Programming

As an Industrial Engineer, I have also developed some programming skills that are needed for business analysis.

Some of the courses and skills can be listed as well:

100-hours Data Science Course Syllabus

S01 Introduction to Data Science/Introduction to R

S02 Basic Data Structures in R - Part 1

S03 Basic Data Structures in R - Part 2

S04 Functions /Conditions/Loops in R

S05 Importing/Exporting Data in R

S06 Data Preprocessing in R - Part 1

S07 Data Preprocessing in R - Part 2

S08 Story Telling with Data/Introduction to ggplot2

S09 Data Visualization Case Study

S10 Introduction to Machine Learning/KNN Algorithm Implementation

S11 Introduction to Linear Regression/ Used Car Price Prediction Case Study – Part 1

S12 Used Car Price Prediction Case Study – Part 2

S13 Step-wise Regression/Salary Prediction for Hitters Data Set Case Study – Part 1

S14 Regularization /Decision Tree/Bagging and Random Forrest/Salary Prediction for Hitters Data Set Case Study – Part 2

S15 Gradient Boost/Stochastic Gradient Boost/XG Boost /Salary Prediction for Hitters Data Set Case Study – Part 3

S16 Introduction to Maximum Likelihood Estimation (MLE)

S17 Logistic Regression/ Marketing Campaign Analysis Case Study

S18 kNN for Classification/Linear Discriminant Analysis/Naïve Bayes Classification/Decision Tree for Classification/Support Vector Machines

S19 Algorithmic Trading Case Study

S20 Clustering Methods/ Clustering Customers Using RFM Case Study

S21 Principal Component Analysis and PCR/ Perceptual Map Case Study

S22 Times Series Analysis – Part 1

S23 Times Series Analysis – Part 2

S24 Demand Prediction Using Times Series Analysis Case Study

S25 Python Basics

S26 Introduction to NumPy and Pandas

S27 Introduction to Matplotlib for Data Visualization in Python

S28 Used Car Price Prediction Case Study in Python

S29 Salary Prediction for Hitters Data Set Case Study in Python

S30 Algorithmic Trading Case Study in Python

S31 RFM Clustering Case Study in Python

S32 Introduction to Artificial Neural Networks and Deep Learning/ Introduction to Keras and TensorFlow Library

+ Project + 24- hours Microsoft SQL Server Course working on the Northwind database

Project Goals Students were expected to achieve the following goals by completing their project:

- Apply the CRISP-DM approach to methodically execute a data project in practice.

- Be able to apply the various machine learning approaches they learned during the course to real data.

- Use one of the R or Python programming languages to analyze the data.

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I did the project of the course and used many of the regression algorithms in Python including:

Linear Regression, Linear Regression with Box-Cox transformation, Linear Regression Using the Best Subset Selection, Forward and Backward Stepwise Selection (AIC, BIC, Adjusted R-squared, K-fold Cross Validation), Ridge Regression, Lasso Regression, Decision Tree, Bagging, Random Forest, Gradient Boost Regression, Extreme Gradient Boost.