Train Hub

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Data Science Course 2nd – 6th September 2024 (Weekdays)

The course provides the entire toolbox you need to become a data scientist. Fill up your resume with in demand data ... Show more
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HPI Train Hub
35 Students enrolled
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The course provides the entire toolbox you need to become a data scientist.

Fill up your resume with in demand data science skills and impress interviewers by showing an understanding of the data science field.

You will learn;

– How to pre-process data

– Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

– Start coding in Python and learn how to use it for statistical analysis

– Perform linear and logistic regressions in Python

– Carry out cluster and factor analysis

– Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

– Apply your skills to real-life business cases

– Unfold the power of deep neural networks

– Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance.

A typical data science course is designed to cover various modules or topics. Here are some modules you will find in our data science course:

  1. Introduction to Data Science: This module usually covers the fundamentals of data science, its applications, and the role of a data scientist.
  1. Mathematics and Statistics: This module often includes topics like linear algebra, calculus, probability, and statistical analysis,  which are essential for understanding data.
  1. Programming: You’ll likely learn programming languages such as Python or R, which are widely used in data science.
  1. Data Collection and Cleaning: This module focuses on gathering data from various sources and cleaning it to make it suitable for analysis.
  1. Data Analysis and Visualization: You’ll learn how to use tools and libraries to analyze data and create visualizations for better understanding.
  1. Machine Learning: This is a crucial module covering the theory and practical application of machine learning algorithms.
  1. Big Data Technologies: Some courses include modules on big data tools like Hadoop and Spark for handling large datasets.
  1. Deep Learning: For more advanced courses, deep learning modules cover neural networks and their applications.
  2. Data Ethics and Privacy: You’ll learn about the ethical considerations and legal aspects of working with data.
  1. Project Work: Many courses involve a final project where you apply what you’ve learned to a real-world data problem.
  1. Domain-specific Applications: Depending on the course, there may be modules focusing on data science applications in specific  fields, like healthcare, finance, or marketing.
  1. Tools and Libraries: You’ll be introduced to various data science tools and libraries, like Jupyter, Pandas, NumPy, scikit-learn, and more.

These modules provide a well-rounded education in data science, preparing students to tackle a wide range of data-related tasks and problems.

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Course details
Duration 10 hours
Video 9 hours
Level Advanced

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