What are 3 Reasons To Join An Investment Banking?

People you work within Team.

Like you, they’ll have come through an exacting selection and training process, and we guarantee that the vast majority will be bright and exciting high achievers who care desperately about what they do every day at work. And in the City banking attracts an international crowd, so you’ll get to know people from all around the world.

Fun at Work.

There’s too much to do in this game! You’ll be working in an industry where the rapid pace of innovation keeps things fascinating, and you’ll get to read about what you’re working on in the news. Some roles will give you the chance to travel abroad extensively, and we hear that the social side of things isn’t wrong at all.

Monetary Perks, and more

What can we say? It’s a lot more than you’ll be able to earn in most other industries. There’re other perks too — private insurance plans, pensions, in-house gyms, travel across the world and (sometimes) freshly baked cookies from your institutions own kitchens. — Wink.

The question remains the same. How?

We make it simpler for you. You need not necessarily study abroad. Imarticus Learning offers multiple internationally recognised training and placement opportunities for careers in financial analysis. The investment banking courses at Imarticus have been customised to impart practical, instead of theoretical knowledge to ensure no gaps in the academia and the industry.

To take your career to the next level, all you need is a counselling session, where you get a chance to meet senior industry experts to guide you, keeping in mind your skills and interest to offer the best-suited training to you.

Article Source : https://imarticus.org/what-are-3-reasons-to-join-an-investment-banking/

Infographics: The Adaptation of Artificial Intelligence in The Industry

AI (Artificial Intelligence) is the simulation of human intelligence processes by machines, especially computer systems. Particular applications of AI include expert systems, speech recognition and machine vision. It (AI) is a branch of computer engineering, designed to create machines that behave like humans. Although AI has come so far in recent years, it is still missing essential pieces of human behavior such as emotional behavior, identifying objects and handling them smoothly like a human.

Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.

Article Source : https://imarticus.org/infographics-the-adaptation-of-artificial-intelligence-in-the-industry/

What are top 15 Data Analyst Interview Questions and Answers?

Data analytics has emerged as the latest hotshot for companies in recent times with huge opportunities arising in the industry every day. However, everyone has to undergo a stringent recruitment process in order to get a job with a company which may comprise of aptitude tests, GD sessions and then the final interview.

Here are some of the most asked interview questions one may encounter while sitting for a data analytics job.

What does a typical data analyst responsibilities consist of?

A typical data analyst’s job responsibilities involve gathering and organisation of the data, finding correlations between the analysed data with the data in possession of the company and then applying the knowledge accumulated from it to solve problems creatively.

What is some essential requirement to be a data analyst?

A comprehensive knowledge of business-related tools along with the knowledge of statistics, mathematics and computer languages such as Java, SQL, C++, etc. are required in order to be a data analyst. One also needs the knowledge of data mining, pattern recognition and problem solving ability along with a good analytics training for the job.

What does the term “data cleansing” means?

Data cleansing refers to the process of detecting and removing any inconsistency or errors from the data in order to improve its quality. Data can be cleansed in many ways.

Name some best tools which can be used for data analysis.

Some of the most useful tools for data analysis are –

  • Google Search Operators
  • KNIME
  • Tableau
  • Solver
  • RapidMiner
  • Io
  • NodeXL

What is KNN imputation method?

KNN imputation method refers to the attribution of the values of missing attributes by using the attribute values nearest to the missing ones. Distance function is utilized to determine the similarity between two attribute values.

Mention some best techniques for data cleansing.

Some of the best techniques for data cleansing are –

  • Sorting of the data which organizes them on the basis of their categories.
  • Focusing attention on the summary statistics for each column that will help identify the most frequent problems.
  • Getting mastery of regular expression
  • Creating a set of utility functions, tools and scripts for handling everyday cleansing tasks.

What is the difference between data mining and data profiling?

Data mining focuses on identifying essential records and analysing data collections along with discovering sequence, etc. while data profiling, on the other hand, is concerned with the analysis of individual attributes of the data and providing valuable information on those attributes such as data type, length, frequency, etc.

What are data validation methods?

Data validation can be done in two manners –

  • Data verification — once the data has been gathered, a verification is done to check its accuracy and remove any inconsistency from it.
  • Data screening — inspection or screening of data is done to identify and remove errors from it (if any) before commencing the analysis of the data.

Name some common issues associated with data analyst career.

Some common issues which data analysts face are –

  • Missing values
  • Miss-spelt words
  • Duplicate values
  • Illegal values

What is an Outlier?

The term outlier refers to a value which appears far away and diverging from an overall pattern in a sample. There are two types of outlier namely univariate and multivariate depending on the number of variables used.

What is logistic regression?

Logistic regression or logit regression refers to a statistical method of data examination where one or more independent values are defining an outcome.

