What is Data Science?

Minerva Singh
A free video tutorial from Minerva Singh
Bestselling Udemy Instructor & Data Scientist(Cambridge Uni)
4.3 instructor rating • 39 courses • 69,997 students

Learn more from the full course

Complete Data Science Training with Python for Data Analysis

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

12:49:50 of on-demand video • Updated July 2019

  • 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
English Data analytics - So before we embark on the course I'm going to talk about a bit about what is state of science now that you have enrolled for the course presumably you want to learn about better science and start working with the science using Python. But then what is data science. Because now based on science it means different things to different people. And as soon as the word data or science is uttered a lot of people they use that interchangeably with machine learning and that is partially correct because machine learning. Now as you can see this figure is an integral component of data science but machine learning and data science. They're not the same thing for a lot of people. Data science means neuro computing and things like neural networks. Fair enough. Again neuro computing is a part of data science and it is not the whole of the IT or science. So then what is data of science now data science is an interdisciplinary field which is used to process analyze and derive insights from different types of data. It relies on a plethora of techniques including visualization statistics and machine learning and you can see all of these here things like machine learning visualization statistics neuro computing all of them feed into the science and in turn help us make sense of the different types of data out there in this world. So these are science it is a sum of a lot of parts and it is a massively interdisciplinary field. So there's no one discipline of field which can claim to be the whole of data or science and not out of the different families and topics with them that are science so you know for a lot of people they work with databases and data processes and that makes them better scientists. Fair enough but out of all of these families within the science some of these some families such as exploratory data analyses data visualization and statistics and machine learning are needed for almost all data science rolls. So if you want to work as a professional data scientist in any capacity you may or may not know about databases or about pattern recognition but you are almost always expected to know about visualization statistics and machine learning and to be very fair. Things like exploratory data analyses visualization statistics machine learning. They really overlap with each other and they really feed into each other. So in this course I'm actually going to introduce you to these foundational building blocks of data or science. And we are and by the time you complete the scores you will be proficient in the most important and fundamental aspects of better science and that prepare you for almost all the data science rules out there in Bison. And with this this is the philosophy of this goes. And in the next lecture I'm going to introduce myself and talk you a bit more about what this course is all about. So you may make up your mind and you will get a feel of what we are going to tackle and then we're actually going to crack on with the intricacies of data science in the subsequent sections.