Data science is a collective pool of various algorithms, tools, machine learning principles that work in unison to extract hidden patterns from raw data. It requires a diverse set of skills and demands knowledge from aspects of mathematics, science, communication, and business. Honing a diverse skill set, data scientists gain the ability to analyze numbers and influence decisions.

The core objective of data scientists lay in bridging the gap between numbers and actions by using…

AI for People and Business is an AI book and AI strategy framework. Here's the official book trailer where I explain important topics that are covered in the book, as well as the beneficial aspects of reading it and the importance of developing a strong AI strategy.

FREE CHAPTER DOWNLOAD -…

The amount of data created globally, to date, reached an overwhelming, 59 zettabytes, in 2020, with 90% of the world’s data being produced in the last two years alone. We are generating 2.5 quintillion bytes of data each day in the present era.

Amount of data produced worldwide…

That data analytics is big is not in dispute. Global revenues for big data software and services are expected to grow from USD 42 billion in 2018 to USD 103 billion in 2027, at a CAGR of 10.48%. And in the United States itself, by 2020, about 2.7 million job postings for data analytics and data science are expected. 

Data inflows into companies are…

SAS Tutorials on 9to5sas are designed for SAS programmers, Data Scientist, Data Analyst, and all the readers who want to learn SAS and need to transform raw data to produce insights for business decisions using SAS.

Why SAS?

Being SAS Professional I can tell you why SAS. I would say, its bit tiny subject however very Unique, competitive and leading within the analytics world. SAS is the main statistic tool certified by the FDA,…

Often touted as one of the top jobs of the 21^st century, a data scientist is an increasingly common requirement across firms of different sizes and from a variety of verticals. This comes from the fact that: 

• Massive amounts of data are flowing into most companies

• This data is looked at as potentially a great source of valuable strategic insights

In the course of a data science career, a data scientist handles a number of…

This the second part of Reinforcement Learning (Q-learning). If you would like to understand the RL, Q-learning, and key terms please read Part 1.

In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article.

Import libraries

We are coding the algorithms using the R base package…

Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers?

A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like…

Over the years I’ve often been asked by beginners where they should start in statistics, what they should do first, and which parts of statistics they should prioritise to get them to where they want to be (which is usually a higher…

Data science, by definition, is an interdisciplinary field which makes use of scientific processes, methods, systems, and algorithms for extracting insights and knowledge from multiple structured and unstructured data.…

Most of the big organizations are struggling with AI transformation. Data science projects are either taking too long to complete or would never get into production. 

Among various reasons, the most common is the lack of a stable data science team. Due to high demand, the turnover rate is very high in data science, unfortunately. Data science managers or leaders typically go around this problem by focusing on the following:

• acquire best data science talent
• retain…

You’re a data scientist, but you didn’t end up in the data science industry.

Are you worried about not making it?

Fret not, we’ve got multiple career options molded perfectly for you.

Ever since data science and big data analytics became the buzzwords in the industry, career options in these fields never went amiss. Job roles in both the data science and the big data analytics industry continue to reign as some of the most coveted jobs a tech professional could…

Did you ever wonder what’s a typical day of a data scientist like? A data scientist needs to explore data given to us and provide actionable insights, but how do we do that and is that all we do? Do we just sit in-front of a computer and code all day? Do we spend most of our day reading papers? Or is it something completely different? Let me walk you…

Data scientist was coined the sexiest job of the 21st century and with good reason. In LinkedIn 2020 Emerging Jobs Reports, Artificial intelligence was named the ‘Jobs of Tomorrow’ due to its strong presence. Furthermore, the potential application of data science in multiple industries has attracted people from all…

Job interviews make everyone nervous. But this is what they are designed to do. It is the most common medium to assess a candidate’s presence of mind and his/her ability to remain calm and composed in a tense situation.  In order to ace the interview, you need to have in-depth knowledge of the role you are interviewing for and what is expected. Presence of mind and strong subject knowledge assumes added significance when you are preparing for a Data…

In the past few years, Big Data has emerged as a prospective game-changer for organizations of all sizes and across all industries. Possible – and actual – use cases include better customer targeting, improved employee hiring and benefits, better ad targeting, and reduced overall costs.

This growth in demand for Big Data has meant that a number of people now want to either get into or grow further in the data science industry. One of the most popular routes…

Machine learning (ML) is an application of Artificial Intelligence (AI) that provides the system with the ability to automatically learn and improve from experience rather than explicit programming. This is possible because today a large amount of data is available which lets machines to be trained rather than programmed. It is…

Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become.  Of course there are also some interesting winner and loser stories.

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020.  The really big news is how many excellent choices are now available.  In a remarkable move, the whole field of…

Many traditional businesses hate to fail. They embrace concepts such as Fail Fast in public, but deep inside their hearts, they are scared to fail. Failure is what gives corporate employees sleepless nights. In fact, many corporate initiatives fail, such as data science projects, for example. Why is failure so unloved, and what can we do about it? We have to change the way we think about failure.



Failure is not an event, it's a process.

This distinction is…