An Introduction to R Programming

For someone with a modest knowledge of Python and nothing else, the syntax of R programming language can be quite intimidating. However, learning R is not all that difficult and a little bit of practice allows you to quickly adapt to the programming syntax of a new language. For the uninitiated, R is a programming language created by the statisticians for the statisticians. Its strong affinity with statistics makes it one of the most important programming languages for data science.

Need to learn R for data science

Before proceeding further, you would like to know the importance of learning R for making a career in Data science.

Powerful Analytic Packages for Data Science

First and foremost R programming has an extremely vast package ecosystem. It offers robust tools to master unique skills associated with data science like data visualization, data manipulation and machine learning. Another primary advantage of R is that it has a vast and active community that works diligently on the programming language by continuously adding to R language’s functionalities.

High Industry Popularity and Demand

R programming boasts of high analytical power which is making it one of the most popular programming languages in the field of data science. It is being heavily employed in industries including big ones like Google and Facebook who are increasingly hiring data scientists to reap the benefits of machine learning and Artificial Intelligence. 

Quickstart Installation Guide

To start programming with R on your computer, you need to install R and RStudio on your computer.

Install R Language

You have to first install the R language on your computer before you start working on it. In order to download R, please visit CRAN, https://cloud.r-project.org/ (the comprehensive R archive network). Choose your system and then select the latest version of R programming language to install.

Install RStudio

You also need a robust and sturdy tool to write and compile R code. And when it comes to robustness and popularity, RStudio is the most popular IDE (integrated development environment) for R. Go to File > New File > R Script to open a new script file.

RStudio Interface

4 panels are used in RStudio interface. They are script, console, environment and output. Script is where the main script is located and console shows the output of the code you run from script. R programming training provided by a good training institute is excellent for people who do not have a programming background but want to acquire job oriented practical skills and wants to make a career in Data Science. 

What do you need to learn to become a financial analyst?

Being a financial analyst is one of the most sought-after career paths in the field of finance. This is mainly because analysts can work in a variety of industries and also because the field has some significant advantages, including a high earning potential. If you’re finance major, a financial analyst role is undoubtedly worth considering.

Here are some of the critical things you need to know about being a financial analyst.

What is a financial analyst?

A financial analyst is someone who makes business suggestions for a company based on analyses they carry out on factors such as market trends, the financial status of an organization and the predicted results of a definite type of deal. Analysts usually have academic backgrounds as a business, finance or accounting majors and are numbers-driven individuals who are comfortable interpreting data and making recommendations based on that data.

What do financial analysts do?

Financial analysts are mainly responsible for building financial models that can forecast the outcome of certain business decisions. To do this correctly, they need to aggregate a massive amount of financial data while also taking into account factors such as financial market trends and past transactions of a related nature. Since the role can be entirely different based on where an analyst works, the industry an analyst chooses to go into describes their day-to-day responsibilities. Overall, analysts play an important part in providing decision-makers with the information they need to boost revenue and manage assets successfully.

What are the challenges of being a financial analyst?

Finance is a very data-driven industry, and one of the most significant challenges of working as an analyst is being able to examine and interpret financial statements, market trends, and microeconomic conditions to offer recommendations on potential business deals and decisions. Besides, the technical challenges involved in aggregating and interpreting this complex data, one of the other challenges analysts face is the quick pace of the finance industry. Fortunately, by knowing what to anticipate and getting the right training, these challenges can be overcome.

What are the benefits of being a financial analyst?

Although working as a financial analyst does pose some challenges, it’s also a career field filled with excellent opportunities, mainly when it comes to having your choice of industries. This is because financial analysts play a crucial part in almost every area of business so whether you’re enthusiastic about music or fascinated towards technology, you’re likely to find an opportunity that’s right for you. Further to having flexibility when it comes to industries, you’ll be playing a decisive role in pulling together the information required to take these decisions and to develop new strategies. Apart from learning new skills like how to develop models in Excel and participating in exciting business processes, being an analyst will also give you the opportunity to establish a strong professional network, an asset which you can continue to foster throughout your career.

Although being a financial analyst comes with some challenges, it's also an excellent opportunity to work in an exciting field and play a vital role in the decision-making processes of an organization. If being a financial analyst sounds like it might be for you, consider taking on a financial analytics course and getting a hands-on feel for the position.

