Data Analyst : Data Analyst
Level 5 Endorsed Data Analysis Diploma | 13-in-1 Bundle | 280 CPD Points I Tutor Support | 24x7 Instant Access
One Education
Summary
- Exam(s) / assessment(s) is included in price
- Tutor is available to students
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Overview
** 13-in-1 Data Analysis Bundle **
Unleash Your Full Data Analysis Potential with Our Comprehensive Bundle!
Are you ready to elevate your data analysis skills to a whole new level? Look no further! Our Data Analysis Bundle is the ultimate solution you've been searching for.
In today's data-driven world, being proficient in a single aspect of data analysis is just not enough. That's why we've carefully curated a bundle of courses that complement each other seamlessly, creating a holistic and powerful learning experience.
The Data Analysis Bundle includes the following courses:
- Diploma in Data Analytics with Tableau
- Data Analytics
- Google Data Studio: Data Analytics
- Big Data Analytics with PySpark Power BI and MongoDB
- Big Data Analytics with PySpark Tableau Desktop and MongoDB
- Learn MySQL from Scratch for Data Science and Analytics
- SQL for Data Science, Data Analytics and Data Visualization
- Data Analysis In Excel
- Diploma in Data Analysis Fundamentals
- Business Data Analysis
- Data Science & Machine Learning with Python
- R Programming for Data Science
- Learn Python, JavaScript, and Microsoft SQL for Data science
Course Significant Points:
- Quality Licence Scheme Endorsed
- CPD Accredited Course
- Unlimited Retake Exam & Tutor Support
- Easy Accessibility to the Course Materials
- 100% Learning Satisfaction Guarantee
- Lifetime Access & 24/7 Support
- Self-paced online course Modules
- Covers to Explore Multiple Job Positions
How is the course assessed?
To assess your learning, you have to complete the assignment questions provided at the end of the course. You have to score at least 60% to pass the exam and to qualify for Quality Licence Scheme endorsed and CPD-accredited certificates. After passing the assignment exam, you will be able to apply for a certificate.
Meet the Endorsement
The Quality Licence Scheme has been designed specifically to recognise high-quality courses. In addition, the QLS certificate enriches your CV and recognises your quality study on the relevant subject.
Meet the Accreditation
CPD Quality Standards (CPD QS) accreditation assures the Data Analysis with Excel course training and learning activities are relevant, reliable, and up to date.
**Enrol Today and Unleash Your Full Data Analysis Potential!**
Achievement
CPD
Description
The Data Analysis Bundle is a comprehensive collection of courses designed to equip individuals with the skills and knowledge necessary for a successful career in data analysis. From foundational concepts to advanced techniques and tools, this bundle covers a wide range of topics in the field of data analysis. Whether you're a beginner looking to get started or a professional seeking to enhance your skills, these courses provide a well-rounded education in data analysis.
