Data science is a new kind of field which has a great significance in your life and has a role in making a decision of your own choice. Most of the well- established companies are adopting data science and making it a cornerstone of their success. As the name suggests this field deals with data. Data here refers to the grouped information of people’s choice, need, liking etc. It’s the work of the data scientist to make that messy data useful and productive.
Making of a data scientist
Data scientist is a combination computer engineer and statistician. Extraction of data needs great knowledge of computing and for handling them, we need Statistics and Probability. In this field knowledge is guided by logic and your multiple personal capability. All these traits and features makes it a dream job.
Role of data science in industries
They play a major role in making the company’s economy. It gives direction to the company’s next move or product. It makes the strategy more effective and productive, leading to better outcomes. As the internet world is developing with great rates, the demand for data scientists will increase.
Entering into the world of data science these are the things you should know
There are many online courses available on the internet, but the best ones are premium. Before entering in those courses, you should consider the following advice
Be prepared before entering the courses. You should not go for data science courses with an empty head, as it will be just a waste of time. You should carry basic knowledge of statistics and computer science.
Choosing the best course. There are many courses on the internet, but picking the best one saves money and time. If you want to make the course more effective you should choose the one which provides you with regular tasks.
Making the course more productive. While taking the course you should keep testing your knowledge by molding your life into that of a data scientist, and start thinking like a data scientist and solve the problems of data science. You could read articles and news related to data science. You could search for recent issues of data science.
Learning computer science doesn’t mean the basics, it involves learning networks and the most important coding or programming. Before involving yourself in data science courses you should learn computer language and become a programming master. Language like Python and R will make your course successful. If you are insanely good with these things, then you deserve to be a data scientist.
Data science course is the future of the IT world, as the new emerging technology is based on data science. That technology is A.I (Artificial Intelligence). This the most broad and wide technology ever. It has many subparts, but the major one is Machine Learning, Data Analysis, Data Mining. A.I will be shaping the future of the world, so involving is a great moral move.
Making your work easier, I find that 360DigiTMG (a course provider) is a great match for a perfect data science course. 360DigiTMG provides you with the most precise and perfect course. It provides you with great courses in the field of data science with certification.
It takes a high level of data analysis to predict the effects of climate change and the implications of our actions to stop and adapt to it. Often, scientists have terabytes of data, but not the computing power to make sense of climate issues like hurricanes. But this level of analysis is possible with artificial intelligence (AI). In fact, AI may be the best weapon we have to combat and adapt to the effects of climate change. That’s because it can analyze large chunks of data from past events and make accurate predictions about future ones.
Today, AI is helping to monitor and predict everything from glacier retreat to commercial waste management. As innovations in “deep learning” march on, AI’s prescience will help inform scientists about climate impacts and policymakers on the most prudent steps for adaptation. Here are some critical ways AI is helping to preserve our planet.
Smarter Home Energy Use
AI is helping save the planet by assisting homeowners through energy-efficient smart homes. The Internet of Things and today’s “smart devices” let homeowners control their energy use and lower their monthly bills. Smart thermostats can adjust temperature settings for specific rooms in a house. Smart water sprinklers can change water usage based on weather forecasts. And smart security systems can cut down on false alarms calls — so fewer gas-guzzling trips by first responders. The automation, connection, and prediction power built into these smart devices allow homeowners to lower their carbon footprint.
But smart energy use is not just about conservation — it’s also about the best time to use energy. Peak energy hours like evenings are higher-demand, higher-cost times. Smart devices can automate energy use for low-demand hours. Plus, off-peak times like mid-day are when alternative energy sources like solar and wind contribute the most. Therefore, smart technology promotes renewable energy.
Soil degradation is a problem often overlooked in the media. But it has serious consequences for humanity’s ability to adapt to and survive climate change. It takes a millennium to generate only three centimeters of topsoil, and soil degradation is happening at a much faster rate. Chemicals, deforestation, erosion, and global warming are major contributors to soil degradation. And if the current rate of degradation continues, the planet’s farmable land could disappear within 60 years, according to United Nations officials.
But farmers and scientists are using AI to help conserve the soil by marshaling complex algorithms along with robots and drones to detect erosion and monitor soil health. For example, one company has developed an agricultural app to help farmers identify nutrient deficiencies within their soil. And farmers are using machine learning to predict the best times to plant, irrigate, and harvest crops based on weather changes. Accurate predictions mean less need for pesticides and fertilizers, which degrade the soil.
