Complete Time Series Analysis With Python
What you'll learn
- Master Time Series Data In Python & Become Proficient In Time Series Data Analysis
- LEARN How To Use Python-based Packages For Time Series Analysis
- APPLY Python Data Science Techniques To REAL LIFE Data
- IMPLEMENT Common Data Processing And Visualisation Techniques For Time Series Data in Python
- BE ABLE To Read In, Pre-process & Visualize Time Series Data
- The Basic Conditions Time Series Data Must Fulfill & How To Check For These
- MODEL Time Series Data To Forecast Future Values
- USE Machine Learning Regression For Forecasting Future Values
- Prior Familiarity With The Interface Of Jupiter Notebooks and Package Installation
- Prior Exposure to Basic Statistical Techniques (Such As p-Values, Mean, Variance)
- Be Able To Carry Out Data Reading And Pre-Processing Tasks Such As Data Cleaning In Python
- Interest In Working With Time Series Data Or Data With A Time Component To Them
THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN PYTHON!
This course is your complete guide to time series analysis using Python. So, all the main aspects of analyzing temporal data will be covered n depth..
If you take this course, you can do away with taking other courses or buying books on Python based data analysis.
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in in analysing time series data in Python, you can give your company a competitive edge and boost your career to the next level.
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:
Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and i have produced many publications for international peer reviewed journals.
Over the course of my research I realised almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic .
So, unlike other instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!
You will go all the way from carrying out data reading & cleaning to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.
Among other things:
You will be introduced to powerful Python-based packages for time series analysis.
You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.
& you will learn to apply these frameworks to real life data including temporal stocks and financial data.
NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED!
You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.
My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real-life.
After taking this course, you’ll easily use the common time series packages in Python...
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will work with real data and you will have access to all the code and data used in the course.
JOIN MY COURSE NOW!
Who this course is for:
- Anyone Who Wants Master Time Series Data In Python
- Anyone Who Wants To Become Proficient In Time Series Data Analysis Working With Real Life Data
- People Interested in Applying Machine Learning Techniques to Time Series Data
- Anyone Who Wants To Become An Expert Data Scientist
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).