Deep Dive into Time Series Analysis Part: 2
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Deep Dive into Time Series Analysis
Part: 2 (Intermediate)

  • Part 2 of Time Series Analysis, where we dive in deeper to study the Time series Data in more
    detail, clean, process and extract valuable insights.

Created by Selva Prabhakaran

  • English

What you will learn

01

What is stationarity and why does it matter in Forecasting?

02

Statistical tests for Stationarity

03

ACF, PACF and the underlying math

04

What is Granger's Causality Test, Caveats, Interpretation

05

Testing for Seasonality

06

Approaches to Deseasonalize, Detrend the a Time Series

07

When to use what approaches to impute missing data

08

Fully worked out Python examples.

Course Curriculum

Requirements

  • Courses Page1 Basics of Python
  • Courses Page1 Foundational knowledge of Data Science
  • Courses Page1 High school maths

Who should attend this course?

  • Data Science Aspirants

  • Data Science Professionals

  • Software/Data engineers interested in quantitative analysis

  • Professionals working with large datasets

  • Data analysts, economists, researchers

About the course

You will learn the following skills by the end of the course:

  • LightGBM
  • XGBoost Random
  • Forest Decision Tree
  • Logistic Regression
  • Hyperparameter
  • Tuning Feature Importance Confusion Matrix
  • ROC AUC
  • Concordance and Discordance
  • Precision Recall Curve
  • Capture Rates and Gains
  • Feature Engineering
  • Label Encoding
  • Frequency Encoding
  • Chi-Square test ANOVA test
  • Exploratory Data Analysis
  • Memory
  • Optimization
  • Data Preprocessing

Instructor

Selva Prabhakaran Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this project!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I’ve put up the series of courses and projects with detailed explanations – just like an on the job experience. Hope you love it!

  • 4.5+Instructor rating

  • 200+ reviews

  • 10K+students

  • 15+ Courses

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