

    R:

    • Data Manipulation (dplyr, tidyr)

    • Data Visualization (ggplot2)

    • Statistical Modeling

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