Faculty of Data Science

To speak with someone,

+234 702 630 7268

So far, many learners have enrolled in the faculty because...

  1. they wanted to add data science skills to their Resume to boost their chances in the labour market. OR
  2. they are working already and need to impress their boss as well as increase their efficiency and relevance at work with their new skill. OR,
  3. they are about to continue their education overseas in data science related field and want to have some practical experience before leaving the country. OR
  4. they are migrating to developed countries and want to operate in the IT space on their arrival there. OR
  5. they need it personally for their research work and/or private business.
Whichever category you belong to, you will be satisfied.


Basic Requirement:

While we take the training classes from the A.B.C for all learners, basic computer appreciation / operations skills and a good personal computer is a requirement for entry into all our programs.

Python is one of the best languages used by data scientist and machine learning engineers for various projects / application. This course will take learners through our

  1. Python Foundation Course where they will learn Python Programming fundamentals.
  2. Data Science and Data Wrangling Course where they will learn about data cleaning, data analysis, data engineering, data preprocessing and more
  3. Machine Learning Course where they will be taught Machine learning in-depth.

Before the end of the data science and machine learning training experience, learners will work on the following projects:

  1. Earthquake prediction
  2. Restaurant Recommendation system
  3. Cryptocurrency Price Prediction for the next 30days
  4. Health Insurance Premium Prediction
  5. Financial Budget Analysis
  6. Ted-Talks Recommendation System
  7. Next word Prediction
  8. Movie Recommendation System
  9. Image Recognition with Machine Learning
  10. Fake News Detection
  11. Uber Data Analysis
  12. Handwritten Character Recognition
  13. Credit Card Fraud Detection
  14. Customer Segmentation Using Machine Learning
  15. Face Land Marks Detection
  16. Car Prices Prediction
  17. Python GUI For Data Entry
  18. Generate Interactive Map
  19. Translate Using Python
  20. Exploratory Data Analysis on Global Terrorism
  21. Send Custom Email with Python
  22. Quiz Game with Python
  23. Calculator GUI with Python


The Training is organized into the following modules:


Python programming language is a popular and highly demanded skill. This training will help prepare learners to master Python programming language well enough to break into the world of AI/Machine Learning, Data Science, Data Analytics, Software Engineering and many more.

MODULE 1: Onboarding and Course Introduction

Understanding the difference between Data Analytics, Data Science, Machine Learning, Deep Learning, and Artificial Intelligence. System specification, Jupyter Notebook and GoogleColab Explained, Program Structure and answering all necessary questions to fix learners properly on their career track.

MODULE 2: Python Foundation

  • Python Basics, Data Types, Containers, Operators
  • If, else, elif, nested if statement, Loops and functions
  • Advance Python

Learners will be given minimum of 5 Projects to work on within the period of 2 weeks and they are advised to submit before the deadline. These projects are applications of what they learnt in module 1 and 2.


This section of the training will introduce the student into the world of Data Collection, Exploratory Data Analysis, Data Cleaning, Feature Engineering, Data Preprocessing and more. After this academy, Students will be able to perfectly clean data, answer all Data Related Questions and derive insights for any company, organization and individuals that need their services.

Data Visualization is a method that uses static and interactive visuals to help people understand the details embedded in the data being collected. This Program will build student with necessary skills to choose the best chart to visualize data for best decision making.

MODULE 3: Data Wrangling

  • Python NumPy
  • Data Analysis with Pandas

Students will be given minimum of 3 Projects to work on within the period of 2 weeks and they are advised to submit before the deadline. The projects take the learners through the experience of practical application of what they learnt in Data Wrangling Academy.

Module 4: Data Visualization

  • Matplotlib Data Visualization
  • Seaborn Data Visualization
  • Plotly and Cufflinks Data Visualization

Students will be given minimum of 2 Projects to work on within the period of 1 week and they are advised to submit before the deadline. This projects are applications of what they learnt in the Data Visualization Class.

This course will expose learners to different machine learning models. At the end of this course, they will be able to build machine learning systems for different sectors.

