Advanced Topics in a Data Analyst Course Syllabus | IABAC

Advanced Topics in a Data Analyst Course Syllabus | IABAC

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  Advanced Topics ina Data Analyst Course Syllabus iabac.org
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  What is DataAnalytics? Definition: Data analytics is the process of examining datasets to draw conclusions about the information they contain, using specialized systems and software. Key Components: Data Collection: Gathering raw data from various sources (databases, APIs, surveys). Data Processing: Cleaning and organizing data to prepare for analysis. Data Analysis: Using statistical and computational techniques to identify patterns, trends, and insights. Data Visualization: Presenting data in graphical formats to communicate findings effectively. iabac.org
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  Objective: To providea comprehensive understanding of advanced data analysis techniques. Target Audience: Intermediate data analysts looking to enhance their skills. Format: Lectures, hands-on workshops, and project work. Course Overview iabac.org
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  Knowledge Requirements: Basic knowledgeof data analysis tools (Excel, SQL, Python/R). Understanding of fundamental statistics and data visualization. Familiarity with databases and basic programming concepts. Requirements iabac.org
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  Topics Covered: Interactive dashboards(Tableau, Power BI) Storytelling with data Advanced chart types (heatmaps, box plots) Visualizing complex data relationships (network diagrams) Learning Outcome: Create compelling visual narratives that effectively communicate insights. Module 1: Advanced Data Visualization iabac.org
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  Module 2: StatisticalMethods for Data Analysis Topics Covered: Hypothesis testing Regression analysis Time series analysis ANOVA and its applications Bayesian statistics overview Learning Outcome: Apply advanced statistical methods to interpret and analyze real-world data. iabac.org
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  Topics Covered: Supervised vs.unsupervised learning Feature selection and engineering Model evaluation metrics (ROC curves, confusion matrix) Introduction to deep learning concepts Model deployment and maintenance Learning Outcome: Build, assess, and deploy machine learning models for predictive analytics. Module 3: Machine Learning for Data Analysts iabac.org
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  Topics Covered: Data cleaningtechniques Working with APIs Handling missing data Data transformation (normalization, encoding) Introduction to data pipelines Learning Outcome: Efficiently prepare and clean datasets for accurate analysis. 8: Module 4: Data Wrangling and Preparation iabac.org
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  Topics Covered: Introduction toHadoop and Spark Processing large datasets NoSQL databases (MongoDB, Cassandra) Data streaming concepts (Kafka) Use cases for big data in various industries Learning Outcome: Utilize big data tools and technologies to analyze large-scale data. Module 5: Big Data Technologies iabac.org
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  Topics Covered: Understanding dataprivacy regulations (GDPR, CCPA) Ethical considerations in data analysis Responsible data use and bias mitigation Data governance frameworks Learning Outcome: Implement ethical data practices and understand governance policies. Module 6: Data Ethics and Governance iabac.org
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  Module 7: AdvancedSQL Techniques Topics Covered: Window functions and their applications Common table expressions (CTEs) Performance tuning and optimization Data aggregation and summarization Advanced joins and subqueries Learning Outcome: Write complex SQL queries for sophisticated data manipulation and retrieval. iabac.org
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  Topics Covered: Best practicesfor presenting data findings Creating effective reports and dashboards Use of storytelling techniques in data presentations Communicating technical information to non- technical audiences Learning Outcome: Effectively communicate data insights to stakeholders. Module 8: Data Communication and Presentation iabac.org
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