A Crash Course in Data Science This one-module course constitutes the first "week" of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself.
About this Course
This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff.What is a crash course in data science?
A Crash Course in Data Science. This one-module course constitutes the first "week" of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself. You'll learn important terminology and how successful organizations use data science.
What is Crash Course on YouTube?
The Crash Course YouTube channel was conceived by the Green Brothers after YouTube approached them with an opportunity to launch one of the initial YouTube-funded channels as part of the platform's original channel initiative.
How many series of Crash Course are there?
To date, there are 38 main series of Crash Course, with John hosting nine and Hank hosting seven. Together with Emily Graslie, they also co-hosted Big History. A second channel, Crash Course Kids, is hosted by Sabrina Cruz and has completed its first series, Science.
What is this data science course?
This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content.
What is data course?
Data science Specializations and courses teach the fundamentals of interpreting data, performing analyses, and understanding and communicating actionable insights.
Which course is best for data?
8 Best Data Science Courses & Certifications for 2022:Data Science Specialization — JHU @ Coursera.Introduction to Data Science — Metis.Applied Data Science with Python Specialization — UMich @ Coursera.Data Science MicroMasters — UC San Diego @ edX.Dataquest.Statistics and Data Science MicroMasters — MIT @ edX.More items...
How do I start learning about data?
Step 0: Figure out what you need to learn. ... Step 1: Get comfortable with Python. ... Step 2: Learn data analysis, manipulation, and visualization with pandas. ... Step 3: Learn machine learning with scikit-learn. ... Step 4: Understand machine learning in more depth. ... Step 5: Keep learning and practicing. ... Join Data School (for free!)
What is data science course for beginners?
The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment. The emphasis in this course is on hands-on and practical learning .
Can I learn data science in 3 months?
To start learning data science, you must have the following capabilities to get a positive result in 3 months: You must have some technical knowledge like a degree in Stat. Math etc. You also need to know about coding schemes and programming languages.
Does data science require coding?
Many data scientists started their careers without prior knowledge or experience in coding. The basic requirements for a non-coder to become a data scientist include: Thorough understanding of probability and statistics. Having a passion for working with numbers.
How do I teach myself data analysis?
7 Tips to Guide Self-Studying Data ScienceStart Anywhere—But Start. To important things to keep in mind as you navigate your learning experience: ... Pick Up a Programming Language. ... Dive into the Technical. ... Delve Into More Advanced Topics. ... Learn The Tools. ... Level Up Your Soft Skills.
Can I be a self taught data analyst?
It's definitely possible to become a data scientist without any formal education or experience. The most important thing is that you have the drive to learn and are motivated to solve problems. And if you can find a mentor or community who can help guide and support your learning then that's even better!
Can I learn data analysis on my own?
Yes, you can learn the fundamentals of data analysis on your own.
What are the 3 main concepts of data science?
This article covers 5 fundamental Data Science Concepts.Data Science Concept #1: Machine Learning. ... Data Science Concept #2: Algorithms. ... Data Science Concept #3: Statistical Models. ... Data Science Concept #4: Regression Analysis. ... Data Science Concept #5: Programming.
Who is eligible for data science?
The eligibility criteria for these programs is a bachelor's degree in science or engineering with basic knowledge of statistics & mathematics. Undergraduate data science courses require students to score more than 50% marks in class 12 exams with mathematics, statistics, or computer science as core subjects.
Can anyone learn data science?
Anyone, including you and I, can become a data scientist if you're motivated enough. After years of being frustrated with how conventional sites taught data science, I recently created Dataquest, a better way to learn data science online.
What is structured data?
Structured data is what most people typically think of when they think of data: numbers and information such as dates, money, names, etc. neatly organized into tables of columns and rows. This easy organization makes structured data easy for computers to analyze.
What is big data?
Big data refers to the massive amount of data that companies collect and analyze. Some people use the term to simply describe the volume of data collected. However, most of the time it’s used to describe the systems and processes used to collect, store, analyze and output data. Big data can use data of any kind — structured or unstructured, ...
What happens when a computer shuts down?
One of the main advantages of this, in addition to efficient storage, is that if a computer shuts down, you don’t lose all of your data.
What is Hadoop cluster?
Enter Apache Hadoop, an open-source software framework that efficiently distributes the storage of large data sets across clusters of computers and servers, called Hadoop clusters. When using Hadoop, you set up your own physical servers and computers, called NoSQL databases, which are networked together.
What is velocity in big data?
Velocity refers to the speed at which data is collected. Big data doesn’t include any kind of data analysis where you collect a few data points per day. Big data refers to analysis that collects data on a constant basis. Things like social media posts, retail transactions and app usage are just a few examples of the type of high-velocity activities that big data tracks.
What is the end goal of big data?
The end goal of big data analytics is to find actionable insights like data trends that reveal changes you can implement to improve your business.
Why is big data important?
