Name: Complete Guide to Data Preparation for Analysis
Price: 20 GBP

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Curriculum

The Complete Guide to Data Preparation for Analysis provides a structured understanding of how to prepare, clean, and manage data for accurate insights and informed decision-making. In today’s data-driven environment, the ability to prepare data effectively is an essential foundation for successful analysis. This course empowers learners to develop the confidence and technical understanding required to transform raw data into reliable, high-quality datasets ready for analysis.

Through the Complete Guide to Data Preparation for Analysis, learners will explore methods for detecting errors, handling missing values, normalising data, and ensuring consistency across multiple data sources. They will also gain practical knowledge of data cleaning tools, data validation techniques, and the importance of maintaining data integrity throughout the analytical process.

Completing this Complete Guide to Data Preparation for Analysis will enable learners to save time, reduce analysis errors, and make informed business decisions based on clean and structured data. Whether preparing data for statistical modelling, visualisation, or reporting, learners will gain a practical understanding of how to prepare and manage data efficiently, ensuring analytical outcomes are both credible and impactful.

Sneak Peek

Learning Outcomes

- Apply structured techniques in the Complete Guide to Data Preparation for Analysis.
- Identify and correct data errors to improve analytical reliability.
- Manage missing or inconsistent data using proven data preparation strategies.
- Evaluate data quality and integrity before conducting analysis.
- Implement data transformation processes for accurate reporting and insights.
- Demonstrate competence in preparing datasets for advanced analysis.

Who is this Course For

Data analysts aiming to enhance data preparation efficiency and accuracy.
Business professionals seeking reliable data insights for decision-making.
Graduates pursuing roles in data analytics or business intelligence.
Researchers working with large datasets requiring cleaning and organisation.
IT professionals managing data for analysis or system integration.
Project managers overseeing data-driven business initiatives.
Students interested in foundational skills for data analysis careers.
Anyone keen to improve data preparation and management techniques.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Accreditation

All of our courses, including this Microsoft Powerpoint with AI, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.

Career Path

Data Analyst – Average Salary: £35,000 per year
Business Intelligence Analyst – Average Salary: £40,000 per year
Data Engineer – Average Salary: £45,000 per year
Market Research Analyst – Average Salary: £32,000 per year
Database Administrator – Average Salary: £38,000 per year
Reporting Analyst – Average Salary: £36,000 per year

Course Curriculum

The detailed curriculum outline of our Do Your Personal SWOT Analysis & Grow in Career & Life is as follows:

Designing Surveys

1 – Introduction to Data Cleaning and How Significant it is
2 – What is Data Cleaning
3 – Key aspects of Data Cleaning
4 – Key Aspects of Data Cleaning
5 – Methods of Data Cleaning
6 – Impact of Data Cleaning
7 – Where Data Cleaning is used
8 – Techniques of Data Cleaning
9 – Handling Missing Values
10 – Examples of Handling Missing Values
11 – Data Deduplication and Identifying Duplicate information
12 – Data Deduplication – Comparison Methods
13 – Duplicate Detection
14 – Data Deduplication Conclusion
15 – Outlier Identification and Treatment
16 – Example of Outlier Identification and Treatement
17 – Data Normalization and Data Standardization
18 – Data Formatting and Data Parsing
19 – Inconsistent Data Handling
20 – Error Correction and Validation
21 – Data Transformation
22 – Feature Engineering
23 – Handling Imbalanced Data

Data Standardization Frameworks

1 – Data Collection Introduction
2 – Where and why we need data collection
3 – Types of Data Collection
4 – Mobile and Web Analytics Framework
5 – User Engagement Analytics Frameworks
6 – Centralized Logging Frameworks
7 – Real time Data Streaming Frameworks
8 -Cloud-Based Data Collection Frameworks
9 – Observability Frameworks
10 – Business Evolution and Frameworks at different stages of the business 2
11 – Tools for Data Collection Frameworks

Data Cleaning Frameworks

1 – Introduction to the Data Standardization Course
2 – Impact of Data Standardization
3 – Aspects of Data Standardization
4-Frameworks of Data Standardization
5 -Data Wrangling
6 – Data Standardization
7 – Data Orchestration
8 – Data Blending
9 – Data Cleaning
10 – Data Transformation
11 – Data Integration and Data Enrichment
12 – Types of Data Standardization and General Data Standards
13 – Demo of Data Standardization for General Data Standardization frameworks
14 – Demo of Complex Data Standardization

