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Applied Data Science & Statistical Analysis Fundamentals
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Applied Data Science & Statistical Analysis Fundamentals

Turn raw data into actionable insights using Python's powerful data science ecosystem.

16 hoursBeginner8 ders
Instructor
Yasin Polat
Data Specialist & Researcher
01

Beginners stepping into data science and AI

02

Professionals looking to improve their data analysis skills

Participation details

Schedule, location, and access information for this course.

Training schedule

Mar15
1. Oturum
15 Mart 202609:00 – 17:00TR Time
Mar22
2. Oturum
22 Mart 202609:00 – 17:00TR Time
Location & Access
Online (Google Meets)

Interactive Simulations

Enroll in the course to access these interactive simulations.

Python ile Biyoinformatik Analizi
01Python

Python ile Biyoinformatik Analizi

Yapay zeka dünyasına ilk adımınızı atın. Bu bölümde temel kavramları ve geleceği konuşacağız. Learn by doing, move from theory to practice.

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deneme
02deneme

deneme

Learn by doing, move from theory to practice.

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What You'll Learn

Skills and knowledge you will gain from this course.

Clean and manipulate messy datasets using Pandas
Perform comprehensive Exploratory Data Analysis (EDA)
Understand and apply core statistical tests to real data
Prepare and preprocess data for machine learning algorithms
Extract meaningful insights from large-scale structured data

About This Course

A comprehensive look at what this course offers.

Build a robust foundation in data science by mastering data manipulation, exploratory data analysis (EDA), and essential statistical concepts. This course is the perfect stepping stone for transitioning into advanced machine learning and AI applications.

Curriculum

A detailed breakdown of everything covered in this course.

3 bölüm · 8 konu

1
Introduction to NumPy and vectorized operations
25 dk
2
Mastering Pandas DataFrames and Series
35 dk
3
Data cleaning, handling missing values, and data wrangling
40 dk
1
Descriptive statistics and probability distributions
30 dk
2
Hypothesis testing and A/B testing fundamentals
45 dk
3
Correlation, variance, and statistical significance
35 dk
1
Encoding categorical variables and scaling techniques
30 dk
2
Dimensionality reduction concepts (PCA)
40 dk

Requirements

What you need before starting this course.

  • Basic Python knowledge (functions, loops, basic logic)
  • High school level mathematics
  • A desire to build a career in data science or AI

Who This Is For

This course is perfect for you if...

  • Beginners stepping into data science and AI
  • Professionals looking to improve their data analysis skills
  • Researchers needing robust statistical tools for their data

Instructor

Your guide throughout this course.

Yasin Polat

Data Specialist & Researcher

Experienced in building data pipelines, statistical analysis, and bridging the gap between raw data and machine learning models.

Course Details

Key information about this course at a glance.

LevelBeginner
Total Duration16 hours
Course TypeLive Course
LanguageTürkçe
CertificateIncluded
Last Updated2026-02

Frequently Asked Questions

Find answers to the most common questions about this course.

Our platform offers both live and pre-recorded training; you can check the specific 'Course Type' at the top of the course page. Our live courses are fully interactive and conducted simultaneously with the instructor.

While many of our live sessions are recorded, this may vary depending on the specific course. If a course is recorded, you will have access to the recordings through your profile. Please check the course description for exact details.

You can ask your questions directly to the instructor during the live sessions. Additionally, you can stay in constant touch with the instructor and other participants through course-specific WhatsApp communication groups.

Yes, upon successful completion of the live training program, a personalized, verifiable certificate of completion is issued to you. You can add this certificate to your resume and LinkedIn profile with a single click.

Depending on the course, the installation of required software (Python, VS Code, etc.) is usually demonstrated step-by-step during the first session. For courses with specific hardware requirements, these are detailed in the 'Requirements' section on the course page.

To put the theoretical knowledge into practice, hands-on projects and assignments are given at the end of each week or module. You will receive instructor feedback by completing these assignments.

Free

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