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30-Day Money-Back Guarantee

This course includes:

  • 13 hours on-demand video
  • 6 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Python

Complete Data Science Training with Python for Data Analysis

Beginners python data analytics : Data science introduction : Learn data science : Python data analysis methods tutorial
Rating: 4.3 out of 54.3 (1,500 ratings)
7,644 students
Created by Minerva Singh
Last updated 7/2019
English
English [Auto], Portuguese [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

13 sections • 123 lectures • 12h 50m total length

  • What is Data Science?
    Preview03:37
  • Preview11:34
  • Data For the Course
    00:03
  • Preview10:57
  • Preview04:05
  • Preview19:15
  • Some Miscellaneous IPython Usage Facts
    05:25
  • Online iPython Interpreter
    03:26
  • Preview02:36

  • Preview00:17
  • Different Types of Data Used in Statistical & ML Analysis
    03:37
  • Different Types of Data Used Programatically
    03:46
  • Python Data Science Packages To Be Used
    03:16
  • Preview01:59

  • Numpy: Introduction
    Preview03:46
  • Create Numpy Arrays
    10:51
  • Numpy Operations
    16:48
  • Preview07:34
  • Numpy for Basic Vector Arithmetric
    06:16
  • Numpy for Basic Matrix Arithmetic
    06:32
  • Broadcasting with Numpy
    03:52
  • Solve Equations with Numpy
    05:04
  • Numpy for Statistical Operation
    07:23
  • Preview02:24
  • Section 3 Quiz
    2 questions

  • Data Structures in Python
    12:06
  • Preview00:07
  • Read in CSV Data Using Pandas
    05:42
  • Read in Excel Data Using Pandas
    05:31
  • Reading in JSON Data
    03:09
  • Read in HTML Data
    12:06
  • Preview02:06

  • Preview04:19
  • Removing NAs/No Values From Our Data
    10:28
  • Basic Data Handling: Starting with Conditional Data Selection
    05:24
  • Drop Column/Row
    04:42
  • Subset and Index Data
    09:44
  • Basic Data Grouping Based on Qualitative Attributes
    09:47
  • Crosstabulation
    04:54
  • Reshaping
    09:26
  • Pivoting
    08:30
  • Rank and Sort Data
    08:03
  • Concatenate
    08:16
  • Merging and Joining Data Frames
    10:47
  • Preview02:06

  • What is Data Visualization?
    Preview09:33
  • Some Theoretical Principles Behind Data Visualization
    06:46
  • Histograms-Visualize the Distribution of Continuous Numerical Variables
    12:13
  • Boxplots-Visualize the Distribution of Continuous Numerical Variables
    05:54
  • Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
    11:57
  • Barplot
    22:25
  • Pie Chart
    05:29
  • Line Chart
    12:31
  • Preview02:14

  • What is Statistical Data Analysis?
    Preview10:08
  • Preview08:38
  • Some Pointers on Exploring Quantitative Data
    00:20
  • Explore the Quantitative Data: Descriptive Statistics
    09:05
  • Grouping & Summarizing Data by Categories
    10:25
  • Visualize Descriptive Statistics-Boxplots
    05:28
  • Common Terms Relating to Descriptive Statistics
    05:15
  • Data Distribution- Normal Distribution
    04:07
  • Check for Normal Distribution
    06:23
  • Standard Normal Distribution and Z-scores
    04:10
  • Confidence Interval-Theory
    06:06
  • Confidence Interval-Calculation
    05:20
  • Preview01:28

  • Preview05:42
  • Test the Difference Between Two Groups
    07:30
  • Test the Difference Between More Than Two Groups
    10:55
  • Explore the Relationship Between Two Quantitative Variables
    04:25
  • Correlation Analysis
    08:26
  • Linear Regression-Theory
    10:44
  • Linear Regression-Implementation in Python
    11:18
  • Conditions of Linear Regression
    01:37
  • Conditions of Linear Regression-Check in Python
    12:03
  • Polynomial Regression
    03:53
  • GLM: Generalized Linear Model
    05:25
  • Logistic Regression
    11:10
  • Preview01:52
  • Section 8 Quiz
    4 questions

  • How is Machine Learning Different from Statistical Data Analysis?
    Preview05:36
  • What is Machine Learning (ML) About? Some Theoretical Pointers
    05:32

  • Preview01:38
  • KMeans-theory
    02:31
  • KMeans-implementation on the iris data
    08:01
  • Quantifying KMeans Clustering Performance
    03:53
  • KMeans Clustering with Real Data
    04:16
  • How Do We Select the Number of Clusters?
    05:38
  • Hierarchical Clustering-theory
    04:10
  • Hierarchical Clustering-practical
    09:19
  • Principal Component Analysis (PCA)-Theory
    02:37
  • Principal Component Analysis (PCA)-Practical Implementation
    03:52
  • Preview02:08

Requirements

  • Be Able To Use PC At A Beginner Level, Including Being Able To Install Programs
  • A Desire To Learn Data Science
  • Prior Knowledge Of Python Will Be Useful But NOT Necessary

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 #analytics

Who 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
  • Anyone With A Prior Knowledge Of Python Looking To Branch Out Into Data Analysis
  • Anyone Looking To Become Proficient In Exploratory Data Analysis, Statistical Modelling & Visualizations Using iPython

Featured review

Rishabh Arora
Rishabh Arora
8 courses
4 reviews
Rating: 4.5 out of 5a year ago
The best part about the course is the instructor being prompt to your queries. As a beginner, you might face a lot of errors but the instructor is helpful enough. Great to build your foundation in python!

Instructor

Minerva Singh
Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)
Minerva Singh
  • 4.3 Instructor Rating
  • 12,553 Reviews
  • 68,796 Students
  • 39 Courses

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

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