Module 5:

  • Understanding Supervised, Unsupervised and Reinforcement Learning
  • Machine Learning Models and how to choose the best for each task
  • Machine Learning with Scikit-learn
  • Linear Regression Models with Single Variable
  • Linear Regression Models with Multiple Variable
  • Machine Learning Project 1

Module 6:

  • Gradient Decent and Cost Function in ML
  • Save and load trained model using Pickle and Sklearn Joblib
  • Categorical, Dummy Variables and One Hot Encoding in ML
    • Using Pandas get_dummies
    • Using Sklearn OneHotEncoding
  • Training and Testing Data In ML
  • Logistic Regression Model in ML (Binary Classification)
  • Logistic Regression Model in ML (Multiclass Classification)
  • Machine Learning Project 2

Module 7:

  • Decision Tree in Machine Learning
  • ML Project 3
  • Support Vector Machine in ML
  • Machine Learning Project 3

Module 8:

  • Random Forest in Machine Learning
  • ML Project 5
  • K Fold Cross Validation and Evaluating Model Performance in ML
  • K Means Clustering Algorithm
  • Machine Learning Project 4

Module 9:

  • Mathematics for Machine Learning (Optional)
  • Statistics For Machine Learning (Optional)
  • Naïve Bayes Classifier Algorithm Part 1
  • Machine Learning Project 5

Module 10:

  • Naïve Bayes Classifier Algorithm Part 2
    • Explore Spam email dataset
    • Sklearn CountVectorizer
    • Types Of Naives Bayes Classifier
    • Sklearn MultinomialNB Classifier
    • Sklearn Pipeline
  • Machine Learning Project 6

Module 11:

  • Hyper Parameter Tuning (GridSearchCV) in ML
    • Train Test Split
    • Kfold Cross Validation
    • GridSearchCV For Hyperparameter Tuning
    • RandomizedSearchCV
    • Choosing best Model
  • Machine Learning Project 7

Module 12:

  • L1 and L2 Regularization Lasso, Ridge Regression in solving
  • Overfitting Issue
  • K Nearest Neighbor Classification
  • Principal Component Analysis (PCA)
  • Bias and Variance in Machine Learning
  • Ensemble Learning – Bagging
  • Image Classification
  • Natural Language Processing (NLP)
  • Machine Learning Project 8

Module 13:

  • Using GitHub to manage Data Science Projects
  • Working on Projects
  • Projects Presentation
  • Building Professional Resume
  • Sessions on how to Get Your First Data Science Job
  • Congratulations


While we give you our Certification of Training in all of the above, we also encourage you to attempt the following global certifications in Data Science with Python:

In addition, if you are looking at enrolling for an undergraduate or post graduate degree in related course in any University overseas, this training prepares you ahead for first class degree.


200 Hours


From ₦190,000

To speak with someone, call +234 (0) 702 630 7268 or +234 (0) 810 402 2323

R is an advanced language that performs various complex statistical computations and calculations. Therefore, it is widely used by data scientists and business leaders in multiple fields, from academics to business.

Our course on Data Science and Machine Learning with R takes you through three stages – the beginners, intermediate and advanced stage over a period of 43 weeks. In 11 months of rigorous training, we make you an expert in R. You will also learn how to use Github to manage your data science projects.

So if your goal is to have a mastery of R for data analysis, data visualization and machine learning, then you should sign up to join the upcoming cohort today.


Module 1

  • History and Overview of R
    • What is R
    • Basic Features of R
    • Limitations of R
  • Getting started with R
    • Installation
    • Getting Started with the R interface
  • Intro to basics
    • Arithmetic with R
    • Variable assignment
    • Basic data types

Module 2

  • Vectors
    • Creating Vectors
    • Naming Vectors
  • Matrices
    • What is a matrix?
    • Naming a matrix
    • Adding columns to matrix
    • Adding rows to matrix
    • Selection of matrix elements
    • Arithmetic with matrix

Module 3

    • Factors
      • What is a factor?
      • Factor levels
      • Summarizing a factor
      • Ordered factors
    • Data frames
      • What’s a data frame?
      • Structure of data frame
      • Creating a data frame
      • Selection of data frame elements
      • Sorting your data frame

Module 4

    • List
      • Creating a List
      • Creating a named list
      • Selecting elements from a list
      • PROJECTS


Module 5

  • Conditional and Control flows
    • Relational operators – Equality, Greater and less than, compare vectors and matrices
    • Logical Operators – & and |, Reverse(!)
    • Conditional statements – If statement, Else statement, Else if statement
    • Loops
      • While loop – conditions with while loop, break
      • For loop – Loop over a vector, loop over a list, loop over a matrix, Control flow and loop with break

