1. Welcome
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1.1 Welcome!
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1.2 Programme introduction and instructions
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1.3 Programme Overview
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1.4 Programme Outline
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1.5 Meet the Faculty
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1.6 Meet your programme learning facilitator
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1.7 Live webinars and engagement information
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1.8 Introduce Yourself
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1.9 What you need to get started
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1.10 Excelling as an online learner
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1.11 Requirements to earn our Certificate of Preparation
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1.12 Capstone project
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1.13 The learning platform
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1.14 Pre-programme survey
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1.15 Frequently asked questions (FAQs)
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1.16 Code of Conduct and Agreement
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1.17 Resources
2. Excel For Data Analysis
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2.1 Introduction to Excel
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2.1 Data Entry, Cleaning, and Preprocessing, Filter, Sort, Trim
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2.2 Create formulas to solve a problem
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2.3 Charts and Graphs
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2.4 Split and Concatenate
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2.5 Excel Formula and Functions – Simple, complex, relative, and absolute references and functions
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2.6 VLOOKUP
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2.7 Pivot table
3. SQL - PostGres
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3.1 SQL Basics
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3.11 Introduction and Installation of tools and resources
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3.12 Overview of PostgreSQL
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3.13 RDBMS Concepts
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3.14 Databases
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3.15 Syntax
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2.16 Data Types
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3.17 Operators
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3.2 Data Definition Language
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3.21 Data Definition Language - Create Databases and Schema
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3.22 Data Definition Language - Drop Database and Schema
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3.23 Data Definition Language - Accessing Database and Schema
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3.24 Data Definition Language - Create Table
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3.25 Data Definition Language - Drop Table
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3.26 Data Definition Language - Insert Query and Values
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3.27 Assignment I
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3.3 Data Manipulation Language
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3.31 Data Manipulation Language - Select Query
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3.32 Data Manipulation Language - Where Clause
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3.33 Data Manipulation Language - AND & OR Clause
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3.34 Data Manipulation Language - Update and Delete Query
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3.35 Data Manipulation Language - Like and Top Clause
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3.36 Data Manipulation Language - Order By and Group BY
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3.37 Data Manipulation Language - Distinct Keyword
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3.38 Data Manipulation Language - Sorting Results
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3.4 Additional Data Manipulation Language
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3.41 Additional Data Manipulation Language - Constraints
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3.42 Additional Data Manipulation Language - Using Joins
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3.43 Additional Data Manipulation Language - Union Clause
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3.44 Additional Data Manipulation Language - NULL Clause
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3.45 Additional Data Manipulation Language - Alias Syntax
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3.46 Additional Data Manipulation Language - Alter Commands
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3.47 Additional Data Manipulation Language - Operators
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3.48 Additional Data Manipulation Language - Using Views
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3.49 Additional Data Manipulation Language - Having Clause
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3.49 Assignment II
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3.5 Advanced SQL
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3.51 Advanced SQL - Transactions
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3.52 Advanced SQL - Wildcards
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3.53 Advanced SQL - Functions and Procedures
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3.54 Advanced SQL - Temporary Tables
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3.55 Advanced SQL - Grouping Sets, Cubes and Rollup
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3.56 Advanced SQL - Sub Queries
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3.57 Advanced SQL - Using Sequences
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3.58 Advanced SQL - Handling Duplicates
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3.6 Capstone Project (SQL)
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Quiz
4. Power BI
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4.1 Power BI Basics
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4.11 Introduction to Power BI and Installation Steps
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4.12 Architecture
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4.13 Supported Data Sources
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4.14 Comparison with Other BI Tools
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4.15 Data Modelling
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4.16 Dashboards Options
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4.17 Visualisation Options
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4.2 Data Transformation and DAX Basics
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4.21 Different Data Importation and Transformation
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4.22 Data Visualisation
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4.23 Dax Basics
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4.24 DAX intermediate /RLS/Administration Role
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4.3 Advanced Power BI Tools and Best Practices
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4.31 Advanced Data Visualisation
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4.311 Advanced Data Visualisation - Custom Visuals
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4.312 Advanced Data Visualisation - Drilldown
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4.313 Advanced Data Visualisation - Drillthrough
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4.314 Advanced Data Visualisation - Controls
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4.315 Advanced Data Visualisation - Filters
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4.32 Power BI Best Practices
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4.4 End-to-end Power BI Project
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4.41 Lifecycle of a Power BI Project
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4.42 End-to-end Power BI project (Hands-On)
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4.5 Capstone Project (Power BI )
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Quiz
5. Python for Data Analysis
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5.1 Introduction and history (3:06)
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5.11 Python Syntax Basics (6:06)
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5.12 Strings and Console Output (7:46)
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5.13 Conditional Statements (7:20)
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5.14 String Operations in Real Data (8:44)
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5.15 Iris Dataset (3:17)
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5.2 Functions, List and Dictionaries, List and Functions, Loops (6:35)
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5.21 Functions in Python (8:00)
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5.22 Lists (9:16)
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5.23 Dictionaries (7:05)
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5.24 Integrating Lists with Functions (6:38)
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5.25 Loops for Data Processing (6:11)
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5.3 Python for Data Science & Machine Learning
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5.31 Numpy Basics
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5.32 Data Handling with Python
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5.33 Visualisation with Matplotlib and Seaborn
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5.34 Introduction to Scikit-Learn
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5.4 Pandas for Data Science & Machine Learning
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5.41 Introduction to Pandas
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5.42 Data Manipulation with Pandas
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5.43 Advanced Data Analysis with Pandas
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5.44 Pandas for Machine Learning Data Preparation
5. Machine Learning
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5.1 Introduction to Machine Learning
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5.11 Overview of Machine Learning and its Applications
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5.12 Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
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5.13 Essential Python for Machine Learning
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5.14 Data Pre-processing and Exploration
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5.2 Supervised Learning
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5.21 Linear Regression and Logistic Regression
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5.22 Decision Trees and Random Forests
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5.23 Support Vector Machines (SVM)
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5.24 Evaluation Metrics for Classification and Regression
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5.3 Unsupervised Learning
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5.31 Clustering algorithms: K-means, Hierarchical, DBSCAN
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5.32 Dimensionality Reduction: PCA and t-SNE
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5.33 Association Rule Mining
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5.4 Advanced Machine Learning Techniques
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5.41 Ensemble Methods and Boosting
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5.42 Feature Engineering and Selection
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5.43 Hyperparameter Tuning and Optimisation
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5.44 Model Deployment and Scalability
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5.5 Special Topics in Machine Learning
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5.51 Neural Network and Deep Learning
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5.52 Natural Language Processing (NLP) basics
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5.53 Introduction to Reinforcement Learning
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5.54 Ethics and Bias in Machine Learning
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5.55 Current Trends and Research Topics