Exploring and Protecting Oceans
Scientists watch and test the health of oceans because they’re the best indicators of Earth’s health. Microplastics, increased CO2 levels, and ocean acidification are changing the surface of the planet. The key to protecting oceans is exploring and monitoring them for changes. Climate scientists and oceanographers are using AI technology to drive autonomous marine vehicles to the deepest depths. And some companies are developing autonomous garbage collection systems that would help remove plastics and floating debris.
Another emerging technology — blockchain — is helping to track fishing and identify illegal behavior. Blockchain is the same technology that powers cryptocurrencies like Bitcoin. The technology acts as a transparent ledger for transactions. Blockchain is a decentralized system, which means it operates autonomously and isn’t subject to misuse and abuse. Trust is critical to international treaties that regulate fishing quotas and manage overfishing. Blockchain technology can record each fish (e.g., tuna) with a scannable code uploaded to the ledger. Therefore, retailers, customers, and regulators can confirm that fish are legally caught.
Air Pollution Detection
AI is becoming an invaluable tool for tracking our air quality and identifying sources of pollution. During accidental emissions, city air quality officials need to identify and respond quickly. Some European cities are using leak sensors and AI to help create emission maps, predict mortality rates, and estimate financial costs of emergency responses. These data points give decision makers a more accurate view of the air pollution along with more targeted remediation.
In addition to monitoring air pollution, AI is also cutting tailpipe emissions. AI manages self-driving cars to make getting from point A-to-B more efficient. Self-driving automobiles can cut oil consumption and greenhouse gas emissions by 2% to 4% annually. AI and global positioning systems operating driverless tractor-trailer rigs will make deliveries non-stop, faster, and less costly to the planet. Complex algorithms, sensors, and traffic lights are directing traffic flow in some cities. These systems are currently reducing travel time by 25%, braking by 30%, and idling time by 40%.
Evaluating the Efficacy of Action
AI is bringing powerful ways to monitor and predict threats to our environment. Synthetic thinking adds value for scientists, officials, and policymakers by giving them deeper looks into current environmental situations. Perhaps, more than anything, AI’s biggest potential lies in figuring out where solutions hit the mark and where they miss. It’s counterproductive to invest resources and time into bad solutions. But that’s highly likely, given the complexity of climate change and adaptation.
Where do we invest? Which coastline needs saving the most? What communities are at a higher risk? With dwindling resources and bigger dangers, we will face some hard decisions in the future about where to deploy our efforts. At some point, those decisions will mean life or death. We will need quick thinking and accurate data. Evaluating our options and predicting their implications is where AI will bring the most value.
Data is transpiring today in large quantities because of the modern equipment, so what is happening with the old data? Why is the old data so important? What can the old data tell us? Globalization is the means of connecting and desegregating people, businesses, and government authorities all across the globe. Owing to the internet and social media the world has become complicated with the overload of information that means more and more piles of data are added to the large volumes of already existing data.
New technologies continue to present a new amount of uncertainties and possibilities for the world. The possibility of billions of people connected by the internet and their mobile devices, with an exceptional path to knowledge is infinite. These possibilities are aggregated by developing technology inventions in fields such as artificial intelligence, business analytics, data analytics, data science, robotics, autonomous transportation, 3-D printing, nanotechnology, and quantum computing.
Every time we use our search engines or our social media platforms to interact or to share pictures digitally, we add more to the pile of data which already exists in a large volume. As the amount of data generation grew, the need to monitor or collect or prepare this data also grew. These became some of the main difficulties and concerns for enterprise industries until the year 2010. Since there’s been a lot of hype about data storing and processing, a new door was opened to a broad field which came into existence as “Data Science”.
What is Data Science?
Data science does not mean the invention of complicated models or making awesome visualizations or writing data codes. Data Science means the usage of data to generate as much influence as plausible for an organization. This influence can be created in the form of multiple things – it could be in the form of insights, in the form of data merchandise, or the form of the product references for an organization. To perform such tasks, you require complicated models, data visualizations or code generators.
Before data science, the term data mining was quite popular. It was then defined as the overall method of identifying valuable information from data. Data science was brought to another level in the 2000s, it was done by combining computer science with data mining. This led to the birth of social media platforms and websites, which additionally contributed to Big data. Technologies and tools like MapReduce, Hadoop, and Spark were launched to extract valuable information from big data. https://diggitymarketing.com/white-hat-seo-techniques/ has a comprehensive guide on white hat SEO techniques with a focus on structured data.
