About the course
If your data analyst team is looking to break free from the limitations of traditional tools like Excel, this hands-on training is your key.
You'll learn how to harness the speed and flexibility of Python, combined with the powerful data manipulation and numerical computation capabilities of Pandas and NumPy, to transform your analytical processes.
This course provides a significant boost to your existing toolkit, while also offering Python developers a focused route into the dynamic world of Data Science.
Expect practical exercises and interactive sessions that allow you to apply your learning directly to real-world data science scenarios, from exploratory analysis to predictive modeling.
Online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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- Write foundational Python programs, utilising core data types, control flow statements, and custom functions.
- Efficiently handle and process raw data from various file formats (e.g., CSV, JSON) and manage date/time objects.
- Apply fundamental Object-Oriented Programming (OOP) principles to structure Python code.
- Perform comprehensive data manipulation using Pandas, including loading, cleaning, and transforming table-like data with DataFrames.
- Conduct exploratory data analysis (EDA), deriving summary statistics and aggregating data with Pandas' groupby() method.
- Create compelling data visualisations directly from Pandas DataFrames to communicate insights effectively.
- Integrate and combine datasets through various join and merge operations with DataFrames.
- Leverage NumPy for high-performance numerical computing, performing essential array operations and statistical analysis.
- Interact with relational databases using Python and SQL, integrating database queries with Pandas for data analysis.
- Understand the principles of scalable data processing with an introduction to tools like PySpark, Polars, and DuckDB.
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This course is aimed at Data Analysts, Data Scientists, and software developers who want to get a practical introduction to Data Analysis with Python.
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You will ideally have some prior experience programming or applying statistical analysis techniques and / or be a Excel super user.
Any exposure to coding with Python would be beneficial but we will give you "just enough" Python pointers to be able to create real-world workable solutions to data analysis challenges.
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This Data Analysis with Python course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Handling Python
Core data types in Python
Control flow statements
Defining and using custom functions
The Python standard library
Working with data:
Iteration and list comprehensions
Accessing raw data on file (CSV, JSON, ...)
Working with dates and times
Basics of Object-Oriented Programming in Python
Playing with pandas
Working with table-like data in pandas
Essential operations with Series and DataFrame object
Loading data from file into DataFrame objects
Summary statistics over DataFrame objects
Data aggregation queries (groupby() method)
Exploratory analysis of new datasets
Data visualisation over DataFrames
Join/merge operations with DataFrames
Working with text data in DataFrames
Working with Time Series data
...and a slice of Numpy
Working with NumPy arrays
Essential operations with NumPy arrays
Stats with NumPy
Doing Databases:
SQL and pandas
Working with relational databases
SQL for Big Data in Python:
PySpark
Polars
DuckDB
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https://www.python.org/ - the home of Python
https://pandas.pydata.org/ - get pandas
https://numpy.org/ - get Numpy
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