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Ways AI and deep learning are now changing the education industry. To know more visit: https://bit.ly/2m3QYY2

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For more information :-https://www.jigsawacademy.com/full-stack-machine-learning-artificial-intelligence/

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SQL for Data Science

SQL, or Structured Query Language, is a powerful programming language that can add, delete, extract, or operate on information within a relational database. You can even use SQL to perform complex analytical functions and change the structure of the database itself – adding or deleting tables, for instance.

Four Reasons Why You Should Learn SQL

Now that we know what SQL is, let’s dig into some reasons why any aspiring data scientist needs this skill in their toolbox:

SQL Mastery is a Must for Most Data Science Jobs

SQL proficiency is a basic necessity for many data science jobs, including data analyst, programmer analyst, database administrator, business intelligence developer, and database developer. You’ll need SQL to communicate with the database and work with the data. Many technical interviews for these jobs test SQL skills in some way, usually in the whiteboard test (i.e. where you solve a problem by writing code on a whiteboard).

SQL Integrates with Scripting Languages

Sometimes querying a database with SQL will give you all the insights you need. But you may want to take it further. Maybe you want to summarize the data in a particular way and then create a nice data visualization for your web application. Or maybe you want to use the query result as one of the inputs for the next step in some code you’re writing. Or maybe you have a working script package and you want to integrate it into the SQL environment.

Luckily, you can convert the result set into an XML or JSON format and use it for subsequent data consumption. Depending on the version of SQL you use, specialized connection libraries allow you to connect a client app to your database.

SQL is Declarative

Machine learning involves self-learning algorithms – algorithms that can adjust their performance without having the process hard-coded in a set of logical rules. In other words, machine learning lets you specify your objective without specifying how it is done. SQL works in a similar way.

SQL is nonprocedural and designed specifically for accessing data. The primary difference between SQL and conventional programming languages (R, Python, Java, etc.) is that SQL statements specify WHAT data operations should be performed rather than HOW to perform them. When you write Python script, the Python interpreter reads your program line by line and carries out the instructions in each line. If you’ve ever written any code, you know how long that takes!

In contrast, SQL’s concise set of commands save time and reduce the amount of programming required to perform intricate queries. Instead of directing a compiler along each step of the way, you simply tell it what you want it to do.

SQL Opens the Door to Data Science

Many people are rushing headlong into machine learning, data science, and artificial intelligence. It is important that you set yourself apart by mastering the foundations of this field as well as the flashier concepts. Learning Data Science with SQL will give you a good understanding of relational databases. It will also boost your professional profile, especially compared to those with inadequate database experience.

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Use a Data Science Bootcamp to Advance Your Big Data Career

A sense of frustration has aroused among some organizations wanting to develop a Big Data Strategy with Data Science and allied technologies. They can’t find people competent of doing the work. Professionals have been cautioning of a Data Scientist scarcity for at least six years, but it’s a weird sounding, “new” career area that various people have never heard of. As a result, some employers are focusing on experience and skills, rather than educational degrees and social connections. They want professionals who can get the job done. One possible way to gaining needed knowledge of the many practices and techniques required is to join a Data Science Bootcamp Online.

Data Science Bootcamps are often full immersion training programs, meant for students coming from a diversity of technical backgrounds, but they can also include part-time online classes. Several professionals from different domains are now learning Big Data skills, and getting good jobs, by taking bootcamp courses.

Bootcamps are short-term, intense training programs designed for people. They are projected to fill in skill gaps. Data Scientists must have an assortment of tools and tricks in processing Big Data for companies, and these Data Science Bootcamps are focused on the facts.

Today’s bootcamps provide their students a good balance of theory and practice. Very few programs dependcompletely on the classroom experience;however, it is still used quite a bit. The concepts related to the technologies are generallyexplicated by an instructor during lectures, with printed materials providing a base for the student. This information is then implemented to simulations and situations in labs, offering students valuable, practical experience.

