Keep hearing buzz words like “Data Science”, “Machine Learning”, “A.I.” but you are unsure where to begin? Want to upskill and become an in-demand top talent?
Our programme is designed to equip you with job-ready Data Science & Python knowledge and skillsets to make your mark in today’s digital economy.
Whether in a boot camp or 1-on-1 format, our experienced instructors will guide you from 0 to 100!
Preface Coding is a global, award-winning programming platform based in London, Tokyo, Melbourne and Hong Kong. We specialise in delivering personalised coding courses both online and in-person, whenever and wherever you are in the world.
In this final module, you’ll have equipped yourself with skills to comb over available data and be able to implement practical machine learning techniques to make predictions as well as deliver insights as well as communicate findings using data visualisation techniques.
It’s your time to build, train and deploy your model at scale now and change the world!
Python for Data Science
A beginner-friendly module that teaches you how to code in Python and work efficiently with big datasets using Google’s Colab. Understand how businesses store, extract and manipulate data through industry use cases and case studies.
Analyzing Data with Python
Learn how to analyze and harvest clean data sets and create data frames to run basic analysis as well as to perform data reporting using powerful data science libraries like Pandas and Numpy to gain actionable insights for your business.
Web Scraping with APIs
Learn how to navigate, collect and organize data from various sources like CSV files, APIs. You’ll leverage existing datasets, scrape web data and learn to access useful information in these data structures.
Intro to Data Visualization
Understand how data scientists present and visualize large datasets across industries. Leverage Matplotlib to create descriptive and interpretable visuals, enabling you to easily extract relevant information, better understand the data, and make effective decisions.
Data Crawling and Data Mining
Learn how to extract and save any data on any websites like Google and Yahoo, process the HTML codes, and build automated tools to crawl the web at scale. From texts to images, fetch and display desired information for your own use with libraries like BeautifulSoup.
Advanced Data Visualisation
Bridge the gap between data and insights and create informative and striking statistical graphics with libraries like Seaborn and Plotly. Draw effective conclusions and analysis by visualizing the multidimensional relationships among the data samples, conduct correlation analysis and even statistical data exploration.
Learn how to explore, clean and model data for supervised machine learning using preprocessing techniques. Implement linear and logistic regression, and build classification models with decision trees like Random Forest to make predictions with Python machine learning library sci-kit learn and k-nearest neighbours algorithm.
Generalization and Overfitting in Machine LearningDetermine whether a model is good or not by implementing both a training set and a test set to ensure its ability to adapt and generalized to new, unseen data. Learn how the reward-punishment mechanism works to penalized models to prevent overfitting using regularization and cross-validation methods to improve model accuracy.
Unsupervised LearningLearn how to find associations, patterns, and relationships present in data using clustering techniques and dimensionality reduction techniques as well as matrix factorization to break data set in groups.
Learn how to extract and identify information from unstructured text using text mining and Natural Language Processing (NLP) techniques to make sense out of data, e.g. how Siri is powered. Know how to pre-process raw image data using normalisation and standardisation techniques as a part of data preparation for deep learning.
Deep Learning with KerasHands-on with popular machine learning tools like Google’s Tensorflow; and leverage on the Keras Library to build, train and test your first deep learning model.
Neural NetworksUnderstand the architecture and key parameters in a neural network and apply different forms of machine learning algorithms like Recurrent Neural Networks (RNNs) for sequential data such as time series and financial data; Natural Language Processing (NLP) text classification and generation and Convolutional Neural Networks (CNN) for image classification.
In this final module, you’ll have equipped yourself with skills to comb over available data and be able to implement practical machine learning techniques to make predictions as well as deliver insights as well as communicate findings using data visualisation techniques.
It’s your time to build, train and deploy your model at scale now and change the world!
