The Python Data Analytics syllabus is designed to help analysts, researchers, BI experts and developers becoming fluent in the use of the Python programming language, with the purpose of automating tasks such as acquisition, cleaning and analysis of digital data.
Using interactive examples and hands-on exercises, the course starts with an introduction to the core concepts of Python programming, which provides participants with the foundations to achieve a high degree of independence and flexibility for their analysis process. The course then continues with a focus on how Python tools can help the audience performing data wrangling and data
analysis effectively.
By attending this course you’ll learn:
• How to load data from different sources, like Excel files and SQL databases, using Python
• How to extract statistical information from your data
• How to query your data by rows/columns and with specific conditions
• How to perform data aggregations
• How to work with time series (indexing by time, resampling, moving average, etc)
• How to plot data effectively
Quants, Data Scientists, Data Analysts, Financial Analysts, Business Intelligence experts who are new to Python.
Software developers who are new to Python and Data Science or want to know more about the Python tools for Data Analysis.
• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook
• Built-in data types in Python
• Working with strings, numbers, lists, tuples and dictionaries
• Control flow statements
• Conditional execution with if statements
• Conditional loops with where statements
• Looping over a sequence with for statements
• Defining and using custom functions
• Working with dates and times
• Accessing data on file (CSV, JSON, ...)
• Working with data in pandas
• Retrieving data in pandas
• Data manipulation in pandas
• Handling missing data in pandas
• Data Analysis in pandas
• Data Visualisation in pandas
• Working with multiple tables
• Time Series with pandas
• Working with text data in DataFrames
• SQL databases
• NumPy
• Data Visualisation with matplotlib and plotly
Classroom or Remote
Remote
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