Module 6

  • Functions
    • Introduction to Functions – Use a function, function inside function
    • Writing functions
    • R Packages – Load an R package, different ways to load a package
  • The Apply Family
    • Lapply – lappy with built-in-function, lapply with your own function
    • Sapply – sapply with your function, sapply with function returning vector
    • Vapply

Module 7

    • Utilities
      • Useful functions
      • Regular expressions – grepl & grep, sub & gsub
      • Times & dates – present date and time, create and format dates and times, calculations with time
      • PROJECT


Module 8

    • Data wrangling
      • Understanding a data frame
      • Filter the verb
      • Arrange the Verb
      • Filtering and arranging
      • The mutate verb
    • Data Visualization
      • Visualizing with ggplot2 – variable assignment, comparing variables
      • Log scales – putting x and y axis on a log scale
      • Additional aesthetics – adding color, adding size
      • Faceting

Module 9

    • Grouping and summarizing
      • The summarize verb
      • The group_by verb
      • Visualizing summarized data
    • Types of visualizations
      • Line Plot
      • Bar Plot
      • Histogram
      • Boxplots


Module 10

    • Transforming data with dplyr
      • Exploring dsts with dplyr – understanding you data, selecting columns
      • The filter and arrange verb – filtering for conditions, arranging observations
      • The mutate() verb – select, mutate, filter and arrange
    • Aggregating Data
      • The count verb
      • The group_by, summarize, and ungroup verbs
      • The slice_min and slice_max verbs

Module 11

  • Selecting and transforming data
    • Selecting
    • The rename verb – rename as part of a select
    • The transmute verb


Module 12

  • Joining tables
    • The inner_join verb – columns to join on
    • Joining with one-to-many relationship
    • Joining three or more tables
    • Left and Right joins
      • The left_join verb
      • The right_join verb
      • Joining tables to themselves
    • Full, Semi, and Anti joins
      • The full_join verb
      • The semi_join and anti_join verbs

Module 13

  • Summary statistics
    • What is statistics – descriptive and inferential statistics
    • Measures of center – mean and median
    • Measures of spread – quartiles, quantiles and quintiles, variance and standard deviation, IQR
    • Random numbers and probability
      • With and without replacement – calculating probabilities
      • Discrete distribution

Module 14

  • Random numbers and probability
    • Continuous distribution
    • Binomial distribution
  • More Distributions and the Central Limit Theorem
    • Normal distribution
    • The central limit theorem
    • The poisson distribution – identifyinf lambda
    • The t-distribution
  • Correlation and Experimental Design
    • Correlation


Module 15

  • Introduction
    • the grammar of graphics
    • ggplot2 layers
  • Aesthetics
    • Color, shape, size
    • Aesthetics for categorical & continuous variables
    • Modifying aesthetics – updating aesthetic labels
    • Aesthetics best practices – appropriate mappings
  • Geometrics
    • Scatter plots
    • Histograms
    • Bar plots
    • Line plots
  • Themes
    • Themes – moving the legend, modifying theme elements
    • Theme flexibility – built-in-themes, Exploring ggthemes, setting themes
    • Using annotate for embellishments

Module 16

  • Statistics
    • Stats with geoms – modifying stat_smooth
    • Stats: sum and quantile
  • Coordinates
    • Coordinates – zooming in, aspect ratios, expand and clip
    • Coordinates vs scales – log-transforming scales, adding stats to transformed scales
    • Double and flipped axes
    • Polar coordinates
  • Facets
    • The facets layer
    • Facet labels and order
    • Facet wrap & margins


Module 17

  • Getting started with R markdown
    • Introduction to R markdown – create Rmarkdown file, adding code chunks,
    • Adding and formatting text- adding sections to your report, including links and images
    • The YAML header
  • Adding Analyses and visualizations
    • Analyzing – filtering
    • Adding plots
  • Improving the report
    • Organizing the report – creating a bulleted list, creating a numbered list, adding a table
    • Code chunk options – comparing code chunk options, collapsing blocks in the knit report, modifying the report using include and echo
    • Warnings, messages, and errors – Excluding messages, excluding warnings.
    • Customizing the report
      • Adding a table of contents – specifying headers and number sectioning, adding table of contents options
      • Creating a report with a parameter
      • Customizing the report – customizing the report style, customizing the header abd table of contents, customizing the title, author, and date.
      • Referencing the CSS file


