Highlights
- Explore Python core concepts and best practices
- Learn Python virtual environment set-up
- Explore the notions of object-oriented programming and functional programming, as applied to Python applications
- Use Python and its statistical computing libraries to analyse and visualise your data, and to gather actionable insights
- Extract and manipulate Excel and SQL data using Pandas
- Gain an overview of Artificial Intelligence, Machine Learning and Big Data
- Learn how to implement Machine Learning systems to perform predictions on data
- Build your AI capability
- Familiarise yourself with the automation in the workplace
- Explore the future of the workplace
- Learn about Machine Learning tools for data scientists and non-data scientists
- Learn more about chatbots and feature engineering
A taster video for Python Remote Training
Course Details
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
- 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
Data Visualisation
- Data analysis benefits from the visualisation of data. If a picture if worth a thousand words, complex data structures can be easier to understand and analyse using effective visualisation techniques. Communicating the results with non-technical users is also a challenge that visualisation techniques help to overcome. We'll showcase how to easily produce beautiful visualisations with matplotlib.
AI Overview
• What is Artificial Intelligence? What's up with the hype?
• Data Science vs. Data Mining vs. Machine Learning
• Machine Learning Problems and Applications
• Python Environment Set-up with Anaconda Python
◦ Jupyter Notebooks
◦ Python Ecosystem for Data Science and Machine Learning
Machine Learning Concepts
• Learning and Prediction
• Feature Engineering
• Training data and Test data
• Cross-validation
• Underfitting and Overfitting
Supervised Learning Problems
• Classification: predicting a label
• Algorithms for classification: k-Nearest Neighbours, Support Vector Machine and Naive Bayes
• Regression: predicting a quantity
• Algorithms for regression: Linear Regression and Polynomial Regression
Unsupervised Learning Problems
• Clustering: grouping similar items
• Algorithms for clustering: k-Means, Hierarchical Clustering and DBSCAN
• Dimensionality Reduction
• Algorithms for dimensionality reduction: Principal Component Analysis
Evaluation of Machine Learning algorithms
• Evaluation metrics for machine learning
• Planning an evaluation campaign on your data
Deep Learning & Neural Network Overview
• Intro to Artificial Neural Networks
• Neural Network concepts
◦ Neural Network Types
◦ Gradient Descend
◦ Back-propagation
◦ Activation Functions
◦ Loss Functions
◦ Hyper-parameters
• Neural Networks in the Wild: examples of successful applications
• Deep Network Architectures
• Deep Learning Libraries
Who should attend
Software developers and software engineers with a basic knowledge of Python. Data Scientists, Data analysts and Business Intelligence professionals who are new to Python.
Developers, engineers, researchers and analysts who want to start learning about Artificial Intelligence and related concepts, including Data Science, Data Mining, Machine Learning and Deep Learning. Some background in Mathematics (e.g. Statistics and Probability, Linear Algebra, Calculus, etc) will be beneficial, but not strictly required.
Feedback
4.8 out of 5 average
"I hadn't integrated Pandas with Python before joining my company. So it's very useful to consolidate my understanding of such skill via this course. The Jupyter notebooks provided will be a valuable resource for revising the materials and are really well laid out."
JL, Data Analyst, Python for Data Science, March 2021
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”
Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022
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