Complete Data Science Training with Python for Data Analysis

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

4.60 (1949 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Complete Data Science Training with Python for Data Analysis
10,697
students
13 hours
content
Oct 2021
last update
$94.99
regular price

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 Learning Techniques On Real Life Data

Implement Supervised Learning (Both In The Form Of Classification & Regression) Techniques On Real Data

Evaluate The Accuracy & Generality Of Machine Learning Models

Build Basic Neural Networks & Deep Learning Algorithms

Use The Powerful H2o Framework For Implementing Deep Neural Networks

Why take this course?

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 #analytics

Screenshots

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Our review

--- **Course Overview:** "Data Science Analysis with Python" is a comprehensive course that has been highly praised by recent reviewers for its practical applications and the instructor's exceptional teaching abilities. The course covers the use of Python for data science analytics and is considered an excellent stepping stone for those new to the subject, as well as ideal for professionals looking to enhance their skills. **Pros:** - **Practical Focus**: The course is designed to be hands-on, allowing learners to apply what they've learned in real-world scenarios. - **Instructor Expertise**: Reviewers consistently commend the instructor's profound knowledge of data science and Python, making lectures both informative and engaging. - **Beginner-Friendly**: The content is organized in a professional manner, making it accessible to learners at all levels. - **Comprehensive Coverage**: The course provides valuable information on various aspects of data analytics and offers a broad overview of data science tools. - **Professional Presentation**: The majority of the course material is presented well, with some reviewers noting it as a good introduction to more advanced studies. - **Motivational**: Many learners report feeling motivated and excited to apply what they've learned, with some expressing their intention to delve deeper into data science after completing this course. **Cons:** - **Statistical Underpinnings**: There are concerns regarding the instructor's understanding of statistical concepts, as evidenced by explanations that seem to lack depth or clarity. - **Outdated Content**: Some reviewers have pointed out that the course content, particularly code and datasets, is outdated, with references to Python 2 and some packages no longer in use. - **Logical Flow Issues**: The flow of information in some lectures is erratic, with a lack of logical progression, making it difficult for learners to follow along. - **Engagement and Clarity**: Some sections of the course, especially those involving statistical formulas and data visualization, are criticized for their lack of engagement and clarity. - **Technical Errors**: There are instances where code contains errors or files are missing, requiring learners to seek additional resources to complete exercises. - **User Experience**: A few reviews mention the presence of outdated URL links and disconnected content flow, which can affect the overall learning experience. **General Feedback:** The course receives high marks for its comprehensive approach to teaching data science with Python and the instructor's expertise. However, several reviewers suggest that the course would benefit from an update to reflect the latest developments in Python and data science tools. The course also needs improvements in explaining statistical concepts and ensuring a logical flow of information throughout all sections. **Recommendations:** - **Update Content**: Modernize the code examples, datasets, and libraries used in the course to align with current standards. - **Enhance Statistical Explanations**: Provide more detailed explanations for statistical formulas and models to help learners build a solid understanding of the underlying principles. - **Improve Logical Flow**: Organize each lecture to have a clear structure and logical progression of information for better learner engagement and comprehension. - **Proofread and Correct Errors**: Ensure all code, datasets, URLs, and associated materials are accurate and up-to-date to prevent confusion or the need for learners to seek additional resources. **Final Verdict:** "Data Science Analysis with Python" is a valuable course that offers a solid foundation in data science with Python, taught by an expert instructor. With some updates and improvements, it can provide an even more enriching learning experience for those looking to enter or advance within the field of data science.

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Coupons

DateDiscountStatus
3/6/202181% OFF
expired
8/25/202379% OFF
expired
1258298
udemy ID
6/18/2017
course created date
10/15/2019
course indexed date
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