Complete Data Wrangling & Data Visualisation With Python
What you'll learn
- Install and Get Started With the Python Data Science Environment- Jupyter/iPython
- Read In Data Into The Jupiter/iPython Environment From Different Sources
- Carry Out Basic Data Pre-processing & Wrangling In the Jupyter Environment
- Learn to IDENTIFY Which Visualisations Should be Used in ANY given Situation
- Go From A Basic Level To Performing Some Of The MOST COMMON Data Preprocessing, Data Wrangling & Data Visualization Tasks In Jupyter
- How To Use Some Of The MOST IMPORTANT Data Wrangling & Visualisation Packages Such As Matplotlib
- Build POWERFUL Visualisations and Graphs from REAL DATA
- Apply Data Visualization Concepts For PRACTICAL Data Analysis & Interpretation
- Gain PROFICIENCY In Data Preprocessing, Data Wrangling & Data Visualisation In Jupyter By Putting Your Soon-To-Be-Acquired Knowledge Into IMMEDIATE Application
- The Ability To Install the Anaconda Environment On Your Computer/Laptop
- Know how to install and load packages in Anaconda
- Interest in Learning to Process and Visualise Real Data
Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course!
I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science- data wrangling and visualisation.
GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION!
This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.
To be more specific, here’s what the course will do for you:
(a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common data wrangling tasks in Python.
(b) It will equip you to use some of the most important Python data wrangling and visualisation packages such as seaborn.
(c) It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation.
(d) You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results.
The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners
After each video you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments.
TAKE ACTION NOW :) You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
Who this course is for:
- Students Interested In Getting Started With Data Science Applications In The Jupyter Environment
- Students Interested in Learning About the Common Pre-processing Data Tasks
- Students Interested in Gaining Exposure to Common Python Packages Such As pandas
- Those Interested in Learning About Different Kinds of Data Visualisations
- Those Interested in Learning to Create Publication Quality Visualisations
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).