Module 18

  • Storytelling with data
  • Asking SMART questions
    • Specific
    • Measurable
    • Action-oriented
    • Relevant
    • Time-bound
    • Things to avoid when asking questions – leading questions, closed-ended questions, vague questions
  • Preparing to communicate the data
    • Selecting the right data
    • Showing relevant statistics
    • Visualizations for different audiences
    • Choosing the appropriate format
  • Structuring written reports
    • Types of reports
    • Reproducibility and references
  • Building compelling oral presentations
    • Planning an oral presentation
    • Building presentation slides
    • Delivering the presentation


Module 19

  • Importing data from flat files with utils
    • Read. csv
    • Read.delim & read.table
  • Readr & data.table
    • Readr: read_csv & read_tsv
    • Readr:read_delim – skipand n_max, col_types with collectors
    • Data.table:fread
  • Importing Excel data
    • Readxl – list the sheets of an excel file, import an excel sheet, reading a workbook, col_names argument, the skipargument
    • Gdata
  • Reproducible excel work with XLConnect
    • Reading sheets – connect to a workbook, list and read excel sheets, customize readworksheet
    • Adapting sheets – add worksheet, populate worksheet, renaming sheets, removing sheets


Module 20

  • Importing data from database
    • connect to a database – establish a connection, inspect the connection
    • import table data – List the database tables, import users, import all tables
  • Importing data from databases(2)
    • SQL Queries from inside R
    • DBI Internals – send – fetch – clear
  • Importing data from the web(part 1)
    • HTTP – Import flat files from the web, secure importing
    • Downloading files – import excel files from the web, Downloading any file, secure or not, reading a text file from the web
    • API’s & JSON – from JSON to R, Quandi API, OMDb API
    • Json & jsonllte – toJSON, minify and prettify
  • Importing data from statistical software packages
    • Haven – import SAS data with haven, import STATA data with haven, Import SPSS data with haven
    • Foreign – import STATA data with foreign, import SPSS data with foreign


Module 21

  • Common data problems
    • Data type constraints – common data types, converting data types, trimming strings
    • Range constraints
    • Uniqueness constraints – full duplicates, removing partial duplicates, aggregating partial duplicates
  • Categorical and Text data
    • Categorical data problems – identifying inconsistency, correcting inconsistency.
    • Cleaning text data – detecting inconsistent text data, replacing and removing

Module 22

  • Advanced Data problems
    • Uniformity – date uniformity
    • Cross field validation
    • Completeness – types of missingness, visualizing missing data, treating missing data
  • Record Linkage
    • Comparing strings – calculating strings, calculating distance, fixing typos with string distance
    • Generating and comparing pairs – link or join, pair blocking, comparing pairs
    • Scoring and linking – score then select or select then score


Module 23

  • Introduction to dates
    • Recognising ISO 8601 dates
    • Plotting
    • arithmetic and logical operators
    • Lubridate
  • Parsing and manipulating Dates and Times with lubridate
    • Parsing dates with lubridate – Selecting the right parsing function, specifying an order with ‘parse_date_time()’
    • Extracting parts of a datetime – what to extract, adding labels, extract for plotting, extract for filtering and summarizing
    • Rounding datetimes

Module 24

  • Arithmetic with Dates and Times
    • Taking differences between datetimes
    • Time spans – Adding and subtracting a time span to a datetime, arithmetic with timesoans, generating sequences of datetimes
    • Intervals – comparing intervals and datetimes, converting to durations and periods
  • Problems in practice
    • Time zones – setting the timezone, viewing the timezone, times without dates
    • More on importing and exporting datetimes – fast parsing with fasttime, fast parsing with lubridaate::fast_strptime


Module 25

  • How to write function
    • Importance of functions – The benefits of writing functions, calling functions
    • Converting scripts into functions – inputs to functions, multiple inputs to functions, renaming GLM
  • Arguments
    • Default arguments – numeric defaults, logical defaults, NULL defaults, categorical defaults
    • Passing arguments between functions – Harmonic mean, dealing with missing values, passing arguments with…
    • Checking arguments – custom error logic, fixing function arguments, errors with bad arguments

