What is Advanced Data Analytics Course?
The Advanced Data Analytics Course is designed to provide Learners with in-depth knowledge of advanced analytical techniques, tools, and methodologies. This course covers predictive analytics, machine learning, statistical modelling, and data visualisation, enabling learners to address complex business challenges and optimise decision-making processes.
Learners will gain hands-on experience working with advanced tools like Python, R, and SQL and specialised platforms for big data and artificial intelligence. Practical exercises, case studies, and projects prepare learners to apply their analytical skills in real-world scenarios and generate actionable insights.
This comprehensive 4-day Advanced Data Analytics Course by Oakwood International empowers professionals to advance their expertise in analytics, positioning them for leadership roles in data-driven industries.
Course Objectives
- To understand advanced concepts in data analytics, including machine learning and AI
- To develop expertise in statistical modelling and predictive analytics
- To master tools such as Python, R, and SQL for data manipulation and analysis
- To explore big data platforms and advanced visualisation techniques
- To address business challenges with data-driven solutions
- To ensure data accuracy, reliability, and security in analytics workflows
- To gain practical experience through real-world projects and case studies
Upon completion, Learners will have the advanced skills to tackle complex analytics challenges and drive strategic decision-making.
Course Outline
Advanced Data Analytics Course
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
- 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
- Page 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
Included
Included
- No course includes are available.
Offered In This Course:
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Video Content
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eLearning Materials
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Study Resources
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Completion Certificate
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Tutor Support
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Interactive Quizzes
Learning Options
Discover a range of flexible learning options designed to meet your needs. Select the format that best supports your personal growth and goals.
Online Instructor-Led Training
- Live virtual classes led by experienced trainers, offering real-time interaction and guidance for optimal learning outcomes.
Online Self-Paced Training
- Flexible learning at your own pace, with access to comprehensive course materials and resources available anytime, anywhere.
Build your future with Oakwood International
We empower you with the skills, knowledge, and confidence to excel in your career. Join us and take the first step towards realising your professional goals.
Frequently Asked Questions
Q. What topics are covered in the Advanced Data Analytics Course?
The course covers statistical
modelling, predictive analytics, machine learning, big data, data
visualisation, and real-world applications.
Q. How can this training benefit my career?
This training enhances your
expertise in advanced analytics, equipping you to tackle complex data
challenges and excel in data-driven roles.
Q. Is this course applicable to all industries?
Advanced analytics skills are
versatile and highly valuable across finance, marketing, healthcare, and
logistics industries.
Q. What support is provided during the training?
Learners receive comprehensive
study materials, hands-on exercises, and expert instructor guidance to ensure
effective learning and application.
Q. Is this course suitable for beginners?
No, this course is designed for
professionals with foundational knowledge of data analytics, statistical
analysis, or related fields.