Location:Main Road, Bangalore


Python For Time Series Data Analysis




Python Tech Training


Job Aspirants


Both Classroom and Online Classes


Week Days and Week Ends

Duration :

Daily 2 hrs during Weekdays

Python Objectives

•Learn Fundamental Concepts of Python
•Learn by example, by writing exciting programs
•Learn how to integrate and customize Python code.
•Different Python practical questions asked during real time interviews .
•Learn Python from scratch & understand core programming concept
•Learn to design and run complex automated workflows for Python
•Learn how to implement the all the functionalities of a Python.
•Create Apps using Python From Scratch and scale it up to any level
•Amazing Step by Step Guide for Beginners to Learn Python Language Quick and Simple!

python for time series data analysis Training Features

•Advanced Topics covered with examples
•Training by Industry expert professionals
•Highly competent and skilled IT instructors
•Trainer support after completion of the course
•We Also provide Case studies for Online Training Courses
•Access to a huge closet containing information about Hadoop
•Flexible group timings to admit freshers, students, and employed professionals
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Who are eligible for Python

•.net, front end developer, Android Development, ios, Big Data, Web Development, java full stack, Service Now, Wintel Servers, Change Management, Database
•Java Developer, Php Mysql, Zend 2.0, java j2ee struts hibernate spring, iOS, Android, html
•Microsoft Azure, Azure, Sql Azure, Cloud Computing, Cloud Testing, SQL, Cognos Framework Manager, Query Studio, Oracle, Business Objects, Issue Resolution
•Oracle Apps Testing, Functional Testing, O2C, Techical Support, Service Desk, IT Helpdesk, IT Support, Tech Support, java, J2ee, Java Developer
•UI/UX Architect, C#, Asp.Net, Javascript, CSS, Ajax, HTML, MS SQL, Azure, SugarCRM, Php, MVC, MYSQL, CodeIgniter, Android Developer, HTML5, CSS3, JQuery


•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.
•We’ll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we’ll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.
•Then we’ll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.
•Afterwards we’ll get to the heart of the course, covering general forecasting models. We’ll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.
•Afterwards we’ll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.
•This course even covers Facebook’s Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.
•So what are you waiting for! Learn how to work with your time series data and forecast the future!
•We’ll see you inside the course!
•Who this course is for:
•Course Overview Check
•1 question
•Course Curriculum Overview
•Course Set Up and Install
•Installing Anaconda Python Distribution and Jupyter
•NumPy Arrays – Part One
•NumPy Arrays – Part Two
•NumPy Indexing and Selection
•NumPy Operations
•NumPy Exercises
•NumPy Exercise Solutions
•Pandas Overview
•Introduction to Pandas
•DataFrames – Part One
•DataFrames – Part Two
•Missing Data with Pandas
•Group By Operations
•Common Operations
•Data Input and Output
•Pandas Exercises
•Pandas Exercises Solutions
•Data Visualization with Pandas
•Overview of Capabilities of Data Visualization with Pandas
•Visualizing Data with Pandas
•Customizing Plots created with Pandas
•Pandas Data Visualization Exercise Solutions
•Time Series with Pandas
•Overview of Time Series with Pandas
•DateTime Index
•DateTime Index Part Two
•Time Resampling
•Time Shifting
•Rolling and Expanding
•Visualizing Time Series Data
•Visualizing Time Series Data – Part Two
•Time Series Exercises – Set One
•Time Series Exercises – Set One – Solutions
•Time Series with Pandas Project Exercise – Set Two
•Time Series with Pandas Project Exercise – Set Two – Solutions
•Time Series Analysis with Statsmodels
•Introduction to Time Series Analysis with Statsmodels
•Introduction to Statsmodels Library
•ETS Decomposition
•EWMA – Theory
•EWMA – Exponentially Weighted Moving Average
•Holt – Winters Methods Theory
•Holt – Winters Methods Code Along – Part One
•Holt – Winters Methods Code Along – Part Two
•Statsmodels Time Series Exercises
•Statsmodels Time Series Exercise Solutions
•General Forecasting Models
•Introduction to General Forecasting Section
•Introduction to Forecasting Models Part One
•Evaluating Forecast Predictions
•Introduction to Forecasting Models Part Two
•ACF and PACF Theory
•ACF and PACF Code Along
•ARIMA Overview
•Autoregression – AR – Overview
•Autoregression – AR with Statsmodels
•Descriptive Statistics and Tests – Part One
•Descriptive Statistics and Tests – Part Two
•Descriptive Statistics and Tests – Part Three
•ARIMA Theory Overview
•Choosing ARIMA Orders – Part One
•Choosing ARIMA Orders – Part Two
•ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part One
•ARMA and ARIMA – AutoRegressive Integrated Moving Average – Part Two
•SARIMA – Seasonal Autoregressive Integrated Moving Average
•SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART ONE
•SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART TWO
•SARIMAX – Seasonal Autoregressive Integrated Moving Average Exogenous – PART 3
•Vector AutoRegression – VAR
•VAR – Code Along
•VAR – Code Along – Part Two
•Vector AutoRegression Moving Average – VARMA
•Vector AutoRegression Moving Average – VARMA – Code Along
•Forecasting Exercises
•Forecasting Exercises – Solutions
•Deep Learning for Time Series Forecasting
•Introduction to Deep Learning Section
•Perceptron Model
•Introduction to Neural Networks
•Keras Basics
•Recurrent Neural Network Overview
•Keras and RNN Project – Part One
•Keras and RNN Project – Part Two
•Keras and RNN Project – Part Three
•Keras and RNN Exercise Solutions
•BONUS: Multivariate Time Series with RNN
•Quick Check on MultiVariate Time Series Notebook and Data
•Facebook’s Prophet Library
•Overview of Facebook’s Prophet Library
•Facebook Prophet Evaluation
•Facebook Prophet Trend
•Facebook Prophet Seasonality