So the rise in big data spiked the rise in data science to support the requirements of businesses to extract insights from their huge unorganized data sets. By this time, the journal of data science defined data as everything that is related to gathering, analyzing and modeling all types of applications. With the abundance of data and the birth of data science, it became possible to instruct machines with a data-driven approach. Let us look at various positions that are required to be filled in the field of administration, processing, and fulfilling of big data science. These positions include:
Data Engineers – these are the people who build and manage data gathering processes. They should be well aware of things such as Aforge.net, Java, and Scikit-learn.
Software Engineers – these are the people who understand what kind of data requires to be examined and develop the software to suit the industry. They should be well aware of things such as Java, SQL, and Python.
Artificial Intelligence Hardware Specialists – these are the people who understand the AI and what knowledge should be searched for in the pile of data sets. They should be well aware of things like Python, Java, and machine learning.
Role of a Data Scientist
Typically, as a data scientist, it’s your job to solve real company issues by gathering data. It doesn’t matter to the organization which kind of tools you use to complete your tasks. Their main goal is to understand their customers well and to deliver excellent results. Being a good data scientist doesn’t mean how advanced your tools are, it means the amount of impact that you can produce with your work.
A data scientist is not a data cruncher, he/she is a problem solver and strategist who are hired by companies to solve their most enigmatic and troublesome problems. They are expected to guide the company in the right direction.
The Need for Data Science
The first question that comes to the mind is what is the need to collect so much raw data and then extract information from it. So, let us learn why we need Data Science and why it is important. There is a cumbersome burst of raw data happening around us and all of it is not inappropriate. We can organize this raw data to make decision making easier. Businesses can use this raw data to derive meaningful insights. It can help them to know their customers better and to enhance their overall performance in the market. As the saying goes “Data is to products what electricity is to gadgets.”
Data Science VS Big Data VS Data Analytics
Data Science means using various tools and algorithms to extract valuable information from raw data. This raw data is drawn from different channels and platforms like cell phones, search engines, surveys, e-commerce sites, and social media. This field is related to the cleansing of data and then preparing and analyzing it. Data science is used in industries like internet searches, digital advertisements and search recommendations.
A huge amount of data is available to the world in the form of structured and unstructured sets, which is termed as Big Data. It means analyzing large sets of extreme data computationally to understand the patterns and trends of today’s civilization. This helps organizations to extract essential information from their data to discover and develop, which makes data a valuable asset for organizations. This helps in understanding human behavior which in turn helps businesses to understand their customers better. Big Data is applied in industries with financial services, communication and retail services or outlets.
Data Analytics courses is the process of revealing hidden patterns and associations, latest business trends, customer preferences, and structured and unstructured data forms. Data analytics can be utilized in healthcare, travel and gaming industries. It helps in boosting the complete performance of the business by refining the financial processes. This increases the visibility of a brand and provides insights into it.
Data Science Courses and Eligibility
More and more companies are now realizing the importance of having an expert on board to analyze their company’s data. So, what are the requirements to become a data scientist? A career in data science requires an understanding of mathematics and statistics. There are many ways to become a data scientist. The most common way is doing a bachelor’s degree in IT, Computer Science, Math, Physics or another related field and then applying for a Data Science certification program.
If you want to specialize in a particular branch or domain then you can also opt for Data Science online training programs. Students gain practical skills and are given hands-on experiences with the help of assignments and programs. Students in data science courses are taught how to collect, interpret and analyze the data, which could help organizations all over the world to make intelligent decision making. They should have effective communication skills as well as high-level analytical thinking.
Why you should become a Data Scientist?
According to studies, there will be a requirement of more than 2.7 million data scientist job openings by the year 2020. In today’s job market, there is a lot of excitement and hype around the role of data scientists and why shouldn’t it be? It is a profitable job that ensures your security in terms of money. Apart from its economic benefits, it is also a discipline that affects our everyday lives in some way or the other.
Data science plays a unique role in improving the lives of people. It has the ability to set a course of an entire business just by analyzing the data that the company has been producing for years. It helps in detecting frauds in the banking sector by analyzing the data of financial institutions. The objective of data science is based more on scientific analysis rather than the notions and experience of just one individual.
Believe it or not, Data Science also helps in solving social problems. It will help you to bring a positive revolution in the country. Big companies like Google, LinkedIn and Amazon to small retail stores – all are looking for experts to analyze their raw data to useful information. The job of a Data Scientist has grown into the most demanding and flexible profession now.
