Data Analytics

Data Analytics Training Program

Kick start your data-driven career with a comprehensive Data Analytics course from GALTech School in Kerala.

Master the core skills of Excel, SQL, Python, and data visualization tools like Power BI and Tableau.

Gain practical experience through real-life projects and industry-relevant case studies.

Secure valuable internships to gain hands-on experience and build your professional network.

Prepare for a successful career in the data analytics field with our fast-paced, results-oriented program.

Why Data Analytics?

*Join our data analytics program and embark on a rewarding career journey. Whether you’re a beginner or a seasoned professional, our program provides:

  • Comprehensive training: Covering both theoretical and practical aspects of data analysis.
  • Supportive learning environment: Fostering growth and encouraging exploration.
  • Industry-relevant skills: Preparing you to handle the complexities of the modern data landscape.

Basic

Data Analytics

Document

Course Details

Course Details

Duration: 3 - 3.5 Months
Mode: Online and Offline
Live Project & Internship: 15 days to 1 month
Hours: 2-3 hours per day, 5 days a week

Week 01

Week 01

Excel Basics

Introduction to Excel interface and navigation.
Basic formulas and functions (SUM, AVERAGE, COUNT, etc.).
Data entry, formatting, and cell referencing.
Sorting, filtering, and conditional formatting.

Week 02

Week 02

Data Manipulation in Excel

Advanced formulas (IF, VLOOKUP, HLOOKUP, INDEX-MATCH).
Data validation and error handling.
Pivot Tables and Pivot Charts.
Working with dates and times.

Week 03

Week 03

SQL Basics

Introduction to databases and SQL syntax.
Data retrieval (SELECT, WHERE, ORDER BY).
Joining tables (INNER JOIN, LEFT JOIN, RIGHT JOIN).
Grouping and aggregation (GROUP BY, HAVING, SUM, COUNT).

Week 04

Week 04

Advanced SQL

Subqueries and nested queries.
Window functions and advanced data manipulation.
Database management and optimization techniques.

Week 05

Week 05

Power BI Basics

Introduction to Power BI interface.
Data import and transformation.
Creating basic reports and dashboards.

Week 06

Week 06

Advanced Power BI

DAX expressions and calculations.
Advanced visualization techniques.
Sharing and publishing reports.

Week 07 - 08

Week 07

Introduction to Tableau

Tableau interface and basic functionalities. Data blending and joining in Tableau.
Creating visualizations and dashboards.
Data blending and joining in Tableau.

Week 08

Advanced Tableau

Advanced chart types (heat maps, treemaps, bullet charts).
Tableau Server and sharing insights.

Week 09

Week 09

Introduction to Python

Python interpreter and environment.
Installation and setup.
Installation of Anaconda and Jupyter Notebook.
Introduction to Google Colab.
Using Python as a calculator.
First steps towards programming.
Keywords in Python.
Identifiers and statements.

Week 10

Week 10

Python Programming - Data Structures & Control Flow

Data Structures: Numbers, Strings, Lists, Tuples, Sets, Dictionary.
File input and output operations.
Modules and packages.
Control flow and indentation.
Decision making with if, elif, and else.
Loop structures (for and while).
Loop control statements (break, continue, pass).

Week 11

Week 12

Functions

Introduction to functions.
Function arguments and parameters.
Recursion and anonymous functions (lambda).
Function return values and scope.

Week 12

Week 12

Exploratory Data Analysis (EDA) with Python

Introduction to Pandas and Numpy.
Basic operations and data manipulation.
Introduction to Matplotlib and Seaborn.
Creating basic plots and visualizations.
Comprehensive data analysis using Pandas, Numpy, Matplotlib, and Seaborn.

Final Project

Apply all learned skills to analyze a real-world dataset, including data cleaning, transformation, visualization, and presentation.
Course Details

Duration: 3 - 3.5 Months
Mode: Online and Offline
Live Project & Internship: 15 days to 1 month
Hours: 2-3 hours per day, 5 days a week

First Month

Week 1:

Excel Basics
Introduction to Excel interface and navigation.
Basic formulas and functions (SUM, AVERAGE, COUNT, etc.).
Data entry, formatting, and cell referencing.
Sorting, filtering, and conditional formatting.

