Course Overview

The comprehensive data analysis course covers a range of topics, including advanced Excel, SQL, Python, machine learning, and Power BI. The course begins with an introduction to data analysis, followed by advanced Excel techniques, such as working with large datasets, creating PivotTables and PivotCharts, and using advanced functions. Next, students learn about SQL, including querying and joining databases.

After that, students are introduced to Python and machine learning, where they learn how to use Python for data analysis, build and evaluate predictive models, and work with algorithms such as linear regression and decision trees. Finally, students learn about Power BI, a data visualisation tool, which helps them create interactive dashboards, reports, and data visualisations, allowing them to communicate their findings effectively. Overall, the course provides students with a comprehensive understanding of the data analysis process and equips them with the skills necessary to succeed in a data-driven world.

Course Benefits

4 Reasons why you should study data analytics

  • Improved problem-solving: With data analytics, you develop the ability to identify patterns, detect anomalies, and solve complex problems using data-driven approaches.
  • High-Paying Career: Data analysts are significant, and with an approaching abilities deficiency not too far off as an ever increasing number of organisations and areas begin working with enormous information, this worth is simply going to increase. 
  • Improved forecasting and planning: You can use data analytics to look at patterns and trends from the past to make accurate predictions and come up with effective business strategies.
  • Data analytics skills are applicable to everyday life: Many of the related skills you learn in data analytics can be used in everyday life. Learning how to effectively organise data can help you with personal budgeting, and explaining complex data can help you improve your communication skills.

Course Modules

  • Advanced Excel in data analysis enables users to manage, manipulate, and analyse large datasets. It includes features such as PivotTables, PivotCharts, and advanced functions like VLOOKUP, INDEX, MATCH, and IF statements. It is a widely-used tool in data analysis due to its versatility, flexibility, and user-friendly interface.
  • Enlarge Your Functions Toolbar
    • 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
    • Organise a Pivot chart
  • Analysing 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
    • Organise 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
  • Descriptive
  • Variability
  • Distribution
  • Probability
  • Linear Functions
  • Linear Algebra
  • Vectors
  • Matrices
  • Tensors
  • SQL is essential to data analysis because it makes it possible to efficiently get and analyse data from relational databases. Large data sets are accurately analysed, and success depends on having a thorough understanding of the process.
  • DDL and DML in MySQL and setup
  • ERD Diagrams and Relational Mapping
  • Data normalisation
  • Basic Queries
  • Database Manipulation
  • Table Manipulation
  • Relational Algebra
  • Advanced SQL - Joining, Subquery, Views
  • Database Security
  • Multiple Activities to Perform
  • By building a strong developer portfolio on GitHub, you can demonstrate your coding abilities and make your work available to others. You may successfully showcase your skills and share your code with the world using this platform.
  • Python is a popular programming language used in data analysis due to its versatility, powerful libraries, and ease of use. With Python, data analysts can clean, manipulate, and analyse data, build predictive models, and create visualisations. Its popularity in the data science community continues to grow due to its efficiency and flexibility.
  • 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
  • Machine learning is a field of artificial intelligence that utilises algorithms to enable computers to learn from data and make predictions without human intervention. It has become an essential part of data analysis, providing insights and building predictive models 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
    • Naïve Bayes
    • Random Forest Classification
    • Support Vector Machines
    • Additional Topics
      • Assessing a Classification Model
      • Adaboos
      • Gradient Boosting
      • XGBoost
      • Grid Search CV
  • Clustering Models
    • Hierarchical
    • K-Means Clustering
  • Association
    • Apriori
    • Eclat
  • Solved Sample Code Files for easy practice
  • Access to Multiple Datasets
  • 7+ Real world data projects
  • R is a highly regarded programming language in the realm of data analysis for its powerful statistical analysis capabilities, comprehensive library support, and graphics facilities. It is favored by statisticians and data analysts for its proficiency in data manipulation, statistical model building, and advanced data visualization.
  • R Introduction
  • R Installing
  • R Syntax
  • Comments, Variables and Data types in R
  • Numbers, Math, Strings, Booleans and Operators in R
  • IF, IF Else, Else if and Nested IF in R
  • Loops in R
  • Functions in R
  • Data Structures in R
  • Graphics in R
  • R Statistics
  • Final Project of Data Analysis with R
  • ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration processes used to extract data from various sources, transform it into a desired format, and load it into a target data warehouse or data lake for analysis and reporting. Both ETL and ELT play a crucial role in consolidating, cleaning, and preparing data for business intelligence and analytics purposes.
  • Introduction to ETL/ELT
    • Extract
    • Transform
    • Load
  • Software Tools
    • Talend Studio
    • Apache Hadoop
    • Apache Kafka
  • Real-world data projects
  • 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 modelling
    • Creating relationships between data tables
  • Visualisation
    • Basic visualisation types
    • Customising visualisations
    • Working with visuals and filters
  • Additional Topics
    • Introduction to DAX functions
    • DAX formulas and expressions
    • Aggregating and summarising data with DAX
  • Sharing and Collaboration
    • Sharing and publishing reports
    • Managing access to reports
    • Collaboration with Power BI
  • Advanced Topics
    • Advanced data modelling
    • 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 data visualization and business intelligence tool that transforms data into interactive visuals, aiding data-driven decision-making for organizations.
  • Tableau Installation
    • Set up and customise Tableau on your PC to conduct data analysis.
  • Tableau UI
    • You will understand to use tool for researching and producing data visualisations.
  • Tableau UI Components
    • Using elements for creating visualisations, such as sheets and legends.
  • Tableau Marks Card
    • Controls the appearance of data points in a visualisation.
  • Tableau Functions
    • Understanding data manipulation computations built-in.
  • Filters in Tableau
    • Using tools for data focusing and refinement in visualisations.
  • Forecasting with Tableau
    • Utilise automated projections to predict future data patterns.
  • Parameters
    • Working with Dynamic values for customising an interactive visualisation.
  • Measures
    • How Calculations are made using quantitative data.
  • Dimensions
    • Grouping and categorising of data using categorical variables.
  • Project In Tableau
    • House Data Dashobard - Project I
    • Jobs Data Dashboard - Project II
  • Learn Tkinter, a well-known GUI package, to create one-of-a-kind custom apps with eye-catching interfaces. Learn how to use Python's built-in SQLite database to store and retrieve data effectively in your applications.


  • Weather Prediction
  • Predicting Cancer Malignant or Benign
  • Predicting Stock Prices
  • Wine Quality Prediction
  • Power BI Projects
  • Tableau Projects

Skills You’ll Learn from Python Course

  • Data visualisation: Skill to effectively present complex data through charts, graphs, and interactive visualisations, making it easier for stakeholders to understand and interpret.
  • Data mining: Competence in using techniques like clustering, classification, and association analysis to discover hidden patterns, trends, and insights from large datasets.
  • SQL and database querying: Proficiency in using SQL (Structured Query Language) to extract, manipulate, and analyse data from relational databases.
  • Programming skills: Knowledge of programming languages like Python or R to perform data analysis tasks efficiently and automate processes.
  • Business acumen: Understanding of business concepts and domain knowledge to align data analysis efforts with organisational goals and make data-driven recommendations.

Career After Data Analysis Certification

  • Data Analyst
  • Business Intelligence Analyst
  • Data Engineer
  • Power BI Analyst
  • Market Research Analyst
  • Financial Analyst
  • Data Quality Analyst
  • Healthcare Analyst

Certificate in Data Analysis Training

  • Participants who successfully complete the training will receive certifications from Future Connect Training to honour their accomplishment and the knowledge they have gained.

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