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Online Instructor-led Training on “Data Analytics with R for Data Science”

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Course Description

Statistical Research Consultants Bangladesh (SRCBD) is offering 06-weeks (15 Classes) of Online Instructor-led training on "Data Analytics with R for Data Science" starting from 📅3rd December 2022.

R is now considered one of the most popular analytics tools in the world. In this certificate program, you will develop the skill set necessary to efficiently perform critical aspects of statistics, data science, and machine learning. The courses cover the application of core analytics concepts in the R programming environment to allow a scalable implementation.

This course will introduce beginners to programming basics of coding and statistical analysis in R data science and machine learning. Participants will learn to handle data, visualize it and perform statistical operations like hypothesis testing, regressions & ML techniques on fairly structured data.

Develop your Data Analytics Skills with Professional Trainer. Learn Statistics and R from Professional Statisticians.

Language: The training will be conducted in Bangla.

𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗲𝗲: 4500Taka.

📣📣 𝗘𝗮𝗿𝗹𝘆 𝗯𝗶𝗿𝗱 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Anyone who completes registration and payment by 3rd November gets 𝟭𝟱% 𝗼𝗳𝗳.  (so, 3825/- Taka after discount till 3rd November 2022)

Program Structure:
Course Classes: 15 Classes (Every class is divided into two sessions)
Course Sessions: 30 Sessions
Two classes in a week (Saturday & Tuesday)
Course Duration: 36 hours
Course Schedule: 8.00 PM - 10.30 PM
The class will be conducted via ZOOM. Download link:  https://zoom.us/download 

Facilities:

  • Continuous help from the facilitators with knowledge building of statistics and computing in R.
  • Resourceful materials on each topic.
  • Practical Problem-Solving Sessions in R
  • A certificate will be given after the successful completion of coursework.

Contact:
For further queries, please e-mail 📧srcbd.training@gmail.com  or call 📞+88-01792087904.
For any queries, please give us a message at m.me/srcbd.org
Facebook page: www.facebook.com/srcbd.org
Subscribe to the SRCBD YouTube channel: https://rebrand.ly/srcbd-yt

1.   Introduction to Data Science and R

a.   What is Data Science

b.   Benefits and Uses of Data Science

c.   What is R

d.   History of R

e.   The R Environment and Statistics

f.   Why Should We Learn R

g.   Installing R and R Studio

h.   Overview of R Screen and R Studio Screen

i.   Installing packages in R

j.   Basic Computations in R

2.   Basic and Intermediate R

a.   Basic Commands in R

b.   Variable and Variable Naming

c.   Data Types in R

d.   Data Structures in R

e.   Conditional and Control Flow

f.   Loops

g.   Functions

h.   The Apply Family

i.   Utilities

3.   Data Manipulation and Joining Using dplyr

a.   Transforming Data in R

b.   Transforming Data with dplyr

c.   Aggregating Data

d.   Selecting and Transforming Data

e.   Case Study (Data Manipulation)

f.   Transforming Data with tidyr

g.   Joining Tables

h.   Left and Right Joins

i.   Full, Semi, and Anti Joins

j.   Case Study (Joining Tables)

4.   Importing and Exporting Data in R

a.   Importing data from flat files with utils

b.   Importing data using readr & data.table

c.   Importing Excel data

d.   Importing data from databases

e.   Importing data from the web

f.   Importing data from statistical software packages

Review Class 1: Lectures 1 to 4

5.   Data Visualization in R

a.   A Quick Introduction to base R graphics

b.   Different plot types

c.   Adding details to plots

d.   Managing plots

e.   Advanced plot customization and beyond

6.   Data visualization with ggplot2

a.   Introduction

b.   Aesthetics

c.   Geometries

d.   Themes

e.   Statistics

f.   Coordinates

g.   Facets

h.   Best Practices

7.   Writing Functions in R and Introduction to Statistics in R

a.   How to write a function

b.   All about arguments

c.   Return values and scope

d.   Case Study (Functions)

e.   Summary Statistics

f.   Random Numbers and Probability

g.   Distributions and Central Limit Theorem

h.   Correlation and Experimental Design

8.   Sampling in R and Hypothesis Testing in R

a.   Introduction to Sampling

b.   Simple, Systematic, Stratified, and Cluster Sampling

c.   Accuracy of Sample Statistics

d.   Bootstraps

e.   A/B Testing, Z-Score, P-Values, Type I and Type II errors

f.   t-test, ANOVA

g.   proportion t-test, chi-square

h.   rank-based non-parametric test

Review Class 2: Lectures 5 to 8

9.   Regressions in R

a.   Simple Linear Regression

b.   Predictions and model objects

c.   Assessing model fit

d.   Multiple linear Regression

e.   Simple logistics regression

f.   Multiple logistic Regression

g.   Training and Evaluating Regression Models

h.   Non-linear Regression

10. Machine Learning with R : Unsupervised Learning (Clustering) & Supervised Learning

(Classification)

a.   Calculating Distance Between Observations

b.   k-means

c.   Hierarchical clustering

d.   Dimensionality reduction with PCA

e.   Case Study

f.   K-Nearest Neighbors

g.   Naïve Bayes

h.   Linear Regression

i.   Classification Tree

11. Machine Learning with R: Supervised Learning

a.   Decision Tree

b.   Random Forest

c.   Support Vector Machine

d.   Artificial Neural Network

12. Reporting with R Markdown and Building Web Application with Shiny

a.   Introduction to Markdown

b.   Getting Started with R Markdown

c.   Adding Analyses and Visualizations

d.  Improving the Report

e.  Customizing the Report

f.   Getting Started with Shiny

g.   Inputs, Outputs, and Layouts

h.   Reactive Programming

i.   Build Shiny Apps

Review Class 3: Lectures 9 to 12

 

Fazle Rabby

Fazle Rabby

Trainer
  • Price৳ 4500
  • Duration 36 Hours
  • Lessons 15

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