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Machine learning & the need for it à

Machine learning is a sub field of Artificial Intelligence, in which a computer system is fed with algorithms that are designed to analyze & interpret different types of data on their own. These learning algorithms obtain the analyzing ability when they are trained for the same using sample data.

It comes in handy when the amount of data to be analyzed is very large & out of human limits. It can be used to arrive at important conclusions & make important decisions.

Some important fields where it is being implemented:

1. Cancer treatment-

Chemotherapy, which is used in killing cancerous cells poses the danger of killing even the healthy cells in the human body. An effective alternative to chemotherapy is radiotherapy which makes use of machine learning algorithms to make the right distinction between cells.

1. Robotic surgery-

Using this technology, risk free operations can be performed in parts of the human body where the spaces are narrow & the risk of a doctor messing up the surgery is high. Robotic surgery is trained using machine learning algorithms.

1. Finance-

It is used to detect fraudulent bank transactions within seconds for which a human would take hours to realize.

The utility of Machine learning is endless & can be used in multiple fields.

What does one learn in Machine Learning?

1. Supervised algorithms-

Supervised learning is the type of learning in which input & output is known, & you write an algorithm to learn the mapping process or relation between them.

Most algorithms are based on supervised learning.

1. Unsupervised algorithms-

In unsupervised learning, the output is unknown & the algorithms must be written in a way that makes them self-sufficient in determining the structure & distribution of data.

Prerequisites

Computer science students & other students with an engineering background find it easier to learn Machine learning. However, anybody with good or at least a basic knowledge in the following domains can master the subject at beginner level: –

1. Fundamentals of programming-

Fundamentals of programming include a good grip of basic programming, data structures & its algorithms.

1. Probability & statistics-

Key probability topics like axioms & rules, Baye’s theorem, regression etc. must be known.

Knowledge on statistical topics like mean, median, mode, variance, & distributions like normal, Poisson, binomial etc. is required.

1. Linear Algebra-

Linear algebra is the representation of linear expressions in the form of matrices & vector spaces. For this, one must be well informed about topics like matrices, complex numbers & polynomial equations.

NOTE: These prerequisites are for beginners.

Job prospects in Machine learning à

Owing to its limitless applications & use in modern & improvised technology, demand for its professionals is increasing day by day, & it would never ever go out of trend.

A professional can find jobs in the following fields: –

• Machine learning engineer
• Data engineer
• Data analyst
• Data scientist

If you are looking for data science online training in Hyderabad, then ExcelR data is the right place for you. Having given world-class training to hundreds of successful beginners & professionals, & having established 3 branches in the city, our training program in the end will surely make you leave enlightened on the subject.

Rapid advances in data collection and storage have enabled many organizations to accumulate vast amounts of data. Traditional analysis tools and techniques cannot be used because of the large sets. Data Science is a blend of traditional data analysis methods with sophisticated algorithms for processing huge amount of sets. It has also made a way to discovering new types of data.

Let’s look at some well-known applications for data analysis-

• Business: when we are doing any business, we need to be sure about the point-of-sale of our products reaching customers. To be specific, consider bar code scanners and smart card technologies, that we use in today’s world, have allowed retailers to estimate the data about the customer’s purchases at the counters. Retailers use this information, along with other business and customer service records, to build a better understanding of the needs of the customers and improve their businesses.
• Medicine, science and engineering: Researchers in this field are rapidly extracting data that is key to further discoveries. For example, satellites in space send us data about whatever is happening in today’s world. Data that the satellite provides ranges from multiple terabytes to petabytes, which is definitely a huge amount.

We have seen some basic applications of data science, now let’s turn our focus towards the challenges-

• Scalability: The advances in data generation and collection – sets with sizes of gigabytes, terabytes, or even petabytes – are becoming common. If some algorithm could handle such massive amount, we can make an algorithm in such a way that we can divide one huge block into several small blocks. This method is known as scalability. Scalability ensures ease of access to individual records in an efficient manner.
• High Dimensionality: Nowadays, handling sets with hundreds and thousands of attributes are common. In bioinformatics, the ICU analysis produces a huge dimension of measurements and many features to track the human health. Also, for some analysis algorithms, the computational complexity increases as dimensionality increases.
• Heterogeneous and complex data: traditional data analysis often deals with sets having attributes of the same type. Now, as data is booming in many industries, data has become heterogeneous and complex.
• Non-Traditional Analysis: Current data analysis tasks often require the valuation of thousands of hypotheses and the development of some of these techniques has been motivated by the desire to automate the process of hypothesis evaluation.

