Advanced Data Analytics

Financial support available*


Welcome to our comprehensive course on data analysis! Our course offers a thorough introduction to programming skills essential in data analysis. We will introduce languages like Python, R, and SQL, and explore the implementation of algorithms and data structures to efficiently handle and analyze extensive datasets. The course will mainly feature on advanced Statistics and Microsoft Excel. Besides, you will explore tools and frameworks such as MS Excel, Power BI, and Tableau, which are essential for data analysis and visualization. You will learn how to extract meaningful insights from complex datasets and present your findings effectively. Our course is designed to provide a solid foundation in statistical concepts and mathematical techniques dedicated explicitly to data analysis. You will learn about probability distributions, hypothesis testing, regression analysis, and much more, making informed decisions and drawing reliable conclusions from data-driven experiments and observations. By enrolling in our course, you’ll take a significant step towards becoming a data analysis expert. Join us today!


Module 1: Introduction to Analytics Explore the fundamentals of analytics, learning key concepts and tools to make informed decisions. Gain a foundational understanding of data analysis techniques and their applications.

Module 2: Data Preparation Dive into the crucial phase of data handling. Master data cleaning, transformation, and integration techniques, ensuring quality and reliability for effective analysis and decision-making.

Module 3: Data Visualization Learn to create compelling charts and graphs to communicate insights effectively. Transform raw data into meaningful visuals for better understanding and decision support.

Module 4: Statistical Analysis Understand and apply statistical methods to draw meaningful conclusions from data. Gain the skills to analyze patterns, trends, and relationships for informed decision-making.

Module 5: Big Data Explore technologies and techniques for handling and processing large volumes of data. Learn to extract valuable insights from bigdata for strategic decision-making.

Module 6: Machine Learning & AI Step into the future of data-driven intelligence. Explore machine learning algorithms and AI applications. Acquire the skills to build predictive models and unlock the potential of automated decision-making.

Throughout the course, students will not only learn the theoretical concepts but also apply their knowledge through practical exercises and real-world case studies. The combination of theoretical and hands-on experience will enable them to become proficient data analysts by the end of the program.

This course is a steppingstone towards a rewarding career in the data analytics field, offering participants the skills and expertise they need to excel in an ever-evolving and data-rich business environment. We look forward to welcoming you on this exciting educational journey!

Case Studies - Data Labs - Projects

Welcome to Softosmith! Explore the world of data analytics through our course featuring 3 diverse case studies. These real-world examples offer insights into our problem-solving approach and demonstrate our success in various scenarios. Discover our commitment to excellence and innovation in data analytics. If you have specific questions or want to delve into a case, reach out—we’re here to guide you!

Personalized meeting for registered students

What you will learn

Details to know


Perform a self-evaluation of analytical thinking by providing particular instances of how analytical thinking has been applied.

Utilize Excel for fulfilling fundamental responsibilities of a data analyst, such as inputting and arranging data.
Explain databases, highlighting their functions and components, while incorporating effective methods for organizing data.

Implement fundamental SQL functions to cleanse string variables within a database.

Explain the significance of arranging data before analysis, incorporating the use of sorts and filters.
Explain the application of data visualizations in communicating data and the outcomes of data analysis.
Explain the R programming language and its programming environment.
Distinguish between a capstone, case study, and a portfolio.