Data Science

This 2-day Python for Machine Learning course teaches professionals how to build supervised ML models using pandas, NumPy, scikit-learn, and Matplotlib—covering prediction, evaluation, visualization, and deployment.

The Leading Data Analytics Projects (3 Days) course empowers managers, analysts, and data leaders to design and execute successful analytics initiatives. Participants will explore the Analytics Project Lifecycle (APLC) — from business framing and prototype development to deployment and ROI measurement. Through interactive discussions and real-world case applications using Power BI, Tableau, and Python, learners will gain the skills to interpret data patterns, build analytics strategies, and manage team capacity for scalable insights. This program bridges business strategy and technical execution, equipping participants to lead data-driven transformation and ensure measurable business impact through analytics.

Data is at the core of every business decision today. The 2-day Advanced Data Analytics and Visualization course equips participants with practical skills to analyze, interpret, and visualize data for actionable insights. Using tools like Microsoft Excel, Power BI, Python, and Tableau, participants will explore key analytics concepts, from data cleaning and basic statistics to dashboard creation and predictive insights. The program blends theory with hands-on exercises to help learners understand data storytelling, KPI monitoring, and trend analysis. Ideal for professionals and executives, this course builds confidence in transforming raw data into compelling visuals that drive better decision-making across any organization.

The Certified Business Analytics Professional (CBAP) program is a 5-day practical course designed to equip participants with the full spectrum of analytics skills using the R programming language. Learners will gain hands-on experience in data management, statistical analysis, data visualization with ggplot2, and advanced modelling techniques including regression, logistic regression, segmentation, time-series forecasting, and decision trees. The course also covers text mining, document classification, and sentiment analysis using R’s text analytics ecosystem. Through guided exercises and real case studies, participants learn how to clean data, interpret statistical outputs, build predictive models, and translate insights into actionable business recommendations. This program is ideal for professionals who want to build strong, job-ready analytics capability using R.

The Basic Statistics and Data Analytics (3 Days) course introduces beginners to the essential skills needed to analyze and visualize data effectively. Participants will learn how to use Python, Pandas, Matplotlib, and Seaborn to clean, transform, and explore datasets. The program covers descriptive statistics, exploratory data analysis (EDA), and regression fundamentals, enabling learners to extract meaningful insights and present them visually. Designed for professionals, students, and aspiring data analysts, this hands-on course provides practical experience using real-world datasets, building a solid foundation for future learning in data analytics, machine learning, and AI.

This 3-day Foundation of Statistics for Data Analytics course equips participants with the statistical foundations and applied modelling skills needed to extract business insights from data. Learners will cover exploratory data analysis, hypothesis testing, clustering and decision-tree techniques, prototype algorithm development, and methods for validating predictive models. Emphasis is placed on translating statistical outputs into actionable recommendations and interactive visual storytelling. Through hands-on case examples and model prototyping, participants will learn to identify meaningful patterns, build simple predictive models, and communicate findings that support business decisions. The program is ideal for aspiring data analysts, BI professionals, and managers who want a practical bridge from statistics to data-driven outcomes.

The Advanced Statistical Analysis Techniques (3 Days) course equips professionals and researchers with practical expertise in data-driven decision-making. Through hands-on exercises using Microsoft Excel and SPSS, participants will learn to apply advanced statistical methods including regression, correlation, ANOVA, and hypothesis testing. The course bridges theory and practice, helping learners master statistical modeling and data interpretation in real-world contexts. Designed for analysts, researchers, and business professionals, this training builds the confidence to perform and evaluate complex statistical analyses accurately and effectively — essential for advanced analytics roles and data-informed strategies.