Module 26

  • Return values and scope
    • Returning values from functions
    • Returning multiple values from functions – returning metadata
    • Environments – creating and exploring environments
    • Scope and precedence – can a function find it’s variable?, can you access variables from inside functions, variable precedence


Module 27

  • Exploring categorical data
    • Exploring categorical data – Bar chart, Bar chart interpretation, side-by-side bar chart
    • Counts vs proportions – conditional proportions,
    • Distribution of one variable – marginal bar chart, conditional bar chart, pie chart
  • Exploring Numerical Data
    • Exploring numerical data – Faceted histogram, Boxplots and density plots
    • Distribution of one variable – Marginal and conditional histograms, three binwidths
    • Box plots – box plots for outliers, plot selection
    • Visualization in higher dimensions – 3 variable plot, interpret 3 var plot
  • Numerical Summaries
    • Measures of center – Choice of center measure, calculate center measures
    • Measures of variability – Choice of spread measure, calculate spread measure, choose measures for center and spread
    • Shape transformation
    • Outlliers – identify outliers



Module 28

  • Linear regression
    • Two variables – response variable
    • Fitting a linear regression – estimate the intercept, estimate the slope, linear regression with lm()
    • Categorical explanatory variables – visualizing numeric vs categorical, calculating means by category, lm() with a categorical explanatory variable
  • Predictions and model objects
    • Making predictions – visualizing predictions, the limit of prediction
    • Working with model objects – extracting model elements, using broom
    • Regression to the mean
    • Transforming variables

Week 29

  • Assessing model fit
    • Quantifying model fit – coefficient of determination, residual standard error
    • Visualizing model fit – residuals vs fitted values, Q-Q plot of residuals, scale location, drawing diagnostic plot
    • Outiliers, leverage and influence – leverage, influence, extracting leverage and influence
  • Simple logistic regression
    • Importance of logistic regression – exploring explanatory variables, visualizing linear and logistic models, logistic regression with glm()
    • Predictions and odds ratios – probabilities, odds ratio, log odds ratio
    • Quantifying logistic regression fit – Measuring logistic model performance, accuracy, sensitivity, specificity

Module 30


  • Parallel Slopes
    • Parallel slopes linear regression – Fitting a parallel slopes linear regression, interpreting parallel slopes coefficients, visualizing each explanatory variable, visualizing parallel slopes
    • Predicting parallel slopes – Predicting with a parallel slopes model
    • Assessing model performances – comparing coefficients of determination, comparing residual standard error
  • Interactions
    • Models for each category – One model per category, predicting multiple models, visualizing multiple models assessing model performance
    • One model with an interaction – specifying an interaction, interactions with understandable coeffs
    • Making predictions with interactions – predictions with interactions, manually calculating predictions with interactions
    • Simpson’s paradox

Module 31

  • Multiple Linear Regression
      • Two numeric explanatory variables – 3D visualizations, modelling 2 numeric explanatory variables, including an interaction
      • More than 2 explanatory variables – visualizing many variables, different levels of


    • How linear regression works – the sum of squares, linear regression algorithm
  • Multiple Logistic Regression
    • Multiple logistic regression – Visualizing multiple explanatory variables, logistic regression with 2 explanatory variables, logistic regression prediction, confusion matrix
    • The logistic distribution – Cumulative distribution function, inverse cumulative distribution function, binomial family argument, logistic distribution parameters
    • logistic regression works – logistic regression algorithms

Module 32


    • Introduction to sampling
      • Sampling and point estimates, reasons for sampling, simple sampling with dplyr, simple sampling with base-R
      • Convenience sampling
      • Pseudo-random number generation – generating random numbers, understanding random seeds
    • Sampling methods
      • Simple random and systematic sampling – simple random sampling, systematic sampling
      • Stratified and weighted random sampling – proportional stratified sampling, Equal counts stratified sampling, weighted sampling
    • Cluster sampling
      • Benefits of clustering, performing cluster sampling
      • comparing sampling methods – 3 kinds of sampling, summary statistics on different kinds of sample

