Data Analytics Training
in Marathahalli

Best Institute for Data Analytics in Bangalore | RIA Institute

If you’re aspiring to embark on a rewarding journey into the realm of Data Analytics, look no

further. Our Data Analytics Certification Course is meticulously crafted to equip you with the

skills and knowledge needed to thrive in the dynamic world of data.

Why Choose Ria Academy ?

Our Key Features

data science courses in bangalore

Industry-Experienced Trainers

Azure devops Training in Bangalore

Hands-on Training

AWS Devops Training in Bangalore

Live Projects and Case Studies

Frequently Asked Questions

What prerequisites are required for the Data Analytics course?

The Data Analytics course at Ria Academy is designed for beginners. Basic knowledge of statistics and a keen interest in data are beneficial but not mandatory.

How long does it take to complete the Data Analytics Certification Course?

The duration of the course is 20 weeks, ensuring a comprehensive understanding of data analytics concepts and hands-on practice.

Are the certifications recognized in the industry?

Yes, our certifications are widely recognized and valued by industry leaders, enhancing your credibility as a skilled data analyst.

What sets Ria Academy apart from other institutes?

Ria Academy stands out for its industry-oriented curriculum, experienced faculty, and a focus on practical, real-world applications of data analytics.

Is financial assistance available for the Data Analytics course?

We offer flexible payment options and occasional scholarship opportunities. Contact our admissions team for more information.

Student Reviews

Mishal Kassim

Ria Academy has been instrumental in shaping my career in data analytics. The practical approach and industry exposure provided have been invaluable


I chose Ria Academy for its reputation as the best institute for data analytics in Bangalore, and it lived up to its name. The placement support was a game-changer.

Syam K S

The Data Analytics Certification Course not only expanded my knowledge but also gave me the confidence to excel in the competitive field of data analytics.

Contact Us for a Brighter Future in Data Analytics

Ready to kickstart your career in data analytics? Contact us using the form below, and our admissions team will guide you on your journey to success


Get a Call Back

Data Analytics

Course Content

A comprehensive Data Analytics course would cover the foundational concepts, tools, and techniques used in the field of data analytics. Here’s a suggested course content for a Data Analytics training course:

  • What is Data Analytics?
  • Importance and Applications of Data Analytics
  • Difference between Data Analytics, Data Science, and Business Intelligence
  • Types of Data: Structured, Semi-structured, Unstructured
  • Data Sources: Databases, Web, Social Media, IoT, etc.
  • Data Quality and Cleaning
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Inferential Statistics: Hypothesis Testing, Confidence Intervals, p-values
  • Probability Distributions: Normal, Binomial, Poisson, etc.
  • Principles of Data Visualization
  • Tools: Excel, Tableau, Power BI, Matplotlib, Seaborn
  • Creating Dashboards and Storytelling with Data
  • Data Cleaning and Transformation
  • Handling Missing Values
  • Feature Engineering and Selection
  • Understanding Data Distributions
  • Correlation and Covariance Analysis
  • Visualization Techniques for EDA
  • Introduction to R and Python
  • SQL for Data Retrieval and Manipulation
  • Excel Advanced Features: Pivot Tables, Power Query, Power Pivot
  • Regression Analysis: Linear, Multiple, Logistic Regression
  • Time Series Analysis and Forecasting
  • Clustering and Classification
  • Supervised vs. Unsupervised Learning
  • Common Algorithms: Decision Trees, Random Forest, k-Means Clustering
  • Model Evaluation Metrics
  • Introduction to Big Data
  • Tools: Hadoop, Spark
  • Big Data Analytics Use Cases
  • Understanding Business Metrics and KPIs
  • Customer Analytics, Marketing Analytics, Financial Analytics
  • A/B Testing and Experimentation
  • Data Privacy and Security
  • Ethical Considerations in Data Analytics
  • Regulatory Frameworks: GDPR, CCPA, etc.
  • Retail Analytics
  • Healthcare Analytics
  • Social Media Analytics
  • Text Analytics and Natural Language Processing (NLP)
  • Recommendation Systems
  • Deep Learning Basic
  • End-to-End Data Analytics Project
  • Data Collection, Cleaning, Analysis, Visualization, and Interpretation
  • Building a Portfolio
  • Data Analytics Certifications
  • Career Paths and Opportunities