Description

Description

Data science has surged in popularity in recent years, and for good reason. Companies across various industries increasingly rely on data to guide their decision-making, leading to a high demand for skilled data scientists. This comprehensive course provides the foundational skills and techniques needed to thrive in this dynamic field.

You will begin by exploring the role of a data scientist and the lifecycle of data science projects within an organization. Then, you’ll develop essential technical skills, such as using Python and its relevant libraries for data analysis and visualization, preprocessing unstructured data, and building AI/ML models.

The course covers key machine learning algorithms, including linear regression, decision tree classifiers, and clustering algorithms. You’ll learn to apply these techniques to real-world problems, such as predicting customer churn and building recommendation engines.

Throughout the training, you’ll engage in hands-on exercises and projects, allowing you to practice your skills and build your portfolio. By the end of the course, you’ll have a thorough understanding of the data science process, the tools and techniques used by data scientists, and the ability to apply these skills to real-world challenges.

Training Objectives

• Differentiate between Predictive AI and Generative AI.
• Translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions.
• Use Python Pandas
• Matplotlib & Seaborn libraries to explore
• analyse
• and visualise data from various sources
• including the web
• word documents
• email
• NoSQL stores
• databases
• and data warehouses.
• Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library
• such as Decision Trees
• Logistic Regression
• and Neural Networks.
• Re-segment your customer market using K-Means and Hierarchical algorithms to better align products and services to customer needs.
• Discover hidden customer behaviours from Association Rules and build a Recommendation Engine based on behavioural patterns.
• Investigate relationships & flows between people and business-relevant entities using Social Network Analysis.
• Build predictive models of revenue and other numeric variables using Linear Regression.
• Test your knowledge with the included end-of-course exam.
• Leverage continued support with after-course one-on-one instructor coaching and computing sandbox.

Course Outline

• Module 1: The Role of a Data Scientist: Combining Technical and Non-Technical Skills<br />
• What is the required skillset of a Data Scientist?<br />
• Combining the technical and non-technical roles of a Data Scientist<br />
• The difference between a Data Scientist and a Data Engineer<br />
• Exploring the entire lifecycle of Data Science efforts within the organisation<br />
• Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models<br />
• Exploring diverse and wide-ranging data sources that you can use to answer business questions<br />
• Examine the difference between Generative AI and Discriminative AI<
• Module 2: Data Manipulation and Visualisation using Python's Pandas and Matplotlib Libraries<br />
• Introducing the features of Python that are relevant to Data Scientists and Data Engineers<br />
• Viewing Data Sets using Python’s Pandas library<br />
• Importing, exporting, and working with all forms of data, from Relational Databases to Google Images<br />
• Using Python Selecting, Filtering, Combining, Grouping, and Applying Functions from Python's Pandas library<br />
• Dealing with Duplicates, Missing Values, Rescaling, Standardising, and Normalising Data<br />
• Visualising data for both exploration and communication with the Pandas, Matplotlib, and Seaborn Python libraries<
• Module 3: Preprocessing and Analysing Unstructured Data with Natural Language Processing<br />
• Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models<br />
• Exploring the most popular approaches to Natural Language Processing (NLP), such as stemming and “stop“ words<br />
• Preparing a term-document matrix (TDM) of unstructured documents for analysis<br />
• Look at how Data Scientists can integrate Large Language Models (LLMs) in their work<
• Module 4: Linear Regression and Feature Engineering for Business Problem Solving<br />
• Expressing a business problem, such as customer revenue prediction, as a linear regression task<br />
• Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)<br />
• Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE<br />
• Exploring the Feature Engineering possibilities to improve the Linear Regression model<
• Module 5: Classification Models and Evaluation for Predictive Analysis<br />
• Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn<br />
• Exploring how AI/ML Classification models are built using Training, Test, and Validation<br />
• Evaluating the strength of a Decision Tree Classifier<
• Module 6: Alternative Approaches to Classification and Model Evaluation<br />
• Examining alternative approaches to classification<br />
• Considering how Activation Functions are integral to Logistic Regression Classifiers<br />
• Investigating how Neural Networks and Deep Learning are used to build self-driving cars<br />
• Exploring the probability foundations of Naive Bayes classifiers<br />
• Reviewing different approaches to measuring the performance of AI/ML Classification Models<br />
• Reviewing ROC curves, AUC measures, Precision, Recall, and Confusion Matrices<
• Module 7: Clustering Techniques for Customer and Product Segmentation<br />
• Uncovering new ways of segmenting your customers, products, or services using clustering algorithms<br />
• Exploring what the concept of similarity means to humans and how you can implement it programmatically through distance measures on descriptive variables<br />
• Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm<br />
• Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm<br />
• Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)<
• Module 8: Association Rules and Recommender Systems for Business Applications<br />
• Building models of customer behaviours or business events from logged data using Association Rules<br />
• Evaluating the strength of these models through probability measures of support, confidence, and lift<br />
• Employing feature engineering approaches to improve the models<br />
• Building a recommender for your customers that is unique to your product/service offering<
• Module 9: Network Analysis for Organisational Insights<br />
• Analysing your organisation, its people, and its environment as a network of inter-relationships<br />
• Visualising these relationships to uncover previously unseen business insights<br />
• Exploring ego-centric and socio-centric methods of analysing connections critical to your organisation<
• Module 10: Big Data Analytics, Communication, and Ethics<br />
• Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics<br />
• Exploring the communications and ethics aspects of being a Data Scientist<br />
• Discuss the ethical implications of recent developments in AI<br />
• Surveying the paths of continual learning for a Data Scientist