Business analytics/Data analytics/ Data science Course Certification

Python is a versatile programming language widely used in data analysis, machine learning, web development, automation, and more.
In data analytics, Python is popular for its libraries such as Pandas (data manipulation), NumPy (numerical computing), Matplotlib and Seaborn (data visualization), Scikit-learn (machine learning), TensorFlow and PyTorch (deep learning), etc.
Python is used for data preprocessing, analysis, modeling, and deployment of machine learning models.

Tableau is a powerful data visualization tool that allows users to create interactive and shareable dashboards and reports.
It connects to various data sources such as Excel, SQL databases, cloud services, etc., enabling users to analyze and visualize data in real-time.
Tableau supports drag-and-drop functionality, making it user-friendly for both technical and non-technical users.

SQL is a programming language used for managing and manipulating relational databases.
It allows users to perform tasks such as querying data, inserting/updating/deleting records, creating and modifying database structures (tables, views, indexes), and managing user permissions.
SQL is essential for extracting, transforming, and analyzing data stored in databases for reporting and analysis purposes.

Power BI is a business analytics tool by Microsoft used for creating interactive reports and dashboards.
It integrates with various data sources and services, enabling users to connect, transform, and visualize data for insightful decision-making.
Power BI offers features like data modeling, DAX (Data Analysis Expressions) for calculations, advanced visualizations, and sharing/collaboration capabilities.

Advanced Excel skills involve proficiency in functions, formulas, data analysis tools, and pivot tables/charts.
It includes features such as conditional formatting, data validation, array formulas, VBA (Visual Basic for Applications) programming, etc.
Advanced Excel skills are essential for data manipulation, analysis, and reporting tasks, especially for handling large datasets and complex calculations.

Advanced statistics involves knowledge and application of advanced statistical techniques beyond basic descriptive statistics.
It includes methods such as regression analysis, time series analysis, multivariate analysis, hypothesis testing, experimental design, Bayesian statistics, etc.
Advanced statistics are used for analyzing complex datasets, identifying patterns, making predictions, and deriving meaningful insights.

R is a programming language and environment specifically designed for statistical computing and graphics.
It offers a wide range of packages for data manipulation, visualization, statistical modeling, machine learning, and more.
R is widely used in academia, research, and industries such as healthcare, finance, and marketing for data analysis and statistical computing tasks.

What We Do