ANSI SQL Part I: Master Database Fundamentals & Query Techniques

Uzair Aslam is an accomplished Data Scientist with extensive expertise in machine learning, artificial intelligence, and advanced analytics. With a proven track record of delivering transformative data-driven solutions across the financial services and telecommunications sectors, he brings a unique combination of technical excellence and business acumen to every project. Currently serving as AVP - Senior Data Scientist at Maybank, Uzair has spearheaded the development of cutting-edge fraud detection models that have revolutionized risk management processes. His innovative machine learning solution reduced false positive review time by 75%, saving approximately 2,940 man hours and significantly improving operational efficiency. He has successfully engineered sophisticated fraud detection systems combining Computer Vision and Natural Language Processing, utilizing OCR technology for document analysis and anomaly detection. His leadership extends to managing cross-functional teams and developing automated business intelligence solutions, including news web-scraping applications for risk event monitoring across the Asia Pacific region. Uzair's professional journey includes valuable experience as a Specialist Data Scientist at The CADS, where he provided strategic advisory services to major international clients including Petronas, Shell, and CIMB. His consulting expertise focuses on translating complex business requirements into actionable data science use cases. He has also demonstrated exceptional training capabilities, delivering corporate training programs on Machine Learning, Deep Learning, and Natural Language Processing to diverse professional audiences. With a Master's degree in Data Science & Business Analytics from Asia Pacific University (achieved with Distinction) and a Bachelor's in Electronics Engineering, Uzair possesses a solid academic foundation complemented by numerous industry certifications. His technical proficiency spans Python, R, SQL, Spark, SAS, and HiveQL, along with expertise in modern data science platforms including Tableau, Power BI, DataRobot, H2O.ai, and Dataiku. His project portfolio demonstrates versatility across fraud detection, credit scoring, opinion spam detection, electricity load forecasting, and neural network applications, showcasing his ability to apply advanced statistical and machine learning concepts to solve real-world business challenges.

Mohammad Mehdi Lotfinejad is a distinguished Chief Data Officer and certified HRDF trainer with over 15 years of experience in computer science instruction and professional data science training. Currently serving as Chief Data and Knowledge Officer at Magna.ai, a Florida-based lawtech company, he leads the development of sophisticated graph databases, data warehouses, and API solutions that power AI-driven legal case analysis systems. His expertise spans the entire data ecosystem, from architecture design to workflow automation and team leadership. With a robust background encompassing more than a decade of hands-on experience in data engineering and data science, Mohammad has successfully implemented enterprise-scale data processing pipelines across multiple industries and geographies. His professional journey includes senior roles at Axiata Digital Advertising (ADA) in Malaysia, where he designed and deployed data pipelines using AWS Redshift, Snowflake, Apache Spark, and Apache Airflow, and at The Center of Applied Data Science, where he led teams delivering training solutions to major corporations including CIMB, PETRONAS, SHELL, and TNB. Mohammad's technical proficiency is comprehensive and current, encompassing cloud platforms (AWS, Google Cloud), data warehousing solutions (Redshift, Snowflake), big data technologies (Apache Spark, Hadoop, Hive), workflow orchestration (Apache Airflow), and multiple database systems (MySQL, PostgreSQL, MongoDB). He holds a Harvard Business School certification in Business Analytics and multiple AWS certifications, including specialized credentials in Big Data, Data Warehousing, and Practical Data Science with Amazon SageMaker. As an educator, Mohammad brings exceptional depth to his training delivery. His academic career includes faculty positions at Payame Noor University and Islamic Azad University, where he served in leadership roles including Chancellor and Department Head. He has authored three technical books and published numerous peer-reviewed papers in international journals. His teaching repertoire covers data engineering, data science, machine learning, software development, and computer architecture, delivered through engaging, hands-on methodologies that bridge theoretical concepts with practical industry applications. Mohammad's unique combination of executive leadership, technical expertise, and proven training capabilities makes him an invaluable resource for organizations seeking to develop their data science and engineering capabilities. His ability to translate complex technical concepts into actionable learning outcomes, coupled with his extensive real-world implementation experience, ensures that training participants gain immediately applicable skills that drive business value.

Dr. Mohammed Al-Obaydee is a distinguished academic and industry professional with extensive expertise in artificial intelligence, machine learning, deep learning, and data science. As an Adjunct Associate Professor at Taylor's University Malaysia and a certified Train the Trainer professional, he brings a unique blend of academic rigor and practical industry experience to his teaching and consulting engagements. With a Ph.D. in Information Science from Universiti Kebangsaan Malaysia (UKM) and over two decades of experience spanning academia and industry, Dr. Al-Obaydee has established himself as a leading authority in advanced computing technologies. His research excellence is evidenced by multiple best paper awards, including recognition at IEEE ACSAT2014 and ITNG 2023 conferences, along with an impressive publication record featuring over 149 citations and an h-index of 6. His research focuses on medical image analysis, computer vision, machine learning applications, and deep learning techniques. As a founder of INNOEYETIVE and AIC4ALL, Dr. Al-Obaydee demonstrates entrepreneurial acumen in leveraging AI and immersive technologies for digital transformation. His industry experience includes successful training and consultation projects with major corporations such as HSBC Bank, OCBC Bank, DELL Malaysia, Amway Malaysia, and EtiQa, where he has delivered specialized programs in data science, analytics, and applied AI engineering. His ability to bridge the gap between theoretical concepts and practical applications makes him an exceptional trainer for professionals seeking to upskill in emerging technologies. Dr. Al-Obaydee's teaching portfolio encompasses a comprehensive range of subjects including Machine Learning, Deep Learning, Data Analytics, Data Engineering, Parallel Computing, and Advanced Data Structures. He has supervised numerous master's students and research projects at both Taylor's University and the International Islamic University Malaysia (IIUM), focusing on cutting-edge topics such as deep reinforcement learning, medical image classification, fraud detection, and natural language processing. His international speaking engagements and extensive training experience across multiple organizations demonstrate his exceptional communication skills and ability to make complex technical concepts accessible to diverse audiences.