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Data Analytics Program

Become an Data Analytics Engineer in 6 months with 4 months of hands-on experience on live projects. This program will help you understand the basics and advanced levels of knowledge to succeed in this field.

Hybrid Mode - Choose your own choice

Office Environment for both offline as well as Online Classes

One to one mentorship

Job Assistance, Excellent Past record of 94% success

Analytics

The Data Analytics Engineering course is designed to equip you with the skills and knowledge necessary to become a proficient Data Analytics Engineer. This comprehensive program covers a range of topics from data collection and preprocessing to advanced analytics and data visualization. By the end of the course, you will be able to transform raw data into meaningful insights that drive business decisions.

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#Business Stats, #Excel, #SQL, #Tableau, #Power BI, and #Python

  • Discover what awaits you as a Data Analytics Expert!

  • Unlock the potential earning opportunities in this field!

  • Explore the vast horizons of Data Analytics Careers!

  • Unveil the promising future prospects!

  • Envision how this course will empower you!

  • Gain insights into the comprehensive curriculum and skills you'll master!

Data on a Touch Pad
Paragliders

Data Analytics Opportunity Insights

More than 2 Lakhs job in 2024

As per estimates there more than 2 lakhs jobs in India for Data Anlytics related roles

Skill Gap

Currently only 62% jobs in Data Analytics field are being full-filled due to lack of skills

Earn 10 Lac+ per annum 

With 4+ years of experience, participants are earning avg package of 20+ Lacs and above

Enroll Now

Call us to reserve your spot in upcoming batches.

Next Batches

Curriculum Designed by Industry Experts

1. Introduction to Data Analytics

Overview of Data Analytics: Understanding the field of data analytics and its significance across various industries.

4. Exploratory Data Analysis (EDA)

Visualization Tools: Utilizing Tableau, Power BI, and Python libraries (Matplotlib, Seaborn) to create insightful visualizations.

7. Big Data Technologies

Big Data Tools: Learning to use Apache Hadoop, Spark, and big data processing frameworks.

10. Data Security and Ethics

Ethical Issues in Data: Discussing the implications of data collection and analysis, focusing on privacy and fairness.

2. Data Collection and Management

Data Storage and Retrieval: Learning about relational databases (SQL) and NoSQL options (MongoDB), data warehousing solutions like Amazon Redshift.

5. Data Analysis Techniques

Data Analysis Tools: R for statistical computing, Python with libraries like NumPy and SciPy for numerical analysis.

8. Data Visualization and Reporting

Reporting and Dashboarding: Building dynamic dashboards in Tableau and Power BI, understanding best practices in data reporting.

11. Capstone Project

Real-World Data Analytics Project: Applying all learned skills to solve a business problem from data collection to insight generation.

3. Data Cleaning and Preprocessing

Data Transformation Tools: Using tools like Excel, Google Sheets, and Python libraries (Pandas) for manipulating datasets.

6. Advanced Analytics

Machine Learning for Data Analytics: Introduction to machine learning using Python’s scikit-learn.

9. Business Intelligence and Decision Making

Tools for BI: Working with enterprise BI tools like SAS BI, Oracle BI, and learning to extract actionable insights.

12. Career Development in Data Analytics

Career Paths in Data Analytics: Exploring different roles and industries where data analytics is crucial.

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