Getting in to a Data Science Bootcamp

The education necessities for bootcamps differ widely. There are bootcamps providing beginner’s courses online, which have no background necessities, and also there are advanced bootcamps requiring a demonstrable background in some programming languages or a higher degree.

Gaining Hands-On Experience

Irrespective of the kind of Data Science Bootcamp being chosen, find out what sorts of projects they work on, and the amount of real-time experience they provide in working on projects. This is really important. This experience may be acquaintance with a particular program (for example, Apache Spark), or familiarity with a wide spectrum of programs.

Words of Warning

Do not integrate a philosophy of minimalistic education. It is a career field needing individuals to continually update their information and develop a sound understanding of current and new technologies. As the technologies progress and change, so must the Data Scientist.

Before making any decision of joining a bootcamp, surf around and analyze a variety of bootcamps, read numerous reviews and make comparisons. Become a conversant consumer, and attend the bootcamp that is a best fit for your requirements.

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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 the better hiring decision. Quality institutions have state-of-the-art resources to facilitate students’ learning.  

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HR Trends: The Present and the Future

While today, technology has revolutionized each and every aspect of your life, and as with every invention trends are changing quicker than time itself, it is now also a fact that the initiatives we take today may become outdated even before the season changes. With Artificial Intelligence and Machine Learning ruling the world, being a people manager in an age like this can be daunting and yet, enriching.

The ever-changing global business scenario and economic trends have also developed a fiercely competitive business landscape. In the present scenario, “People” are becoming the most crucial asset for all organizations and to them, your employer brand. Your people become the single distinguishing factor, and their talent empowers the organization to overcome the challenges. Realizing the necessity to hire and retain superior talent, business leaders and CEOs are putting more emphasis on developing a workforce that considers the requirements of the present generation.

With global changes across all the industry sectors, HR has transformed from being a personnel department to a strategic business partner. Today HR is not just about managing recruitments and processing payrolls. With increasing cost pressures and the growing need for business efficiency, the role of an HR of an organization has from being HR personnel to that of a Business Strategy Head.

As an enabler, technology is assisting HR teams across organizations understand people better. The age of data is also substituted by the age of insight. It infers that merely having a chatbot might not be sufficient. What also matters today is that data and analytics help you take business decisions based on tangible insights. While the future of HR department seems to be one where we are quickly adopting upcoming technologies like Predictive Analytics and Artificial Intelligence; HR teams also need to develop skills that help highlight the human aspect of people management, including mentoring, coaching, counseling, and career shaping.

With organizations accepting this change, the need for better talent management and engagement initiatives has increased tremendously. People engagement and wellness is taking center stage, with overall wellbeing being the primary focus. It is right to say that today; the focus of HR personnel has shifted from personal transactional issues to solving genuine people matters to ensure they stay longer.

If you are an HR personnel and want to better your hiring and talent retention skills, then enrolling yourself in a human resource analytics program can be your best bet. Quality institutions have the right resources, experienced faculty, and state-of-the-art infrastructure to facilitate students' learning.

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Big data and HR: The potential of predictive analytics

Big data is quite a simple concept. It’s a lot of gathered data, and hoarded within its depths are the solutions to all the developments, patterns and relations you require to precisely foretell the future. The question then, is how can an organization extract and utilize this knowledge. One way is using predictive analytics, like a doctor inspecting your medical past to determine your future health and risk of disease. As organizations continue to better leverage their staff, predictive analytics can provide a valuable opportunity for SMEs to efficiently allocate inadequate resources and predict and plan for future workers’ commotion.

The HR competitive advantage

Predictive analytics is comparatively new to HR, and within organizational circles it was a tool chiefly utilized by the marketing department. Though, organizations are determining that it has the potential to assist gain a better understanding of talent models and how to foresee future performance based on present and past behavior.

This offers an opportunity for organizations to gain the competitive edge through:

●       Forecast of future jobs and leadership requirements

●       Turnover analysis

●       Data-based risk management

●       Estimation of future performance and pre-hire flight threat.  

By leveraging the mass mines of data, organizations are able to enhance every aspect of their HR analytics approach, saving businesses money on turnover and up skilling. Though the potential is extreme, there are several issues that must be measured before jumping on the bandwagon.