Module 33

  • Sampling distributions
    • Relative error of points estimates – calculating relative errors, relative error vs. sample size
    • Creating a sampling distribution – replicating samples, replication parameters
    • Approximate sampling distributions – exact sampling distribution, exact vs. approximate
    • Standard errors and the central limit theorem – population and sampling distribution means, population and sampling distribution variation
  • Bootstrap distribution
    • Introduction to bootstrapping – principle of bootstrapping, with or without replacement, generating a bootstrap distribution
    • Comparing sampling and bootstrap distributions, bootstrap statistics vs bootstrap distribution, compare sampling and bootstrap means, comparing sampling and bootstrap standard deviations.
    • Confidence intervals – confidence interval interpretation, calculating confidence intervals

Module 34


  • Introduction to hypothesis testing
    • Hypothesis tests and z-scores – Uses of A/B testing, calculating the sample mean, calculation a z-score
    • P-values – criminal trials and hypothesis tests, left tail, right tail, two tails, calculating pvalues
    • Statistical significance – Decisions from p-values, calculating confidence intervals, type 1 and type 2 errors
  • Two-sample and ANOVA Tests
    • Performing t-tests – hypothesis testing workflow, two sample mean test statistics
    • Calculating p-values from t-statistics – the t-distribution, from t to p
    • Paired t-tests – visualizing the difference, using t.test()
    • ANOVA tests – conducting an Anova test, pairwise t-tests

Module 35

  • Proportion tests
    • One-sample proportions tests – test for single proprtions
    • Two-sample proportiontests – test of two proprtions, prop_test() for two samples
    • Chi_square test of independence – The chi-square distribution, performing a chi-square test
    • Chi-square goodness of fit tests – visualizing goodness of fit, performing a goodness of fit test
  • Non-parametric tests
    • Assumptions in hypothesis testing – testing sample size
    • The “there is only one test” framework – specifying and hypothesizing
    • Continuing the infer pipeline – Generating & calculating, observed statistic and p-value
    • Non-parametric ANOVA and unpaired t-tests – simulation-based t-test, rank sum tests

Module 36


  • K-Nearest Neighbours (kNN)
    • Classification with Nearest Neighbors –
    • What about the ‘k’ in kNN? – understanding the impact of ‘k’, testing other ‘k’ values
    • Data preparation for kNN
  • Naïve-Bayes
    • Understanding Bayesian methods – computing probabilities, a simple Naïve Bayes location model, understanding dependent events, examining “raw” probabilities, understanding independence
    • Understanding NB’s “naivety” – a more sophisticated location model, understanding the Laplace correction
    • Applying Naïve Bayes to other problems – handling numeric predictors

Module 37

  • Logistic Regression
    • Making binary predictions with regression – building simple logistic regression models, making a binary prediction, the limitations of accuracy
    • Model performance tradeoffs – Calculating ROC curves and AUC, Comparing ROC curves
    • Dummy variables, missing data, and interactions – coding categorical features, handling missing data, understanding missing value indicators, building a more sophisticated model
    • Automatic feature selection – the dangers of stepwise regression, building a stepwise regression model
  • Classification Trees
    • Making decisions with trees – Building a simple decision tree, visualizing classification trees, understanding the tree’s decisions
    • Growing larger classification trees – creating random test datasets, building and evaluating a larger tree, conductinga fair performance evaluation
    • Tending to classification trees – preventing an overgrown tree, creating a nicely pruned tree
    • Seeing the forest from the trees – building a random forest model

Module 38


  • What is regression
    • Introduction – identify the regression tasks
    • Linear regression – code a simple one-variable regression, examining a model
    • Predicting once you fit a model – multivariate linear regression
  • Training and evaluating regression models
    • Evaluating a model graphically – gain curve
    • Root Mean Squared Error (RMSE) – calculate RMSE
    • R-squared – calculate R-squared, correlation and R-squared
    • Properly Training a Model – Generating a random test/train split, train a model using test/train split, create a cross validation plan, evaluate a modeling procedure using n-fold cross-validation.