Data Collection Frameworks

1 – Course Introduction
2 – Where Surveys are Used
3 – Impact of a Survey – McDonald Example
4 – Types of Surveys
5 – Components of a Survey
6 – Introduction to the Project
7 – Background Information and Defining Objectives
8 – Creating Survey Questions – Section Introduction
9 – Thinking Themes – Key Themes
10 – Brainstorming Questions
11 – Prioritizing Questions
12 – Question Types
13 – Crafting Clear Questions
14 – Avoid Double Barreled Questions
15 – Consider Response Options
16 – Neutral Language Question Flow and Sensitivity
17 – Forming Questions in Chatgpt
18 – Creating the Quesstionaire in Google forms and Creating Options
19 – Logical Branching
20 – Piloting and Administrative Modes
21 – Admistrative Methods
22 – Conclusion

Course Curriculum

Designing Surveys
	1 – Introduction to Data Cleaning and How Significant it is	00:05:00
	2 – What is Data Cleaning	00:03:00
	3 – Key aspects of Data Cleaning	00:05:00
	4 – Key Aspects of Data Cleaning	00:04:00
	5 – Methods of Data Cleaning	00:01:00
	6 – Impact of Data Cleaning	00:02:00
	7 – Where Data Cleaning is used	00:02:00
	8 – Techniques of Data Cleaning	00:01:00
	9 – Handling Missing Values	00:04:00
	10 – Examples of Handling Missing Values	00:04:00
	11 – Data Deduplication and Identifying Duplicate information	00:02:00
	12 – Data Deduplication – Comparison Methods	00:01:00
	13 – Duplicate Detection	00:01:00
	14 – Data Deduplication Conclusion	00:03:00
	15 – Outlier Identification and Treatment	00:06:00
	16 – Example of Outlier Identification and Treatement	00:02:00
	17 – Data Normalization and Data Standardization	00:04:00
	18 – Data Formatting and Data Parsing	00:05:00
	19 – Inconsistent Data Handling	00:04:00
	20 – Error Correction and Validation	00:04:00
	21 – Data Transformation	00:05:00
	22 – Feature Engineering	00:05:00
	23 – Handling Imbalanced Data	00:06:00
Data Standardization Frameworks
	1 – Data Collection Introduction	00:04:00
	2 – Where and why we need data collection	00:01:00
	3 – Types of Data Collection	00:02:00
	4 – Mobile and Web Analytics Framework	00:05:00
	5 – User Engagement Analytics Frameworks	00:03:00
	6 – Centralized Logging Frameworks	00:03:00
	7 – Real time Data Streaming Frameworks	00:02:00
	8 -Cloud-Based Data Collection Frameworks	00:04:00
	9 – Observability Frameworks	00:03:00
	10 – Business Evolution and Frameworks at different stages of the business 2	00:07:00
	11 – Tools for Data Collection Frameworks	00:10:00
Data Cleaning Frameworks
	1 – Introduction to the Data Standardization Course	00:02:00
	2 – Impact of Data Standardization	00:03:00
	3 – Aspects of Data Standardization	00:07:00
	4-Frameworks of Data Standardization	00:01:00
	5 -Data Wrangling	00:03:00
	6 – Data Standardization	00:03:00
	7 – Data Orchestration	00:03:00
	8 – Data Blending	00:02:00
	9 – Data Cleaning	00:02:00
	10 – Data Transformation	00:02:00
	11 – Data Integration and Data Enrichment	00:04:00
	12 – Types of Data Standardization and General Data Standards	00:11:00
	13 – Demo of Data Standardization for General Data Standardization frameworks	00:04:00
	14 – Demo of Complex Data Standardization	00:12:00
Data Collection Frameworks
	1 – Course Introduction	00:01:00
	2 – Where Surveys are Used	00:01:00
	3 – Impact of a Survey – McDonald Example	00:01:00
	4 – Types of Surveys	00:02:00
	5 – Components of a Survey	00:02:00
	6 – Introduction to the Project	00:01:00
	7 – Background Information and Defining Objectives	00:04:00
	8 – Creating Survey Questions – Section Introduction	00:01:00
	9 – Thinking Themes – Key Themes	00:01:00
	10 – Brainstorming Questions	00:01:00
	11 – Prioritizing Questions	00:01:00
	12 – Question Types	00:01:00
	13 – Crafting Clear Questions	00:01:00
	14 – Avoid Double Barreled Questions	00:02:00
	15 – Consider Response Options	00:01:00
	16 – Neutral Language Question Flow and Sensitivity	00:02:00
	17 – Forming Questions in Chatgpt	00:07:00
	18 – Creating the Quesstionaire in Google forms and Creating Options	00:09:00
	19 – Logical Branching	00:02:00
	20 – Piloting and Administrative Modes	00:01:00
	21 – Admistrative Methods	00:03:00
	22 – Conclusion	00:01:00