R Training

Data is the new crude oil. And business success in the 21st century is heavily reliant on the ability to mine and use relevant data about consumers, internal operations, financial operations and industry trend to drive business decisions and make predictions.

Remoik Data & Analysis, Nigeria’s leading business data analysis company is putting together this special training for proactive business professionals who already have some experience with business reporting and quantitative data analysis.

We will be focusing on building analysis models with R. We will provide you all the software and materials needed.

    ENROLL IN THE R COURSE

    This training is coming up on a soon to be announced date. It covers our industry recognized certificate, practice materials, required software, tea break + lunch and other training materials.

    The training will be facilitated industry experts who have built commercial models. And we have had participants of our different trainings from Promasidor, Citi Bank, Dalberg, SaveTheChildren, Mobil, Total, Vodacom, Nestle, Guinness Nigeria, Nigerian Breweries, Delta Afrik, Biofem Pharmaceuticals, American Tower Corporation, LATC Marine, Broll, Habanera (JTI), SABMiller, IBM, Airtel, Diamond Bank, ECOWAS, Ministry of Finance, Palladium Group, Nokia Siemens Networks and DDB.

    R for Data Analysis Training:

     

    1. Understanding R programming language
    2. Installing and setting up R
    3. Introduction to R Studio
    4. Vectors, Matrices, Arrays, Lists, Factors, Data Frames
    5. Data manipulation in R 
    6. Loop functions
    7. Basic Statistics

     

    1. Important R packages
    2. ggplot2
    3. dplyr, lubridate, tidyr
    4. car, caret, vcd
    5. zoo, xts
    6. readxl, readr
    7. Shiny
    8. Different machine learning algorithms

     

    1. Practical Projects
    2. Project 1: Analysing Nigeria stocks and economic data
    3. Project 2: Analysing Social Media data
    4. Project 3: Churn analysis
    5. Project 4: Market Basket Analysis
    6. Project 5: Web scrapping

     

    1. Weekly Assignments (spread over 4 weeks)
    2. Week 1 assignment project
    3. Week 2 assignment project
    4. Week 3 assignment project
    5. Week 4 assignment project