Why this course?

Description

curriculum designed to equip learners with fundamental and advanced skills in data analytics and visualization. It covers essential programming with Python, utilization of leading visualization tools such as Tableau and Power BI, foundational statistical concepts, and practical data analysis using Microsoft Excel. Integrated assignments and projects provide hands-on experience, fostering proficiency in interpreting and presenting data to inform decision-making.

Module 1: Python for Data Analytics

• This module introduces the core concepts of Python programming essential for data analytics. Topics include Python basics, data structures, programming fundamentals, and the use of specialized libraries such as Pandas for data manipulation, NumPy for numerical computation, and visualization libraries Matplotlib and Seaborn. Learners consolidate their understanding through a series of practical assignments supported by downloadable learning materials.

Module 2 to 6: Tableau for Data Visualization

• These modules provide an in-depth study of Tableau, a premier data visualization platform. Beginning with introductory concepts, learners progress to advanced techniques including the creation of bins and calculated fields, use of joins, and development of comprehensive visualizations. The curriculum culminates in a project creating a COVID-19 dashboard, which integrates the knowledge acquired throughout these modules.

Module 7 to 12: Power BI for Interactive Visualizations

• Power BI is explored through practical instruction on installation, query editing, and management of data relationships. Learners gain expertise in creating measures and calculated columns, applying conditional formatting, and employing popular visualization types. A capstone project involving HR data analytics demonstrates the application of these skills to real-world business scenarios.

Module 13: Statistics and Probability for Data Science

• This module provides foundational knowledge of statistics and probability theory critical to data analysis. Learners study key concepts through structured content and downloadable resources that underpin data-driven modeling and inference.

Module 14 to 18: Microsoft Excel for Data Analysis

• Focusing on Microsoft Excel, these modules cover pivot tables, formatting, sorting, filtering, grouping, and advanced features such as calculated fields and pivot charts. Real-world case studies—including salary data analysis, stock market trends, and weather data—facilitate practical understanding and skill application.

Applied Projects

• The curriculum concludes with a series of applied machine learning projects aimed at reinforcing analytics concepts. Projects include train-test model building, credit score classification, and stress prediction using machine learning techniques. Each project is supplemented with downloadable resources, enabling learners to engage in end-to-end data science workflows.