Is this one of your needs? Then course is for you
Forecasting Online Users ?
Forecasting Traffic ?
Forecasting the expected performance of their loan portfolio?
Forecasting real-estate properties?
Forecasting User Spending Habits ?
If there is some time dependency, then you know it - the answer is: time series analysis.
Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!
We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python.
Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.
We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.
Then we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.
This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.
In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice.
We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…
With these tools we will master the most widely used models out there:
· AR (autoregressive model)
· MA (moving-average model)
· ARMA (autoregressive-moving-average model)
· ARIMA (autoregressive integrated moving average model)
. SARIMA (seasonal autoregressive integrated moving average model)
Why wait? Every day is a missed opportunity.
Click the “Buy Now” button and start mastering time series in Python today.
Who this course is for:
Aspiring data scientists.
Programming beginners.
People interested in quantitative finance.
Programmers who want to specialize in finance.
Finance graduates and professionals who need to better apply their knowledge in Python.
