Time Series Analysis Real world use-cases in python

Time Series Analysis Real world use-cases in python, available at $54.99, has an average rating of 4.3, with 43 lectures, based on 49 reviews, and has 12331 subscribers.

You will learn about 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 This course is ideal for individuals who are One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis.. It is particularly useful for One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis..

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Summary

Title: Time Series Analysis Real world use-cases in python

Price: $54.99

Average Rating: 4.3

Number of Lectures: 43

Number of Published Lectures: 41

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 41

Original Price: ?1,199

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis..

Target Audiences

One who is curious about Data Science, AI, Machine Learning, Natural Language Processing, Time Series Analysis..

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..

Course Curriculum

Chapter 1: Introduction

Lecture 1: Intro to this course & course Benefits

Lecture 2: Utilize QnA Section ( Golden Opportunity ) !

Lecture 3: How to follow this course-must watch

Lecture 4: How to download Anaconda Navigator & do set-up

Lecture 5: Quick Summary of Jupyter Notebook

Chapter 2: Project 1–>> Predict the prices of a Bitcoin

Lecture 1: Introduction to Business Problem

Lecture 2: Dataset & resources

Lecture 3: Prepare your data for Analysis

Lecture 4: What is re-sampling & how to perform it..

Lecture 5: Perform In-depth Analysis on Data..

Lecture 6: What is trend & how to analyse trend of closing price..

Lecture 7: Building a Base-line Model..

Lecture 8: What is seasonality & stationarity & how to examine it.

Lecture 9: Performing Statistical Test to detect Stationarity..

Lecture 10: How to Smoothen your series

Lecture 11: Build model using Facebook Prophet

Lecture 12: How to cross validate your Facebook Prophet model..

Chapter 3: Project 2–>> Predict Number of Births on a given day

Lecture 1: Dataset & resources

Lecture 2: Understanding distribution & trend of data..

Lecture 3: What is rolling & how to perform rollling on our data..

Lecture 4: Building a naive model & evaluate it..

Lecture 5: Intuition behind ARIMA –part 1

Lecture 6: Intuition behind MA Model– ARIMA part 2

Lecture 7: Intuition behind AR model ARIMA part 3

Lecture 8: Intuition behind Integrating ARIMA part 4

Lecture 9: Building a ARIMA Model..

Lecture 10: What is Normalization & How to normalize your data..

Lecture 11: How to Featurize your Data.

Lecture 12: Performing Dickey Fuller Test on Data

Lecture 13: How to Hypertune your Time Series Model

Chapter 4: Project 3–>> Predict the prices of Stocks

Lecture 1: Dataset & resources

Lecture 2: Collecting our Data for use-case

Lecture 3: Perform Exploratory Data Analysis on data

Lecture 4: Building Interactive Plots using Plotly

Lecture 5: What is Co-relation & how to check your Features are co-related or not..

Lecture 6: Perform Value at Risk analysis

Lecture 7: Finding Relationship between your data

Lecture 8: Analysing Historical Prices using Candle-stick

Lecture 9: Prepare your data for Time Series Modelling..

Lecture 10: Building & Intrepreting Facebook Prophet Model

Chapter 5: Bonus lesson

Lecture 1: Bonus Lecture

Instructors

Shan Singh
Top Rated & Best-Selling Udemy Instructor , Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 7 votes

4 stars: 13 votes

5 stars: 28 votes

Frequently Asked Questions

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