**Diploma in Data Analytics with Tableau**
- Introduction To The Course
- Project 1: Discount Mart (Sales And Profit Analytics)
- Project 2: Green Destinations (HR Analytics)
- Project 3: Superstore (Sales Agent Tracker)
- Northwind Trade (Shipping Analytics)
- Project 5: Tesla (Stock Price Analytics)
- Bonus: Introduction To Database Concepts
- Tableau Stories
**Data Analytics**
- Introduction To The World Of Data
- Basics Of Data Analytics
- Statistics For Data Analytics
- Actions Taken In The Data Analysis Process
- Gathering The Right Information
- Storing Data
- Data Mining
- Excel For Data Analytics
- Tools For Data Analytics
- Data-Analytic Thinking
- Data Visualisation That Clearly Describes Insights
- Data Visualisation Tools
**Google Data Studio: Data Analytics**
- Introduction
- Google Sheets
- Google Data Studio
**Big Data Analytics with PySpark Tableau Desktop and MongoDB**
- Introduction
- Setup And Installations
- Data Processing With PySpark And MongoDB
- Machine Learning With PySpark And MLlib
- Creating The Data Pipeline Scripts
- Tableau Data Visualization
- Source Code
**Learn MySQL from Scratch for Data Science and Analytics**
- Getting Started
- SQL Server Setting Up
- SQL Database Basics
- SQL DML (Data Manipulation Language)
- SQL DDL (Data Definition Language)
- SQL DCL (Data Control Language)
- SQL Statement Basic
- Filtering Data Rows
- Aggregate Functions For Data Analysis
- SQL Data Analyticstatements
- SQL Group By Statement
- JOINS
- SQL Constraints
- Views
- Advanced SQL Functions
- SQL Stored Procedures
- Import & Export Data
- Backup And Restore Database
**Data Analysis In Excel**
- Modifying A Worksheet
- Working With Lists
- Analyzing Data
- Visualizing Data With Charts
- Using PivotTables And PivotCharts
- Working With Multiple Worksheets And Workbooks
- Using Lookup Functions And Formula Auditing
- Automating Workbook Functionality
- Creating Sparklines And Mapping Data
- Forecasting Data
**Diploma in Data Analysis Fundamentals**
- Introduction
- Agenda And Principles Of Process Management
- The Voice Of The Process
- Working As One Team For Improvement
- Exercise: The Voice Of The Customer
- Tools For Data Analysis
- The Pareto Chart
- The Histogram
- The Run Chart
- Exercise: Presenting Performance Data
- Understanding Variation
- The Control Chart
- Control Chart Example
- Control Chart Special Cases
- Interpreting The Control Chart
- Control Chart Exercise
- Strategies To Deal With Variation
- Using Data To Drive Improvement
- A Structure For Performance Measurement
- Data Analysis Exercise
- Course Project
- Test Your Understanding
**Business Data Analysis**
- Introduction To Business Analysis
- Business Environment
- Business Processes
- Business Analysis Planning And Monitoring
- Strategic Analysis And Product Scope
- Solution Evaluation
- Investigation Techniques
- Ratio Analysis
- Stakeholder Analysis And Management
- Process Improvement With Gap Analysis
- Documenting And Managing Requirements
- Business Development And Succession Planning
- Planning & Forecasting Operations
- Business Writing Skills
**Data Science & Machine Learning with Python**
- Course Overview & Table Of Contents
- Introduction To Machine Learning
- System And Environment Preparation
- Learn Basics Of Python - Assignment
- Functions
- Data Structures
- NumPy Array
- NumPy Data
- NumPy Arithmetic
- Learn Basics Of Matplotlib
- Learn Basics Of Pandas
- Understanding The CSV Data File
- Load And Read CSV Data File Using Python Standard Library
- Using NumPy
- Using Pandas
- Dataset Summary - Peek, Dimensions And Data Types
- Class Distribution And Data Summary
- Explaining Correlation
- Explaining Skewness - Gaussian And Normal Curve
- Dataset Visualization - Using Histograms
- Dataset Visualization - Using Density Plots
- Dataset Visualization - Box And Whisker Plots
- Multivariate Dataset Visualization - Correlation Plots
- Multivariate Dataset Visualization - Scatter Plots
- Data Preparation (Pre-Processing) - Introduction
- Re-Scaling Data
- Standardizing Data
- Normalizing Data
- Binarizing Data
- Feature Selection - Introduction
- Uni-Variate Part
- Recursive Feature Elimination
- Principal Component Analysis (PCA)
- Feature Importance
- Refresher Session - The Mechanism Of Re-Sampling, Training And Testing
- Algorithm Evaluation Techniques - Introduction
- Train And Test Set