Module 39

  • Issues to consider
    • Categorical inputs – Examining the structure of categorical inputs, modelling with categorical inputs
    • Interactions – modeling an interaction
    • Transforming the response before modeling – relative error, modelling log-transformed monetary output, comparing RME and root-mean-square Relative Error
    • Transforming inputs before modeling
  • Dealing with Non – Linear Responses
    • Logistic regression to predict probabilities – fit a model of sparrow survival probability, predict sparrow survival
    • Poisson or quasipoisson
    • GAM to learn non-linear transforms – writing formulas for GAM models

Module 40

    • Tree-Based Methods
      • The intuition behind tree-based methods – predicting with a decision tree
      • Random forests – Build a random forest model, predict with the random forest model
      • One-Hot_encoding categorical variables – vtreat, novel levels
      • Gradient boosting machines – find the right number of trees for a gradient boosting machine, fit an xgboost, visualize the xgboost

Module 41


  • Unsupervised learning in R
    • Introduction to k-means clustering – k-means clustering, results of Kmeans(), visualizing and interpreting results of Kmeans()
    • How k-means works – handling random algorithms, selecting number of clusters
  • Hierarchical clustering
    • Introduction to hierarchical clustering
    • Selecting number of clusters – interpreting dendrogram, cutting the tree
    • Clustering linkage and practical matters – linkage methods, comparing linkage methods, practical matters: scaling, comparing kmeans() and hclust()

Module 42

  • Dimensionality reduction with PCA
    • Introduction to PCA – PCA using prcomp()
    • Visualizing and interpreting PCA results – interpreting biplots, variance, visualize variance
    • Practical issues with PCA – practical issues:scaling
    • What is law of large numbers
    • Example of law of large numbers
    • Law of large numbers in finance
 Module 43


  • Getting familiar with Git and Github
    • What is Git
    • What is Github
  • Understand Git Terminology
    • What is repository
    • Understand cloning
    • Commit
    • Understanding push
    • Understanding pull
  • Get started with Git
    • Install Git in your system
    • Let’s initialize Git
    • Configure Git in your system
    • Committing files in Git
    • View logs in Git


387 Hours


From ₦250,000

To speak with someone, call +234 (0) 702 630 7268 or +234 (0) 810 402 2323

This course will empower our learners with Microsoft Excel and Power BI basic to advanced skills to work with data for your graphs, data organization and programming. The following topics are covered:

Excel for Data Analytics and Visualization

  1. Getting started with Excel
  2. Working with data and Excel tables
  3. Performing calculations on data
  4. Changing workbook appearance
  5. Focusing on specific data by using filters
  6. Reordering and summarizing data
  7. Combining data from multiple sources
  8. Analyzing data and alternative data sets
  9. Creating charts and graphics
  10. Using PivotTables and PivotCharts
  11. Printing worksheets and charts
  12. Working with macros and forms
  13. Working with other Office programs
  14. Collaborating with colleagues
  15. Manage and share workbooks
  16. Apply custom formats and layouts
  17. Create advanced formulas
  18. Create advanced charts and formulas
  19. Using the Analysis Toolpak (Data Analysis)
  20. Using the Excel Solver

Power BI for Data Visualization

  1. Introduction into Data Visualization Concepts
  2. Understanding the basic concept of data visualizations – Understanding Datasets, Data Visualization, Visualization Interpretation
  3. Importing Datasets into Power BI
  4. Navigating Power BI
  5. Understanding the basis of Hierarchy Formation – Drilling Up, Drilling Down
  6. Advanced Drilling Options
  7. Colours for Enhanced Visualizations
  8. Time Series, Aggregation, Granularity and Filters
  9. Maps, Scatter Plots and Interactive
  10. Custom Visualization Installation
  11. Data / Business Forecast with Power BI
  12. Creating Relationship among Tables
  13. DAX – Data Analysis Expression with Power BI
  14. Publishing Report on powerbi.com


36 Hours


From ₦100,000

To speak with someone, call +234 (0) 702 630 7268 or +234 (0) 810 402 2323

Learning how to work with SQL, this course takes you through the following modules:

  • Use SQL for data mining, data analysis, data science, and data visualization
  • Transition from the Very Basics to a Point Where You can Effortlessly Work with Large SQL Queries
  • Become a Master SQL Developer
  • Build awesome dashboards with Google Data Studio and Google Big Query as the backend
  • Be confident in using the Google Big Query Tool and Ecosystem
  • Practice Every Step of the Way by Working through myriads of practice files.


110 Hours


From ₦180,000

To speak with someone, call +234 (0) 702 630 7268 or +234 (0) 810 402 2323

“Data is a tool for enhancing intuition.”

– Hilary Mason

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