The Data Science Course 2019: Complete Data Science Bootcamp (updated 7/2019)

Category: Tutorial

Tag: Database/SQL


Posted on 2019-08-23, by nokia241186.

Description


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The Data Science Course 2019: Complete Data Science Bootcamp (updated 7/2019) | 15.5GB

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.



What you'll learn
The course provides the entire toolbox you need to become a data scientist
Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
Impress interviewers by showing an understanding of the data science field
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
Use state-of-the-art Deep Learning frameworks such as Google's TensorFlowDevelop a business intuition while coding and solving tasks with big data
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
Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Requirements
No prior experience is required. We will start from the very basics
You'll need to install Anaconda. We will show you how to do that step by step
Microsoft Excel 2003, 2010, 2013, 2016, or 365
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