Data Analytics Online Training [Real-Time]

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Dur: 3 months
Course fee : 15000 /-

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Data Analytics

Course Overview

The Data Analytics course is designed to equip participants with the essential skills and knowledge needed to analyze and interpret data effectively. It covers a range of techniques and tools to extract valuable insights from datasets, enabling informed decision-making in various domains. This course provides a solid foundation in data analytics principles and practices.

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Full Stack Data Analytics Course

Description

In this course, participants will learn key concepts related to data analytics, including data exploration, statistical analysis, and data visualization. Practical applications of analytics tools and techniques will be covered, allowing participants to gain hands-on experience in extracting meaningful patterns and trends from diverse datasets. The course also introduces common data analytics tools and programming languages used in the field.

Course Highlights:

04 Resume Based Projects
Complete concepts from Basics to Advance Level
Lifetime Assess for Materials
Everyday’s Task
Every Week Test (Mock Interview Test)
Microsoft Certification in Data Analyst
300+ Certification based Sample Questions - Resume Building
300+ Interview Questions & Answers
Interview Skills & Preparation
Multiple Mock Interviews

Course Objectives

The primary objectives of the Data Analytics course are as follows:

Fundamental Concepts: Introduce participants to fundamental concepts in data analytics, including data types, exploratory data analysis (EDA), and statistical measures.
Data Exploration and Cleaning: Teach techniques for exploring and cleaning datasets to ensure data quality and reliability.
Statistical Analysis: Provide an understanding of statistical methods and tests for drawing inferences from data.
Data Visualization: Explore the principles of data visualization and introduce tools for creating compelling and informative visual representations of data.
Introduction to Tools: Familiarize participants with popular data analytics tools, such as Python libraries (e.g., Pandas, NumPy) or specialized software (e.g., Tableau, Power BI).
Practical Applications: Apply data analytics techniques to real-world scenarios, enabling participants to solve analytical problems and extract actionable insights.
Interpretation and Communication: Develop skills in interpreting analytical results and effectively communicating findings to both technical and non-technical audiences.

Prerequisites

- Basic understanding of data and its importance.
- Familiarity with spreadsheet applications (e.g., Microsoft Excel, Google Sheets).
- Knowledge of basic statistical concepts (e.g., mean, median, standard deviation).
- Understanding of data visualization principles.
- Awareness of data analysis tools and software (e.g., Tableau, Power BI).
- Experience with interpreting and presenting data in a meaningful way.

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Course Curriculum

Basic Module

Introduction to Microsoft Excel
Installing Excel: Windows / Mac
Getting Familiar With Excel
Introduction to Tables
Input data into cells
Introduction to Formulas
Formula Behavior
Built in Functions
Combining Data From Two Tables

Advance Module

Pivot Tables
Nested IF statements
VBA to automate tasks
Custom Functions

Descriptive Statistics

Data
Types of Data
Collection of Data
Population & Sample
Sampling Techniques
Measures of Central Tendency
Measures of Spread
Measures of Shape
Percentiles
Quartiles
Inter Quartile Range (IQR)
Outliers
Correlation
Covariance
Probability
Probability Distributions
Calculation of Probability using
Standard Error
Central Limit Theorem
Confidence Intervals

Inferential Statistics

Hypothesis Testing
Formulation of Null & Alternate Hypothesis
Type-I error & Type-II error
P value
Left tail vs Right tail vs Two tail
1 Sample test (Z test & T test)
2 Sample test (independent test & paired test)
ANOVA Test
Chi-square Test

Basic Module

Introduction to Python
Installation of Python
Variables
Input
Output
Data types
Data Structures
Operators
Condition Statements
Loops
Functions

Advance Module

Advance Functions
File handling
Errors
Exception Handling

Numpy

Introduction to Numpy
Numpy Attributes
Array creation
Indexing & Slicing
Iteration over a array
Array Manipulation
Mathematical Operators
Relational Operators
Functions