Business goals vs. HR goals

Big data is only of use if organizational goals are united.

Organizations that apply a piecemeal method may reveal, through the examination of HR data, a pack of interesting, albeit un-actionable facts. For instance your analytics may tell you that a surge in performance assessments directly relates with employee job satisfaction. This is an exciting insight, the question is how can you take that info and transform it into a beneficial outcome?

Part of the issue businesses can come across is that HR aren’t attentive to business outcomes, but their own objectives. For instance, if the business goal is to decrease costs, HR’s objective should hinge on increasing employee morale to decreasing turnover and growing retention. So the test is for HR to determine how these insights can offer real value for the organization not just them. Interestingly the manner marketing analytics have been utilized are paving the path for HR. Earlier predictive analytics, the marketing teams extracted their data to determine trends related to their purposes:

●       Gathering higher click rates

●       How many whitepapers would be required to double the lead base

●       Which channels to employ to surge brand awareness?

Eventually these metrics meant very little to the organizations as a whole. As the technology evolved, marketing professionals were able to move their attention and began classifying the relationship between marketing events with sales and conversions.

Seeing the future

HR predictive analytics may never reach the point of taking the ‘human’ out of human resources. Though, it can be a dominant tool to leverage the multitudes of employee data that your organization has stored in its servers. It can be a means of enhancing your workforce, advance its longevity and efficiently assign available resources. HR can safeguard their investment is valuable by breaking down departmental silos and always keeping the objectives of the business as the central point of their design.

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Importance of Business Analytics for HR Professionals

The process of recruiting a right candidate is difficult. An HR has to flip through hundreds of resumes to choose the best ones. Employing manual screening, the procedure becomes difficult, error-prone, and lengthy. For this reason, having an HR analytics system in place can save you much time.

Hiring-as a procedure is data-driven. The use of data and effectual analytical processes can be excellent tools for a recruiter. It not only saves valuable time in screening the resumes but also helps in faster and enhanced decision-making. The insights produced can drastically assist human resources (HR) to measure how successful an employee would be in the company.

Advantages of integrating data analytics in the talent acquisition and retention procedure:

Collecting the right data

Candidates’ data can be tabulated into different parameters like age, qualification, years of experience, etc. This data can be combined and ranked based on the requirement of the profile. Recruiters can also unify candidate’s public data from different social media platforms to put in value to their profiles. This would give in better returns in the form of appropriate hires which can enhance the overall quality of the company. However, recruiters require keeping in mind that overpopulating the date with needless attributes might confuse the recruitment process.

Data cleansing

Once the data is collected, it needs to be cleaned to feed it to different models. This is the most important step where a small error might skew the overall result. A lot of tools have advanced options to slice a large volume of data. The incidents of resumes with exaggerated facts and false statements are increasing every day. By identifying such profiles at this step, recruiters would be able to select right candidates for the profiles they are searching for.

Applying Analytical Techniques

By using analytics, professionals decide whether new candidates meet expected performance levels to contribute to the achievement of the company. With factors such as candidates’ achievements, period at previous companies, employers would get a clear signal of whom to hire for the position. Predictive modeling helps organizations to match the correct candidate to the right position by recognizing the traits that differentiate high performers.

Attrition Analytics

Machine learning can be taken into use to build models to know the probable employee attrition rate in the approaching time. It uses parameter like age, income, years at the company, satisfaction level, and other significant factors to recognize how many employees are on a watch for a change. This analysis can be utilized to take preventive measures and keep valuable workers in the organization.

Optimization

Analytics can assist recruiters find a vast range of candidates they would not get using conventional search methods. It can assist them get the best cost per recruit, and also enhance their job postings response. This allows organizations to attain better responses to their jobs postings depending on different factors such as duration, location, occupation, and industry.

Human Resource Analytics allows for improved processes that enhance human aptitude and efficiency. People are crucial to the success of any organization. With the correct use of analytics, companies would be able to draw right competencies, manage talent, employ capacity, and keep employees for long-term success.

