Module 1: Foundations of Data Exploration and the EDA Process

·       Defining EDA and its role in the data science lifecycle

·       The CRISP-DM framework overview and where EDA fits

·       Understanding data types (Nominal, Ordinal, Interval, Ratio)

·       The five-step EDA workflow

·       Identifying the business context and defining analysis goals

·       Practical session: Loading a raw dataset and generating initial statistical summaries (e.g., using Python/R/Excel)

Module 2: Descriptive Statistics for Data Understanding

·       Measures of Central Tendency (Mean, Median, Mode) and when to use each

·       Measures of Dispersion (Variance, Standard Deviation, IQR)

·       Understanding and interpreting Skewness and Kurtosis

·       Calculating correlation and covariance for bivariate analysis

·       Using quantiles and percentiles to analyze distribution

·       Practical session: Calculating and comparing descriptive statistics for continuous and categorical variables

Module 3: Univariate and Bivariate Data Visualization

·       Best practices for data visualization (chart selection, color theory)

·       Creating and interpreting Histograms and Density plots for distribution

·       Using Box Plots and Violin Plots to identify spread and outliers

·       Generating Bar Charts and Pie Charts for categorical data

·       Visualizing relationships with Scatter Plots and Heatmaps (correlation matrix)

·       Practical session: Generating a comprehensive set of univariate and bivariate visualizations to profile a new dataset

Module 4: Data Quality Assessment and Missing Value Imputation

·       Identifying data quality issues (missingness, inconsistencies, duplication)

·       Types of missing data (MCAR, MAR, MNAR) and their implications

·       Common imputation techniques: Mean/Median/Mode imputation

·       Advanced imputation: K-Nearest Neighbors (KNN) and regression imputation

·       Handling duplicate and inconsistent records

·       Practical session: Identifying missing data patterns and applying two different imputation methods (e.g., Mean and KNN) to compare results

Module 5: Outlier Detection and Treatment

·       Defining outliers and anomalies in different contexts

·       Statistical methods for outlier detection: Z-Score and IQR methods

·       Visualization techniques for outlier identification (Box Plots, Scatter Plots)

·       Data transformation techniques (Log, Square Root) to manage outliers

·       Strategies for treating outliers: Capping, Trimming, and Separation

·       Practical session: Implementing the IQR method to flag outliers in a numerical feature and applying capping to mitigate their effect

Module 6: Foundations of Feature Engineering

·       Defining Feature Engineering and its impact on model performance

·       The difference between raw data, features, and variables

·       Domain expertise as the primary driver of effective feature creation

·       Creating simple interaction features (multiplication, division)

·       The feature creation workflow

·       Practical session: Creating new interaction features (e.g., density = mass/volume) based on domain knowledge of a dataset

Module 7: Handling Categorical Variables

·       Strategies for encoding nominal categorical features (One-Hot Encoding)

·       Strategies for encoding ordinal categorical features (Label/Ordinal Encoding)

·       Dealing with high-cardinality categorical variables (Target/Frequency Encoding)

·       Binary encoding and its use cases

·       Handling text features using Bag-of-Words or TF-IDF overview

·       Practical session: Applying One-Hot Encoding to a nominal feature and Target Encoding to a high-cardinality feature

Module 8: Feature Scaling and Transformation

·       The necessity of feature scaling for distance-based algorithms

·       Implementing Standardization (Z-score scaling)

·       Implementing Normalization (Min-Max scaling)

·       Power transformations (Box-Cox and Yeo-Johnson) for achieving normality

·       Discretization (Binning) of continuous features into categorical bins

·       Practical session: Comparing the effects of Standardization and Normalization on a skewed numerical feature using Python

Module 9: Dimensionality Reduction and Feature Selection

·       The Curse of Dimensionality and its effects on model performance

·       Feature Selection methods: Filter (Chi-squared, Correlation), Wrapper (Forward/Backward Selection), and Embedded (Lasso)

·       Introduction to Principal Component Analysis (PCA) for dimensionality reduction

·       Interpreting PCA components and selecting the optimal number of components

·       Feature importance extraction from machine learning models

·       Practical session: Implementing PCA to reduce the dimensionality of a dataset and visualizing the explained variance ratio

Module 10: Time-Based and Automated Feature Engineering

·       Creating date and time-based features (day of week, month, holiday flags)

·       Generating lagged features and rolling window statistics (mean, sum, std dev)

·       Aggregating features for sequential and transactional data

·       Automating Feature Engineering using tools or libraries overview

·       Documenting the final feature set and preparing for modeling

·       Practical session: Creating lagged features and a 7-day rolling average for a time-series feature in the dataset

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