Top AI Development Frameworks

Artificial Intelligence has been around for almost a decade now which has given intelligent products that we are using or at least we have the test prototypes in hand yet there is a lot to be achieved yet. Till today, we have only achieved AI development code libraries which generally work with supervised learning. Now the technology giants like Facebook, Microsoft, and Google are working to develop programs that can operate over existing AI development libraries for cross-platform libraries and unsupervised learning support. AI development will leverage quantum computing, big data, 5G communication, and distributed computing for unsupervised learning based AI products development.

Top AI development Frameworks

KERAS

KERAS is an open source python-enabled neural networks library which can run over Tensorflow, Microsoft CNTK (Cognitive Toolkit), and many other frameworks. It is best to be used by beginners in AI development.

TENSORFLOW

It is the most famous framework for AI development which uses machine learning methods like neural networks. It was developed by the Google Brain team; it is behind the auto-completion recommendation for phrases that we type in the Google search engine’s text box.

PYTORCH

It is a python based open source machine learning code library for natural language processing.  Pytorch works in combination with CAFFE for deep learning which were joined together in early 2018 by the Facebook team.

SONNET

Sonnet is an AI development code library based on python and built on top of TensorFlow to make complex neural networks for deep learning. It is the best for Artificial Intelligence research and development it is not easy for beginners to develop in SONNET. 

MXNET

It is an open source deep learning system framework for training and deploying neural networks. It has a scalable training model that supports multiple programming languages R, Scala, Go, Perl, Python, C++, Julia, JavaScript, Matlab for AI development. Apache MXNET is used for installing neural networks on shared hosting services such as AWS and Microsoft Azure.

CNTK

Microsoft CNTK is a deep learning AI development kit where neural networks are defined as a series of computational graphs through a directed graph. The leaf nodes are input values, and other nodes signify a matrix of operations on input values. It allows users to syndicate popular deep learning models like DNNs, CNNs, and RNNs.

DL4J

Deeplearning4j is an open source deep learning AI development programming library developed for Java and JVM (Java Virtual Machine). DL4J is enabled by its own numerical computing library and can work on both CPUs and GPUs.

ONNX

It is a deep learning platform which is collaboratively developed by Microsoft and Facebook. The platform was designed and developed for interoperability of AI development models. With ONNX it is probable to work in Pytorch on AI development model which was developed in Microsoft CNTK or TensorFlow etc.

AI training online from a quality analytics institution can help you learn all the essential frameworks of AI and make a successful career in the field of analytics. Top institutions with their quality resources and experienced faculty ensure quality education for their students. 

How Machine Learning Is Impacting HR Analytics

Like all other facets of modern business, technology is also changing the way we work. This applies to all departments in the organization and HR is no exception. From mobility to cloud computing, big data, blockchain technology, VR and augmented reality, Internet of Things” (IoT) and a variety of developing technologies are now finding their way into the more progressive HR departments of many organizations.

One technology that is presently making significant developments in streamlining and simplifying the functions of HR is machine learning. The technology is not new but its applications for human resources have only lately begun to gain momentum, and they are already making a substantial impact.

Machine learning can competently handle the following:

• Streamlining HR operation such as interviews, group meetings, performance appraisals, and a host of other general HR tasks.
• Analytics and reporting on related HR data
• Streamlining workflows
• Enhance recruitment procedures
• Decreasing staff-turnover
• Personalize training
• Gauge and manage engagement
• Improve rewards and recognition programs

As machine learning acquires a deeper understanding of the organization and has absorbed all significant information, machine learning will be able to:

• Recognize knowledge gaps or shortcomings in training
• Personalize training to make it more relevant for the employee
• Help in performance reviews
• A track, guide and improve employee growth and development

Insights from data

HR collects massive amounts of data on all aspects of worker activity, but without some kind of machine learning to digest and evaluate this information and provide usable reports, it will be almost impossible to recognize essential trends, opportunities, and threats. The data needs to give meaningful insights, and machine learning can do this.

Machine Learning Applications in HR

Automation of workflows

It was one of the primary application of machine learning in HR. Scheduling is usually a time-consuming and tedious task. Whether it is improving onboarding, scheduling interviews, performance reviews, testing, training, and managing the repetitive HR queries, machine learning can reduce most of this dreary work for HR.

This will simplify the process and provide the HR department more time to concentrate on the “bigger issues” at hand.

Attracting top talent

A variety of machine learning applications are already being employed by many organizations to improve their probabilities of attracting the right recruits. Organizations like Glassdoor and LinkedIn have efficiently used machine learning to limit searches and seek outright candidates based on advanced smart algorithms.

