Faculty of Data Science

• 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.

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

• 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

• 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

• • 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

INTRODUCTION TO TIDYVERSE

• • 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

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

• PROJECTS

DATA MANIPULATION WITH DPLYR

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

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

JOINING DATA WITH DPLYR

• 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
  • PROJECTS

INTRODUCTION TO STATISTICS IN R
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

• 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
• PROJECTS

DATA VISUALIZATION WITH GGPLOT2

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
• PROJECTS

• 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
• PROJECTS

REPORTING WITH R MARKDOWN

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

• PROJECTS

DATA COMMUNICATION CONCEPTS

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
• PROJECTS

INTRODUCTION TO IMPORTING DATA IN R

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
• PROJECTS

INTERMEDIATE IMPORTING DATA IN R

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
• PROJECTS

CLEANING THE DATA IN R

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

• 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
• PROJECTS

WORKING WITH DATES AND TIMES IN R

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

• 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
• PROJECTS

INTRODUCTION TO WRITING FUNCTIONS IN R

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

• 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
• PROJECTS

EXPLORATORY DATA ANALYSIS IN R

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
• PROJECTS

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

• 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
• PROJECTS

Module 30

INTERMEDIATE REGRESSION IN R

• 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

• 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

  interaction

  • 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
• PROJECTS

Module 32

SAMPLING IN R

• • 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

• 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
• PROJECT

Module 34

HYPOTHESIS TESTING IN R

• 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

• 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
• PROJECT

Module 36

SUPERVISED LEARNING IN R: CLASSIFICATION

• 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

• 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
• PROJECT

• 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.

• 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

• • 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

• PROJECT

Module 41

UNSUPERVISED LEARNING IN R

• 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()

• 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
• LAW OF LARGE NUMBERS
  
  • What is law of large numbers
  • Example of law of large numbers
  • Law of large numbers in finance
• PROJECT

• 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

DURATION:

387 Hours

COST: 

From ₦250,000

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