Data Science Basics for Absolute Beginners | Data Science Tutorial

Great Learning brings you this live session on “Data Science basics for absolute beginners”. In this session, you will be learning about various aspects of Data Science, such as its significance in the coming years, components that fit into the ecosystem of Data Science and its architecture which denotes its working. You will also get to understand the various Data Science job roles along with the observed salary trends. This session is aimed at anyone willing to start their career in Data Science. Since Data Science is a huge domain, it becomes very important that you understand how you can begin and eventually land a good job. There are multiple ways you can begin with Data Science but there is a good chance that you might get confused when you begin, tending to which the instructor breaks the concepts down into bite-sized pieces along with giving you a sense of direction that you can consider when beginning to learn this fantastic domain.

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How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

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Learn Data Science Tutorial - Full Course for Beginners. DO NOT MISS!!!

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

• Welcome (1.1)
• Demand for Data Science (2.1)
• The Data Science Venn Diagram (2.2)
• The Data Science Pathway (2.3)
• Roles in Data Science (2.4)
• Teams in Data Science (2.5)
• Big Data (3.1)
• Coding (3.2)
• Statistics (3.3)
• Business Intelligence (3.4)
• Do No Harm (4.1)
• Methods Overview (5.1)
• Sourcing Overview (5.2)
• Coding Overview (5.3)
• Math Overview (5.4)
• Statistics Overview (5.5)
• Machine Learning Overview (5.6)
• Interpretability (6.1)
• Actionable Insights (6.2)
• Presentation Graphics (6.3)
• Reproducible Research (6.4)
• Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

• Welcome (1.1)
• Metrics (2.1)
• Accuracy (2.2)
• Social Context of Measurement (2.3)
• Existing Data (3.1)
• APIs (3.2)
• Scraping (3.3)
• New Data (4.1)
• Interviews (4.2)
• Surveys (4.3)
• Card Sorting (4.4)
• Lab Experiments (4.5)
• A/B Testing (4.6)
• Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

• Welcome (1.1)
• Spreadsheets (2.1)
• Tableau Public (2.2)
• SPSS (2.3)
• JASP (2.4)
• Other Software (2.5)
• HTML (3.1)
• XML (3.2)
• JSON (3.3)
• R (4.1)
• Python (4.2)
• SQL (4.3)
• C, C++, & Java (4.4)
• Bash (4.5)
• Regex (5.1)
• Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

• Welcome (1.1)
• Elementary Algebra (2.1)
• Linear Algebra (2.2)
• Systems of Linear Equations (2.3)
• Calculus (2.4)
• Calculus & Optimization (2.5)
• Big O (3.1)
• Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

• Welcome (1.1)
• Exploration Overview (2.1)
• Exploratory Graphics (2.2)
• Exploratory Statistics (2.3)
• Descriptive Statistics (2.4)
• Inferential Statistics (3.1)
• Hypothesis Testing (3.2)
• Estimation (3.3)
• Estimators (4.1)
• Measures of Fit (4.2)
• Feature Selection (4.3)
• Problems in Modeling (4.4)
• Model Validation (4.5)
• DIY (4.6)
• Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

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What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

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