Data science is a “concept to unify statistics, data analysis, machine learning, 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 context of mathematics, statistics, computer science, and information science. Data Science is a multi-disciplinary subject field that uses scientific methods, processes, algorithms, technology, and systems to extract knowledge and insights from both structured and unstructured data.
TABLE OF CONTENTS
Data science is a “concept to unify statistics, data analysis, machine learning, 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 context of mathematics, statistics, computer science, and information science.
Who should study this course? (Data Science)
- Anyone interested in Data Science
- Anyone interested in a Data Scientist career
- Software developers or programmers
BEFORE YOU START
This means that you need to be skilled in maths, programming, and statistics. One way of complying with the prerequisite is to have a resonating academic background. Data scientists usually have a Ph.D. or Master’s Degree in statistics, computer science or engineering
About the Course
Welcome to this course on Data Science. Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles. This course covers the necessary tools and concepts used in the data science industry, including machine learning, statistical inference, working with data at scale and much more. First, we’ll start by showing you the entire process for data science projects and the different roles and skills that are needed. Then you’ll learn the basics of obtaining data through a variety of sources, including web APIs and page scraping. We’ll show you how to use tools like R, Python, the command line, and even spreadsheets to explore and manipulate data. We’ll also take a look at powerful techniques for analyzing data. We’ll be covering a variety of techniques for planning, performing, and presenting your projects to help you get started in data science and making the most of the data that’s all around you.
WHAT YOU’LL LEARN
- Programming with R, Python, and SQL
- Understand roles and careers in data science
- Sourcing data
- Data science in math and statistics
- Data science and machine learning
It empowers professionals with data management technologies like Hadoop, R, Flume, Sqoop, Machine learning, Mahout, etc. The knowledge and expertise of the skills is an added advantage for a better and competitive career.
As stated by the United States of Labor Statistics, the employment of all computer and information research scientists is expected to rise 19 percent by the year 2026, which is deemed much faster than the average for all professions. About 5,400 new jobs are projected over the decade.
Real Career Impact
According to Glassdoor, in 2016 data science was the highest paid field to get into. The demand for data science is very high, while the supply is too low. Think about computer science years ago. The internet was becoming a thing and people were making a lot of money on it. Not only are Data Scientists responsible for business analytics, but they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms. Some of the prominent Data Scientist job titles are Data Scientist. Data Analyst. Fueled by big data and AI, demand for data science skills is growing exponentially, according to job sites. The supply of skilled applicants, however, is growing at a slower pace. It’s a great time to be a data scientist entering the job market. That’s according to recent data from job sites Indeed and Dice.
Statistician Business intelligence reporting professional Data Analyst
Data Mining or Big Data Engineer Program / Project Manager
Basic/Nano Degree Certificate
Individual Certificates for each course
DATA SCIENCE / DATA ANALYSIS
Data Science is a multi-disciplinary subject field that uses scientific methods, processes, algorithms, technology, and systems to extract knowledge and insights from both structured and unstructured data.
At the core of the subject is data. Troves of raw information, streaming in and stored in enterprise data warehouses. There is much to learn by mining it. We can build advanced capabilities with it. Data Science is ultimately about using this data in creative ways to generate business value.
Data Scientists play a central role in developing data product. This involves building out algorithms, as well as testing, refinement and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at a wide scale.
Data Science is a blend of 3 major skill sets:
- Mathematics Expertise
- Technology and Hacking Skills
- Strong Business Acumen
A common personality trait of data scientists is that they are deep thinkers with intense intellectual curiosity. They accept challenges and are passionate about what they do Academic credentials may be helpful. But that alone will not suffice. A Ph.D. statistician may still need to pick up a lot of programming skills and also gain business experience. The real motivation is ‘uncovering’ the truth that has hidden beneath the surface. Data Science is a relatively new and rising discipline where universities are still grappling with the
subject’s structure and training curriculum. Till then it will remain a self-taught subject and Data Scientists autodidacts.
ANALYTICS & MACHINE LEARNING:
How does this tie-up with Data Science? Analytics has become a popular business lingo over recent years. It is loosely used to describe critical thinking that is quantitative in nature. Technically, it is the ‘science of analysis’ – the practice of analyzing information to make a decision.
MACHINE LEARNING is a term closely associated with Data Science. It refers to a broad class of methods that revolve around data modeling to:
- algorithmically make predictions
- algorithmically decipher patterns in data
The wide-ranging breadth of Machine-learning techniques comprises an important part of the Data Science tool-box.
Raw data can be unstructured and messy with information coming from disparate data sources. Data Munging is a term used to describe the data wrangling to bring together data into cohesive views, cleaning and polishing data and keeping it ready for downstream usage.
Most companies that wish to enhance their business by being more data-driven, Data Science provides the secret sauce. Data Scientists are short in supply in the twenty-first-century corporate market even after offering sky-high salary (90k to 100k dollars). Such problem-solvers are in much demand.
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