This comprehensive five-day training course is designed to equip participants with the essential skills and knowledge required for effective data collection and data cleaning. The program covers the entire data lifecycle, from planning and sourcing data to its final preparation for analysis. Through a blend of theoretical instruction and hands-on practice, you'll learn to navigate the complexities of real-world datasets, ensuring the integrity and reliability of your information.
The course delves into various data collection methodologies, including both manual and automated techniques, alongside crucial ethical considerations. We'll then transition to the core of data cleaning, focusing on practical strategies for identifying and rectifying common data issues such as missing values, inconsistencies, and errors. A significant portion of the training is dedicated to data validation and quality assurance, culminating in a final project where you will apply all learned skills to build an end-to-end data pipeline.
Who Should Attend the Training
- Data analysts
- Researchers
- Business intelligence professionals
- Database administrators
- Students or anyone interested in a career in data science
Objectives of the Training
By the end of this course, you will be able to:
- Understand the full data lifecycle, from collection to analysis.
- Select and apply appropriate data collection methods for various projects.
- Identify, handle, and correct common data quality issues, including missing values and inconsistent data.
- Implement data validation techniques to ensure the accuracy and reliability of datasets.
- Develop automated workflows to streamline the data cleaning process.
- Apply your skills in a practical project, demonstrating an end-to-end data pipeline.
Personal Benefits
- Enhance your analytical and problem-solving skills, making you a more effective data professional.
- Gain hands-on experience with industry-standard tools and techniques.
- Boost your career prospects in the rapidly growing fields of data science and analytics.
- Build a strong foundation for more advanced data-related studies.
Organizational Benefits
- Improve the quality and accuracy of data-driven decisions within your organization.
- Reduce time spent on manual data cleaning through the use of efficient, automated workflows.
- Minimize risks associated with poor data quality, such as flawed analysis and incorrect reporting.
- Foster a culture of data literacy and quality consciousness among employees.
Training Methodology
- Interactive lectures and group discussions to facilitate knowledge sharing.
- Hands-on practical exercises to reinforce key concepts.
- Case studies and real-world examples to provide practical context.
- Group project work to simulate a collaborative work environment.
- Post-training support to address any follow-up questions.
Trainer Experience
Our trainers are seasoned professionals with extensive experience in data science and analytics. They have worked on diverse projects across various industries, including finance, healthcare, and technology. Their expertise isn't just theoretical; they bring a wealth of practical, real-world knowledge to the classroom, ensuring the training is relevant, current, and directly applicable to your professional needs.
Quality Statement
We are committed to delivering high-quality training that exceeds your expectations. Our course content is meticulously crafted and regularly updated to reflect the latest industry standards and best practices. We believe in providing a learning environment that is both challenging and supportive, ensuring every participant gets the most out of their training experience.
Tailor-made Courses
We understand that every organization has unique needs. This course can be customized to fit your specific requirements, focusing on the tools, datasets, and challenges most relevant to your business. Contact us to discuss how we can create a bespoke training solution for your team.
Course Duration: 5 days
Training fee: USD 1500
Module 1: Fundamentals of Data and the Data Lifecycle
- Understanding what data is and its importance in decision-making.
- Introduction to the data lifecycle: from collection to analysis and application.
- Exploring different types of data: structured, semi-structured, and unstructured.
- Data quality dimensions: accuracy, completeness, consistency, timeliness, and validity.
- Practical session: A case study analysis of a real-world dataset to identify its characteristics and potential quality issues.
Module 2: Data Collection Methods and Tools
- Primary vs. Secondary data collection: pros, cons, and use cases.
- Manual data entry and survey methods: best practices and potential pitfalls.
- Automated data collection using web scraping and APIs.
- Introduction to data sources and repositories.
- Practical session: Writing a simple web scraping script to collect data from a public website.
Module 3: Data Collection Planning and Ethics
- Designing a data collection plan: defining objectives, scope, and resources.
- Ethical considerations in data collection: privacy, consent, and bias.
- Data governance and compliance (e.g., GDPR, CCPA).
- Documentation and metadata management.
- Practical session: Developing a comprehensive data collection plan for a hypothetical research project, including ethical review.
Module 4: Introduction to Data Cleaning
- Why data cleaning is a critical step in the data lifecycle.
- Identifying common data issues: outliers, duplicates, and formatting inconsistencies.
- Tools for data cleaning: spreadsheets (Excel), Python (Pandas), and R.
- Exploratory data analysis (EDA) as a preliminary step to data cleaning.
- Practical session: Using Pandas to perform an initial exploratory analysis on a messy dataset to identify cleaning tasks.
Module 5: Handling Missing Data
- Understanding the different types of missing data: Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR).
- Strategies for dealing with missing data: deletion of rows/columns.
- Data imputation techniques: mean, median, mode, and advanced methods.
- Tools and functions in Python for detecting and handling missing values.
- Practical session: Applying various imputation techniques to a dataset with missing values and comparing the results.
Module 6: Dealing with Inconsistent and Erroneous Data
- Identifying and correcting data type inconsistencies.
- Standardizing data formats (e.g., dates, text fields).
- Finding and removing duplicate records.
- Handling outliers: detection and treatment methods.
- Practical session: Writing a script to standardize and de-duplicate a dataset with inconsistent entries.
Module 7: Data Validation and Quality Assurance
- Establishing data validation rules: range checks, format checks, and logical checks.
- Building a data quality assurance framework.
- Error logging and reporting.
- Continuous monitoring of data quality.
- Practical session: Creating a set of validation rules and writing a function to flag invalid data points in a new dataset.
Module 8: Automating Data Cleaning Workflows
- Introduction to functions and loops for repetitive cleaning tasks.
- Creating a reusable data cleaning pipeline.
- Using version control (e.g., Git) for data cleaning scripts.
- Best practices for documenting cleaning workflows.
- Practical session: Building a complete, reusable data cleaning script for a specific type of dataset.
Module 9: Introduction to Data Visualization
- The role of visualization in understanding and presenting data quality.
- Using plots (histograms, box plots, scatter plots) to detect outliers and anomalies.
- Creating before-and-after visualizations to demonstrate the impact of cleaning.
- Visualizing relationships and patterns.
- Practical session: Creating a series of visualizations to showcase the improvements made to a dataset after the cleaning process.
Module 10: Project-Based Application: End-to-End Data Pipeline
- Synthesizing all learned skills into a single project.
- Project planning: from problem definition to final output.
- Step-by-step implementation of a full data collection and cleaning pipeline.
- Presenting the final clean dataset and the insights gained.
- Practical session: Participants will work on a capstone project that involves collecting, cleaning, and validating a real-world dataset from scratch.
Requirements:
· Participants should be reasonably proficient in English.
· Applicants must live up to Armstrong Global Institute admission criteria.
Terms and Conditions
1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free
2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.
3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.
4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.
5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.
Booking for Training
Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.
Or call us on +254720272325 / +254725012095 / +254724452588
Payment Options
We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:
1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.
2. Invoice: We can send a bill directly to you or your company.
3. Deposit directly into Bank Account (Account details provided upon request)
Cancellation Policy
1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.
2. Participants may cancel attendance 14 days or more prior to the training commencement date.
3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.
Tailor Made Courses
This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com
Accommodation and Airport Transfer
Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588
