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Big Data Practitioners 

Ön Koşul

Temel İstatistik, Veri Bilimi ve Büyük Veri konsept bilgisi olması yeterlidir.

Eğitimin Hedefleri

Eğitim sonrasında katılımcıların Veri Bilimi ve Büyük Veri süreçlerine hakim olmaları hedeflenmektedir.

Eğitimin İçeriği

 

1. Gün

Part 1 : Introduction

The Field of Data Science – The Various Data Science Disciplines
The Field of Data Science – Connecting the Data Science Disciplines
The Field of Data Science – The Benefits of Each Discipline
The Field of Data Science – Popular Data Science Techniques
The Field of Data Science – Popular Data Science Tools
The Field of Data Science – Careers in Data Science
The Field of Data Science – Debunking Common Misconceptions

Part 2: Probability
Probability – Combinatorics
Probability – Bayesian Inference
Probability – Distributions+
Probability – Probability in Other Fields

Part 3: Statistics
Statistics – Descriptive Statistics
Statistics – Practical Example: Descriptive Statistics
Statistics – Inferential Statistics Fundamentals
Statistics – Inferential Statistics: Confidence Intervals
Statistics – Practical Example: Inferential Statistics
Statistics – Hypothesis Testing
Statistics – Practical Example: Hypothesis Testing

Part 4: Mathematics

Matrices & Vectors & Tensors

 

2. Gün

Part 5: Introduction to Python
Python – Variables and Data Types
Python – Basic Python Syntax
Python – Other Python Operators
Python – Conditional Statements
Python – Python Functions
Python – Sequences
Python – Iterations

 

3. Gün

Part 6 : Deep Learning – Introduction to Neural Networks
Deep Learning – How to Build a Neural Network from Scratch with NumPy

Part 7 : Data Science With BigData

Hadoop components and architecture
Hadoop configuration, administration, and management
How to use common Hadoop tools

 

Kayıt

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