Welcome to Eddoc Technology – Your Gateway to Data Analytics Excellence!

In today’s data-driven world, the ability to analyze and interpret data is one of the most sought-after skills across industries. At Eddoc Technology, we offer cutting-edge Data Analytics Training designed to empower aspiring professionals to thrive in this rapidly evolving domain.

Key Features of Data Analytics

50+ live sessions spread around seven months

Hands-On Projects with Real-World Datasets

Expert Instructors with Industry Experience

Practical Experience with Popular ML Tools and Frameworks

Resume Preparation and LinkedIn Profile Review

Career Guidance and Placement Assistance

COURSE CURRICULUM

Module 1: Introduction to Data Analytics

➤ What is Data Analytics?
➤ Importance and Applications of Data Analytics
➤ Types of Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
➤ Data Analytics Process
➤ Career Opportunities in Data Analytics

Module 2: Data Understanding and Preprocessing

➤ Data Collection Methods
➤ Understanding Data Types and Structures
➤ Data Cleaning and Preparation
➤ Handling Missing Data
➤ Removing Duplicates
➤ Data Transformation (Scaling, Encoding)
➤ Exploratory Data Analysis (EDA)
➤ Identifying Trends and Patterns
➤ Correlation Analysis

Module 3: Statistical Analysis

➤ Basics of Statistics
➤ Mean, Median, Mode, Variance, Standard Deviation
➤ Probability and Probability Distributions
➤ Hypothesis Testing
➤ T-Test, Chi-Square Test, ANOVA
➤ Regression Analysis
➤ Linear and Logistic Regression

Module 4: Data Visualization

➤ Importance of Data Visualization
➤ Principles of Effective Visualization
➤ Tools for Data Visualization:
➤ Excel (Charts and Graphs)
➤ Tableau (Dashboards, Storyboards)
➤ Power BI (Reports, Insights)
➤ Creating Interactive Visualizations

Module 5: SQL for Data Analytics

➤ Introduction to SQL
➤ Working with Databases and Tables
➤ Writing Queries
➤ SELECT, INSERT, UPDATE, DELETE
➤ Filtering and Sorting Data
➤ Joins, Subqueries, and Aggregations
➤ Advanced SQL Functions

Module 6: Programming for Data Analytics

Python for Data Analytics
➤ Basics of Python Programming
➤ Libraries: Pandas, NumPy, Matplotlib, Seaborn
➤ Data Manipulation with Pandas
➤ Visualizing Data with Matplotlib and Seaborn

R for Data Analytics (Optional)
➤ Introduction to R
➤ Data Manipulation with dplyr
➤ Visualizing Data with ggplot2

Module 7: Machine Learning Fundamentals

➤ Overview of Machine Learning in Data Analytics
➤ Supervised Learning: Regression and Classification
➤ Unsupervised Learning: Clustering
➤ Implementing Machine Learning Models with Python

Module 8: Advanced Topics in Data Analytics

➤ Big Data Analytics Overview
➤ Introduction to Hadoop and Spark
➤ Real-Time Analytics with Streaming Data
➤ Business Intelligence and Analytics Integration

Module 9: Capstone Projects

➤ Work on real-world datasets
➤ End-to-end project implementation
➤ Problem definition
➤ Data preprocessing
➤ Analysis and visualization
➤ Reporting insights
➤ Example Projects:
➤ Sales and Revenue Analysis
➤ Customer Segmentation
➤ Predictive Maintenance

Module 10: Soft Skills and Career Development

➤ Resume Building for Data Analytics Roles
➤ Interview Preparation
➤ Communication of Insights to Stakeholders
➤ Presentation of Analytics Projects

📞FOR ENQUIRY

Our Career Services

More than 100 employer partners and a committed recruiting team work together to provide you with a variety of possibilities.

Connect with our large Eddoc student community to exchange employment opportunities

Practice simulated interviews with professionals in the field.

Use our experienced professionals to optimize your LinkedIn profile and resume.

We not only help you get ready for the job, but we also help you get ready for your search and interviews

Looking for In-Depth Syllabus Information

"Discover, Analyze, Dominate: The World of Data Awaits You!"

Online Learning with Weekdays / Weekend

Live Classes & Mentoring Sessions

Frequently Asked Questions (FAQ) – Data Analytics Training

1. What is Data Analytics, and why is it important?

Data Analytics is the process of analyzing raw data to uncover trends, patterns, and insights that support decision-making. It is important because it helps organizations optimize processes, improve customer experience, and drive business growth.

2. Who can enroll in this Data Analytics course?

This course is suitable for:
➤ Freshers and graduates looking to build a career in data analytics.
➤ IT professionals seeking to upskill.
➤ Business professionals wanting to leverage data for decision-making.
➤ Entrepreneurs aiming to grow their businesses through data insights.

No prior coding or analytics knowledge is required.

3. What tools and technologies will I learn in this training?

You will master the following tools and technologies:
➤ Python
➤ R (optional)
➤ SQL
➤ Tableau
➤ Power BI
➤ Excel
➤ Google Analytics

4. What are the prerequisites for joining the course?

No specific prerequisites are required. A basic understanding of mathematics and logical reasoning is helpful but not mandatory.

5. What job roles can I apply for after completing this course?

After completing this course, you can apply for roles such as:
➤ Data Analyst
➤ Business Analyst
➤ BI Developer
➤ Data Scientist (with additional skills in ML)
➤ Financial Analyst

6. Will there be hands-on training during the course?

Yes! Our course emphasizes practical learning through:
➤ Real-world datasets
➤ Live projects and case studies
➤ Interactive sessions with industry experts

7. Will I receive a certificate upon course completion?

Yes, you will receive a globally recognized certificate that validates your expertise in data analytics.

8. What is the duration of the course?

➤ Beginner to Intermediate Level: 8-10 weeks
➤ Advanced Level: 4-6 additional weeks

9. Is job assistance provided after completing the training?

Yes, we offer a job assistance program that includes:
➤ Resume building
➤ Interview preparation
➤ Placement support with our network of hiring partners

10. What kind of projects will I work on?

You will work on real-world projects such as:
➤ Sales and revenue analysis
➤ Customer segmentation
➤ Predictive maintenance
➤ Business performance dashboards

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