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Use Predictive HR Analytics to Transform HR Strategy

HR Predictive analytics is all about evaluating present human resources data to get valuable insights into patterns, and make useful predictions about the future. Predictive analytics recognizes trends related to employee performance, attrition rate, extreme employee absenteeism, return on investment on talent management, identification of bad performers, employee engagement, compensation strategies, and many other employees’ related aspects.

As per a CEB report, “Only 12 percent of companies presently use predictive analytics for talent acquisition and retaining decision making. Though 49 percent of enterprises intends to do so in the near future.” Nowadays, HR departments are generating more data than ever before, but they struggle to transform this data into useful insights.

So far, very few companies have been successful in taking benefit of predictive analytics with the assistance of prevailing HR systems that they have been using for years. There are a number of reasons behind this:

●    Lack of skilled employees who can utilize data reports and dashboards,

●    Lack of understanding to use data insights to their benefit,

●    Complicated HR analytics tools, or in some cases,

●    Companies underestimate the requirement to examine real-time predictive analytics.

HR Analytics Is a Game Changer

Analytics have played a significant role in adding predictability to various facets of an enterprise and usually you will find Finance, Customer Support, Sales, and also Demand Generation having refined analytic modeling. HR departments seem to be still catching up in this domain. HR Analytics gives insights into each procedure by using data to make conversant decisions, enhance the processes and operational performance to get a competitive edge.

Arrival of advanced HR analytics tools is quickly changing human resource departments into a data-powered mindset. Though the technology acceptance rate is low, most companies are taking a step ahead to implement HR analytical tools. Human Resources is in a better position to make well-versed decisions, if decisions are made on accurate insights.

Analytics offers useful insights on worker’s personal information, compensation, behavior, performance, advantages, risks to leave the company and more, from time to time so that the data is inferred to know the future trends and fast and well-timed action is taken instead of a hasty action when the event takes place.

Predictive HR Analytics Is Useful as it:

●    Finds the factors responsible for employee engagement and satisfaction

●    Find the main reasons for augmented attrition of the top talent

●    Forecast future workforce necessities and ways to cater to those demands

●    Examine factors influencing employee performance and apply data insights to enhance business performance

●    Define the impact of particular KRAs on the business

●    Gauge business outcomes

 Use Predictive HR Analytics to get a clear framework to create HR strategies. Recognize the source cause of many employee problems. In the approaching years, expect predictive analytics to rule HR by implementing employee data in recruiting, engagement, retention, performance reviews and more.

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The Role of Data Science in Corporate Development

Data - it’s one of the most influential four-letter words out there, but it’s useless without the ability to draw meaningful insights from it. Enter data science. This amazing field harnesses the power of deep learning and statistics for near-endless applications, such as better corporate decision-making, development and much more.

Here’s a closer look at how organizations are using data science, along with one-way ambitious business leaders who can position themselves to optimize results through data science.

What are Data Science Platforms?

Google's Chief Economist Hal Varian once stated, “The ability to take data -- to be able to understand it, to process it, to extract value from it, to visualize it, to communicate -- it’s going to be a hugely important skill in the next few decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So, the rare factor is the ability to recognize that data and extract value from it.”

Dataconomy elucidates, “Data science platforms are meant to embrace the whole of a data scientist’s work. It means they characteristically offer tools that help users incorporate and explore data from diverse sources, build and deploy replicas, and make the results of those models operational. Basically, this suite of tools is intended to keep data science work reproducible, transparent, and scalable — and make it convenient for a data scientist to push dynamic results to the people who make decisions based on those outcomes, replacing static (and rapidly outdated) reports.”

In other words, getting access to data is only a part of the equation. Organizations must also have tools through which to integrate that data into their operations and outcomes. Those who are adapting data science platforms as a strategic enterprise -- taking the focus off of the data itself and emphasizing on data-driven insights instead -- are gaining the competitive advantage. Due to this, the adoption of data science platforms is predicted to skyrocket from 29 percent to 69 percent over the next year, as per a current study organized by Forrester Consulting on behalf of DataScience, Inc.

