Data Science with Python

Simpliv

Course

Online

£ 10 + VAT

The best Other Education courses

See all

See all

Description

  • Type

    Course

  • Methodology

    Online

  • Start date

    Different dates available

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

Writing simple Python scripts to do basic mathematical and logical operations
Loading structured data in a Python environment for processing
Creating descriptive statistics and visualizations
Finding correlations among numerical variables
Using regression analysis to predict the value of a continuous variable
Building classification models to organize data into pre-determined classes
Organizing given data into meaningful clusters
Applying basic machine learning techniques for solving various data problems

Questions & Answers

Add your question

Our advisors and other users will be able to reply to you

Who would you like to address this question to?

Fill in your details to get a reply

We will only publish your name and question

Reviews

This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 4 years

Subjects

  • Install
  • Programming
  • Advertising
  • Wine
  • Statistics

Course programme

Introduction 2 lectures 35:32 Introduction to Python This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] Statistical Essentials with Python This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] Introduction 2 lectures 35:32 Introduction to Python This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] Statistical Essentials with Python This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] Introduction to Python This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] Introduction to Python This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] Introduction to Python This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] Introduction to Python This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] This segment introduces Python and doing some basic programming tasks with it. It's important that you first install and configure the necessary tools (Python, Anaconda, Spyder, etc.) before starting this video. [16:48] Statistical Essentials with Python This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] Statistical Essentials with Python This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] Statistical Essentials with Python This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] Statistical Essentials with Python This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] This video segment shows how we can use Python to do some basic statistical processing on data. It's important that you know how to install packages for your Python distribution before starting this segment. [18:45] Statistical Processing 4 lectures 49:36 Analyzing Structured Data This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] Statistical Analysis with Python Advertising Longley Statistical Processing 4 lectures 49:36 Analyzing Structured Data This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] Statistical Analysis with Python Advertising Longley Analyzing Structured Data This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] Analyzing Structured Data This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] Analyzing Structured Data This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] Analyzing Structured Data This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] This video segment provides a bit of theory for various measurements and techniques we will use for analyzing structured data. This includes descriptive statistics, correlation, and regression. [10:31] Statistical Analysis with Python Statistical Analysis with Python Statistical Analysis with Python Statistical Analysis with Python Advertising Advertising Advertising Advertising Longley Longley Longley Longley Machine Learning with Python 5 lectures 01:02:53 Introduction and Classification Clustering This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] Density Estimation This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] Wine Iqsize Machine Learning with Python 5 lectures 01:02:53 Introduction and Classification Clustering This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] Density Estimation This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] Wine Iqsize Introduction and Classification Introduction and Classification Introduction and Classification Introduction and Classification Clustering This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] Clustering This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] Clustering This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] Clustering This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] This video introduces the concept of clustering and shows how we could use Python to do it. Clustering is useful when we don't know class labels or even the number of classes, and yet we want to organize and explain the data in some way. [18:34] Density Estimation This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] Density Estimation This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] Density Estimation This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] Density Estimation This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] This video segment describes one more branch of machine learning where we have even less information than we had for clustering. Here, we want to estimate the density of data distribution as a way to describe the underlying phenomenon. [13:44] Wine Wine Wine Wine Iqsize Iqsize Iqsize Iqsize

Additional information

Writing simple Python scripts to do basic mathematical and logical operations Loading structured data in a Python environment for processing Creating descriptive statistics and visualizations Finding correlations among numerical variables Using regression analysis to predict the value of a continuous variable Building classification models to organize data into pre-determined classes Organizing given data into meaningful clusters Applying basic machine learning techniques for solving various data problems

See related categories

Data Science with Python

£ 10 + VAT

The best Other Education courses

See all

See all