Time Series Analysis Real world use-cases in python

Time Series Analysis Real world use-cases in python | Udemy/b]
English | Size: 3.16 GB
Genre: eLearning

What you’ll learn
Carry out time-series analysis in Python and interpreting the results, based on the real world challenges
Forecast the future based on patterns observed in the past.
Gain Hands-on by solving Real World challenges
Comprehend stationarity and how to perform Stationary Test

Are you looking to land a top-paying job in Data Science , AI & Time Series Analysis & Forecasting?

Or are you a seasoned AI practitioner who want to take your career to the next level?

Or are you an aspiring data scientist who wants to get Hands-on Data Science and Time Series Analysis?

If the answer is yes to any of these questions, then this course is for you!

This course will teach you the practical skills that would allow you to land a job as a quantitative financial analyst, a data analyst or a data scientist.

In business, Data Science , AI is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science & Time Series applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!

This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.

1.Task #1 @Predict closing Price of Bitcoin : Develop an Time Series model to predict closing price of Bitcoin

2.Task #2 @Predict Number of Births: Develop Time Series Model to predict number of births on a particular day…

3.Task #3 @Predict the Stock Prices: Predict the prices of stock using Facebook Prophet..

Who this course is for:
One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis..

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