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Python Course

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Overview

Not available. Please contact.

Audience

Who will the Course Benefit? Who will the Course Benefit?

This course will benefit anyone who requires a solid practical foundation in Data Analysis, including descriptive statistics and visualisation in Python.

Skills Gained

The delegate will learn and acquire skills as follows:

Data Analysis Python

  • Numpy
    • Create and manipulate NumPy arrays and Matrices
    • Generate random numbers from various distributions
    • Use NumPy vectorized functions
    • Red array data from various common file formats
  • Pandas
    • Understand the composition, relation and main features of Pandas Series and DataFrame structures
    • Read Data from cvs, json, the web and relational database into DataFrames and Series
    • Data Cleaning and Preparations
    • Data Wrangling: Join, Combine and Reshape
    • Data Aggregation and Group operations
    • cvs, excel and other format data into Pandas DataFrame objects
  • Clean, group, manipulate and summarise tabular data using Pandas data processing features
  • Visualisation with Matplotlib (and Seaborn)
    • Plot
      • Bar, Column and Pie charts
      • box-plots
      • histograms
      • scatterplots and line-plots
  • Other
    • Use Jupyter Notebook and Jupyter Lab with the anaconda distribution

Statistics

  • Distinguish between different data types
  • Summarize Categorical and Numerical Data
  • Calculate basic descriptive statistical measures such as
    • Measures of Central Tendency:
      • Mean
      • Median
      • Mode
    • Measures of Dispersion:
      • Variance
      • Standard deviation
      • Quantiles
  • Understand the advantages and disadvantages of the various summary statistics
  • Understand Bivariate data and perform Correlation and basic Linear Regression
  • Produce various visual representation (or plots) of data

Produce various visual representation (or plots) of data Course Objectives Course Objectives

This course aims to provide the delegate with the knowledge to be able to:

  • Determine the type of data at hand and decide of the most appropriate analysis and visualisation
  • Perform numerical calculations using the Python NumPy library
  • Use Pandas to read, explore, manipulate and process tabular data from various sources, including excel, csv, Json files and relational databases
  • Visualise and generally explore data using Matplotlib and Seaborn
  • Carry out descriptive statistical summaries on data in Python
  • Interpret graphs and statistical results correctly

Prerequisites

Not available. Please contact.

Outline

The delegate will learn and acquire skills as follows:

Data Analysis Python

  • Numpy
    • Create and manipulate NumPy arrays and Matrices
    • Generate random numbers from various distributions
    • Use NumPy vectorized functions
    • Red array data from various common file formats
  • Pandas
    • Understand the composition, relation and main features of Pandas Series and DataFrame structures
    • Read Data from cvs, json, the web and relational database into DataFrames and Series
    • Data Cleaning and Preparations
    • Data Wrangling: Join, Combine and Reshape
    • Data Aggregation and Group operations
    • cvs, excel and other format data into Pandas DataFrame objects
  • Clean, group, manipulate and summarise tabular data using Pandas data processing features
  • Visualisation with Matplotlib (and Seaborn)
    • Plot
      • Bar, Column and Pie charts
      • box-plots
      • histograms
      • scatterplots and line-plots
  • Other
    • Use Jupyter Notebook and Jupyter Lab with the anaconda distribution

Statistics

  • Distinguish between different data types
  • Summarize Categorical and Numerical Data
  • Calculate basic descriptive statistical measures such as
    • Measures of Central Tendency:
      • Mean
      • Median
      • Mode
    • Measures of Dispersion:
      • Variance
      • Standard deviation
      • Quantiles
  • Understand the advantages and disadvantages of the various summary statistics
  • Understand Bivariate data and perform Correlation and basic Linear Regression
  • Produce various visual representation (or plots) of data

Produce various visual representation (or plots) of data Course Objectives Course Objectives

This course aims to provide the delegate with the knowledge to be able to:

  • Determine the type of data at hand and decide of the most appropriate analysis and visualisation
  • Perform numerical calculations using the Python NumPy library
  • Use Pandas to read, explore, manipulate and process tabular data from various sources, including excel, csv, Json files and relational databases
  • Visualise and generally explore data using Matplotlib and Seaborn
  • Carry out descriptive statistical summaries on data in Python
  • Interpret graphs and statistical results correctly

Talk to an expert

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. Its a cost effective option. One on one training can be delivered too, at reasonable rates.

Submit an enquiry from any page on this site and let us know you are interested in the requirements box, or simply mention it when we contact you.

All $ prices are in USD unless it’s a NZ or AU date

SPVC = Self Paced Virtual Class

LVC = Live Virtual Class

Please Note: All courses are availaible as Live Virtual Classes

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