Complete Data Science Training with Python for Data Analysis (Udemy.com)

Beginners python data analytics : Data science introduction : Learn data science : Python data analysis methods tutorial

Produced in 2022

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What you will learn

  • Python data analytics - Install Anaconda & Work Within The iPytjhon/Jupyter Environment, A Powerful Framework For Data Science Analysis
  • Python Data Science - Become Proficient In Using The Most Common Python Data Science Packages Including Numpy, Pandas, Scikit & Matplotlib
  • Data analysis techniques - Be Able To Read In Data From Different Sources (Including Webpage Data) & Clean The Data
  • Data analytics - Carry Out Data Exploratory & Pre-processing Tasks Such As Tabulation, Pivoting & Data Summarizing In Python
  • Become Proficient In Working With Real Life Data Collected From Different Sources
  • Carry Out Data Visualization & Understand Which Techniques To Apply When
  • Carry Out The Most Common Statistical Data Analysis Techniques In Python Including T-Tests & Linear Regression
  • Understand The Difference Between Machine Learning & Statistical Data Analysis
  • Implement Different Unsupervised Le

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Quality Score

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Overall Score : 88 / 100

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Course Description

Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More
THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS:
It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python!
HERE IS WHY YOU SHOULD TAKE THIS COURSE:
First of all, this course a complete guide to practical data science using Python...
That means, this course covers ALL the aspects of practical data science and if you take this course alone, 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 & 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 the student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modelling to visualization to machine learning.
Unlike other Python instructors, I dig deep into the statistical modelling 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
DISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING):
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, bar plots, 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...& MUCH MORE!
With this course, you'll have the keys to the entire Python Data Science kingdom!
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 packages like Numpy, Pandas, and Matplotlib to work with real data in Python.
You'll even understand deep concepts like statistical modelling 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 and put into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your data science abilities.
HERE IS WHAT THIS COURSE WILL 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, a perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One.
This course will:
(a) Take students without a prior Python and/or statistics 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 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 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, the 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.
JOIN THE COURSE NOW!

#data #analysis #python #anaconda #analyticsWho this course is for:
  • Anyone Who Wishes To Learn Practical Data Science Using Python
  • Anyone Interested In Learning How To Implement Machine Learning Algorithms Using Python
  • People Looking To Get Started In Deep Learning Using Python
  • People Looking To Work With Real Life Data In Python

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Reviews

4.4

100 total reviews

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By Md Saiful Islam Sajol on 3 weeks ago

The instructor doesn't go deep in the topics. She just reads the text and code as well. Sometimes I got lost what she is trying to do reading (!) codes after codes. Even she doesn't describe the library functions properly. What is the basic protocol of writing a particular library function, where can I get the documentation of these functions, how it works all of these were missing. It seems like she was always in a hurry to finish the video. Many basic explanation was missing. Repetition of unnecessary words are very annoying. For example, I heard thousands of times the word "essentially" and "you know " - that don't make any sense. At the beginning of every video, she could briefly declare what she is going to do with the relevant data sets, so that we can keep track with the lecture.

By Sadia mumu on a month ago

Good course materials. Learned lots of important things about data analysis using python. Hats of for the quality of the content.

By Topu Islam on a month ago

Fabulous course for python in data analysis. I am fully satisfied with the sessions, lectures and content quality. Though the course duration was long but totally worthy! Thanks for such quality course.

By Shammi Akter on a month ago

Analysis data with python is awesome! Course sessions are very organized! Instructor gave excellent knowledge delivery in this course.

By Hafsa Talat on 2 months ago

the instructor seem to have a good grip on the topic, looking forward to a great learning experience

By Melanie Parker on 2 months ago

Towards the end of the course (sections 10 - 12), there was little to no explanation about the commands being used and the options being set in certain function calls. It seemed to become more of a reading through of the notebook which was very hard to keep up with while mirroring the work in my own notebook and I don't feel I have a solid understanding of the purpose of all of the options and the selected values in the supervised learning, unsupervised learning, and neural networks sections.I also ran into issues again in these later sections where the functions have changed with newer versions of some packages such that I was not getting the exact same results for the same models being trained and I assume that relates to different default values of different function options. It would be helpful for the instructor to check the current version of particularly the machine learning model functions to ensure the lectures correspond to the version of these functions that course participants will be using.

By Siam aaron on a month ago

Marvelous course materials, outline and lecture contents. Beyond expectation. Thank you for such quality course.

By Agboola Temitope on 4 months ago

she was pretty fast and i couldnt run most of the packages because i use a windows 10 and most of the packages wouldnt run on mine.

By Adarsh Trivedi on 3 months ago

It's a very good and informative video series I have learned a lot from this course.

By Ilham Zannuary on 2 months ago

The instructor talks very quickly and explains also, for example, less detailed code to be explained.

By Divakar V on 5 months ago

The examples in the lessons are not explained properly at all. The instructor just reads the variable names and function names but she does not care to explain the what the function does and what is the function of the parameters. The outputs are also not explained properly. She does not properly explain how to use the concepts in real life. One of the worst tutorials I have come through.

By Ankita Kothari on 3 weeks ago

Instructor should explain a bit more about all the methods and packages they are importing. Things are working out but no proper explantion has given about many methods, fucntions and packages they are importing; being stated that this is course for Beginners with Python