DSCI300

Introduction to Modern Data Science

A comprehensive introduction to the basics of data science.









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Estimated Time Commitment: 18 hours

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What you’ll learn

  • Modern data science practices
  • The latest in machine learning algorithms
  • Deep Learning with Tensorflow 2.0, Keras, and Sci Kit Learn

Who this course is for

People that want to learn data science but already know Python.

Requirements

SMNR002, SMNR003, SMNR004

Description

Data science is a fast-moving career field. Many data science classes skip over the foundational knowledge required to do the job in favor of just focusing on how to code algorithms.

This problem is exacerbated by the reality that, with online classes in this field, you can wind up with out-of-date information if they have not been updated in a few years.

At Mass Street Data School, DSCI300: Introduction to Modern Data Science is taught with practical academic rigor. Theory, practice, and code are all weaved together to present the most sophisticated data science course on the internet.

While getting instruction from university professors is valuable, it is always better to get trained by practitioners. At Mass Street Data School, we have a practicing data scientist and data engineer teach our Introduction to Modern Data Science course. They will teach you the mathematical theory behind algorithms, as well as the business context in which those algorithms are deployed.

As the data science career field evolves, DSCI300 will be updated accordingly. Our students have lifetime access to the course so they will never be out of date with bleeding edge data science technology. Students will get a thorough understanding of the state of the art of the profession.

Our students will also receive a solid education in the theory of data science—however, lessons are delivered with an eye towards the practical. Unlike university professors, we teach data science from the perspective of how you perform data science in the real world based on our personal experience of performing this work for large organizations. At the end of this course, our students will know how to apply these concepts every day in a business setting.

Enroll in DSCI300 to get on the cutting edge of data science excellence.

Syllabus

Course Introduction
Instructor Introduction
Course Overview
Why You Should Take This Course
How To Get Help With This Course
Getting The Course Material
How To Get The Most Out Of This Course

The Data Science Process
Data Science Process Overview
Ask A Question
Get The Data
Explore The Data
Model The Data
Communicate Results
Reproducible Research

Getting Started With Data
Environment Setup
Jupyter Notebook
Imports
Packages Overview
Exploratory Data Analysis

Machine learning Overview
Types of Machine Learning
Supervised Learning
Regression
Decision Trees
Ensemble
Support Vector Machines
Unsupervised Learning
SciKit Learn
Feature Engineering
Hyperparameter Tuning

Deep Learning in Theory
Artificial Neural Networks
Back Propagation And Activation Functions
Loss And Optimizer Functions
Deep Learning Strategies
Convolutional Neural Networks
Recurrent Neural Networks
Residual Neural Networks

Examples of Deep Learning
Working with Keras and Tensorflow
Creating Deep Models
Using CNN's
Using RNN's
Creating models with ResNets
Creating your own Project

Course Conclusion
A Final Word
TEVAL Instructions
TEVAL: Student Evaluation Of Instruction