Data Science is all about extracting knowledge from data. Data Science is the integration of methods from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. This interdisciplinary and cross-functional field leads to decisions that move an organization forward in terms of proposed investment, decisions regarding a product or business strategy.

Data Science is a buzzword, often used interchangeably with analytics or big data. At times, Analytics is synonymous with Data Science, but at times it represents something else. A Data Scientist using raw data to build a predictive behaviour model, falls in to the category of analytics.

Data science is a steadily growing discipline that is driving significant changes across industries and in companies of every size. It is emerging as a critical source for insights for enterprises dealing with massive amounts of data.

1.  Introduction to Data Science with R – Data Analysis Part 1

Part 1 in a in-depth hands-on series of videos introducing the viewer to Data Science using R. The video series illustrates the complete Data Mining project lifecycle via Kaggle’s Titanic 101 competition. All source code from videos are available from GitHub at: https://github.com/EasyD/IntroToDataS….

NOTE – The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo.

 

2.  Predictive Modelling Techniques | Data Science With R Tutorial

This lesson will teach you Predictive analytics and Predictive Modelling Techniques.

After completing this lesson you will be able to:
1. Understand regression analysis and types of regression models
2. Know and Build a simple linear regression model
3. Understand and develop a logical regression
4. Learn cluster analysis, types and methods to form clusters
5. Know more series and its components
6. Decompose seasonal time series
7. Understand different exponential smoothing methods
8. Know the advantages and disadvantages of exponential smoothing
9. Understand the concepts of white noise and correlogram
10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc
11. Understand all the analysis techniques with case studies

Regression Analysis:
• Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables.
• It predicts the value of a dependent variable based on one or more independent variables
• Coefficient explains the impact of changes in an independent variable on the dependent variable.
• Widely used in prediction and forecasting

Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-…

 

3.  Data Science Tutorial for Beginners – 1 | What is Data Science? | Data Analytics Tools | Edureka

This Data Science course is designed to provide knowledge and skills to become a successful Data Scientist. The course covers a range of Hadoop, R and Machine Learning Techniques encompassing the complete Data Science study.
Course Objectives

After the completion of the Data science Course at Edureka, you should be able to:

Gain an insight into the ‘Roles’ played by a Data Scientist.
Analyse Big Data using Hadoop and R.
Understand the Data Analysis Life Cycle.
Use tools such as ‘Sqoop’ and ‘Flume’ for acquiring data in Hadoop Cluster.
Acquire data with different file formats like JSON, XML, CSV and Binary.
Learn tools and techniques for sampling and filtering data, and data transformation.
Understand techniques of Natural Language Processing and Text Analysis.
Statistically analyse and explore data using R.
Create predictive using Hadoop Mappers and Reducers.
Understand various Machine Learning Techniques and their implementation these using Apache Mahout.
Gain insight into the visualisation and optimisation of data.

 

4.  “Data Science: Where are We Going?” – Dr. DJ Patil (Strata + Hadoop 2015)

Data Science, where are we going? What impact can we expect? With a special introduction from President Barack Obama.

Dr. DJ Patil has been the VP of Product at RelateIQ and the Data Scientist in Residence at Greylock Partners.  He has held a variety of roles in academia, industry, and government, including Chief Scientist, Chief Security Officer, and Head of Analytics and Data Teams at the LinkedIn Corporation. Additionally he has held a number of roles at Skype, PayPal, and eBay.

 

5.  Data Science – Part I – Building Predictive Analytics Capabilities

This is the first video lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.

 

6.  How To Become A Data Scientist – SF Data Science

Ryan Orban of Zipfian Academy and Dennis O’Brien of Idle Games talking about becoming a data scientist.

www.zipfianacademy.com

 

7.  Data Science – Eindhoven University of Technology

Data Science is a new engineering discipline and the main driver for innovation in the years to come. The data scientist will be the engineer of the future and scientific research and innovations will be data driven.

Data is collected about anything at any time, at any place. The ultimate goal is not to collect more data but to turn it into real value. Data can answer questions that have never been asked before. Visualization of data can really help discovering remarkable patterns that were never noticed before.

 

8.  Gigs: A Day in the Life of a Data Scientist

In this edition of “Gigs,” RCRtv takes a look at a day in the life of a data scientist at the AT&T Foundry in Plano, Texas.

 

9.  Data Scientist vs Data analyst , their Roles and Qualification

 

10.  The Future of Data Science – Data Science @ Stanford

Data science holds the potential to impact our lives and how we work dramatically. Despite its promise, many questions about data science remain. How real is this emerging discipline? What opportunities and challenges does it present? How can Stanford nurture data science in research and education? Watch the video and hear some of Stanford’s thought leaders debate the answers to these questions.