Advanced Data Analysis with Python - eLearning
450,00 EUR
- 30 hours
Unlock the power of data with Advanced Data Analysis using Python and transform raw information into meaningful business insights. This comprehensive course is designed to help you master modern data analysis techniques using Python’s most powerful libraries, including NumPy, Pandas, and Matplotlib.
Key Features
Language
Course and material in English
Level
Intermediate-advanced level
Access
1 Year access to the learning platform
9+ Hours of On-Demand Videos
with 30+ hours recommended study time
8 Auto-Graded Assessments
29 Guided Hands-On Exercises
4 Assignments
and 55 Recall Quizzes
Certification
Program completion certification included
Learning Outcomes
At the end of this Course, you will be able to:
Manipulation
Use Python for advanced data manipulation and analysis
Pandas
Clean, transform, and preprocess complex datasets using Pandas
NumPy
Perform numerical computations efficiently using NumPy
Matplotlib
Create meaningful data visualizations using Matplotlib
Exploratory
Apply exploratory data analysis (EDA) techniques to uncover insights
Data format
Work with structured and unstructured data formats
Decision
Perform statistical analysis for better decision-making
Datasets
Combine multiple datasets for deeper analytical insights
Problem Solving
Build confidence in solving real-world data problems using Python
Course timeline
Exploratory Data Analysis in Python
Lesson 01
- What is EDA?
- Profiling a Dataset
- Summarizing Categorical Variables
- Summarizing Quantitative Variables
- Merging Datasets
- Reshaping Datasets
- Correlation
Confirmatory Data Analysis in Python
Lesson 02
- Introduction to CDA
- From Exploration to Confirmation
- Case Study: A/B Testing
- Checking for Normality
- Basics of Linear Regression
- Regression with Stats Models
- Independent Samples with T-Test
- Multiple Regression
Intermediate Pandas
Lesson 03
- Introduction to Intermediate Pandas
- Multi-Index
- Dates and Times
- Resampling
- Window Functions
- Missing Values
- Recoding Values
- Regular Expressions
Unstructured Data in Python
Lesson 04
- Overview of Unstructured Data
- Pre-Processing Images
- Image Segmentation
- Image Representation
- The Basics of NLTK
- Accessing Corpora
- Processing Corpora
- Analyzing and Tagging Corpora
Working with APIs in Python
Lesson 05
- What is an API?
- REST API Basics
- REST APIs in Python
- Streaming APIs in Basics
- Streaming APIs in Python
- Processing Streaming Data
- API Token Best Practices
Parallel Computing with Dask
Lesson 06
- Dask and Parallel Computing
- Dask Dataframes
- Processing Data in Dask
- Operating on Dask Dataframes
- Visualizing Dask Dataframes
- Visualizing Performance with Dask Dashboard
Intermediate Seaborn
Lesson 07
- Visualizing Distribution
- Custom Themes and Color Pallettes
- Custom Fonts and Annotations
- Jointpilot and Jointgrid
- Visualizing Regression
- Facetgrid
Interactive Visualization with Plotly and Bokeh
Lesson 08
- Static vs Dynamic Visualizations
- The Basics of Plotly
- Plotly Express in Depth
- Graph Objects
- Introducing Bokeh
- Customizing Bokeh Plots
- Customizing Bokeh Layouts
Who Should Enroll in This Program?
Prerequisites
- Basic understanding of Python programming
- Familiarity with fundamental programming concepts (variables, loops, functions)
- Basic knowledge of mathematics or statistics is helpful but not mandatory
- Interest in data analysis and problem-solving
Aspiring data analysts and data scientists
Software developers transitioning into data roles
Business analysts working with data-driven decisions
Professionals looking to upgrade Python and analytics skills
Statements
Licensing and accreditation
This course is offered according to Partner Program Agreement and complies with the License Agreement requirements
Equity Policy
Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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