Quality over Quantity

While there is a time and place for quantitative data – specifically for developing and testing - the significance of qualitative data should not be ignored within the milieu of corporate development. TechRepublic explains this by stating, “Qualitative research is about study and in terms of data science that, at least in part, interprets to investigative data analysis. Qualitative data scientists are looking for themes in a huge sea of data. This data could be unstructured, structured, or unavailable. If they're successful, they'll uncover ideas and patterns in your data that you never knew existed.”

The Fine Line

With so much consideration focused on how to make the most of the data available, another important topic is sometimes ignored: using data without negotiating client trust and loyalty. This means that forward-thinking companies must also recognize ethical inferences of using all of the data at disposal. After all, only because you have access to data doesn’t mean it’s yours to use -- or that your clients will respond positively if they see that you have crossed a line with sensitive information.

An online data science bootcamp from a reputed institution can help you understand the essentials of data science and make a successful career in this promising domain. Reputed institutions have the right faculty and resources to facilitate students’ learning.  

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What is Data Analysis and How Can You Start Learning it Today?

WHAT IS DATA ANALYSIS?

Data analysis involves sorting through enormous amounts of unstructured data and drawing key insights from it. These insights are extremely valuable for decision-making at organizations of all sizes.

Becoming a data analyst opens the door to lucrative careers like data engineering and data science as you gain more experience on the job.

KEY DATA ANALYSIS SKILLS TO LEARN TO BECOME A DATA ANALYST

Beyond great problem solving, creativity skills and communication, you’ll also require certain tech skills to be successful at data analysis.

Here are the most common skills and tools you’ll need to learn to get a career in data analysis:

EXCEL (SPREADSHEETS)

What it is: Microsoft Excel is a spreadsheet program that lets you perform complex data analysis. Its integrated pivot tables are one of the most prevalent analytic tools.

Why learn it: Diego Fernandez, instructor of Excel for Data Analysis says - Basic to Expert Level, “Learning Excel is necessary for any professional or academic career based on data analysis. It is the most generally used data analysis software both professionally and academically and it’s a solid base before learning any other.”

SQL (DATABASE LANGUAGE)

What it is: SQL (Structured Query Language) is a database language used to interrelate with databases that store data, allowing us to recover data quickly and easily.

Why learn it: SQL lets you perform operations on millions of rows of data. It’s the second most demanded skill for data analysis jobs.

R (PROGRAMMING LANGUAGE)

What it is: R is a programming language for statistical computing and graphics. It is widely used among statisticians, data miners, data analysts, business analysts, and data scientists for developing statistical software, data analysis, and machine learning and so on.

Why learn it: R provides aspiring analysts and data scientists the ability to denote complex data sets in an impressive way. R has been adopted by numerous high-profile enterprises like Facebook and Google as the language of choice to analyze data.

DATA VISUALIZATION

What it is: Data visualization significantly assists key decision-makers in a business setting. Analytics are presented visually in charts, graphs, etc. so they can recognize trends and patterns to understand complex information.

Why learn it: If you are creative, this may be the ideal skill to learn. Learning data visualization can provide you an edge over other job applicants as employers are increasingly looking for experts who understand both the science and art behind data analysis.

Beginner programs in Analytics from a premier institution can help you accelerate your learning. Popular institutions have the right resources and faculty to help students understand the fundamentals of analytics quickly and efficiently.

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How to become a machine learning engineer?

Machine learning engineers — advanced programmers who build artificial intelligence (AI) systems that can learn and apply knowledge — are in huge demand, as more organizations accept these technologies. These professionals do sophisticated programming and work with complex algorithms and data sets to train intelligent systems.

 While many think that AI will soon substitute jobs, it is still creating new job positions such as machine learning engineers, as organizations need highly-skilled employees to develop and sustain a wide range of applications.

What is machine learning?