Week 2:

Data Manipulation in Excel
Advanced formulas (IF, VLOOKUP, HLOOKUP, INDEX-MATCH).
Data validation and error handling.
Pivot Tables and Pivot Charts.
Working with dates and times.

Week 3:

SQL Basics
Introduction to databases and SQL syntax.
Data retrieval (SELECT, WHERE, ORDER BY).
Joining tables (INNER JOIN, LEFT JOIN, RIGHT JOIN).
Grouping and aggregation (GROUP BY, HAVING, SUM, COUNT).

Week 4:

Advanced SQL
Subqueries and nested queries.
Window functions and advanced data manipulation.
Database management and optimization techniques.

Second Month

Week 5:

Power BI Basics
Introduction to Power BI interface.
Data import and transformation.
Creating basic reports and dashboards.

Week 6:

Advanced Power BI
DAX expressions and calculations.
Advanced visualization techniques.
Sharing and publishing reports.

Week 7:

Introduction to Tableau
Tableau interface and basic functionalities. Data blending and joining in Tableau.
Creating visualizations and dashboards.
Data blending and joining in Tableau.

Week 8:

Advanced Tableau
Advanced chart types (heat maps, treemaps, bullet charts).
Tableau Server and sharing insights.

Third Month

Week 9:

Introduction to Python
Python interpreter and environment.
Installation and setup.
Installation of Anaconda and Jupyter Notebook.
Introduction to Google Colab.
Using Python as a calculator.
First steps towards programming.
Keywords in Python.
Identifiers and statements.

Week 10:

Python Programming - Data Structures & Control Flow
Data Structures: Numbers, Strings, Lists, Tuples, Sets, Dictionary.
File input and output operations.
Modules and packages.
Control flow and indentation.
Decision making with if, elif, and else.
Loop structures (for and while).
Loop control statements (break, continue, pass).

Week 11:

Functions
Introduction to functions.
Function arguments and parameters.
Recursion and anonymous functions (lambda).
Function return values and scope.

Week 12:

Exploratory Data Analysis (EDA) with Python
Introduction to Pandas and Numpy.
Basic operations and data manipulation.
Introduction to Matplotlib and Seaborn.
Creating basic plots and visualizations.
Comprehensive data analysis using Pandas, Numpy, Matplotlib, and Seaborn.


Final Project
Apply all learned skills to analyze a real-world dataset, including data cleaning, transformation, visualization, and presentation.

Data Science

Why GALTech?

Classes from Industry Experts

Project-based

Job-focused

Placement Assistance

Internship Programs

HD Video Recordings

Weekly Projects & Assignments

Industry-specific Curriculum

Peer-to-peer

Self-paced

Certification

Affordable Fees

Data Analytics

Who can join

Recent Graduates

Anyone who has just finished their degree.

Working Professionals

People currently employed who want to learn new skills.

Career Changers

Individuals looking to switch to a data analytics career.

Students

Current students wanting to add data skills to their education.

Data Enthusiasts

Anyone interested in learning about data analysis.

Prerequisites

Basic Computer Skills

Familiarity with computers is helpful.

Mathematical Aptitude

A basic understanding of math is useful.

No Prior Experience Required

Beginners are welcome!

Common Queries

Frequently Asked Questions

 Anyone: fresh graduates, working professionals, students, or those who are interested in data analytics.

No! The Data Analytics Program is designed for those with no background or experience. Zero experience is perfectly fine!

You will learn how to work with Excel, SQL, Power BI, Tableau, and Python, along with all the data analytics techniques.

3 to 3.5 months

Yes, the program offers a certificate if one completes it successfully.

There is not any age liming to attend the Data Analytics program.

It can be a classroom program, though along with that, there are also online options.

Yes, you will get the opportunity to do so, as it would be a part of your final project.

Application can be done online and in person with the admissions office, which can be easily contacted for more information.

Data Analytics

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