Big data often provides companies with answers to the questions they did not know they wanted to ask. Therefore, there is an inherent usefulness to the information being collected in big data. Businesses must set relevant objectives and parameters in place to glean valuable insights from big data.
What is clustering in statistics?
A cluster is therefore a set of core samples, each close to each other (measured by some distance measure) and a set of non-core samples that are close to a core sample (but are not themselves core samples).”. This is also one of standard techniques, it might sounds complicated at first but the principle is easy:
Why is scraping good?
Scraping is great, because you can learn Data Science by playing around with more information about your hobby. Say if you like video games and want to analyse different statistics then scraping is the best option.
What is data science?
So, data science is involved in formulating those quantitative questions, identifying the data that could be used to answer the questions, cleaning it, making it nice, then analyzing the data, whether that's with machine learning, or with statistics, or with neural networks or whatever.
How to describe the role of data science?
1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. ...
When is data science useful?
So the key issue when you're analyzing a data set, or when you're trying to use data to help your business, or to help your organization move forward is to know that data science is only useful when you're actually using that data to answer a specific, concrete question that could be useful for your organization.
What is a one module course?
This one-module course constitutes the first "week" of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself. You'll learn important terminology and how successful organizations use data science.
What is a crash course?
For other uses, see Crash Course (disambiguation). Crash Course (sometimes stylized as CrashCourse) is an educational YouTube channel started by John and Hank Green (collectively the Green brothers ), who first achieved notability on the YouTube platform through their VlogBrothers channel. Crash Course was one of the hundred initial channels funded ...
Who is the founder of Crash Course?
Website. Crash Course (sometimes stylized as CrashCourse) is an educational YouTube channel started by John and Hank Green (collectively the Green brothers ), who first achieved notability on the YouTube platform through their VlogBrothers channel. Crash Course was one of the hundred initial channels funded by YouTube's $100 million original ...
What was the video that Hank posted on Crash Course?
However, that April, John detailed that Crash Course was going through financial hardships; in July, Hank uploaded a video titled "A Chat with YouTube", in which he expressed his frustration with the ways YouTube had been changing and controlling its website.
How many subscribers does Crash Course have?
The channel launched a preview on December 2, 2011, and as of January 2021. , it has accumulated over 12 million subscribers and 1.4 billion video views.
When did Crash Course Biology start?
Hank Green's first series, Crash Course Biology, then launched on January 30, 2012, with its first episode covering carbon. A new episode aired on YouTube every Monday until October 22 of that year.
Where was Crash Course Kids filmed?
In addition, Economics was filmed at the YouTube Space in Los Angeles, while Crash Course Kids was filmed in a studio in Toronto, Ontario.
Who is the host of Crash Course Kids?
A second channel, Crash Course Kids, is hosted by Sabrina Cruz and has completed its first series, Science. The first foreign-language course, an Arabic reworking of the original World History series, is hosted by Yasser Abumuailek.
Introduction
- We use Python for Data Science
Python is a perfect language for beginners and experts alike, due to its popularity and clear structure. It’s easy to pick up and thanks to the community you’ll be able to use it both for your first experiments as well as the most recent machine learning research. - Overview of Data Science Crash Course
First of all, I’ll talk about setting up your environment. In our case, that means installing Anaconda on your computer, which will allow you to quickly run Jupyter Notebooks and there you’ll be running short Python programs straight away from your browser. So from now until your first pr…
Anaconda and Jupyter Notebooks
- Anaconda is a free, open-source distribution of both Pythonand R programming languages for data science and machine learning applications. It aims to simplify package management and deployment. Ok, that might sound complicated but the truth is, it’s all about giving you a framework where you can code. You need a compiler to run Python code, think text editor which …
Processing Data
- Let’s finally do something in Python with Data. I’ll review basic techniques for processing data. How can you store information. We’ve already learned we want to represent our data as vectors and matrices.
Getting Data
- Data is crucial for doing Data Science. Often we have to clean it first in order to start using it. Let’s discuss how to do it. If you don’t have any interesting data on your computer, then the best way is to just scrape information from the web. It’s pretty easy with Python with packages like ‘requests’ and ‘BeautifulSoup’ (to clean data). Most websites are easily scraped using requests and it’s all j…
Classification and Supervised Learning
- We have already learned about storing data and where to get data from. Let’s now cover standard techniques for classifying data, which is the basic application of Data Science.
Clustering and Unsupervised Learning
- We learned about supervised learning and what to do when you have a dataset with labels. Let’s now look at datasets with no labels provided and talk about unsupervised learning.
Neural Networks
- We’re going to talk about Neural Networks and how to use them to classify data. It’s going to be a gentle introduction to neural networks as I’m assuming you have never used them.
Dimensionality Reduction
- Let’s talk about how to reduce a number of dimensions in our data, so that it can be visualised and better understood.
Visualisation
- Finally I’m going to discuss how to present a Data Science project so that it’s appealing and instructive to others. In other words, let’s talk about visualisation.
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