Master Data Science with Python - From Zero to Master

U1: Introduction to Data Science with Python

→ Introduction to Python for data analysis
→ Setting up Python and Jupyter
→ Load libraries and data sources
→ Basic graphics with Matplotlib
→ Practical exercise: Basic graphics with Matplotlib
→ Flowchart in a Data Science project


U2: Python Fundamentals from Scratch

→ Handling variables in Python
→ Lists: creation, extraction and modification
→ Advanced techniques for creating lists
→ Using internal functions in Python
→ How to create functions and use flexible arguments in Python
→ Using lambda functions
→ Methods in programming with Python
→ Creating dictionaries in Python
→ Creating dictionaries with the zip function
→ Using Comparators in Python
→ Loop structures in Python
→ List Comprehension in Python

U3: Basic Statistics for Data Analysis

→ Fundamentals of variables and basic concepts
→ ​Calculating the variance of a variable
→ ​Analysis of the correlation of variables
→ ​Creating and analyzing histograms
→ Studying data with percentiles
→ Calculation of probability density functions
→ Forecasting and calculation of moving averages

U4: Numpy for numerical computation

→ Numpy library basics
→ Selecting data using arrays in Numpy
→ ​Working with 2D arrays in Numpy
→ Statistical operations with Numpy

U5: Pandas for Data Analysis

→ Pandas Basics: Introduction to Dataframes
→ ​Generating dataframes from dictionaries
→ ​Import data from plain text files
→ Data selection techniques in Pandas dataframes
→ Useful tools for dataframes in Pandas
→ Data cleaning in data frames: duplicates and errors
→ Data interpolation techniques
→ ​Applying filters to Pandas dataframes
→ Sorting data in a dataframe
→ ​Add columns with text strings in dataframes
→ Generating columns in dataframes using maps and dictionaries
→ Using lambda functions to create columns in dataframes
→ Creating conditional columns in dataframes
→ Modifying column names and order in dataframes
→ Creating pivot tables with Pandas
→ Grouping data with groupby in Pandas
→ ​Union of data frames by concatenation
→ Combining data frames using merge

U6: Import and Export with Pandas

→ Importing data from Excel files
→ ​Fundamentals of relational databases and data models
→ Importing data from SQL databases
→ Extraction of data from a web page
→ Web scraping techniques to import web data
→ Importing data from JSON files
→ Data extraction from social networks
→ ​Load data from Cloud platforms (AWS, Azure, Google)
→ Export data to CSV and Excel files
→ Exporting data to SQL databases

U7: Data Visualization in Python - Matplotlib Library

→ ​Recommendations for data visualization
→ Matplotlib Basics
→ How to create line, bar and scatter charts
→ Advanced graphics customization
→ How to create box and whisker plots
→ Generation of histograms and cumulative distribution functions
→ Creating charts with moving averages
→ Using subplots for multiple graphs
→ How to apply styles to charts
→ Charts using groupby objects
→ Generation of two-dimensional histograms
→ Geographic maps with basemap

U8: Data Visualization in Python - Seaborn Library

→ ​Seaborn Library Basics
→ Linear regression with Seaborn
→ ​Creating stripplots with Seaborn
→ Creating swarmplots with Seaborn
→ ​Creating violinplots with Seaborn
→ Creating jointplots with Seaborn
→ Creating pairplots with Seaborn
→ Visualizing correlations with heatmaps

U9: Using Time Series in Python

→ Extraction and analysis of time series
→ ​Time series filtering
→ Resampling techniques in time series
→ Data interpolation in time series
→ How to visualize time series
→ Generating forecasts using historical data

U10: Expanding the use of Python with other platforms

→ Task automation with Python scripts
→ Python in BI tools like Power BI

U11: Conclusions

→ Conclusions

Course evaluation

→ This course contains a final exam

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