Machine learning is a branch of AI which provides computer systems with the capacity to automatically learn and enhance from experience, rather than being unequivocally programmed. In ML, computers use huge sets of data and employ algorithms to train on and make predictions.

Machine learning systems are able to quickly apply knowledge and training from huge data sets to do facial recognition, object recognition, translation, speech recognition, and many other tasks.

Programming Languages You Need to Learn to Become a Machine Learning Engineer

Python and R are the most important programming languages for machine learning, analytics and data science. An IBM report ranked Python, R and Java as the top programming languages for machine learning engineers, followed by C, C++, Scala, JavaScript, and Julia.

When creating machine learning applications, the training and operational phases for algorithms are different. Hence, some engineers use one programming language for the training phase and a different language for the operational phase.

Other Skills Required to Become a Machine Learning Engineer

Generally, machine learning professionals must be skilled in programming, mathematics, computer science, statistics, deep learning, data science, and problem-solving. Here is a list of some of the skills required.

Computer science fundamentals and programming: Data structures (queues, stacks, multi-dimensional arrays, graphs, trees), algorithms (probing, categorization, optimization, dynamic programming), complexity and computability (P vs. NP, NP-complete problems, big-O notation, estimated algorithms), and computer architecture (memory, bandwidth, cache, scattered processing, deadlocks).

Probability and statistics: Characterization of probability (Bayes' rule, likelihood, conditional probability, independence) and methods derived from it (Markov Decision Processes, Bayes Nets, Hidden Markov Models). Statistics measures (median, mean, variance), distributions (normal, binomial, uniform, Poisson), and analysis techniques (ANOVA, hypothesis testing).

 Data modeling and evaluation: Discovering patterns (clusters, eigenvectors, correlations), predicting properties of earlier unseen instances (regression, classification, sequential vs. randomized cross-validation, anomaly detection), and finding out the right accuracy/error measure and an assessment strategy (training-testing split).

 Applying machine learning algorithms and libraries: Standard applications of machine learning algorithms are available through packages, libraries, and APIs (like Theano, scikit-learn, H2O, Spark MLlib, and TensorFlow). Applying them efficiently means choosing the right model (nearest neighbor, decision tree, support vector machine, neural net, ensemble of various models) and a learning process to fit the data (gradient descent, linear regression, bagging, boosting, genetic algorithms, and other model-specific techniques), as well as understanding how hyper-parameters influence learning.

If you are interested in pursuing your career in AI and don't know where to start, then joining a machine learning and AI training program through a reputed institution can help you kick-start your career.

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The Importance of HR Analytics in an Organization

In the modern technologically driven world, sharing and transmitting of data has become easy. ‘Big Data’ in particular has gained significant prominence and importance in recent times. The quantity of data collected by an organization goes on growing over time but this data has to be utilized efficiently. It needs to be converted into useful information as it offers valuable insights into the working of a company. This information then needs to be analyzed and presented in a way that can be simply understood. HR analytics has the answers to various valuable questions such as how many people a company needs to recruit and how much salary is to be paid to these employees. These are some of the questions which help in improving the performance of a company by enhancing the productivity and motivating staff. The HR manager should know what sort of information is to be mined from existing data as it could be very beneficial for realizing the goals of an organization. At times the data might be huge and detached from each other. To prevent yourself from getting lost in the heaps of data, managers need to start with the right questions. They need to think about what is essential for the company for running its business and functional policies. The data necessary to answer these questions should be prioritized.

Human resources have now become competencies of companies which cannot be easily imitated. Therefore it is crucial to analyze their impact on the organization. Through HR analytics, the HR manager can get the required information necessary for an organization to grow. It helps the HR manager evaluate not just the past or present scenario but also assists them in preparing for the future. It helps in taking necessary actions to confront the future.

Below are the three steps to be followed for HR Analytics:

●    Predictive modeling helps in predicting future necessities. It can provide information on crucial matters such as talent forecasting, retention rate, etc.

●   The Human Resource Information System (HRIS) incorporates HR with IT. It is a system used to collect employee data. While HRIS employs descriptive modeling, HR analytics uses predictive modeling. HRIS data provides information on the past while the predictive modeling on the data predicts the future.

