In this video I cover Seasonal Autoregressive Integrated Moving Averages. We analyze whether CO2 levels are going down, make predictions about the future and more.

All the Files Used are Here for Free : https://github.com/derekbanas/TimeSeriesAnalysis Continue reading Time Series Forecasting 6 : SARIMA →

In this video I show how to make predictions with your ARIMA model. I also show how to make your data sets stationary and exactly how auto ARIMA finds the best parameters for ARIMA.

All the Files Used are Here for Free : https://github.com/derekbanas/TimeSeriesAnalysis Continue reading Time Series Forecasting 5 : ARIMA Prediction →

In this video I cover numerous topics. I talk more about the ARIMA Model Parameters, Resampling, Auto Correlation Function, Partial Auto Correlation Function, Grid Search, Pyramid ARIMA, Akaike Information Criterion, Bayesian Information Criterion and a lot more.
All the Files Used are Here for Free : https://github.com/derekbanas/TimeSeriesAnalysis Continue reading Time Series Forecasting 4 : Grid Search →

In this video I continue teaching you how to predict the future! This time I’ll cover the Granger Causality Test which will for example show you that when one stock goes up in price whether it causes others to go up. That could helpful at making money! I’ll also show you how to concat data, check for errors in predictions, resample data into different time frames and graph seasonality in interesting ways.

All the Files Used are Here for Free! Continue reading Time Series Forecasting 3 : Causation & Seasonality →

In this video I continue teaching you how to predict the future! This time I’m talking about AutoRegressive Integrated Moving Average (ARIMA). ARIMA is the basis for many other models. It focuses on examining differences between values to forecast future values.

I’ll start by making sure you completely understand regressions with AutoReg. Along the way I’ll teach you how to work with DateTimes, Frequency, Dropping Columns, Cleaning Up Missing Data, and Selecting Data based on Conditions. All Files are Here.
Continue reading Time Series Forecasting 2 : ARIMA →

If you want to analyze Covid 19 data on your own from reliable sources using the same software used by professional data scientists you found the right video. This is the most detailed tutorial on how to use web scraping to gather data. I then show how to search for and manipulate CSV files to organize your data. Then I show you how to use professional data science software to search organize and plot that data for analysis. Everything is Free also which is great!

All the Files Used are Here for Free Continue reading Covid 19 Data Analysis →

In this video we start learning how to predict the future with the Holts-Winter Method! I work up slowly so you completely understand it. I cover moving averages, weighted moving averages, single exponential smoothing, double exponential smoothing, triple exponential smoothing and how all of the formulas work with a ton of examples. All the Files are on GitHub Continue reading Time Series Analysis Holt Winters →

I cover a great deal about Time Series Analysis with StatsModels in this video and I also answer a bunch of common questions. I focus on Trends, Error Trend Seasonality Decomposition and Exponential Weighted Moving Averages. I also cover pulling data based on date ranges, rolling mean, smoothing results, built-in datasets, creating and indexing datetimes and much more. All the files are on GitHub. Continue reading Time Series Analysis StatsModels →

In this video I’ll cover Time Series Visualizations available using Matplotlib, Seaborn and Plotly. I’ll cover numerous ways to style charts. The files are available on GitHub.

Next I’ll cover Time Series Analysis with StatsModels. Soon I’ll cover more with StatsModels, SciPy, Scikit-learn, TensorFlow, PyTorch, Keras, Scrapy, Linear Algebra, Calculus and more. Continue reading Time Series Analysis Visualizations →

In this video I’ll show how to create Linear Regressions with StatsModels. We’ll explore the awesome datasets provided by StatsModels. I’ll also be showing how to work through 2 examples using a combination of NumPy, Pandas, Matplotlib, StatsModels and Seaborn.

We’ll explore whether a professors rating has anything to do with how attractive they are. Also we’ll explore how to get bigger tips. Continue reading Linear Regressions with StatsModels →

Linear Algebra is an essential branch of mathematics in the fields of chemistry, computer graphics, physics, economics, statistics, machine learning, engineering, etc.