Mention the various steps in an analytics project.

Various steps in an analytics project –

  • Definition of problem
  • Exploration of data
  • Preparation of data
  • Modeling
  • Validation of data
  • Implementation and tracking

What are the missing patterns generally observed in data analysis?

Some of the commonly observed missing patterns are –

  • Missing completely at random
  • Missing at random
  • Missing that depends on unobserved input value
  • Missing that depends on the missing value itself

How can multi-source problems be dealt with?

One can deal with multi-source problems by –

  • Restructuring schemas for attaining schema integration
  • Identifying similar records and merging them together containing all relevant attributes without redundancy

What is a hierarchical clustering algorithm?

Hierarchical clustering algorithm merges and divides existing groups creating a hierarchical structure in the process which showcases the order of the merging or division of the group.

Article Source: https://imarticus.org/what-are-top-15-data-analyst-interview-questions-and-answers/

What are The Top 10 Algorithms in Machine Learning?

Machine learning is the essential part of the developing technology of Artificial Intelligence. It analyses enormous amounts of data and comes at customized predictions which can help the user to deal logically with an overload of information. A student of Machine Learning course must be aware of the need of making algorithms since these are what enhance the self-teaching capacities of the system. There are three primary techniques to design an algorithm- supervised, unsupervised and reinforced.

Here is a list of the top 10 algorithms which every Machine Learning student must know about –

  1. Decision Tree is one of the most comfortable supervised structures that is very useful to form deep connections and is based on questions in Boolean format. The fabric is systematic and easy to understand, and it is beneficial to determine model decisions and outcomes of chance-events.
  2. Naive Bayes is a simple and robust algorithm for classification. The “naive” term implies that it assumes every variable to be independent which can turn out as impractical sometimes. However, it is a great tool which is successfully used in spam detection, face recognition, article classification and other such operations.
  3. Linear Discrimination Analysis or LDA is another simple classification algorithm. It takes the mean and variance values across classes and makes predictions based on the discriminated value assuming that the data has a Gaussian curve.
  4. Logistic Regression is a fast and effective statistical model best used for binary model classifications. Some real-world applications of this algorithm are scoring credit points, understanding rates of success in market investments and earthquake detection.
  5. Support Vector Machines or SVMs are a well-known set of algorithms which is binary based. The principle is to find the best separation of variables in a hyperplane. The support vectors are the points which define the hyperplane and construct the classifier. Some successful sites to try this algorithm is image classification and display advertising.
  6. Clustering algorithm follows the unsupervised technique, and it works on the principle of determining the more similar characteristics of nearby parameters to patch themselves up in a set cluster or group. There are different types of clustering algorithms such as centroid-based algorithms, dimensionality reduction and neural networks.
  7. Linear regression is a very well understood form of the algorithm which works on quite the same mathematical formula of a linear equation in two dimensions. It is a well-practised algorithm to determine the relationship between two variables and can be used to remove unnecessary variables from your target function.
  8. Ensemble methods are a group of learning algorithms working on the principle of predictive analysis. They construct a chain of classifiers such that the final structure is established to be a superior one. They are very efficient regarding averaging away with biases in poll decisions, and the algorithms are entirely immune to the problem of over-fitting.
  9. Principal component analysis or PCA employs an orthogonal transformation to convert relatable variables into a set of uncorrelated variables called principal components. Some essential uses of the method are compression and data simplification
  10. Independent Component analysis or ICA is a statistical method to determine underlying data which come obscured in data signals and variables. Relative to PCA, this is a more powerful method and works well with applications like digital images, documented databases and psychometric detections.

While no algorithm in itself can be guaranteed for a specific result, it’s always ideal to test multiple algorithms cumulatively. The ultimate task of an algorithm is to create a target function which can process a set of input into detailed output data.

Article Source: https://imarticus.org/what-are-the-top-10-algorithms-in-machine-learning/

What are 15 Data Science Mistakes To Avoid As A Data Scientist?

We all commit mistakes, and there is nothing shameful about it. Learning from those mistakes is what makes your life a step ahead. But, while working as a data scientist, you have to be cautious as one number here, and there will end up giving completely different results.

Here are some of the unintended mistakes you might make while working on piles of data. As a data scientist, you must try to dodge off these mistakes.

