Advanced Data Analytics Course

• Professional Background: No prior experience in data analytics is required; however, a basic understanding of statistics, data analysis, and familiarity with business processes will help maximise the learning experience. 

• Language Proficiency: Learners must have a strong command of English, as all course content, discussions, and assessments are delivered in English. 

• Interest in Data Analytics: The course is ideal for professionals looking to deepen their data analysis skills to drive business strategy, optimise performance, and enhance decision-making through data. 

• Master Data Analysis Techniques: Learn how to apply advanced analytical methods, including predictive modelling, regression analysis, and machine learning algorithms, to extract meaningful insights from complex datasets. 

• Develop Data Visualisation Skills: Gain proficiency in using data visualisation tools to create impactful charts, graphs, and dashboards that communicate insights clearly and effectively to stakeholders. 

• Refine Data Interpretation: Learn to interpret and analyse large datasets, recognise patterns, and use data to solve business problems, improve processes, and enhance decision-making. 

• Implement Data-Driven Strategies: Understand how to integrate data analytics into strategic business decisions, using data to forecast trends, optimise operations, and enhance business performance. 

• Utilise Cutting-Edge Analytics Tools: Master the latest tools and platforms for data analysis, including Python, R, SQL, and advanced Excel techniques, to work more efficiently and effectively with large datasets. 

The Advanced Data Analytics Course is designed for professionals looking to deepen their analytical capabilities to make data-driven decisions and optimise business outcomes. Individuals who can benefit from this course include: 

• Data Analysts 

• Business Analysts 

• Managers 

• Marketing Professionals 

• Financial Analysts 

• Operations Managers 

• IT Professionals 

• Project Managers 

• Product Managers 

• Consultants 

Domain 1: Data Analytics 

Module 1: Introduction to Data Analytics 

• Data Analytics Overview 

• Types of Data Analytics 

• Descriptive Analytics 

• Diagnostic Analytics 

• Predictive Analytics 

• Prescriptive Analytics 

• Benefits of Data Analytics 

• Data Visualisation for Decision Making  

• Data Types, Measure of Central Tendency, Measures of Dispersion 

• Graphical Techniques, Skewness and Kurtosis, Box Plot 

• Descriptive Stats 

• Sampling Variation, Central Limit Theorem, Confidence Interval 

• Optimisation Techniques for Data Analytics
   

Module 2: Introduction to Statistical Analysis 

• Counting, Probability, and Probability Distributions 

• Sampling Distributions 

• Estimation and Hypothesis Testing 

• Scatter Diagram 

• ANOVA and Chi-Square 

• Imputation Techniques 

• Data Cleaning 

• Correlation and Regression 

Module 3: Data Wrangling with SQL 

• Introduction to SQL 

• Database Normalisation 

• Entity-Relationship Model 

• SQL Operators 

• Join, Tables, and Variables 

• SQL Functions 

• Subqueries 

• Views and Stored Procedures 

• User-Defined Functions 

• SQL Performance and Optimisation 

• Advanced Concepts 

• Correlated Subquery 

• Grouping Sets 

Module 4: Presto 

• Introduction to Presto 

• Writing Queries in Presto on Large Data Sets 

Module 5: Feature Engineering 

• Handling Unstructured Data 

• Machine Learning Algorithms 

• Bias Variance Trade-Off 

• Imbalance Data 

• Handling Unbalanced Data 

• Boosting 

• Model Validation 

• Hyper Parameter Optimisation 

• Advanced Machine Learning Libraries – Xgboost 

• Solving Problems on Kaggle
   

Domain 2: Business Analytics with Excel 

Module 6: Introduction to Data Analysis with MS Excel 

• Steps to Analyse Data 

• Introduction to Tables 

Module 7: Cleaning Data with Text Functions 

• Removing Unwanted Characters from the Text 

• Steps for Data Cleaning
   

Module 8: Sorting and Filtering 

• What is Sorting and Filtering? 

