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