Free Download Data Analytics Zero to Hero 5 Experts from FAANG (Advanced)Published 1/2026
Created by H MetaCode
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert |
Genre: eLearning |
Language: English |
Duration: 133 Lectures ( 36h 15m ) |
Size: 30.5 GB
Develop the ability to analyze large-scale platform and product datasets to extract business-critical insights.
Apply analytical techniques to media data, evaluating campaign performance, engagement, and attribution.
Understand and implement foundational machine-learning concepts, including supervised and unsupervised models.
Use Python to clean, transform, visualize, and model data across multiple domains.
Perform financial data analysis, identifying patterns, risks, trends, and forecasting opportunities.
Conduct advanced Python-based analysis on insurance and medical datasets to uncover domain-specific insights.
Build end-to-end analytical workflows that integrate industry datasets, modeling techniques, and actionable reporting.
RequirementsBasic Python, SQL, and Visualization Skills
DescriptionThis advanced data analytics program is designed for learners who are ready to apply analytical skills to real-world domains and specialized industries. Through a structured progression of platform data, media data, financial analytics, and machine-learning fundamentals, this course equips you with the capabilities required in modern data-driven organizations. You will work with real datasets, explore practical business scenarios, and use Python-based analytical techniques to solve complex problems across multiple industries.The course begins with Using Data Analysis Tools for Platform and Product Data, where you learn to interpret customer behavior, product performance, funnel metrics, and retention patterns. You will practice analyzing large-scale platform datasets to uncover growth drivers and improve product decision-making.Next, you dive into Media Data Analytics, where you work with advertising metrics, campaign performance, impression data, attribution modeling, and engagement metrics. By exploring how media organizations measure content performance and optimize marketing efforts, you will learn how analytics drives strategy in digital media environments.You then transition into Introduction to Machine Learning, gaining a solid foundation in supervised and unsupervised learning. You will learn essential concepts such as model evaluation, feature engineering, linear models, classification algorithms, clustering, and basic pipelines-preparing you for more specialized domain applications.The course expands into industry-level use cases through Financial Data Analytics, where you analyze revenue patterns, risk indicators, transaction behavior, and forecasting. You will apply advanced analytical techniques to financial datasets commonly used in banking, fintech, and investment analytics.Building on this foundation, you progress into two specialized Python analysis modules: Advanced Python for Insurance Data and Advanced Python for Medical Data. Here, you will work with domain-specific datasets such as claim histories, underwriting indicators, patient records, and medical cost data. You will perform high-level feature extraction, pattern detection, and risk modeling-skills essential for analysts working in insurance or healthcare data environments.By the end of this course, you will have hands-on experience analyzing multiple forms of real-world data using Python, SQL, and machine-learning principles. You will be able to handle complex datasets, interpret industry-specific insights, and solve practical business problems across platform, media, finance, insurance, and medical domains. This course is ideal for learners who want to elevate their career into advanced analytics, data science, and applied machine learning roles.
Who this course is forThis course is designed for learners who want to advance their data analytics skills using Python, SQL, and machine-learning techniques to analyze real-world platform, media, financial, insurance, and medical datasets.
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