- K-Fold Cross Validation
- Leave One Out Cross Validation
- Repeated Random Test-Train Splits
- Algorithm Evaluation Metrics - Introduction
- Classification Accuracy
- Log Loss
- Area Under ROC Curve
- Confusion Matrix
- Classification Report
- Mean Absolute Error - Dataset Introduction
- Mean Absolute Error
- Mean Square Error
- R Squared
- Classification Algorithm Spot Check - Logistic Regression
- Linear Discriminant Analysis
- K-Nearest Neighbors
- Naive Bayes
- CART
- Support Vector Machines
- Regression Algorithm Spot Check - Linear Regression
- Ridge Regression
- Lasso Linear Regression
- Elastic Net Regression
- K-Nearest Neighbors
- CART
- Support Vector Machines (SVM)
- Compare Algorithms
- Pipelines
- Performance Improvement: Ensembles - Voting
- Bagging
- Boosting
- Performance Improvement: Parameter Tuning Using Grid Search
- Performance Improvement: Parameter Tuning Using Random Search
- Export, Save And Load Machine Learning Models : Pickle
- Export, Save And Load Machine Learning Models : Joblib
- Finalizing A Model - Introduction And Steps
- Finalizing A Classification Model - The Pima Indian Diabetes Dataset
- Quick Session: Imbalanced Data Set - Issue Overview And Steps
- Iris Dataset : Finalizing Multi-Class Dataset
- Finalizing A Regression Model - The Boston Housing Price Dataset
- Real-Time Predictions: Using The Pima Indian Diabetes Classification Model
- Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset
- Real-Time Predictions: Using The Boston Housing Regression Model
**R Programming for Data Science**
- Data Science Overview
- R And RStudio
- Introduction To Basics
- Vectors
- Matrices
- Factors
- Data Frames
- Lists
- Relational Operators
- Logical Operators
- Conditional Statements
- Loops
- Functions
- R Packages
- The Apply Family - Lapply
- The Apply Family – Sapply & Vapply
- Useful Functions
- Regular Expressions
- Dates And Times
- Getting And Cleaning Data
- Plotting Data In R
- Data Manipulation With Dplyr
**Learn Python, JavaScript, and Microsoft SQL for Data science**
- JavaScript Introduction
- JavaScript Basics
- JavaScript Operators
- JavaScript Conditional Statements
- JavaScript Control Flow Statements
- JavaScript Functions
- JavaScript Error Handling
- JavaScript Client-Side Validations
- Python Introduction
- Python Basic
- Python Strings
- Python Operators
- Python Data Structures
- Python Conditional Statements
- Python Control Flow Statements
- Python Core Games
- Python Functions
- Python Args, KW Args For Data Science
- Python Project
- Python Object Oriented Programming [OOPs]
- Python Methods
- Python Class And Objects
- Python Inheritance And Polymorphism
- Python Encapsulation And Abstraction
- Python OOPs Games
- Python Modules And Packages
- Python Error Handling
- Microsoft SQL[MS] Introduction
- MS SQL Statements
- MS SQL Filtering Data
- MS SQL Functions
- MS SQL Joins
- MS SQL Advanced Commands
- MS SQL Structure And Keys
- MS SQL Queries
- MS SQL Structure Queries
- MS SQL Constraints
- MS SQL Backup And Restore
Who is this course for?
Data Analysis course is ideal for:
- Professionals working in fields such as finance, business, and healthcare who need to analyse data and make decisions based on their findings.
- Students and researchers who want to develop their skills in data analysis and visualisation using Excel.
- Individuals who are looking to pursue a career in data analysis and want to gain a foundational understanding of Excel and its capabilities.
- Business owners and entrepreneurs who want to learn how to use Excel to analyse and interpret data for their own businesses.
- Anyone who wants to improve their data analysis skills for personal or professional purposes and gain proficiency in Excel as a tool for data analysis.
Requirements
There are no specific prerequisites to enrol in Data analysis course. Anyone and everyone can take this course.
Career path
The course will assist you in establishing a solid understanding of data analysis. After the course, you will be able to explore career options such as:
- Data Analyst (Average annual salary £35,000)
- Research Analyst (Average annual salary £28,552)
- Business Analyst (Average annual salary £42,618)
- Risk Analyst (Average annual salary £41,340)
- Financial Analyst (Average annual salary £50,300)
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