SQL INSTRUCTOR AND PYTHON MENTOR - CANADA LEARNING CODE Sep. 2023 till dateCurriculum Development, Instruction, and Delivery:- Create and update instructional materials, including course outlines, lesson plans, and learning resources.- Develop real-world, practical examples to illustrate SQL concepts and their applications.- Lead classroom or online sessions, delivering SQL lectures, tutorials, and hands-on exercises.- Foster an engaging and interactive learning environment, encouraging questions and discussions.Skill Assessment and Individualized Learning:- Evaluate student performance through assignments, quizzes, and exams to gauge their understanding of SQL or Python concepts.- Adapt teaching methods to accommodate various learning styles and skill levels- Review and provide feedback on students' code, offering suggestions for code optimization, best practices, and style guidelines. - Offer hands-on assistance with Python programming, helping students write, test, and debug Python code. Mentoring:-Act as a mentor and guide for learners at various skill levels, from beginners to advanced SQL and Python developers, and give career advice to students interested in pursuing SQL or Python-related careers.- Provide ongoing support and constructive feedback to help students improve their Python and SQL skills.Learning Resources:- Recommending learning materials, online tutorials, books, and courses to help students self-learn Python and SQL effectively.

Data Collection, Cleaning and Analysis: - Collect and consolidate data from various sources, ensuring data accuracy and consistency. - Clean and preprocess data to eliminate errors, missing values, and outliers. - Use SQL to query databases and extract relevant data for analysis. - Utilize Excel for data manipulation, creating pivot tables, and performing statistical analysis. - Apply Python for data analysis, scripting, and automation.Data Visualization, Reporting and Insights: - Create interactive and informative data visualizations using Power BI. - Develop dashboards and reports to convey insights to non-technical stakeholders. - Prepare and deliver regular reports, highlighting key findings and trends. - Provide actionable insights and recommendations based on data analysis.Data Mining, Predictive Modeling, and Statistical Analysis: - Conduct statistical tests and analyses to identify patterns, correlations, and trends in the data. - Use Python for data mining and predictive modeling to support business forecasting and decision-making.Data Quality Assurance, Collaboration, and Continuous Learning: - Monitor and ensure data quality and consistency, recommending data quality improvements. - Collaborate with cross-functional teams and departments to understand their data needs and provide analytical support while staying updated with industry trends, best practices, and emerging tools related to data analysis.

Data Collection and Analysis: - Collect and consolidate data from various sources, such as databases, spreadsheets, and external data providers.- Analyze data to identify trends, patterns, and correlations, providing actionable insights.Report and Dashboard Creation:- Develop reports, dashboards, and data visualizations using BI tools (Tableau or Power BI) to present insights to stakeholders.- Customize reports to meet the specific needs of different departments or business units.Data Interpretation and Quality Assurance:- Translate complex data into clear, understandable information for non-technical stakeholders.- Collaborate with business teams to define key performance indicators (KPIs) and metrics.- Monitor and maintain data quality, identifying and addressing discrepancies and errors.- Implement data validation processes and data cleansing as needed.Performance Monitoring, Data Forecasting, and Competitive Analysis: - Track and monitor business performance against established KPIs. - Generate alerts and notifications when performance deviates from expected levels.- Use historical data and statistical analysis to create forecasts and predictive models. - Assist in budgeting and long-term planning based on data-driven predictions.- Research and analyze market and industry trends, comparing the organization's performance to competitors.- Provide recommendations for improving competitiveness.Data Governance and Compliance:- Ensure data compliance with relevant regulations (e.g., GDPR) and maintain data governance policies.- Develop and maintain data dictionaries and metadata for effective data management.Stakeholder Communication:- Collaborate with various departments and teams to understand their data needs.- Present findings and insights to executives and stakeholders, communicating the impact on business strategy.

Data CapturingData EntryData ManagementData Analysis