Pandas

Introduction to Pandas
Series & Data Frame
Create Data Frame
Column Selection, Addition & Deletion
Row Selection, Addition & Deletion
Merging & Concatenation
Import of Data from various sources
Basic insights of datasets
Summarizing Data
Sorting
Discretization
Indexing and Selecting Data
Filtering data
GroupBy
Exporting Data
Statistical Functions

Univariate Analysis
Bivariate Analysis
Multivariate Analysis
Matplotlib

Histogram
Box plot
Scatter Plot
Line Plot
Pie Chart
Bar Chart
Subplots

Seaborn

Bar Plot
Count Plot
Box Plot
Line Plot
Scatter Plot
Regression Plot
Pair Plot
Heatmap
Violin Plot

Dealing wrong Data
Dealing wrong data types
Treating the duplicates
Dealing Missing Values
Handling Outliers
Drop unnecessary columns

Basic Module

Introduction to Databases
Databases vs Spreadsheets
DBMS vs RDBMS
Introduction to SQL
SQL vs NoSQL
Installation of MySQL
Data Types in SQL
Keys - Primary Key & Foreign Key
Constraints
CRUD Operations
SQL Languages
SQL Commands
SELECT
SQL Clause
Operators
Wild cards
Aggregation functions

Advance Module

SQL Joins
Normalization
De-Normalization
SQL Functions
Sub queries
Common Table Expressions (CTE)
Views
Stored procedures

Basic Module

Introduction to Power BI
Connectivity Modes
Power BI Desktop and Data Transformation
Data Visualization & Dashboard

Advance Module

Introduction to DAX
Data Types in DAX
DAX Calculation Types
Steps to Create Calculated Columns
Measures in DAX
DAX Syntax
DAX Functions
DAX Operators
DAX Tables and Filtering

Basic Module

Introduction to Tableau
Connections
Visual Analyticsc
Basic Charts
Sorting
Filtering
Grouping
Sets
Built-in Functions (Number, String, Date, Logical and Aggregate)
Operators and Syntax Conventions
Table Calculations

Advance Module

Types of Calculations
Trend lines
Reference lines
Forecasting
Advance Plots
Dashboard

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Who can learn this course

This course is suitable for a diverse range of individuals, including:

Business Analysts: Professionals seeking to analyze data to support business decision-making and strategy.
Data Enthusiasts: Individuals curious about working with data, whether beginners or those looking to enhance their existing skills.
Students and Graduates: Those pursuing studies in data-related fields or aiming to build a foundation in data analytics for future careers.
Managers and Executives: Decision-makers who want to understand and leverage data analytics for organizational success.
Researchers: Those looking to apply analytics to research projects and draw insights from data.
Anyone Interested in Data: Enthusiasts keen on exploring the world of data analytics and gaining practical skills in data-driven decision-making.

The course caters to a broad audience, providing valuable skills in data analytics for both beginners and individuals with some prior experience in the field.

Average package of course (Data Analytics)

100% Avg
salary hike

4 - 8L Avg
Package

Upcoming Batches

Course Name	Faculty	Date	Time	Mode of Training	Batch Type	Meeting Link
Data Analytics	Real-Time Expert	25 Nov	9:00 AM (IST)	online	Online Training
Data Analytics	Real-Time Expert	25 Nov	9:00 AM (IST)	offline	Classroom Training

Training Features

Comprehensive Course Curriculum

Elevate your career with essential soft skills training for effective communication, leadership, and professional success.

Experienced Industry Professionals

Learn from trainers with extensive experience in the industry, offering real-world insights.

24/7 Learning Access

Enjoy round-the-clock access to course materials and resources for flexible learning.

Comprehensive Placement Programs

Benefit from specialized programs focused on securing job opportunities post-training.

Hands-on Practice

Learn by doing with hands-on practice, mastering skills through real-world projects

Lab Facility with Expert Mentors

State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session

Our Trainees are Working with

Reviews

I recently attended Naresh IT’s Data Analytics program in Hyderabad, and it was an exceptional learning experience with top-notch training and practical insights.

Keerthi Yeakambaram

course : Data Analytics

Great course on Data Analytics! The instructor made complex topics easy to understand, and the hands-on projects enhanced my confidence in data analysis.

Navya Bhathuku432