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.

-------------------------------------------------------------------------------------------------------------------------------------------------

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

We believe in learning-by-doing, so we have taken an integrated and problem-solving approach to delivering our training. The course is broken into sessions, each centred on a few related core concepts and skills. The relevant background is discussed at the beginning of the session, in a just-in-time approach. This is followed by illustrative examples, which includes the introduction of library features, syntax and semantics. For the second half, which is most of the session, the delegates are expected to solve relevant problems of graduated difficulty. Example solutions will be available for the delegates to take away at the end of the course.

This approach is effective as it integrates the learning of statistical theory, library features and Python language syntax, increasing retention by providing meaningful context for each. Immediate practice also helps delegates cement their understanding of concepts on which we build gradually.

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
• Decide on the best visual representation of any presented data
• Understand Bivariate data and perform Correlation and basic Linear Regression
• Produce various visual representation (or plots) of data