Another machine learning- enabled application used to draw top talent is software created by PhenomPeople. It uses keywords to seek out candidates on many social media sites and job platforms.

Greater accuracy in recruiting

One of the most significant yet exceptionally time-consuming tasks of HR is recruiting. Appropriately implemented machine learning technologies can save a lot of time through the use of predictive analysis to decrease time wasting in hiring and make the process more accurate.

Machine learning can help HR in handling the recruitment process from scratch to finish. It will simplify the process, decrease errors and improve results. Though the human element is still necessary to get a feel for the aspirant, machine learning will offer accurate and functional analytics to enhance the effectiveness of recruitment. On the other hand, it will also remove human bias that could be hampering your organization from recruiting suitable candidates.

Unilever, an FMCG giant, uses machine learning platforms to screen the massive amount of job applications they receive. Aspirants have to clear three rounds of machine learning based assessments before actually meeting a human for the final interview. The outcome was a saving of more than 50,000 hours spent on hiring.

Forward planning and improvements

Machine learning can better comprehend the data to give practical insights that will assist HR with predicting communication issues, project progress, turnover trends, employee engagement and a host of other vital developments and issues. This will allow them to gain a prompt awareness of any problems and take corrective measure before these problems become significant issues.

Attrition Detection and understanding

Finding and hiring the right talent is an essential function of HR. Retaining the hired employees rests on more than just the HR department; however, it is vital for them to predict and manage attrition rates.

Machine learning is able to offer valuable insights into these aspects allowing HR to deal with this more efficiently and quickly.

The prediction functionality will allow them to plan in advance before they face skill gaps. More notably, by comprehending the data around employee turnover, they will also be in a better position to take corrective measure and make the required changes to minimize the problem.

HR analytics training online from a top quality institution can help HR professionals in making a better hiring decision. Quality institutions have state-of-the-art resources to facilitate students’ learning.  

Why Students Need to Learn Business Analytics

As the name implies, Business Analytics combines data and analysis. You observe the collected data and examine it through the subject matter with different methods:

●        Predictive models based on math

●        Different types of testing to find essential variables and outliers

●        Statistical tools such as Microsoft Excel, software, etc.

●        Interactive displays like charts, graphs, dashboards, scorecards

●        Predicting old data

When data is analyzed using these ways, it gives significant results, which can be utilized by essential decision makers like company management, stakeholders, etc. to devise smart business strategies.

There are several reasons why learning business analytics will be beneficial for business management students in the future:

You Make Better Decisions Immediately 

Numerous students pursuing a business management program aim to achieve managerial positions as quickly as possible in the future. This is not only due to higher pay of managers in comparison to entry-level positions, but also since they have the right to take decisions that will have an enormous impact on the company they will work for. However, being able to make crucial business decisions also means you are at risk of significant consequences that the organization may not have prepared for.

The reasons why lousy business decisions are made are because of the attitudes of the managers themselves. They choose faultily out of:

●        Sticking to old company strategies

●        Performing impudently based on the wrong insights

●        Not being cautious about the details

Business analytics solve all these issues by showing managers data-driven evidence on why traditional business tactics will not work anymore, what aspects were neglected in their previous decisions, and how wrong insights were poorly implemented.

You Can Come Out With Revolutionary Ideas

Examining data in newer ways may give you fresh ideas and perspectives. In a research conducted by Google, 64% of the interviewed people believed that first; data was making businesses breaking their traditional boundaries. Second, non-traditional organizations are able to disrupt the industry and succeed. Business analytics assists marketers to become more creative by informing them of new trends based on customer behavior and data and provide them with different techniques of testing their ideas.

You Are Warned About Problems Faster

Experts on how to start a business repeatedly state how ignorance will be the collapse of any company. Problems will occur in an organization without being observed because its people don’t have the resources to warn them of these problems when they are still small.

Business analytics have a solution for all these issues. It alerts you to the problems by examining adverse changes in your organization. Displays like dashboards and KPI metrics, ensure that employees are performing to their fullest.

A business analytics program from a top-quality institution can help you in gaining the insight which will help you in making crucial business decisions in the future. Premier institutions have quality faculty and resources to facilitate your learning procedure. 

Role of Apache Spark in Big Data

Apache Spark has appeared as a much handier and compelling substitute for Hadoop. Apache Spark, like all other advanced Big Data tools, is really powerful and well-equipped for dealing with enormous datasets efficiently.

What is Apache Spark?