●   Therefore, HR analytics helps in drawing useful information which can be used to form business policies. It gives insights into a company’s workings which assists in making decisions.

HR analytics training course from a premier institution can help HR personnel take better hiring decisions and also in improving employee retention rate.  

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Artificial Intelligence: The Time Is Now

Artificial Intelligence (AI) or at least the concept has been there for a long time. However, until recently it was within the domain of either theoretical scientists or science fiction writers. It is only within the last few years that AI is slowly but surely turning into reality with many real-world and practical realisations of this exciting technology bringing a collective awe from different quarters. However, any new and exciting technology that possesses the ability to change our lives in more ways than one has the ability to generate extra market hype and AI is certainly no exception to it.  As AI gathers momentum and its awesome potential becomes more widely known, technology vendors of all hues and colours are falling over each other to claim a connection with AI or any of its related approaches. This excessive hype it seems is creating a certain kind of negativity among technology professionals. The people who have the knowledge and the skills to make the best use of AI and play an important part in developing its cause are unfortunately the one cynically dismissing it as overrated marketing hype.

However, it would be naive to dismiss this technology as a passing fad given its business-altering potential. Positioning it as mere smoke and mirrors or overrated marketing hype or more perplexingly an old wine in a new bottle often makes one wonder if all this brouhaha about AI is little more than hyped-up fluff?

Is AI only about Algorithm?

It is a fact that AI-powered solutions are very real and in the near future are going to significantly impact almost every aspect of a business operation. If in the near future AI is reduced to an algorithm, some form of AI can be used to automate it. However, it is important to realize the difference between artificial general intelligence, which is still a far way off, and artificial narrow intelligence that we are talking about presently. What is for certain is that almost in any job function in an organization in the not so distant future will be in the position to use machines to handle any process or activity that they can deconstruct into an algorithm.

An Artificial Intelligence course from a top rated online institute offers comprehensive learning in applying Machine Learning, Deep Learning and Artificial Intelligence techniques to both structured and unstructured data including text, images, audio and video data. This will help you deploy machine and deep learning algorithms as per the given situation.

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What makes Data Science such a Hot Career Option?

According to Glassdoor, data scientists commanded the highest salary in the job market. The basic economics of supply and demand were primarily responsible for this, as the availability of skilled data scientists is much lower than their demand in the job market. The pattern that is being followed here is similar to that witnessed by IT industry in the previous decades. The internet was making its presence felt and everybody was jumping into it to further their career. 

Everybody wanted to be a programmer, a web designer, a developer, system analyst, network administrator, etc. Salaries for IT professionals were super-high at that time, and a job in the IT sector bought both money and social prestige. As time passed, the salaries started to slide southwards as more and more people started joining the industry thus narrowing the gap between supply and demand.

Data Science industry is experiencing a similar phenomenon right now, as there is a big gap between supply and demand and the salaries are exceptionally high.

Demand

A highly competitive business environment demands quick and correct decision making. Data-driven decision making is gaining traction with organizations analyzing a huge amount of data at their disposal to reach meaningful decisions. Previously, analysts made use of software tools like Excel to analyze data, while only academics made use of Stata, SPSS, etc.

The advent of Big Data and the introduction of sophisticated analytical tools and software has given an altogether different dimension to data science. We presently have Google Analytics, SAP, Microsoft Dynamics, Tableau, Sisense, Microsoft Power BI and Big Data programming languages like R and Python which help organizations sift through a mammoth amount of data to gain valuable insights from them. Organizations that are aware of the importance of these tools, and how it can help them take their profit and revenue northwards, are not averse to employing people with the requisite knowledge of these tools. This is where the importance of Data Science professionals enters the equation.

Supply

Data Science is driven by technology change. And the rapidly transforming technology has made data science a reality, but the fact is that traditional teaching methods have failed to catch up with the market requirements. It is the reason there are relatively few programs that educate aspiring data scientists. This leads to supply shortage and heightened demand for data scientists.

