We are in the information age. Almost everything depends on data and information. Especially businesses keep optimizing the way they handle the enormous amount of data using practices of Data Science and Machine Learning to accelerate their growth and profits.
Data science is a popular field, mostly because of the continuing technology evolution. Machines today can process and storage large amounts of data quickly and efficiently serving the competitive marketing demands.
What is Data Science and Why is Important?
Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. Data science is a “concept to unify statistics, data analysis and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.
So, a Data Scientist can use data to understand things and build a useful model on a particular pattern. Using data platforms or statistics and algorithms, he can collect data, for example on people’s behavior on Internet, that can be useful for later software applications or for businesses to change their marketing strategy and promote a new product based on customer’s needs.
Data today create opportunities for business growth and profit like never before. Advanced data technologies and superior analytics tools has made it possible for businesses to gain numerous benefits from their data assets, in the last decade.
A survey of 300 senior executives in the UK, France and Germany found that 42% of UK companies are looking to recruit data scientists in big data analytics roles. But 58% of the executives asked said they were struggling to find candidates with the right mix of skills. Although technical experience remains of greatest importance, other essential attributes include problem-solving skills (cited by 43%, rising to 55% in Germany), analytical skills (42%) and creativity (35%).
If you are interested in landing a job as a data scientist you have to develop the right combination of skills. The following is an extensive list of Data Science courses and resources that give you the needed skills to become a data scientist:
1) Machine Learning (Stanford University)
About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
2) Introduction to Big Data (University of California, San Diego)
About this course: Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible — increasing the potential for data to transform our world!
3) Neural Networks for Machine Learning (University of Toronto)
About this course: Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
4) Introduction to Data Science in Python (University of Michigan)
About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
5) R Programming (Johns Hopkins University)
About this course: In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
All these above mentioned five courses are very important. I just want this information to join these courses., thank you.