Mastering Time Series Analysis and Forecasting with Python

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Akhil Vydyula

2:45:33

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1. Foundations of Time Series Analysis

1. Introduction to Time Series Data.mp4

10:01

2. Understanding Time Series Components.mp4

12:38

3. Stationarity and Its Importance.mp4

08:06

2. Time Series Modeling with ARIMA

1. ARIMA Model Fundamentals.mp4

09:32

2. Building and Evaluating ARIMA Models.mp4

12:51

3. Seasonal Time Series and Decomposition.mp4

11:42

3. Statistical Concepts for Time Series

1. Probability Distributions in Time Series.mp4

14:12

2. Descriptive Statistics and Exploratory Data Analysis.mp4

16:54

3. Hypothesis Testing and Confidence Intervals.mp4

11:52

4. Forecasting with Time Series Models

1. Forecasting with ARIMA Models.mp4

15:35

2. Model Selection and Evaluation.mp4

09:24

3. Practical Forecasting and Model Improvement.mp4

07:15

5. Advanced Time Series Topics and Applications

1. Data Visualization for Time Series.mp4

08:17

2. Time Series in Python Practical Implementation.mp4

07:45

3. Real-world Case Studies and Applications.mp4

09:29

Description

Comprehensive guide to time series analysis and forecasting techniques with Python, covering ARIMA, SARIMA, Prophet

What You'll Learn?

Understand the fundamentals of time series analysis, including trends, seasonality, and noise.
Implement various time series forecasting methods such as ARIMA, SARIMA, and Prophet using Python.
Evaluate and tune time series models to improve accuracy and performance.
Apply time series analysis techniques to real-world datasets and interpret the results for actionable insights.
Students and researchers interested in applying time series techniques to their projects.
Data analysts and scientists looking to enhance their time series analysis skills.
Professionals working in fields like finance, economics, and operations who deal with time-series data.
Anyone curious about understanding and predicting patterns in time-dependent data.

Who is this for?

Aspiring data scientists and analysts looking to specialize in time series analysis and forecasting.

Professionals in finance, marketing, operations, and other fields where time series data is commonly used for decision-making.

Students and researchers in academia who need to analyze time series data for their studies or projects.

Anyone interested in gaining practical skills in time series analysis to enhance their data science toolkit.

What You Need to Know?

Basic knowledge of Python programming. Familiarity with libraries such as pandas and matplotlib is beneficial.

A computer with internet access to follow along with coding exercises and access datasets.

Basic understanding of statistical concepts such as mean, variance, and correlation.

Willingness to learn and apply analytical thinking to solve time series problems.

A curious mind and willingness to learn!

Familiarity with statistical concepts (mean, median, standard deviation).

Basic understanding of Python programming.

More details

Description
Unlock the power of time series analysis and forecasting with Python! This course is designed to provide a thorough understanding of the key concepts, techniques, and tools needed to analyze and predict time series data effectively. Whether you're a data scientist, analyst, student, or professional, this course will equip you with the skills to tackle time series problems in various domains.
What You'll Learn:
Understand the fundamentals of time series analysis, including trends, seasonality, and noise.
Implement and apply popular time series forecasting methods such as ARIMA, SARIMA, and Prophet using Python.
Evaluate and tune time series models to improve their accuracy and performance.
Work with real-world datasets to gain hands-on experience and extract actionable insights.
Course Highlights:
Detailed Explanations: Comprehensive coverage of essential concepts and techniques in time series analysis.
Hands-On Projects: Practical exercises and projects to apply what you've learned.
Expert Guidance: Learn from an experienced data scientist with a proven track record in the field.
Community Support: Join a community of learners to discuss and share insights.
Requirements:
Basic knowledge of Python programming.
Familiarity with libraries such as pandas and matplotlib is beneficial.
A computer with internet access to follow along with coding exercises and access datasets.
Basic understanding of statistical concepts such as mean, variance, and correlation.
Willingness to learn and apply analytical thinking to solve time series problems.
Who Should Enroll:
Aspiring data scientists and analysts looking to specialize in time series analysis and forecasting.
Professionals in finance, marketing, operations, and other fields where time series data is commonly used for decision-making.
Students and researchers in academia who need to analyze time series data for their studies or projects.
Anyone interested in gaining practical skills in time series analysis to enhance their data science toolkit.
Join us on this exciting journey and master the art of time series analysis and forecasting with Python. Enroll today and start transforming data into meaningful insights!
Who this course is for:
Aspiring data scientists and analysts looking to specialize in time series analysis and forecasting.
Professionals in finance, marketing, operations, and other fields where time series data is commonly used for decision-making.
Students and researchers in academia who need to analyze time series data for their studies or projects.
Anyone interested in gaining practical skills in time series analysis to enhance their data science toolkit.

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Time Series Analysis

Akhil Vydyula

Instructor's Courses

Hi There!My Name is Akhil Vydyula, I am a Data Scientist I was previously worked on BFSI data analysis and modelling skills to oversee the full-life cycle of development and execution. He possess strong.ability to data wrangling, feature engineering, algorithm development, model training and implementation.SKILLS AND COMPETENCIESExpert knowledge and experience with C/C++/python Programming and SQL.Should be able to learn and Implement new technologies quickly and effectively.Excellent Mathematical Skills, Problem Solving & Logical Skills.Actively Participating in hackathons in various platforms and writing blogs in medium.TECHNICAL SKILLSMachine Learning, Natural Language Processing(NLP),Computer Vision,Regression, Multi LabelClassification.Transfer Learning, Transformers, Ensembles, Stacking Classifiers.AutoML, SQL, Python, Keras, Pandas, NumPy, Seaborn,Matplotlib,Clustering,Recommendation Systems,Time Series Analysis.

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