CSBD

Cognitive Solution Bangladesh

(+880) 1307762223

R programming for Data Analytics

Instructor

Md Naimul Hasan

CTO, Cognitive Solution Bangladesh
Top-rated Data Analyst and
Statistical Programmer, Fiverr (Fiverr Pro)

📝 Description:

R programming is a robust data analytics platform that is frequently used for machine learning, statistical analysis, and data visualization. Its vast ecosystem of packages, which includes dplyr for data manipulation and ggplot2 for visualizations, enables analysts to manage and interpret complex datasets with ease. Because of its adaptability, R may be used for a wide range of tasks, including corporate intelligence and scholarly research. R is a crucial tool for anyone wishing to use data for insights and decision-making because of its vibrant community and thorough documentation.

Send Money: 3800 taka (bkash 01688131445) 

Registration Link: https://forms.gle/fMqWtH8yLdPPAzHSA

  • Total 20 Classes
  • 4 Portfolio Projects
  • Job Preparation Sessions

📚 Course Outlines:

  1. Installing R on Mac or Windows
  2. R programming vs Python Programming
  3. Useful of R in data science
  4. R important in research
  5. R for web development
  6. R for web scraping
  1. R environment introduction
  2. Writing code and setting the working directory
  3. Loading built-in dataset
  4. Show built-in function
  5. Data types in R (Vectors, lists, matrices, factors, missing values, data frames, Integer, Numeric)
  6. Creating List, matrices, vectors, data frame
  7. Introduction to different signs in R (pipe, assignment operator, equal sign, etc)
  1. Data Import and Export
    1. Import and Export different types of datasets
  2. Data Exploration
    1. Exploring datasets
    2. Descriptive statistics
  3. Data Manipulation and Transformation:
    1. Data type conversions
    2. Missing value imputation
    3. Outlier detection and solution
    4. Date operations
    5. String operations
    6. One-hot encoding
  4. More about Data cleaning
    1. Remove specific row
    2. Missing value checking
    3. Mean imputation into a missing value
    4. Median imputation into missing value
    5. Valuable name changing
    6. Creating new data frame
    7. Unique value checking
    8. Order factor coding
    9. Dataset summary view
  5. Advanced function use (Dplyr, filter, lubricate, tidyr, etc )[use a dataset and show advanced data cleaning such as using pipe and connecting various functions for data cleaning]
  6. Data Integration:
    1. Creating a single dataset out of multiple datasets
      1.  
  1. If Statement
  2. If-else statement
  3. Switch Statement
  4. While Loop
  5. For Loop
  6. Example of by creating a function multiple t-test and after testing it will give a table into a word file. (this will cover nested loop too)
  1. Data Visualization Ethics
    1. Theoretical perspectives
    2. Aesthetical perspective and customizations
  2. Bar Chart
    1. Reason and interpretations
    2. Simple bar chart
    3. Grouped/Clustered bar chart
    4. Stacked bar chart
  3. Line Chart
    1. Reason and interpretations
    2. Simple line chart
    3. Multiple line chart
  4. Histogram
    1. Reason and interpretations
    2. Simple histogram
    3. Multiple histogram
  5. Boxplot
    1. Reason and interpretations
    2. Simple boxplot
    3. Multiple boxplot
  6. Scatterplot
    1. Reason and interpretations
    2. Simple scatterplot
    3. Multiple scatterplot
  7. Dual Axis Plots
  8. Multiple Plots Together
    1. Plots grouped by separate variables
    2. Arranging plots in single image
  9. Interactive Visualisations
    1. Interactive html plots
    2. 3D Network
  1. Mean Comparison (Parametric Tests)
    1. One-sample t-test
    2. Two independent sample t-test
    3. Paired sample t-test
    4. One-way ANOVA (with Post hoc test)
  2. Non-parametric Alternative Tests of above Tests
    1. Wilcoxon signed rank test
    2. Wilcoxon rank sum test or Mann-Whitney test
    3. Paired samples Wilcoxon test
    4. Kruskal-Wallis test (with Post hoc test)
  3. Correlation/Association Analysis
    1. Chi-square test
    2. Fisher exact test
    3. Pearson correlation test
    4. Spearman rank correlation
  4. Multiple Linear Regression
    1. Assumption Checking
    2. Model Interpretation
    3. Prediction and Model Implementation
  5. Assumption Violation Solution
    1. Sqrt transformation
    2. inverse transformation
    3. Quantile regression
    4. Robust regression
  6. Logistic Regression
    1. Introduction and Analysis of Binary Logistic Regression
    2. Introduction and Analysis of Multinomial Logistic Regression
  1. Run only the decision tree and explain its hyperparameters
  1. Portfolio website Making
  2. LinkedIn Profile Making (How one can get job from LinkedIn such as regularly uploading new projects)
  3. Connect with seniors who are in this data science field
  4. How to impress a company for a job
  1. Fiverr or Upwork
    1. Create an account
    2. Create a gig
    3. Tips and tricks to rank a gig