As we know the data is interrelated, making use of attributes, we can distribute it into categories:

1. Distinctness: Equal and not equal
2. Order: <, >, <=, >=
3. Addition: + and-
4. Multiplication: * and /

As we can observe, there are so many areas that are in need of data scientists, it becomes very important to learn and build a career in such an emerging field. The future jobs depend on data science to a maximum extent; in the field of science, commerce, engineering etc.

Ever wondered how Google, Yahoo, and other search engines pull out information from the millions of websites online? How advertising companies always know what’s on your shopping list and target you with the perfect ads that will sway make you click? Or how websites that let you compare prices of products or hotels so that you can get the best deals, get all the information? The answer is quite simple, data analysis.

What is data analysis?

Data science is an umbrella term for research, collection, sorting and presenting data in a pleasing and more understandable manner. Algorithms search for defined patterns within given parameters and provide available data in a more relevant and understandable form. This data is later compiled and structured into a simpler form that suits research. As we move into a digital era where numbers are necessary to prove one’s point, the necessity of data science has been increasing.

What does a data scientist do?

A data scientist is tasked with the job of filtering through piles of information by utilizing algorithms more sorted for usage. Patterns are found within this data to provide a correlation for easier representation. They can sometimes be essential to trap and verify errors in the information that they have acquired while figuring out algorithms that help in storage of this information along with big data. They are required to be adept with software, statistics and need to have the trait of persistence. Their final step is to present all the data, with the help of the patterns found, in a more meaningful manner to make sense to the layman.

What do you need to be a data scientist?

Apart from your technical knowledge, a data scientist must be curious and thirsty to know more. There is so much data that a data scientist had to go through that it often helps if they are the kind of individual who wants to know more.

They need to have great organizational skills that would help them in sorting and arranging data for further use.

Scavenging for data and patterns that may make sense in any way can be tiresome and an exhausting job. Being a bit stubborn might do the trick and help through the stage of boredom, that may later lead to the final wonderful moment.

How to become a data scientist?

As the importance of the job increases, so does the complexity. A data scientist requires adept knowledge in maths, statistics, business and software. Few essential languages are SQL and python. Renowned training institutes that teach data science and the essentials like math, stats and coding are springing up everywhere. They also assist in placements with firms post the training period. Institutes provide adequate education in the field while keeping the classes open minded and free for enquiry.

What are the job opportunities?

Post your course, you can apply for the post of statistician, business intelligence reporting, data analysis, data mining or a big data engineer, program or a project manager. Some prospects may lead you to other countries with salaries as big as 110,000 USD a year.

What is Data science?
In this information age, a lot of data about everyone and everything is generated. The companies are developing upon this big data to analyze the needs of the customer and deliver to them what they want. It is a mixture of algorithms, technology and inference.

Job Prospects after the course:
Several courses are offered in this field. A person is eligible to pursue any career within this field. However, one can specifically choose the data science segment. It can be:

• Data Analyst: The person who analyses the already collected data and derives useful insights that help the companies in further processing the data and streamlining it.
• Data Architect: The one who builds the data. It generally includes the collection of data and putting into the database for further analysis.
• Data engineer: The one who looks at the data and thinks of ways in which it can be presented in an even better way. Developing on the mechanisms and new algorithms is what they are good at. Development of new languages is their work.
• Statistician: A person who draws insights with the help of developed mathematical and statistical ways and predicts the situations that might occur.

Eligibility criteria for the course:
As such, no specifically laid qualifications are listed for this course. But prior knowledge of computer languages like JAVA, Python, C++ etc. would aid in learning. Any person having a graduate degree can go for the course and get the certificate of completion after completing the course.

However, anyone who is interested in pursuing the course can do so. There are no restrictions for those who want to pursue the course.

Future of Data science:
Data science is seen as the most revolutionary and futuristic field that is promising not only in the aspects of providing jobs to the youth but also growing prospects in the field. The speed of growth in the field is really good. Websites like Glassdoor represent a true and fair view of the compensation that companies provide and the opportunities that a person can get.

With the advent of Artificial intelligence, the importance of big data analytics is going to grow as the machines would not be able to draw inferences and that is something that would stay with the humans to decide. Therefore, the field is considered to be a bright one.

Training Institutes:
Many training centers are focusing on providing quality and imparting expertise, knowledge with the help of learned individuals in this field and anyone who is interested in following their career in this field could do so by easily enrolling themselves with the course. The fee on an average is quite affordable for the students. There are some training institutes that even provide job assistance and could help you land a job easily provided you show the traits of a good data science expert. So, what are you waiting for? If this is the field that interests you and number crunching and being analytical is your forte, then this is the field for you!

Being a Data Scientist is a position of great esteem. It is held in high regards, the sky-high pay is also one of the reasons that makes it so in demand. However, there is a scarcity in the number of data scientists available in the nation. If you are planning to make a career out of Data Science, then read on.

Starting with the fundamentals, one has to have the knowledge of Algebraic functions and matrices. Along with this, relational algebra, binary tree and hash functions are to be learned. Other topics are inclusive of Business Intelligence vs. Reporting vs. Analytics. Extract Trans form Load (ETL) is also included in the fundamentals category.

Then comes statistics, this includes the Bayes theorem, probability theorem, outliers and percentiles, exploratory analysis of the data, random variables and CDF (Cumulative Distribution Function), and skewness. Other fundamentals of statistics are also included here.

In case of Programming, the essential languages to be learned are ‘Python’ and ‘R’.

For Machine Learning, one should possess the understanding of concepts such as unsupervised learning, supervised learning and reinforcement learning. Under the algorithms of unsupervised and supervised learning, one should understand clustering, random forest, logistic regression, linear regression, decision tree and K nearest neighbour.

When it comes to Data Visualization, one should have a hands-on knowledge about the visualization tools such as Google Charts, Kibana, Tableau, and Datawrapper.

We all know that Big data can be found everywhere and anywhere. Data is being generated every second, and therefore there is a need for the storage and collection of this data. Data analytics has become a crucial tool for business companies as well as organizations, because of the fear that they might lose out on something important. In the long run, there is a need for this to keep up as well as surpass the competition. The tools that are important for learning the framework of Big Data are Spark and Hadoop respectively.

One comes across the feature selection while in the process of performing data analysis, this is before they have applied the analytical model to data. Therefore one can say that the activity performed so that the raw data is free of any impurities before input into the analytical algorithm is known as data munging. For this process of data munging, one can make use of either ‘Python’ or ‘R’ packages. For a person that deals with data, one should know the concepts and features regarding this important process, along with this data scientists should also be able to recognize their dependent label or variable. The process of Data Munging is also called as Data Wrangling.

Finally, the tool box. One shouldn’t take this lightly, as it is quite crucial and comes in handy at all times. A data scientist should possess hands-on good knowledge on the tools such as Python and R along with Spark, Tableau, and MS Excel. They should also have knowledge of high-speed tools such as Hadoop.

Data Science is a newly emerging interdisciplinary science which is impacting almost all the global business sectors. The application of data science offers a huge potential in the field of agriculture as well. The more the farmers can understand and see what is happening in the fields, the more they are able to make the right as well as strategic choices, both as a business owner and in making better use of land resources.

Digital technology helps farmers to collect various information from the field. It can also enable them to closely monitor each piece of land so that they can precisely determine what is needed for a particular crop to thrive, while at the same time enabling them to avoid or reduce the resources which are not essential for the crop. Farmers can use data science to determine how much fertilizer, water, and other inputs are needed to harvest the best crop. It can also help them to decide how much seeds to be planted in order to get maximum seed performance.

Inter-disciplinary Field

Agricultural science is a complex field which merges together many disciplines. Fundamentals of biology, chemistry, mathematics, physics, statistics, business management, and economics are being used here. Just like in any other industry, the role of an agriculture data scientist is very complex and responsible and requires experts with versatile skill sets. Aspiring data scientists in the field of agriculture need an exposure to plant biotechnology, plant science, animal science, and soil science in order to make an impact and so that they can make sense out of the sets of unstructured data from various resources.

Currently, interactions with farmers prove that they are ready for any technology which can help in improving farm economics. Now they need to be educated regarding the possible risk mitigation and other potential upsides of data science technologies. Farmers are open to accepting new technology, in general. Agricultural ‘data is currently a precious commodity in the global agricultural market and it can impact agriculture in different ways.

Helps to Control of Food inflation

The usual cause of the unpredictable and sudden sharp increase in food inflation is a lack of timely supply. Even though demand patterns are more or less predictable, the challenge is to estimate supply in the food category. Perishable crops usually have price volatility, which is a major setback for farmers. Timely availability of data for sowing, harvest, and production is the only solution for this.

Helps to Reduce wastage of farm produce

Major loss in agriculture comes from wastage of produce, the reasons of which can be lack of proper storage, handling, and planning. If factors which cause wastage can be monitored using remote sensors or devices during storage and transportation, that will be one way to solve the problem. Data science technology can be used to alert farmers if supply is much more than current market demand. Thus stocks can be retained or sowing can be controlled to reduce criminal wastage of crops, which is a boon for farmers.

Big data, as a term, was coined in the year 2005. Post the era when the internet was introduced to us, the general public had to take a moment to take in the magnitude of the internet. While they were hit by a barrage of information, the government was wondering how to track and store the same. And as they grappled with an existing problem, the public eventually started adding to the pile of existing information. Eventually, Google and yahoo came up with innovations that helped in the storage of these with MapReduce and Hadoop respectively. Now that the issue of storing all this information was put out of the way, it was now time for sorting.

With more and more information coming onto the internet everyday, it became harder to go through or find the exact sort of data that was required by an individual. One could find a lot of junk data -as it is being termed- online. This came down to the advent of data science.

Data science is the application of algorithms, machine learning and various such methods and options for bringing out patterns by going through a lot of data. If you’re wondering what sort of patterns could a bunch of numbers have, well the kind that makes everything easier. For example, let’s say one would like to predict the kind of weather for tomorrow or maybe teach a computer how to play chess, one could feed all of this data and find out the probabilities of similar weather or train a computer to react by producing a certain counter move or moves. It gives one the ability to predict with a very high accuracy or learn and adapt from a newfound instance. Number-crunching, as it is sometimes called, is turning to be a necessity in an era where everything is backed by solid data and numbers that add authenticity.

Data scientists are essential analysts when it comes to research and content curation and extraction to make all of this data reader worthy and visual worthy. They become the backbone of providing valuable information in the junkyard of data we have to filter through everyday. As data representation becomes the new trend and the new demand, it may turn out to be a driving force in multiple platforms. From newspapers to machine learning, it’s everywhere already. All it takes to become one is inquisition and the mental set to be ready to work for it. As far as degrees and formal education go, it’s all good to have a PhD but not an absolute requirement. So start your data science training today and benefit with your newfound research capabilities as it becomes a secondary task at the back of the head.

The demand for data scientists is increasing day by day. Data science is a new technology and though not enough material is available on the internet to study it. Reputable institutes are teaching data science to their students. But students from other institutes can also study data science course.

There are online courses available on the internet which teaches whole data science technology through video tutorials and theory as well. There would be assignments which you have to complete. These are certified courses, so there is no need to worry. ExcelR data science can provide such data science course.

Data science definitely plays a larger part in our everyday life today. In fact, it has deliberately made our lives easier than in the past. For example, when someone does not possess knowledge of a particular word or topic, the first thing they do is turn to a search engine. ‘Google’ being the most commonly used search engine of all times. This has become an everyday thing, and it couldn’t be made possible without the support of Data Science in the respective field. Not only Google, but also other search engines, namely – AOL, Ask, Yahoo and Bing implement the usage of the algorithms of data science for dishing out the best possible results in a matter of milliseconds. It is estimated that data of about 20 petabytes is processed by Google on a daily basis.

Detection of Fraudulent Activities

Debts as well as losses occur in large numbers in companies each year. However, with the implementation of Data science in the finance sector, the losses and debts have been reduced to some extent. The banking companies have come to learn over time to divide the data as well acquire it through the past expenditures, the customer’s profiling, etc. along with other variables necessary for analyzation of the chances of defaults and risks. It has also aided them in pushing their products of banking in accordance with the purchasing power of the customer(s).

Medicinal / Drug Discovery.

The process of discovery of drugs is full of complications and is inclusive of several disciplines. It takes long lengths, extending up to even decades of testing and then the discovery of a particular drug for a particular disease / ailment. However, since the arrival and use of data science this process has been reduced in length. Along with this, the expenditure has also gone down and a lot of time is also saved which would otherwise be wasted.

Through the implementation of learning algorithms and data science, one can introduce a perspective for each step starting from the beginning screening for drug compounds to predict the success rate based upon the biological factors.

The purpose of these algorithms is to predict what kinds of effect will the respective compound cause in the body, with the help of advanced level of mathematical simulation and modeling rather than the lengthy experiments carried out in the lab. Computer models and simulations of them are created in the role of a biologically network that makes the predictions of the future easier to make along with more accuracy in the outcomes.

Customer Support

With the implementation of data science, we can help promote healthier lifestyles for patients, encouraging them to make healthy decisions. Along with this, it enables the doctors to concentrate their focus to the cases that are more critical. By simply describing one’s symptoms, and asking questions they can receive the key information regarding their medical condition. With the help of apps, one can be reminded of taking medicines on time and these also provide healthcare support to patients.