Despite a growing interest in the field of data science, there is an acute shortage of skilled professionals with good professional skills. Organizations are in dire need of data scientists who can use the collected data to choose the most intelligent business path, dynamic processes, advanced product marketing, a better understanding of the opponent and all this turns into satisfying customer assistance.
Machine learning, as well as its endgame, artificial intelligence, is proving its value in a wide variety of industries. Renewable energy is yet another sector that can benefit from machine learning’s smart data analysis, pattern recognition and other abilities. Here’s a look at why the two are a perfect match.
Predicting and Fine-Tuning Energy Production
One of the biggest misconceptions about solar power is that it’s only realistic in parts of the world known for year-round heat and intense sunshine. According to Google, around 80% of rooftops they’ve analyzed through their Sunroof mapping system “are technically viable for solar.” They define “viable” as having “enough unshaded area for solar panels.”
Even with this widespread viability, it’s useful to be able to predict and model the energy yield of a renewable energy project before work begins. This is where machine learning enters the equation.
Based on the season and time of day, machine learning can produce realistic and useful predictions for when a residence or building will be able to generate power and when it will have to draw power from the grid. This may prove even more useful over time as a budgeting tool as accuracy improves further. IBM says their forecasting system, powered by deep learning, can predict solar and wind yield up to 30 days in advance.
Machine learning also helps in the creation of solar installations with physical tracking systems, which intelligently follow the sun and angle the solar panels in order to maximize the amount of power they generate throughout the day.
Balancing the Smart Energy Grid
Predicting production is the first step in realizing other advantages of machine learning in clean energy. Next comes the construction of smart grids. A smart grid is a power delivery network that:
Is fully automated and requires little human intervention over time
Monitors the energy generation of every node and the flow of power to each client
Provides two-way energy and data mobility between energy producers and clients
A smart grid isn’t a “nice to have” — it’s necessary. The “traditional” approach to building energy grids doesn’t take into account the diversification of modern energy generation sources, including geothermal, wind, solar and hydroelectric. Tomorrow’s electric grid will feature thousands and millions of individual energy-generating nodes like solar-equipped homes and buildings. It will also, at least for a while, contain coal and natural gas power plants and homes powered by heating oil.
Machine learning provides an “intelligence” to sit at the heart of this diversified energy grid to balance supply and demand. In a smart grid, each energy producer and client is a node in the network, and each one produces a wealth of data that can help the entire system work together more harmoniously.
Together with energy yield predictions, machine learning can determine:
Where energy is needed most and where it is not
Where supply is booming and where it’s likely to fall short
Where blackouts are happening and where they are likely
When to supplement supplies by activating additional energy-generating infrastructure
Putting machine learning in the mix can also yield insights and actionable takeaways based on a client’s energy usage. Advanced metering tools help pinpoint which processes or appliances are drawing more power than they should. This helps energy clients make equipment upgrades and other changes to improve their own energy efficiency and further balance demand across the grid.
Automating Commercial and Residential Systems
The ability to re-balance the energy grid and respond more quickly to blackouts cannot be undersold. But machine learning is an ideal companion to renewable energy on the individual level as well. Machine learning is the underlying technology behind smart thermostats and automated climate control and lighting systems.
Achieving a sustainable future means we have to electrify everything and cut the fossil fuels cord once and for all. Electrifying everything means we need to make renewable energy products more accessible. More accessible renewable energy products means we need to make commercial and residential locations more energy-efficient than ever.
Machine learning gives us thermostats, lighting, and other products that learn from user preferences and patterns and fine-tune their own operation automatically. Smart home and automation products like these might seem like gimmicks at first, but they’re actually an incredibly important part of our renewable future. They help ensure we’re not burning through our generated power, renewable or otherwise, when we don’t need to be.
To summarize all this, machine learning offers a way to analyze and draw actionable conclusions from energy sector data. It brings other gifts, too. Inspections powered by machine learning are substantially more accurate than inspections performed by hand, which is critical for timely maintenance and avoiding downtime at power-generating facilities.
Machine learning also helps us predict and identify factors that could result in blackouts and respond more quickly (and with pinpoint accuracy) to storm damage.
Given that the demand for energy is only expected to rise across the globe in the coming years, now is an ideal time to use every tool at our disposal to make our energy grids more resilient, productive and cost-effective. Machine learning provides the means to do it.
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