Spark is a general-purpose data handling and the processing tool that is appropriate for use in a range of situations. Data scientists utilize Apache Spark to enhance their questioning and analysis and also for the transformation of data. Tasks which are most frequently completed using Spark comprise interactive queries across huge data sets, examination, and processing of streaming data from sensors and other sources, and also from machine learning tasks.

What Does Spark Do?

Spark is capable of processing petabytes of data at a time. This data is disseminated across a collection of thousands of cooperating servers –virtual or physical. Apache spark comes with a broad set of libraries and API which support all the generally used languages such as R, Python, and Scala.

Some distinctive use cases of Apache Spark comprise:

Spark streaming and processing: Nowadays, managing “streams” of data is a real challenge for any data expert. This data comes, often from many sources and all at one time. While one way to store this data is- in disks and analyze it retrospectively, this would cost organizations huge. Streams of financial data, for instance, can be processed in real-time to recognize—and refuse—potentially false transactions. Apache Spark helps with this precisely.

Machine learning: With the growing volume of data, ML approaches are also becoming much more accurate and feasible. Today, the software can be taught to recognize and act upon triggers and then apply similar solutions to new and unknown data. Apache Spark’s unique feature of keeping data in-memory aids in quicker querying and therefore makes it an outstanding choice for training ML algorithms.

Interactive streaming analytics: Data scientists and business analysts want to examine their data by asking a question. They no longer wish to work with pre-defined inquiries to produce static dashboards of sales, production-line productivity, or stock prices. This collaborating query process needs systems like Spark that is able to reply quickly.

Data integration: Data is created by a range of sources and is rarely clean. ETL (Extract, transform, load) processes are often done to pull data from diverse systems, clean it, standardize it, and then stock it into a distinct system for analysis. Spark is progressively being used to decrease the cost and time essential for this.

Apache spark certification from a premier institute can help you learn the essentials of this domain quickly. Top institutions have the right resources and faculty to facilitate students’ learning.

6 Excellent Python Tools for Data Science and Machine Learning

Experts have made it fairly clear that 2018 will be a bright year for machine learning and artificial intelligence. Some of them have also conveyed their view that “Machine learning inclines to have a Python flavor as it’s more user-friendly than Java”.

When we talk about data science, Python’s syntax is the closest to the mathematical syntax and, hence, is the language that is most simply understood and learned by professionals such as mathematicians or economists.

6 Python Tools for Data Science and Machine Learning

Machine learning tools                                       

                                                   

Shogun – Written in C++, Shogun is an open-source machine learning toolbox with an emphasis on Support Vector Machines (SVM) and it’s among the oldest ML tools, created in 1999! It gives a broad range of combined machine learning approaches and the objective behind its creation is to offer machine learning with transparent algorithms and machine learning tools to anyone interested in this domain.

Shogun provides a well-documented Python interface and it is generally designed for integrated large-scale learning and gives a high-performance speed. Though, some find its API tough to use.

Pattern – Pattern is a web mining module which provides tools for data mining, network analysis and visualization and machine learning. It comes with well-documentation and more than instances as well as above 350 unit tests. And most outstandingly, it’s free!

Keras – It is a high-level neural networks API and offers a Python deep learning library. It is the best option for any beginner in machine learning as it provides an easier way to represent neural networks as compared to other libraries. Written in Python, Keras is capable of running on top of famous neural network frameworks such as TensorFlow, CNTK or Theano.

Data science tools

SciPy – It is a Python-based ecosystem of open-source software for science, engineering and mathematics. It uses numerous packages like IPython or Pandas, NumPy to deliver libraries for common math- and science-based programming tasks. This tool is an excellent option when you need to manipulate numbers on a computer system and display the outcomes and it is free as well.

Dask – Dask is a tool offering parallelism for analytics by incorporating into other community projects like Pandas, NumPy, and Scikit-Learn. With this too, you can speedily parallelize prevailing code by altering only a few lines of code, because its DataFrame is the similar as in the Pandas library, its Array object functions like NumPy’s has the capacity to parallelize jobs written in pure Python.

HPAT – High-Performance Analytics Toolkit or HPAT is a compiler-based framework for big data. HPAT automatically scales machine learning/ analytics codes in Python to bare-metal cloud/ cluster performance and can enhance certain functions with the @jit decorator.

If you wish to learn data science with Python along with data manipulation, interlacing theory and basic constructs, then you should join a Data Science with Python program through a reputed institution. This will help you gain knowledge of the domain from the scratch.