It is used to model many types of natural phenomena, create unbreakable cryptography algorithms, maximize yield in agriculture, make stock market predictions, improve file compression, etc. This playlist of videos will be updated daily until I’ve covered everything! Continue reading Learn Linear Algebra →

This video provides complete coverage of Plotly with many real world examples. Plotly allows you to make beautiful static as well as animate charts!

If you want a video that condenses the whole Plotly API and all that it can do into 1 video with a ton of examples, then this video is for you.
The Jupyter Notebook is here on GitHub. Continue reading Learn Plotly in One Video →

This video provides complete coverage of Seaborn with many real world examples. Seaborn is a data visualization library built on top of Matplotlib. Seaborn is often used because it makes attractive visualizations and works easily with Pandas and NumPy.

While with Matplotlib you often have to write multiple lines of code to create a plot, Seaborn makes assumptions on what you want which often translates into getting the same plot with 1 line of code. Here is the cheat sheet on GitHub. Continue reading Seaborn Tutorial 2020 →

This video provides the most complete coverage of Matplotlib with many real world examples. Matplotlib provides numerous ways to create wonderful visualizations. It is the most popular plotting library for Python. It works easily with both Numpy and Pandas arrays. Seaborn, which I’ll cover in my next tutorial further extends it, but it is important to learn both.

If you want a video that condenses the whole Matplotlib API and all that it can do into 1 video with a ton of examples, then this video is for you. Continue reading Learn Matplotlib in One Video →

Pandas is an amazing framework used to work with tabular data, input data from numerous file / database types, clean data, analyze it and visualize it. If you want a video that condenses the whole Pandas API and all that it can do into 1 video with hundreds of examples, then this video is for you.

All the files and the Pandas Jupyter Notebook Cheat Sheet is here. Next I’ll cover MatPlotLib, Seaborn, SciPy, StatsModels, Plotly, Scikit-learn, TensorFlow, PyTorch, Keras, Scrapy, Linear Algebra, Calculus and more. Continue reading Learn Pandas in 1 Video →

I was asked by a local teacher to create an Algebra course that quickly reviewed all the key knowledge required. This course is centered around showing how to solve Algebra problems. For best results copy down the problem, watch me solve it, and then solve it on your own.

I’ll cover everything including Pre-Algebra, Solving Equations, Solving Inequalities, Graphing, Systems of Equations, Quadratic Equations, Matrices, Polynomials, Functions, Rational Equations and so much more. Continue reading Learn Algebra →

NumPy is an amazing scientific computing library that is used by numerous other Python Data Science libraries. NumPy contains many mathematical, array and string functions that are extremely useful. Along with all the basic math functions you’ll also find them for Linear Algebra, Statistics, Simulation, etc.

I cover most everything you’ll learn by reading the API here. Next I’ll cover SciPy, Pandas, StatsModels, MatPlotLib, Seaborn, Plotly, Scikit-learn, TensorFlow, PyTorch, Keras, Scrapy, Linear Algebra, Calculus and more. Continue reading Learn NumPy in 1 Video →

People ask me to do more videos so I’m making 2 weeks full of videos on Statistics with Python. Every day at 1 PM Eastern Standard Time I’ll upload a new video. If this goes well, maybe I’ll go back to every day uploads like I used to do? It is up to you and the number of views these videos get.

This tutorial series will cover all of the Statistics needed for Data Science and machine learning. I’ll also write all the Python code to make everything work. I hope you like it. Continue reading Statistics Every Day →

The most in demand skills in the world right now are in Data Science & Machine Learning! In this one video I will teach you a key part of the Math of Machine Learning and Data Science which is Statistics.

I took everything in a standard 500 page text book on Statistics and put it in this one video. I will cover every formula, but also will solve real world problems with each formula. If you pause your way through this video while taking notes you will master Statistics. Continue reading Statistics in 1 Video →

There is nothing more exciting in the world right now then Machine Learning and Data Analytics! In this one video I will teach you a key part of the Math of Machine Learning and Data Analytics which is Probability.

I took everything in a standard 500 page text book on Probability in put it in this one video. I will cover every formula, but also will solve a real world problem with each formula.

After this video on Probability I will continue with the Math of Machine Learning by covering Statistics, Linear Algebra and Calculus. If you want to see those videos click the Notification Bell. Continue reading Probability in One Video →