Complete Data Science Training with Python for Data Analysis
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Complete Data Science Training with Python for Data Analysis

Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More
Best Seller
4.4 (8 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
118 students enrolled
Created by Minerva Singh
Last updated 9/2017
Current price: $12 Original price: $200 Discount: 94% off
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  • 12.5 hours on-demand video
  • 5 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Install Anaconda and work within the iPytjhon/Jupyter environment, a powerful framework for data science analysis
  • Become proficient in the use of thee most common Python data science packages including Numpy, Pandas, Scikit and Matplotlib
  • Be able to read in data from different sources(including webpage data) and clean the data
  • Carry out data exploratory and pre-processing tasks such as tabulation, pivoting and data summarizing in Python
  • Become proficient in working with real life data collected from different sources
  • Carry out data visualization and understand which techniques to apply when
  • Carry out the most common statistical data analysis techniques in Python including t-tests and linear regersiion
  • Understand the difference between machine learning and statistical data analysis
  • Implement different unsupervised learning techniques on real life data
  • Implement supervised learning (both in form of classification and regression) techniques on real data
  • Evaluate the accuracy and generality of machine learning models
  • Build basic neural networks and deep learning learning algorithms
  • Use the powerful H2o framework for implementing deep neural networks
View Curriculum
  • Students should be able to use PC at a beginner's level, including being able to install programs
  • Desire to learn data science
  • Prior knowledge of Python will be useful but not necessary


              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.


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.

Who is the target audience?
  • 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 and visualizations using iPython
Compare to Other Data Science Courses
Curriculum For This Course
119 Lectures
Introduction to the Data Science in Python Bootcamp
9 Lectures 01:00:58

Introduction to the Course & Instructor

Data For the Course

Some Miscellaneous IPython Usage Facts

Online iPython Interpreter

Conclusion to Section 1
Introduction to Python Pre-Requisites for Data Science
5 Lectures 12:55

Different Types of Data Used in Statistical & ML Analysis

Different Types of Data Used Programatically

Python Data Science Packages To Be Used

Introduction to Numpy
10 Lectures 01:10:30

Create Numpy Arrays

Numpy Operations

Numpy for Basic Vector Arithmetric

Numpy for Basic Matrix Arithmetic

Broadcasting with Numpy

Solve Equations with Numpy

Numpy for Statistical Operation

Section 3 Quiz
2 questions
Introduction to Pandas
7 Lectures 48:23
Data Structures in Python

Read in Data

Read in CSV Data Using Pandas

Read in Excel Data Using Pandas

Read in JSON Data

Read in HTML Data

Data Pre-Processing/Wrangling
13 Lectures 01:36:26

Removing NAs/No Values From Our Data

Basic Data Handling: Starting with Conditional Data Selection

Drop Column/Row

Subset and Index Data

Basic Data Grouping Based on Qualitative Attributes




Rank and Sort Data


Merging and Joining Data Frames

Introduction to Data Visualizations
9 Lectures 01:29:02

Some Theoretical Principles Behind Data Visualization

Histograms-Visualize the Distribution of Continuous Numerical Variables

Boxplots-Visualize the Distribution of Continuous Numerical Variables

Scatter Plot-Visualize the Relationship Between 2 Continuous Variables


Pie Chart

Line Chart

Statistical Data Analysis-Basic
13 Lectures 01:16:53

Some Pointers on Exploring Quantitative Data

Explore the Quantitative Data: Descriptive Statistics

Grouping & Summarizing Data by Categories

Visualize Descriptive Statistics-Boxplots

Common Terms Relating to Descriptive Statistics

Data Distribution- Normal Distribution

Check for Normal Distribution

Standard Normal Distribution and Z-scores

Confidence Interval-Theory

Confidence Interval-Calculation

Statistical Inference & Relationship Between Variables
13 Lectures 01:35:00

Test the Difference Between Two Groups

Test the Difference Between More Than Two Groups

Explore the Relationship Between Two Quantitative Variables

Correlation Analysis

Linear Regression-Theory

Linear Regression-Implementation in Python

Conditions of Linear Regression

Conditions of Linear Regression-Check in Python

Polynomial Regression

GLM: Generalized Linear Model

Logistic Regression

Section 8 Quiz
4 questions
Machine Learning for Data Science
2 Lectures 11:08

What is Machine Learning (ML) About? Some Theoretical Pointers
Unsupervised Learning in Python
11 Lectures 48:03


KMeans-implementation on the iris data

Quantifying KMeans Clustering Performance

KMeans Clustering with Real Data

How Do We Select the Number of Clusters?

Hierarchical Clustering-theory

Hierarchical Clustering-practical

Principal Component Analysis (PCA)-Theory

Principal Component Analysis (PCA)-Practical Implementation

2 More Sections
About the Instructor
Minerva Singh
4.4 Average rating
454 Reviews
5,210 Students
7 Courses
Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)

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