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Dur: 3 months
Course fee : 15000 /-

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

Python | Excel | Statistics
SQL | ETL | Data Cleaning | Data Analysis
PowerBI | Tableau

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:

  1. Fundamental Concepts: Introduce participants to fundamental concepts in data analytics, including data types, exploratory data analysis (EDA), and statistical measures.
  2. Data Exploration and Cleaning: Teach techniques for exploring and cleaning datasets to ensure data quality and reliability.
  3. Statistical Analysis: Provide an understanding of statistical methods and tests for drawing inferences from data.
  4. Data Visualization: Explore the principles of data visualization and introduce tools for creating compelling and informative visual representations of data.
  5. 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).
  6. Practical Applications: Apply data analytics techniques to real-world scenarios, enabling participants to solve analytical problems and extract actionable insights.
  7. 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.
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
Who can learn this course

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

  1. Business Analysts: Professionals seeking to analyze data to support business decision-making and strategy.
  2. Data Enthusiasts: Individuals curious about working with data, whether beginners or those looking to enhance their existing skills.
  3. Students and Graduates: Those pursuing studies in data-related fields or aiming to build a foundation in data analytics for future careers.
  4. Managers and Executives: Decision-makers who want to understand and leverage data analytics for organizational success.
  5. Researchers: Those looking to apply analytics to research projects and draw insights from data.
  6. 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
Live Training Batches Timetable
Course Name Faculty Date Time Mode of Training Batch Type Meeting Link
Data Analytics Real-Time Expert 16 Dec 7:30 AM (IST) online Online Training
Data Analytics Real-Time Expert 16 Dec 7:30 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

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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.

Angie M. 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.

Angie M. Navya Bhathuku432
course : Data Analytics

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