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37 - ITC - Information Technology - Miscellaneous

ITC 115 - Data Science Process

Code Start Date Duration Venue
ITC 115 09 October 2023 5 Days Istanbul Registration Form Link
ITC 115 04 December 2023 5 Days Istanbul Registration Form Link
Please contact us for fees


Course Description

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. 

This course aims how to predict the future from acquired data. For example Customer Churn Prediction, House Prices Prediction, Customer classification, consumption rates...

As data scientists work their magic on huge sets of apparently disparate information to unveil surprising insights in fields as varied as accounting and law enforcement, the process they follow is a mystery to most outside the field.

Course Objectives

  • Becoming familiar with the data science process
  • Asking and interpreting the right questions 
  • Getting the relevant data sampled 
  • Exploring the collected data 
  • Building a model and validating it 
  • Classification and Regression Modeling
  • Learning basically R Programming
  • Data Visualization

Who Should Attend?

  • IT technical services specialists
  • IT specialists
  • IT relationship managers
  • IT architects
  • Consultants
  • Business and IT management
  • Business process owners
  • Those who are dealing directly or indirectly with IT
  • Those who wants to be a data scientist
  • Those who wants to manage and understand acquired data
  • Those who wants to predict the future 
  • Anyone who aspire to know more about the field

Course Details/Schedule

Day 1

  • Introduction to data science and basic terminology
  • Installing R and R studio
  • Packages
  • Basic R syntax
  • Reading data in a file
  • Saving data
  • Generating data
  • Manipulating objects 

Day 2

  • CRISP-DM Methodology
  • Data Preprocessing (Missing value, outlier)
  • Data Types (Data Types, Vector, Matrix, List, Data Frame, Array)
  • Data Understanding with visualization
  • Basic Statistics (mean, median, Max Value, Min Value, Variance)
  • Basic Structural Programming in R
  • Classification and Regression Modeling

Day 3

  • Descriptive Analytics
  • Predictive Analytics
  • Overfitting/ Under fitting
  • Bayes Theorem
  • Training/Testing/Validation
  • Logistic Regression
  • Decision Tree
  • Confusion Matrix
  • False Positive and False Negative concepts
  • Accuracy and ROC Curver

Day 4

  • Linear Regression
  • Multiple Linear Regression
  • Normalization and standardization
  • Support Vector Regression
  • Model Evaluation (Mean absolute error, accuracy) 
  • Data Visualization (Histogram, Scatter Pilot, GIS(map))

Day 5

  • Course Revision
  • Daily Life Examples
  • Processing common sample data like “titanic”, “Fruit”
  • Visualize Processed Data
  • Evaluate Processed Data