• Applying Sorting on Two Columns 

• Steps to Sort Dates and Columns by Colours 

• Apply Filtering 

• Clear Filter 

• Apply Filter on Text
   

Module 9: Exploring Lookup Functions 

• VLookUp Functions in Excel 

• HLookUp Functions in Excel
   

Module 10: Introduction to Power Pivot and Formula Auditing 

• Working with Pivot Tables 

• How to Use Power Pivot? 

• Measures 

• Dimension Tables 

• Relationships 

• Advanced Functions 

• Data Visualisation and Analysis 

• Show Formulas 

• Trace Precedents 

• Trace Dependents 

• Evaluate Formula
   

Module 11: DAX Variables and Formatting 

• What is DAX? 

• Data Types and Operators 

• DAX Variables 

• Formatting DAX Code 

• Debugging Errors in DAX Code 

• Progressive DAX Syntax and Functions
   

Module 12: Introduction to Power Map 

• Create a Power Map 

• Explore Sample Datasets in Power Map 

• Visualise Data in Power Map 

• Create a Custom Map in Power Map
   

Module 13: Design a Dashboard Using Data Model 

• Using PowerPoint and Excel 

• Make a Dashboard in Excel 

• Customise with Macros, Colour, etc. 

• Make a Dashboard in Smartsheet
   

Domain 3: Programming Basics and Data Analytics with Python 

Module 14: Python for Data Analysis - NumPy 

• Introduction to NumPy 

• NumPy Arrays 

• Aggregations 

• Computation on Arrays: Broadcasting 

• Comparison, Boolean Logic and Masks 

• Fancy Indexing 

• Sorting Arrays 

• NumPy’s Structured Arrays
   

Module 15: Python for Data Analysis – Pandas 

• Installing Pandas 

• Pandas Objects 

• Data Indexing and Selection 

• Operating on Data in Pandas 

• Handling Missing Data 

• Hierarchical Indexing 

• Concat and Append 

• Merge and Join 

• Aggregations and Grouping 

• Pivot Tables 

• Vectorised String Operations 

• Working with Time Series
   

Module 16: Python for Data Visualisation – Matplotlib 

• Overview 

• Object-Oriented Interface 

• Simple Line Plots and Scatter Plots 

• Visualising Errors 

• Contour Plots 

• Histograms, Binnings, and Density 

• Customising Plot Legends 

• Customising Colour Bars 

• Multiple Subplots 

• Text Annotation 

• Three-Dimensional Plotting
   

Module 17: Python for Data Visualisation – Seaborn 

• Installing Seaborn and Load Dataset 

• Plot the Distribution 

• Regression Analysis 

• Basic Aesthetic Themes and Styles 

• Distinguish Between Scatter Plots, Hexbin Plots, and KDE Plots 

• Use Boxplots and Violin Plots
   

Domain 4: Tableau Training 

Module 18: Get Started 

• What is Tableau? 

• Steps in Creating Tableau Data Analysis Report 

• Navigation 

• Data Terminology 

• Design Flow 

• File Types 

• Data Types 

• Show Me
   

Module 19: Data Sources 

• Types of Data Sources 

• Custom Data View 

• Extracting Data 

• Fields Operations 

• Editing Metadata 

• Data Joining 

• Data Blending
   

Module 20: Worksheets 

• Add and Rename 

• Save and Delete 

• Reorder Worksheet 

• Paged Workbook 

Module 21: Calculations 

• Operators 

• Functions 

• Calculations 

• Numeric 

• String 

• Date 

• Table 

• LOD Expressions
   

Module 22: Sort and Filters 

• Basic Sorting 

• Basic Filters 

• Filters 

• Quick 

• Context 

• Condition 

• Top Filters 

• Filter Operations
   

Module 23: Tableau Charts 

• Chart 

• Bar 

• Line 

• Pie 

• Crosstab 

• Scatter Plot 

• Bubble Chart 

• Bullet Graph 

• Box Plot 

• Tree Map 

• Bump Chart 

• Gantt Chart 

• Histogram 

• Motion Charts 

• Waterfall Charts