Learn the basic components of Data Science in this crash course for beginners. You will learn: Statistics; Data visualization; Programming

Learn the basic components of Data Science in this crash course for beginners.

In this course for beginners, you will learn about:

Statistics: we talk about the types of data you'll encounter, types of averages, variance, standard deviation, correlation, and more.

Data visualization: we talk about why we need to visualize our data, and the different ways of doing it (1 variable graphs, 2 variable graphs and 3 variable graphs.)

Programming: we talk about why programming helps us with data science including the ease of automation and recommended Python libraries for you to get started with data science.

⭐️ Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:10:52) Statistical Data Types ⌨️ (0:25:10) Types of Averages ⌨️ (0:38:55) Spread of Data ⌨️ (0:50:54) Quantiles and Percentiles ⌨️ (0:55:52) Importance of Data Visualization ⌨️ (1:05:14) One Variable Graphs ⌨️ (1:12:04) Two Variable Graphs ⌨️ (1:25:08) Three and Higher Variable Graphs ⌨️ (1:31:20) Programming

This Python tutorial for Data Science and Machine Learning will kick-start your learning of Python concepts needed for data science, as well as programming in general. Understand how to use the Jupyter Notebook, Understanding of Python from the beginning, Learn to use Object Oriented Programming with classes, Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more!

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

Python has been the go-to choice for Machine Learning, Data Science and Artificial Intelligence developers for a long time. Python libraries for modern machine learning models & projects: TensorFlow; Numpy; Scipy; Scikit-learn; Theano; Keras; PyTorch; Pandas; Matplotlib; ...