5.33 Association Rule Mining | Datacy

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1. Welcome

1.1 Welcome!
1.2 Programme introduction and instructions
1.3 Programme Overview
1.4 Programme Outline
1.5 Meet the Faculty
1.6 Meet your programme learning facilitator
1.7 Live webinars and engagement information
1.8 Introduce Yourself
1.9 What you need to get started
1.10 Excelling as an online learner
1.11 Requirements to earn our Certificate of Preparation
1.12 Capstone project
1.13 The learning platform
1.14 Pre-programme survey
1.15 Frequently asked questions (FAQs)
1.16 Code of Conduct and Agreement
1.17 Resources

2. Excel For Data Analysis

2.1 Introduction to Excel
2.1 Data Entry, Cleaning, and Preprocessing, Filter, Sort, Trim
2.2 Create formulas to solve a problem
2.3 Charts and Graphs
2.4 Split and Concatenate
2.5 Excel Formula and Functions – Simple, complex, relative, and absolute references and functions
2.6 VLOOKUP
2.7 Pivot table

3. SQL - PostGres

3.1 SQL Basics
3.11 Introduction and Installation of tools and resources
3.12 Overview of PostgreSQL
3.13 RDBMS Concepts
3.14 Databases
3.15 Syntax
2.16 Data Types
3.17 Operators
3.2 Data Definition Language
3.21 Data Definition Language - Create Databases and Schema
3.22 Data Definition Language - Drop Database and Schema
3.23 Data Definition Language - Accessing Database and Schema
3.24 Data Definition Language - Create Table
3.25 Data Definition Language - Drop Table
3.26 Data Definition Language - Insert Query and Values
3.27 Assignment I
3.3 Data Manipulation Language
3.31 Data Manipulation Language - Select Query
3.32 Data Manipulation Language - Where Clause
3.33 Data Manipulation Language - AND & OR Clause
3.34 Data Manipulation Language - Update and Delete Query
3.35 Data Manipulation Language - Like and Top Clause
3.36 Data Manipulation Language - Order By and Group BY
3.37 Data Manipulation Language - Distinct Keyword
3.38 Data Manipulation Language - Sorting Results
3.4 Additional Data Manipulation Language
3.41 Additional Data Manipulation Language - Constraints
3.42 Additional Data Manipulation Language - Using Joins
3.43 Additional Data Manipulation Language - Union Clause
3.44 Additional Data Manipulation Language - NULL Clause
3.45 Additional Data Manipulation Language - Alias Syntax
3.46 Additional Data Manipulation Language - Alter Commands
3.47 Additional Data Manipulation Language - Operators
3.48 Additional Data Manipulation Language - Using Views
3.49 Additional Data Manipulation Language - Having Clause
3.49 Assignment II
3.5 Advanced SQL
3.51 Advanced SQL - Transactions
3.52 Advanced SQL - Wildcards
3.53 Advanced SQL - Functions and Procedures
3.54 Advanced SQL - Temporary Tables
3.55 Advanced SQL - Grouping Sets, Cubes and Rollup
3.56 Advanced SQL - Sub Queries
3.57 Advanced SQL - Using Sequences
3.58 Advanced SQL - Handling Duplicates
3.6 Capstone Project (SQL)
Quiz

4. Power BI

4.1 Power BI Basics
4.11 Introduction to Power BI and Installation Steps
4.12 Architecture
4.13 Supported Data Sources
4.14 Comparison with Other BI Tools
4.15 Data Modelling
4.16 Dashboards Options
4.17 Visualisation Options
4.2 Data Transformation and DAX Basics
4.21 Different Data Importation and Transformation
4.22 Data Visualisation
4.23 Dax Basics
4.24 DAX intermediate /RLS/Administration Role
4.3 Advanced Power BI Tools and Best Practices
4.31 Advanced Data Visualisation
4.311 Advanced Data Visualisation - Custom Visuals
4.312 Advanced Data Visualisation - Drilldown
4.313 Advanced Data Visualisation - Drillthrough
4.314 Advanced Data Visualisation - Controls
4.315 Advanced Data Visualisation - Filters
4.32 Power BI Best Practices
4.4 End-to-end Power BI Project
4.41 Lifecycle of a Power BI Project
4.42 End-to-end Power BI project (Hands-On)
4.5 Capstone Project (Power BI )
Quiz

5. Python for Data Analysis

5.1 Introduction and history (3:06)
5.11 Python Syntax Basics (6:06)
5.12 Strings and Console Output (7:46)
5.13 Conditional Statements (7:20)
5.14 String Operations in Real Data (8:44)
5.15 Iris Dataset (3:17)
5.2 Functions, List and Dictionaries, List and Functions, Loops (6:35)
5.21 Functions in Python (8:00)
5.22 Lists (9:16)
5.23 Dictionaries (7:05)
5.24 Integrating Lists with Functions (6:38)
5.25 Loops for Data Processing (6:11)
5.3 Python for Data Science & Machine Learning
5.31 Numpy Basics
5.32 Data Handling with Python
5.33 Visualisation with Matplotlib and Seaborn
5.34 Introduction to Scikit-Learn
5.4 Pandas for Data Science & Machine Learning
5.41 Introduction to Pandas
5.42 Data Manipulation with Pandas
5.43 Advanced Data Analysis with Pandas
5.44 Pandas for Machine Learning Data Preparation

5. Machine Learning

5.1 Introduction to Machine Learning
5.11 Overview of Machine Learning and its Applications
5.12 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
5.13 Essential Python for Machine Learning
5.14 Data Pre-processing and Exploration
5.2 Supervised Learning
5.21 Linear Regression and Logistic Regression
5.22 Decision Trees and Random Forests
5.23 Support Vector Machines (SVM)
5.24 Evaluation Metrics for Classification and Regression
5.3 Unsupervised Learning
5.31 Clustering algorithms: K-means, Hierarchical, DBSCAN
5.32 Dimensionality Reduction: PCA and t-SNE
5.33 Association Rule Mining
5.4 Advanced Machine Learning Techniques
5.41 Ensemble Methods and Boosting
5.42 Feature Engineering and Selection
5.43 Hyperparameter Tuning and Optimisation
5.44 Model Deployment and Scalability
5.5 Special Topics in Machine Learning
5.51 Neural Network and Deep Learning
5.52 Natural Language Processing (NLP) basics
5.53 Introduction to Reinforcement Learning
5.54 Ethics and Bias in Machine Learning
5.55 Current Trends and Research Topics

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5.33 Association Rule Mining

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