  1. Being too bookish: No doubt, knowledge comes from books but while working as a data scientist, you have to be practical in approach. Not every problem is solved similarly. There is so much to read- Algorithms, derivatives, statistical functions and again books. But, it is of no use, if you are unable to recall it when the time to apply it arrives. Therefore, try to supplement your bookish knowledge with a bit of practicality.
  2. Start from the basics: Don’t jump to machine learning without even knowing to mean, median, mode. Start with the basics of data science. Once you have a good hold over mathematical operations, fundamentals of mathematics, command over a programming language and an aptitude to apply all these skills for executing real-life problems, start with machine learning and AI.
  3. Choice of wrong visualisation tools: Concentrating on a limited number of technical aspects of data science inhibits your learning process. You should try to diversify your selection of visualisation tools to look at the assigned data with different elements. Because a particular trick may apply for a few data problems but not for every question, you will encounter as a data scientist. Similarly, not every data problem comes with a pre-assigned mechanism to solve it.
  4. Analyzing data without a decided objective: A data should be analysed keeping in mind what you wish to achieve as a result or else you will end up digging diversified results which might be of no use.
  5. Do not forget ethical issues concerning data: A data can have sensual entries. While working on the data, try to unearth the results which can be helpful for your organisation. As a data scientist, protection of data is also one of your responsibilities.
  6. Never be too proud of your certificates and degrees: Having a degree from a renowned institution is right for your academic health but never be too overwhelmed with those certificates in your hand. After all, the experience is an excellent teacher which you will eventually gain through the passage of time.
  7. Inconsistent learning process: Never pile up your work. Make sure you practice daily whatever you have learnt.
  8. Paying no heed towards the development of communication skill: Your knowledge is incomplete if you cannot dissipate among others. Once you are done with your graphics, charts and accomplishment of the daily task, try explaining it to others. Even a person from a non-technical background should be able to comprehend what you are trying to say.
  9. Avoid manipulating the data: Don’t make unnecessary additions to the data to achieve desired goals. Be honest about your job as a data scientist.
  10. Biased sampling: All the sections of the population must be covered. Otherwise, the results would be considered discriminatory.
  11. Doing anything to get your work published: This is a cardinal sin which a data scientist would ever commit.
  12. Never be too obsessed with the data.
  13. Learn the skill of segregating the data well.
  14. Never focus too much on model accuracy.
  15. Have limited sources to achieve the results.

Article Source: https://imarticus.org/what-are-15-data-science-mistakes-to-avoid-as-a-data-scientist/

Top 5 Reasons to Embrace Machine Learning in Your Organization

The economic sector is now extremely digitized to the benefit of companies and organizations. The techniques to reach out to customers and have a position in the market are no more in need of a physical entity. Digital transformations along with machine learning applications have cut out on various limitations to reach out to customers.

Let’s have a look at the top 5 reasons at how machine learning is the cause for these prospects:

  • Removal of physical constraints has opened up several opportunities for trading sites and market campaigns. One doesn’t need to go to a bookshop to get a book but can quickly look up on the internet- not only restricted to a hardbound but can also opt for the more economical option of eBook. The physical retail outlets have also been linked to online stores so that customers can reach out to the product even if they don’t have the physical access to the shop. Machine learning gives you the best options for your shopping preferences, and there’s almost always a guarantee to get what you want. A digitized shop doesn’t need a limited space to spread out its resources.

Also Read: Everything You Need To Know About Machine Learning and Deep Learning

  • Consumer customized feeds are the latest trend possible exclusively because of machine learning. It’s an unquestionable fact that your organization can expect growth only if it attracts the right consumers. The flow of consumers is subject to competition and convenience. Every person has ample amount of options to go for his specific needs and hence, you have to have something exclusive to provide them such that they return to you if not because of the product, then because of the convenience in getting the product. The present age values individualism and customers should feel prized.
  • Filtering of advertisements on the World Wide Web can help the inflow of the right customers to a brand with the usage of Machine learning. It’s a fact that billions of data are fed to the internet with every click of users and if all this data is classified with the help of algorithms, it can be determined where to broadcast a particular advertisement to expect a maximum number of clicks. Ads are a costly commodity, and one can either thoughtlessly waste them or smartly utilise for the growth of the brand.
  • Automation of the process makes it instantaneous and efficient. On digital websites, there’s no reason for the delay in your order since every user command works independently and simultaneously along with multiple others. Hence, you don’t have to wait in a line depending on large mechanical machines and waste several minutes and even hours on something that can be done within seconds with AI.
  • The benefit of the digital interface reaches both the retailer and the customer simultaneously. If you place an order, while the money gets directly transferred into the bank account of the seller, the customer also receives the assurance of getting the product hassle-free to her doorstep. There’s no scope of mistrust, and it’s a transaction of mutual satisfaction. Several other units of the common benefit from this transaction. One may also notice how the operation has acquired several dimensions of linkages benefiting not just the giving and gaining parties but also other units involved in it.

Finally, one can assume that Machine learning through its ability of implicit learning has given greater power to the people involved in a specific transaction. On the one hand, one can never forget that it all follows the policy of pleasing the consumer. As market competition rises, machine learning is just another necessary addition to your organization.

Article Source: https://imarticus.org/top-5-reasons-to-embrace-machine-learning-in-your-organization/