The ancient Silk Road, once the longest overland trade route, ran over 4,000 miles long. Today nestled in Eurasia's heart along the Silk Road is the national railway company – Kazakhsthan Temir Zholy (KTZ). Though traversing across this historic pathway sounds rather cool, the complex geography and harsh weather conditions propose serious challenges in operating cargo and passenger transportation. KTZ is a crucial part of Kazakhstan – the world's ninth-largest country – and its economy. This railway company knew the importance of having a seamless operating model in order to maintain its current operations, both on and off the track.
All You Need Is Covered!! What you'll learn Do you want to know the best ways to clean data and derive useful insights from it? Do you want to save time and easily perform Exploratory Data Analysis(EDA)? Then this course is for you!! According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organising the data..." In this course, you will not just get to know the industry level strategies but also I will practically demonstrate them for better understanding. This course aims to help beginners, as well as an intermediate data analyst, students, business analyst, data science, and machine learning enthusiasts, master the foundations of confidently working with data in the real world.
Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than every, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand – mostly because of what they can achieve with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three. While there are other technology stacks for AI-based projects, Python has turned out to be the best programming language for AI.
Algorithms are the heartbeat of applications, but they may not be perceived as entirely benign by their intended beneficiaries. Most educated people know that an algorithm is simply any stepwise computational procedure. Most computer programs are algorithms of one sort of another. Embedded in operational applications, algorithms make decisions, take actions, and deliver results continuously, reliably, and invisibly. But on the odd occasion that an algorithm stings -- encroaching on customer privacy, refusing them a home loan, or perhaps targeting them with a barrage of objectionable solicitation -- stakeholders' understandable reaction may be to swat back in anger, and possibly with legal action.
When COVID-19 hit, organizations using traditional analytics techniques that rely heavily on large amounts of historical data realized one important thing: Many of these models are no longer relevant. Essentially, the pandemic changed everything, rendering a lot of data useless. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on "big" data to a class of analytics that requires less, or "small" and more varied. Transitioning from big data to small and wide data is one of the Gartner top data and analytics trends for 2021. These trends represent business, market and technology dynamics that data and analytics leaders cannot afford to ignore.
Today, companies across society are applying AI to optimize internal processes to improve the quality and performance of their existing products, to design new products and/or to further optimize the workforce. AI has proven to be critical for managing and predicting operations of a telecommunication network. However, most of the time, AI is restricted to data scientists and data analysts who are specialists specifically trained in AI. At the same time, it's the subject matter expert, i.e., experienced engineers and technicians who have the expert knowledge in a specific business or technical area. They generally also own the data. One way of bringing AI closer to the subject matter expert (SME) is by democratizing AI.
Apache Spark Streaming – Every company produces several million pieces of data every day. Properly analyzed, this information can be used to derive valuable business strategies and increase productivity. Until now, this data was consumed and stored in a persistent. Even today, this is an important step in order to be able to perform analyses on historical data at a later date. Often, however, analysis results are desired in real time.
At their core, data scientists have a math and statistics background. Out of this math background, they're creating advanced analytics. Just like their software engineering counterparts, data scientists will have to interact with the business side. This includes understanding the domain enough to make insights. Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. Finally, their results need to be given to the business in an understandable fashion. This requires the ability to verbally and visually communicate complex results and observations in a way that the business can understand and act on them. Thus, it'll be extremely valuable for any aspiring data scientists to learn data mining -- the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. Here is a simple list of reasons on why you should study data mining? There is a heavy demand for deep analytical talent at the moment in the tech industry. You can gain a valuable skill if you want to jump into Data Science / Big Data / Predictive Analytics. Given lots of data, you'll be able to discover patterns and models that are valid, useful, unexpected, and understandable. Use some variables to predict unknown or future values of other variables (Predictive). You can activate your knowledge in CS theory, Machine Learning, and Databases. Last but not least, you'll learn a lot about algorithms, computing architectures, data scalability, and automation for handling massive datasets.
There's no denying the fact that we live in a period where technology has inevitably become less counterfeit but rather more intelligent. Regardless of whether we talk about AI applications or the uses of its subsets specifically machine learning and deep learning, the scope is huge on what people could have or can envision. Given that, would it be bizarre to realize that AI applications have outperformed our customary lives and are currently taking control over space (Indian moon mission – Chandrayaan-2, for example)? Expanding the levels of automation and autonomy utilizing strategies from artificial intelligence takes into account a more extensive variety of space missions and furthermore frees people to zero in on tasks for which they are more qualified. At times, autonomy and automation are crucial to the success of the mission.
With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.