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Data Science

Overview

Course Details

Schedule

Program Length: 8 weeks  

Class schedule

Class Length: 2 hours

Academy Courses Pricing

Original Price: 1200

With Growth Labs Academy Scholarship: 799 €*

Outcomes

By the end of this course, you will be proficient with:

  • Gain proficiency in Python, Jupyter Notebooks, and NumPy.
  • Develop a solid understanding of statistical methods, EDA, and data visualization.
  • Apply machine learning and deep learning concepts, including neural networks, to solve real-world data challenges.
Please ensure to have a computer that meets the specified requirements:

 

  • Supported operating systems: macOS, Linux, or Windows (Pro edition required).
  • Latest OS version, fully up to date.
  • All security updates installed.
  • At least 100GB of free space on the hard drive.
  • At least 16GB of RAM, 32GB RAM is strongly preferred.
  • Support for video conferencing and screen-sharing, with a reliable webcam and microphone.

Python

  • Intro to Python, basic syntax, data types, variable declarations, conditions, loops and strings
  • Data structures, lists, tuples and ranges
  • Dictionaries and sets
  • Functions and modules
  • Virtual Environments

Data Science Introduction

  • What is Data science?
  • Where is Data Science Needed?
  • Real-world examples
  • What Data Scientist work?

Data Science Python

  • Linear functions
  • Plotting functions in Python
  • Slope and intercept

Data Science Statistics

  • Percentiles
  • Standard deviation
  • Variance
  • Correlation
  • Correlation Matrix
  • Correlation vs Causality

Data Science Advanced

  • Linear Regression
  • Regression table
  • Regression info
  • Regression Coefficients
  • Regression P-Value
  • Regression R-Squared
  • Linear Regression Case

Exploratory Data Analysis (EDA)

  • Introduction to exploratory data analysis
  • Data Cleaning and Preprocessing
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariate Analysis
  • Feature Engineering

Machine Learning

  • Data
  • Features and Labels
  • Algorithms
  • Model Parameters
  • Training Models
  • Testing
  • Validation
  • Hyperparameters
  • Loss Functions
  • Evaluation Metrics
  • Overfitting and Underfitting
  • Bias and Variance
  • Feature Engineering

Machine learning in practice

  • Understanding the domain
  • Clear goal definition
  • Data quality
  • Data integration
  • Learning models
  • Result interpretation
  • Knowledge deployment

Deep Learning

  • Neural Networks
  • Deep Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Transfer Learning
To successfully pass the class, students should aim to reach a minimum of 90% of the available points. We’ve created a flexible environment which will enable you to have the best learning experience and elevate you on to greater heights. 
Punctuality, participation in discussions, completion of assignments, and demonstration of professional courtesy to others are required, in accordance with our Code of Conduct. Attendance will be taken at the beginning of every class. Passing requires at least 90% attendance. Students should always contact the instructors ahead of time if they are unable to attend all or part of the published class/lab hours.

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