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Python for Data Analysts & Quants training course

Use Python and its statistical computing libraries to analyse and visualise your data and to gather some actionable insights

Next 7 June (Remote)
3 days £2,000.00 + VAT

JBI training course London UK

  • Learn core concepts of the Python environment, language and data science
  • Use Python to get your data in shape, and take advantage of its features and techniques to gain actionable Insights
  • Explore Python data science tools such as NumPy and Pandas  
  • Explore the opportunity to apply the proposed concepts on real data science applications
  • Acquire knowledge on how to access and prepare data 
  • Use data analysis to perform the computation of summary information and basic statistics
  • Use effective data visualisation techniques to help you with complex data structures 

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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


FULL COURSE DETAILS
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JBI training course London UK
JBI training course London UK

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.

 

 


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Related Courses

Installation & Packaging

  • The Anaconda distribution as Python Data Science platform
  • Overview on Python virtual environment set-up
  • Running Python code in Jupyter notebook
  • Installation, packaging and virtualisation of Python using Conda.
  • We'll set up Python using the Anaconda distribution, a free and enterprise-ready Python distribution that includes hundreds of the most popular Python packages for science, math, engineering and data analysis.
  • Anaconda comes with Conda, a cross-platform tool for managing packages and virtual environments. We'll also set up Jupyter, a web-based interactive environment where users can organise, write and run their Python code in notebooks.

Environment Set-up

• The Anaconda distribution as Python Data Science platform
• Overview on Python virtual environment set-up
• Running Python code in Jupyter notebook

Python core concepts

• 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, ...)

Python Data Science libraries

• Working with data in pandas

  • Working with table-like data in pandas
  • Creating Series and DataFrame objects
  • Loading data from file into DataFrame objects
  • Adding, removing and updating databases

• Retrieving data in pandas

  • Querying data to extract specific rows and columns
  • Filtering data on specific conditions
  • Understanding pandas indexing

• Data manipulation in pandas

  • Data transformation
  • Applying functions to transform individual values
  • Applying functions to aggregate values by rows and columns

• Handling missing data in pandas

  • Identifying missing data points
  • Filtering out missing data
  • Filling missing data with given values, interpolation and other filling strategies

• Data Analysis in pandas

  • Extracting summary statistics over DataFrame objects
  • Performing data aggregation queries (groupby() method)
  • Aggregating multiple columns in the same query
  • Exploratory analysis of new datasets

• Data Visualisation in pandas

  • Plotting data from a Series or DataFrame object
  • Bar plots, line plots, scatter plots, histograms and other common charts
  • Basic customisation of charts

• Working with multiple tables

  • Concatenation of multiple tables based on structure/schema
  • Join/merge operations with DataFrame objects based on values
  • Reindexing operations, dealing with duplicate labels in the index
  • Dealing with duplicate records
  • Renaming columns

• Time Series with pandas

  • ◦ Working with date and time data types in pandas
  • Creating ranges of date/time data points
  • Indexing by time
  • Resampling: data aggregation over time
  • Moving window operations, e.g. moving average
  • Defining custom calendars, custom business days, custom holidays

• Working with text data in DataFrames

  • Using the str attribute in pandas objects
  • String manipulation functions in pandas
  • Filtering data with string pattern matching

• SQL databases

  • Connecting to SQL databases with SQLAlchemy
  • Loading data from SQL to pandas
  • Sending SQL queries to a database and retrieving the results in Python and pandas

• NumPy

  • Working with multi-dimensional arrays with NumPy
  • Arithmetic operations with NumPy arrays
  • Vectorised operations with NumPy arrays
  • Stats and linear algebra with NumPy
  • Slicing and indexing NumPy arrays

• Data Visualisation with matplotlib and plotly

  • Overview on the basic types of charts available with the Python libraries
  • Bar plots, line plots, histograms, scatter plots, pie charts
  • Customising the layout and format of a chart
  • Examples of static visualisation with matplotlib
  • Examples of interactive visualisation with plotly

 

 

 

 
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