PCED™ – Certified Entry-Level Data Analyst with Python

Ziele

In dieser 5-tägigen Schulung "PCED™ – Certified Entry-Level Data Analyst with Python" erwerben Sie Fähigkeiten Daten effizient mit Python zu erfassen, zu bereinigen und zu validieren. Der Fokus liegt auf praxisnahen, anwendungsorientierten Fähigkeiten in Python, insbesondere in den Bereichen Datenerfassung, Datenbereinigung und Standardisierungstechniken, sowie auf grundlegenden Kenntnissen in Programmierung und Datenbankmanagement. Darüber hinaus werden Sie darauf vorbereitet, statistische Analysen durchzuführen und grundlegende Methoden des maschinellen Lernens in Python anzuwenden, um reale Datenherausforderungen zu bewältigen. Dabei kommen essenzielle Python-Bibliotheken wie Pandas und NumPy sowie Visualisierungstools wie Matplotlib und Seaborn zum Einsatz. Nach Abschluss der Schulung sind Sie in der Lage, komplexe Datensätze zu verarbeiten, fundierte statistische Analysen durchzuführen und aussagekräftige Datenvisualisierungen zu erstellen. Diese Zertifizierung bietet eine solide Grundlage für den Einstieg in die Welt der Datenanalyse und ebnet den Weg für weiterführende Karrieren in den Bereichen Data Science und Analytics.

Zielgruppe

• Data Analyst

Voraussetzungen

• PCEP & PCAP (or equivalent) + field-specific skills

Agenda

Data Acquisition and Pre-Processing

• Data Collection, Integration, and Storage
• Understand different data collection methods and their roles in decision-making and research
• Explain the data gathering process and various data sources
• Aggregate data from multiple sources and integrate them into datasets
• Explain various data storage solutions

Data Cleaning and Standardization

• Understand structured and unstructured data and their implications in data analysis
• Identify, rectify, or remove erroneous data
• Understand data normalization and scaling
• Apply data cleaning and standardization techniques

Data Validation and Integrity

• Execute and understand basic data validation methods
• Establish and maintain data integrity through clear validation rules

Data Preparation Techniques

• Understand File Formats in Data Acquisition
• Access, manage, and effectively utilize datasets
• Extract data from various sources
• Enhance data readability and format in spreadsheets
• Prepare, adapt, and pre-process data for analysis.
• Understand the importance of the surrounding context, objectives and stakeholder expectations to guide the preparation steps

Python Proficiency

• Apply Python syntax and control structures to solve data-related problems
• Analyze and create Python functions
• Evaluate and navigate the Python Data Science ecosystem
• Organize and manipulate data using Python's core data structures
• Explain and implement Python scripting best practices
• Import modules and manage Python packages using PIP
• Apply basic exception handling and maintain script robustness

SQL for Data Analysts

• Perform SQL queries to retrieve and manipulate data
• Execute fundamental SQL commands to create, read, update, and delete data in database tables
• Execute fundamental SQL commands to create, read, update, and delete data in database tables
• Establish connections to databases using Python
• Execute parameterized SQL queries through Python to safely interact with databases
• Understand, manage and convert SQL data types appropriately within Python scripts
• Understand essential database security concepts, including strategies to prevent SQL query injection

Descriptive Statistics

• Understand and apply statistical measures in data analysis
• Analyze and evaluate data relationships

Inferential Statistics

• Understand and apply bootstrapping for sampling distributions
• Explain when and how to use linear and logistic regression

Data Analysis with Pandas and NumPy

• Manage data effectively with Pandas
• Understand and Utilize the Relationship Between DataFrame and Series in Pandas
• Perform Array Operations and Differentiate Data Structures with NumPy
• Apply and Analyze Data Organization Techniques in Pandas and NumPy

Statistical Methods and Machine Learning

• Apply Python's descriptive statistics for dataset analysis
• Recognize the importance of test datasets in model evaluation
• Analyze and Evaluate Supervised Learning Algorithms and Model Accuracy

Data Visualization Techniques

• Demonstrate essential proficiency in data visualization with Matplotlib and Seaborn
• Assess the pros and cons of different data representations
• Label, annotate, and test insights from data visualizations
• Improve the clarity and accuracy of data interpretation by managing display features such as colors, labels and legends

Effective Communication of Data Insights

• Tailor communication to different audience needs, and combine visualizations and text for clear data presentation
• Summarize key findings and support claims with evidence and reasoning