COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS
A Full 12-Hour Python Data Science Boot Camp! : Learn statistical modelling, data visualization, machine learning and basic deep learning in Python
With so many Python based Data Science & Machine Learning courses around, why should you take this course?
As the title name suggests- this course your complete guide to practical data science using Python. This means, this course covers ALL the aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge –and boost your career to the next level.
THIS IS MY PROMISE TO YOU -COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE
But first things first. 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 data science related techniques and producing publications for international peer reviewed journals.
Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modeling to visualization to machine learning. Unlike other Python instructors, I dig deep into the statistical modeling features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning based models using Python
Inside this course, you’ll discover 12 complete sections addressing every aspect of Python Data Science:
• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda
• Getting started with Jupyter notebooks for implementing data science techniques in Python
• A comprehensive presentation about basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc.
• Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data
• How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc.
• Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and more!
• Statistical analysis, statistical inference, and the relationships between variables
• Machine Learning, Supervised Learning, Unsupervised Learning in Python
• You’ll even discover how to create artificial neural networks and deep learning structures!
With this course, you’ll have the keys to the entire Python Data Science kingdom!
You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started
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 packages like Numpy, Pandas, and Matplotlib to work with real data in Python. You’ll even understand deep concepts like statistical modeling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques). I will even introduce you to deep learning and neural networks using the powerful H2o framework!
With this Powerful All-In-One Python Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning!
The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your data science abilities.
WHAT WILL THIS COURSE DO FOR YOU?
This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will:
(a) Take the students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks
(b) Equip students to use Python for performing the different statistical data analysis and visualization tasks for data modelling
(c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation
(d) Students will get a strong background in some of the most important data science techniques.
(e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results
It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.
TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.
Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler