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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.