Course Code: PYPDA
Duration: 5 days
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This course aims to equip delegates with a substantial knowledge of Python libraries (NumPy, Pandas, Matplotlib and others) and data analysis techniques to enable them to engineer enterprise level solutions in a data-driven environment.
Content in General:
The Pandas library, with its data preparation and analysis features will be our ultimate focus. After familiarizing ourselves with its two data structures, the Series and the DataFrame, we will use the latter to read, manipulate and generally process tabular data sourced from excel, csv and other file formats. However, before that we will thoroughly familiarize ourselves with the NumPy library, not only because it is the foundation of Pandas but, also because it offers powerful tools for numerical calculations and forms the basis of practically all of Pythons Data Science Libraries. We will explore its vectorized functions, basic linear algebra features and use its random library to demonstrate the sampling of different distributions.
Statistics, at least in the descriptive form must be an integral part of any meaningful data analysis course. So, we will learn various data classifications, applicable summary statistics. We also discuss and explore by example, the strengths and weaknesses of the various statistical summaries.
Visualization is another vital component of data analysis. To paraphrase, a graph is worth a thousand words. In this course we will learn the most appropriate visualisation for any given data set and use Matplotlib (and Seaborn) to produce Bar-charts, Pie-charts, histograms, box-plots, scatter-plots and line-graphs.
Finally, for our programming environment, we will use Jupyter Notebook (or Jupyter Lab according to our preference) on the Anaconda platform. This is the cutting edge of editor technology in Pythons Data Science ecosystem.
Approach:
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
Statistics
This course will benefit anyone who requires a solid practical foundation in Data Analysis, including descriptive statistics and visualisation in Python.
This course aims to provide the delegate with the knowledge to be able to:
This course has two requirements, programming experience and mathematical knowledge.
To fulfil the programming pre-requisites, our Python Programming 1, or its equivalent is required. Exceptions could be made for delegates with extensive experience in a different programming language that includes object-oriented concepts.
To fulfil the Mathematics pre-requisites, ideally A-Levels but at a minimum GCSE level is required. Delegates will be expected to understand simple formulas, percentages and proportions and interpret simple graphs.
Data Analysis in Python
Duration: 5 days
RRP: £2,995.00 exc. VAT
Virtual & London Schedule
We are running a full Virtual schedule | |||
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Start Date | Spaces | Book | |
11 May 2020 | Spaces | Book | |
06 Jul 2020 | Spaces | Book | |
07 Sep 2020 | Spaces | Book | |
02 Nov 2020 | Spaces | Book |
Public Scheduled and Closed
Virtual
UK and Overseas
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