Business Analyst Course

Beginners in business analysis will gain a strong foundation in principles and their organizational role. They'll develop effective communication, problem-solving, data analysis, and process optimization skills. The course also covers software tools, project management, stakeholder engagement, critical thinking, documentation, quality assurance, and ethical considerations.

• Introduction to Business Analyst
• BA understanding Through Story ( Example )
• Business Process Modeling (BPM)
• CATWOE (Customers, Actors, Transformation Process, Worldview, Owner, Environmental Constraints)
• MoSCoW (Must or Should, Could or Would)
• MOST (Mission, Objectives, Strategies, and Tactics) Analysis
• PESTLE Analysis
• SWOT Analysis
• 6 Thinking Hats

Excel skills enable users to manage, modify, and analyze massive datasets with ease. It includes vital components like PivotTables and PivotCharts as well as sophisticated operations like VLOOKUP, INDEX, MATCH, and IF expressions. Advanced Excel is well known and frequently used in data analysis because of its adaptability, flexibility, and user-friendly interface.

• The basic IT functions
• Creating and using a range of name
• Locate formulas
• IFERROR
• VLOOKUP & HLOOKUP
• INDEX & MATCH
• Analyzing Your Data
  • Set up Pivot tables
  • Grouping your data
  • Amend Pivot Tables with new data
  • Use a slicer to filter your data
  • Combining slicers to more than one Pivot Table
  • Using a timeline
  • Organize a Pivot chart
• Reducing Your Audit Risk
  • Data Recognition
  • Use of Trace Precedents
  • Use of Trace Dependents
  • Eliminate Arrows
  • Flaws checking
  • Check out Formula
  • Watch Window
• Enhance Your Workflow
  • Set up Macro security
  • Recording Macros
  • How to edit Macro
  • Understanding the VBA edit window
  • Allow & run a Macro from the ribbon
  • Saving & using a Macro-enabled Workbook
  • Deleting your Macro
• Additional Topics
  • Scenarios
  • Goal Seek

Mathematics and statistics are crucial in machine learning, providing tools for creating models that learn from data and make accurate predictions. Linear algebra, calculus, and probability theory are used in algorithms, while statistics analyzes data, identifies patterns, and predicts outcomes. Strong knowledge in these areas is necessary for success in machine learning.

1. Mathematics

• Linear Functions
• Linear Algebra
• Vectors
• Matrices
• Tensors

2. Statistics

• Descriptive
• Variability
• Distribution
• Probability

SQL is critical in machine learning, allowing efficient data retrieval and analysis from relational databases. It ensures accurate analysis of large data sets, with solid understanding essential for success.

• DDL and DML in MySQL and setup
• ERD Diagrams and Relational Mapping
• Data normalization
• Basic Queries
• Database Manipulation
• Table Manipulation
• Relational Algebra
• Advanced SQL - Joining, Subquery, Views
• Database Security
• Multiple Activities to Perform

Python is a widely-used programming language in machine learning. It offers powerful libraries, tools, and frameworks for data analysis, modeling, and visualization. Python's simplicity and flexibility make it a popular choice for building machine learning models and deploying them in real-world applications.

• Python Setup and What is Python?
• Data Types and Syntax
• Comparison Operators
• Python Loop
• Python Statements
• Logical Operators
• Methods and Functions
• Error and Exception Handling
• Modules Packages and libraries
• Debugging
• Advanced python Modules (DateTime)
• File Management
• Multiple Activities to Perform
• Multiple Projects to Build

R is a popular language for data analysis, statistical modeling, and data visualization. Here are some of the things you can learn in R:

• Basics: Installation, data types, and simple operations
• Data Structures: Vectors, data frames, and lists
• Data Import/Export: Loading and saving data
• Manipulation: Subsetting, filtering, and transformations
• Visualization: Basic plotting and ggplot2
• Statistics: Summary stats and basic analysis
• Control Structures: If-else and loops
• Functions: Creating and using functions
• Packages: Extending R's capabilities
• R Markdown: Creating documents
• Help/Documentation: Accessing assistance
• Data Analysis: Exploring datasets
• Error Handling: Troubleshooting errors

Machine learning is a subset of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It involves the use of algorithms, statistical models, and data to build predictive models and uncover insights from large datasets.

• Data Preprocessing
• Supervised Learning
• Unsupervised Learning
• Regression Models
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Random Forest Regression
  • Topics such as
    • Assessing a Regression Model
    • Bias vs Variance
    • Regularisation
    • Gradient Descent
• Classification Models
  • Decision Tree Classification
  • K-Nearest Neighbor
  • Logistic Regression
  • Na誰ve Bayes
  • Random Forest Classification
  • Support Vector Machines
  • Additional Topics
    • Assessing a Classification Model
    • Adaboost
    • Gradient Boosting
    • XGBoost
    • Grid Search CV
• Clustering Models
  • Hierarchical
  • K-Means Clustering
• Association
  • Apriori
  • Eclat
• Build Dashboards for Data Visualisation
• Solved Sample Code Files for easy practice
• Access to Multiple Datasets
• 7+ Real world data projects

Power BI is a powerful data visualization tool that enables analysts to create interactive dashboards, reports, and visualizations. With its user-friendly interface and robust features, Power BI empowers users to transform raw data into insightful and visually engaging representations. Analysts can easily explore data, identify trends, and communicate findings effectively, making Power BI an indispensable tool for data-driven decision-making.

• Introduction to Power BI
  • Overview of Power BI
  • Advantages of Power BI
  • Power BI components
  • Power BI service vs Power BI desktop
• Data Sources and Connections
  • Data sources overview
  • Connecting to different data sources
  • Working with data in Power BI
• Data Transformation and Modeling
  • Data transformation and cleaning
  • Data modeling
  • Creating relationships between data tables
• Visualization
  • Basic visualization types
  • Customizing visualizations
  • Working with visuals and filters
• DAX Functions
  • Introduction to DAX functions
  • DAX formulas and expressions
  • Aggregating and summarizing data with DAX
• Sharing and Collaboration
  • Sharing and publishing reports
  • Managing access to reports
  • Collaboration with Power BI
• Advanced Topics
  • Advanced data modeling
  • Custom visuals and extensions
  • Power BI embedded
• Case Studies and Hands-On Projects
  • Real-world case studies
  • Hands-on projects with Power BI

Tableau is a user-friendly business intelligence and data visualisation application that creates interactive visualisations from data to help organisations make decisions based on that data.

• Tableau Installation

  Installing Tableau on your computer will allow you to undertake data analysis.

• Tableau UI

  You will learn how to utilise the Tableau user interface to do research and create data visualisations.

• Tableau UI Components

  Utilising elements for building visualisations, including as sheets and legends, using Tableau UI components.

• Tableau Marks Card

  Manages how data points look in a display.

• Tableau Functions

  Understanding built-in data manipulation algorithms using Tableau Functions.

• Filters in Tableau

  Using tools for data focus and visualisation refinement, such as filters in Tableau.

• Forecasting with Tableau

  Utilise automated predictions to forecast future data patterns when forecasting using Tableau.

• Parameters

  Customising an interactive visualisation by working with dynamic values.

• Measures

  The process for using quantitative data in calculations.

• Dimensions

  Data categorization and grouping using categorical variables.

Project In Tableau

• House Data Dashobard - Project I
• Jobs Data Dashboard - Project II