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

Created by Selva Prabhakaran

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Learning duration ~ 2-3 weeks
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What you will learn

What is stationarity and why does it matter in Forecasting?

Statistical tests for Stationarity

ACF, PACF and the underlying math

What is Granger's Causality Test, Caveats, Interpretation

Testing for Seasonality

Approaches to Deseasonalize, Detrend the a Time Series

When to use what approaches to impute missing data

Fully worked out Python examples.

Course Curriculum

Requirements

Basics of Python
Foundational knowledge of Data Science
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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