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

**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

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**

1

What is Data Science?

2

Introduction to the Course & Instructor

3

Data For the Course

4

Introduction to the Python Data Science Tool

5

For Mac Users

6

Introduction to the Python Data Science Environment

7

Some Miscellaneous IPython Usage Facts

8

Online iPython Interpreter

9

Conclusion to Section 1

1

Rationale Behind This Section

2

Different Types of Data Used in Statistical & ML Analysis

3

Different Types of Data Used Programatically

4

Python Data Science Packages To Be Used

5

Conclusions to Section 2

1

Numpy: Introduction

2

Create Numpy Arrays

3

Numpy Operations

4

Matrix Arithmetic and Linear Systems

5

Numpy for Basic Vector Arithmetric

6

Numpy for Basic Matrix Arithmetic

7

Broadcasting with Numpy

8

Solve Equations with Numpy

9

Numpy for Statistical Operation

10

Conclusion to Section 3

11

Section 3 Quiz

1

Data Structures in Python

2

Read in Data

3

Read in CSV Data Using Pandas

4

Read in Excel Data Using Pandas

5

Reading in JSON Data

6

Read in HTML Data

7

Conclusion to Section 4

1

Rationale behind this section

2

Removing NAs/No Values From Our Data

3

Basic Data Handling: Starting with Conditional Data Selection

4

Drop Column/Row

5

Subset and Index Data

6

Basic Data Grouping Based on Qualitative Attributes

7

Crosstabulation

8

Reshaping

9

Pivoting

10

Rank and Sort Data

11

Concatenate

12

Merging and Joining Data Frames

13

Conclusion to Section 5

1

What is Data Visualization?

2

Some Theoretical Principles Behind Data Visualization

3

Histograms-Visualize the Distribution of Continuous Numerical Variables

4

Boxplots-Visualize the Distribution of Continuous Numerical Variables

5

Scatter Plot-Visualize the Relationship Between 2 Continuous Variables

6

Barplot

7

Pie Chart

8

Line Chart

9

Conclusions to Section 6

1

What is Statistical Data Analysis?

2

Some Pointers on Collecting Data for Statistical Studies

3

Some Pointers on Exploring Quantitative Data

4

Explore the Quantitative Data: Descriptive Statistics

5

Grouping & Summarizing Data by Categories

6

Visualize Descriptive Statistics-Boxplots

7

Common Terms Relating to Descriptive Statistics

8

Data Distribution- Normal Distribution

9

Check for Normal Distribution

10

Standard Normal Distribution and Z-scores

11

Confidence Interval-Theory

12

Confidence Interval-Calculation

13

Conclusions to Section 7

1

What is Hypothesis Testing?

2

Test the Difference Between Two Groups

3

Test the Difference Between More Than Two Groups

4

Explore the Relationship Between Two Quantitative Variables

5

Correlation Analysis

6

Linear Regression-Theory

7

Linear Regression-Implementation in Python

8

Conditions of Linear Regression

9

Conditions of Linear Regression-Check in Python

10

Polynomial Regression

11

GLM: Generalized Linear Model

12

Logistic Regression

13

Conclusions to Section 8

14

Section 8 Quiz

1

How is Machine Learning Different from Statistical Data Analysis?

2

What is Machine Learning (ML) About? Some Theoretical Pointers

1

Unsupervised Classification- Some Basic Ideas

2

KMeans-theory

3

KMeans-implementation on the iris data

4

Quantifying KMeans Clustering Performance

5

KMeans Clustering with Real Data

6

How Do We Select the Number of Clusters?

7

Hierarchical Clustering-theory

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