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!

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!

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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.