Data science has been a buzzword in the market for quite some time, it has even been labelled the "Best Career of the 21st Century”. Data Science is a multi-disciplinary study, and heavily utilizes scientific methodologies.
For a complete beginner, what is data science?
Data science is an exciting field,there is something for everyone. There is a little bit of math, a bit of coding, visualization, and communication. There are a lot of ways to be a Data Scientist. If you are feeling overwhelmed at all, start with one thing. Pick one type of math or one Python concept and then keep moving up. Little steps every day.
The data portion is developing algorithms that will help us learn about our world in a more structured way. We're essentially looking for patterns. “Data" doesn’t just mean numbers. Data could be a lot of unstructured things like text, images, or audio files.
The science part is how we make sense of data. We found out pretty quickly that you can't put data into black-box algorithms. We need to take a scientific and rigorous approach to design the setup, ask the right questions, and challenge our hypotheses. All of that falls under the scientific approach.
Maths and statistics form the basis of most data science techniques. As a data scientist you will need a good foundational knowledge of these core concepts in order to understand how to perform exploratory data analysis, forecast future events, or apply machine learning and deep learning well. When trying to make sense of data, statistics is an invaluable tool as it wrangles the data in an approachable manner.
For data science, the components are math concepts. We are not playing around with data only for the fun of it.We are solving real problems that a user or customer is facing. We often have to be creative with how we solve those problems to meet an overarching goal. That creativity can be in data sourcing, feature engineering, or how we're going to combine those results. Any of those things require a high level of creativity.
If you're a strong programmer, focus on math first. If you're strong in math, focus on coding
To become a data scientist, you must have a strong understanding of mathematics, statistical reasoning & at least one programming languages especially python . You must understand statistical concepts, how to use key statistical formulas, and how to interpret and communicate statistical results. It include the key concepts of probability distribution, statistical significance, hypothesis testing and regression. Bayesian thinking is also important for machine learning; its key concepts include conditional probability, priors and posteriors, and maximum likelihood.
It’s also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, we’re analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination—charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what’s been presented. This is especially important to note—the “audience” for our output is human.
As a Machine Learning engineer, on the other hand, our final “output” is working software (not the analyses or visualizations that you may have to create along the way), and our “audience” for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves.
Machine learning is the natural progression of artificial intelligence using extensive data. In machine learning, we develop computer programs which automatically learn from the data set available to them and don’t need to be explicitly programmed. An example of Machine learning (ML) would be the kind that Uber or Ola use on the ride sharing apps. The app automatically estimates the cost of our ride, the distance between our locations and also the surge pricing depending on various factors. All these are possible due to machine learning capability of the app with the availability of different data like users in the area, past price trends etc.
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Over the years, data science has become an integral part of many industry like agriculture, marketing optimization, fraud detection and public policy among others.
By using data preparation, statistics, predictive modeling and, data science tries to resolve many issues within individual sectors and the economy at large.
Data science emphasizes the use of general methods without changing its application, irrespective of the domain. This approach is different from traditional statistics tend to focus on providing solutions that are specific to particular sectors or domains.
The traditional methods depend on providing sectors with solutions that tailored to each problem rather than applying the standard solution.
Today, data science has far reaching implications in many fields, both academic and applied research domains like machine translation, speech recognition, digital economy on one hand and fields like healthcare, social science, medical informatics, on the other hand.
The world is going through what is popularly called ‘digital transformation’ – and this is revolutionizing the way we live- the way we communicate, consume, use time, and work.
A lot of what we know today as work is being taken over by progressively intelligent machines.
A career in data science and machine learning makes sure you are also part of the revolution.
Examine recent breakthroughs in subjects like Deep Learning and their applications in domains like Computer Vision and Natural Language Processing to get a sense of AI's potential.
A mix of technologies is enabling machines to make sense of enormous amounts of data and execute cognitive activities, causing a massive disruption in development.
AI is transforming our environment, with far-reaching implications for how we work, live, cooperate, make decisions, and participate as a society.
Artificial intelligence (AI) is a technology that allows systems to encompass cognitive processes as well as adaptive and learning capabilities, resulting in self-improvement.
AI-powered systems can capture and ‘understand' their surroundings, allowing them to make the best decisions in real time to achieve specified goals.
Streams of data on significant human activities have aided AI's rapid advancement. These include, but are not limited to, online communication, social interaction, device usage, searches, content consumption, and IoT data streams.
AI systems use cloud computing and specialised machine learning algorithms to make sense of these massive volumes of complex data. World-scale data centres with massive, labelled data sets are being utilised to train AI algorithms to perform certain cognitive tasks.
Algorithms will soon be able to infer even the implicit setting, such as a children's party, a sporting event, a business meeting, or a random group of individuals in a park.
Digital assistants will grow more intelligent, contextualised, and proactive as time goes on.
AI's widespread deployment will radically alter all sectors.
With the move from owning a car to using on-demand transportation services, consumer habits will be significantly impacted.
AI will aid any industry that requires a large quantity of data processing and content handling.
Financial institutions will automate significant processes regarding transaction validation, fraud identification, stock trading, recommendation, and advisory services.
To improve risk estimations, insurance companies will use the large volumes of data accessible as well as predictive and machine learning technology. As a result, businesses will be able to provide superior products that are tailored to the specific needs of a specific customer.
The adoption of smart, driverless automobiles will also have a huge impact on car insurance firms.
Artificial intelligence (AI) brings new capabilities that alter the traditional product development process, whether for digital or physical items. The AI-powered potential for innovation are growing exponentially as advanced cognitive technologies (cloud-based commercial AI services via easy-to-consume APIs) and low-cost integration scenarios become more widely available.
On top of world-scale digitised information, data, and scientific and general knowledge, AI will drastically improve the overall education system.
Intelligent education agents will capture a student's demands in order to create ideal individualised educational programmes that fit the student's goal, the appropriate level, pace, preferred sorts of content, and other factors.
An AI system will be able to ‘understand' each internet user in terms of interests, daily habits, and future demands, and it will be able to make astonishing calculations and forecasts, ranging from purchasing preferences to the emotional condition of the user.
We will see big changes in the workforce and markets in the next years. Roles and jobs will be rendered obsolete, industries will undergo drastic transformations, and employment patterns and relationships will be re-imagined.