Data Analytics

Data Analytics involves the systematic analysis of raw data to uncover meaningful patterns, insights, and trends that can inform decision-making and drive business strategies.

It encompasses a range of techniques, from statistical analysis and data mining to machine learning, to extract valuable information from large and complex datasets.

Data Analytics is widely employed in diverse industries to gain a deeper understanding of customer behavior, optimize operations, and enhance overall organizational performance.

By leveraging statistical models, visualization tools, and data processing technologies, analysts can transform raw data into actionable intelligence, enabling businesses to make data-driven decisions and stay competitive in today's information-driven landscape.Explore the world of data with our specialized Data Analytics Training in Chennai.

Syllabus:

• Introduction to Data Analytics:
• Overview of Data Analytics and its applications.
• Key concepts in data analysis and statistics.
• Data Exploration and Cleaning:
• Techniques for exploring and understanding datasets.
• Cleaning and preprocessing data for analysis.
• Statistical Analysis and Inference:
• Descriptive statistics and data visualization.
• Inferential statistics and hypothesis testing.
• Introduction to Programming (Python/R):
• Basic programming concepts.
• Coding fundamentals using Python or R.
• Data Wrangling and Manipulation:
• Data wrangling using libraries like Pandas (Python) or dplyr (R).
• Handling missing data and outliers.
• Data Visualization:
• Principles of effective data visualization.
• Tools and techniques for creating visualizations.
• Predictive Analytics:
• Introduction to predictive modeling.
• Building and evaluating predictive models.
• Machine Learning for Data Analytics:
• Overview of machine learning algorithms.
• Applying machine learning to solve data analytics problems.
• Database Management and SQL:
• Basics of database management.
• Writing SQL queries for data retrieval.
• Data Analytics Tools and Software:
• Introduction to popular data analytics tools (e.g., Excel, Tableau, Power BI).
• Hands-on exercises using these tools.