Completing a Full Stack Data Science Program from a top of the line online analytics institute can do wonders for your career aspirations. The latest teaching methodologies that include books, research papers, and online courses are more efficient at helping participants gain valuable skills in the field of data science.

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Top trends in Data Science and Analytics

Data Science is growing at an astounding rate and organizations are increasingly looking at ways to store and analyse that data to gain competitive advantage in the marketplace. In the following paragraphs we shall look at some of the important trends in data science and analytics that has been helping organizations analyse and subsequently monetise the huge amount of data at their disposal.

Demand for expert talent in Data Analytics industry

The fast pace growth of data science and AI industry has given rise to new employment opportunities.  More and more IT professionals as well as people with strong analytical and reasoning capabilities are moving towards this field to take advantage of the huge career advancement opportunities this fast emerging field offers.

Data Science and Healthcare industries

Data science has been making its presence felt big time in predicting the outbreak of epidemics as well as patients’ behaviour. Doctors are increasingly making use of data science to diagnose a patient’s condition without making them go through the painful biopsies.

Embedded Analytics

Embedded Analytics is the fastest growing area of business intelligence. Consumer facing BI and analytics tools are integrated into software applications, and operate as a component of the native application itself rather than operating as a separate platform. Embedded Analytics allow organizations to get access to high quality data for more meaningful and productive insights without the need for an external agent.

In-memory analytics

This technology allows data loading and reading directly from RAM, thus helping in superfast data processing as the need for disk Input/output is eliminated. And when it comes to big data it is the availability of terabyte systems and massively parallel processing powers that takes the relevance of in-memory analytics to an all new level.

Mobile Analytics

With the massive increase in popularity of smartphones, mobile analytics has become the prime focus area of organizations who are keen on taking advantage of this technology to woo customers. The development of motion sensing and location technologies has made it increasingly simple for organizations to collect information about people’s activities. App developers are developing app analytics to understand the users’ profile and transaction behaviour in order to provide customized solutions for enhanced user experience.

Data Science certification from an online institute of repute can give an entire new meaning and definition to your career as there is a huge demand for data scientist professionals. Equipped with latest knowledge and expertise, you can prove to be a real asset to any organization.

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Impact of Machine Learning in HR Analytics

Rapid pace of digitization and advancement in technology has completely revolutionized the way modern businesses operate and function.  This holds true for all the departments that operate within a modern organization including HR. Many people, however, bear the misconception that HR owing to the human element involved in it could not be very receptive to technological advances.  It could probably be the reason that HR has been slow to catch onto machine learning and artificial intelligence (AI) like other fields such as marketing, operations, finance, communications, etc.  However, on the positive side it is heartening to note that the value of machine learning in HR can now be measured owing to advanced algorithms and ever increasing computational power. This along with block chain technology, Internet of Things (IoT), big data, data analytics, virtual reality, cloud computing and augmented reality among host of other developing and emerging technologies has made machine learning integral to functioning of HR department of leading organizations. Machine Learning is relatively new but its importance and use in various aspects of HR has made it an important and exciting prospect in streamlining and improving various HR functions.

In the following paragraphs we shall look at some of the important areas where machine learning and AI is making a significant impact in various functional areas of HR.

Applicant tracking and assessment

This is one area where machine learning and AI have been playing a critical role especially in those organizations which receive large number of applications from job seekers. Machine learning tools ease the tasks of recruiting personals by tracking a candidate’s journey through the entire hiring process.

Soliciting talent

Applicant tracking and assessment will simply not be there if there are no candidates to be interviewed. It is imperative for modern organizations operating in a highly complex and dynamic environment to attract the right kind of talent. Many organizations as such are increasingly making use of job finding sites like LinkedIn, Seek and Glassdoor, which make use of machine learning and powerful algorithms to build interaction map based on a job seeker’s data related to his previous job searches, interests, experience, posts, clicks, etc.

Also, the vast amount of data generated by all the actions and activities of employees needs the power of machine learning to analyze the information and present usable reports.

HR Analytics course from a premium quality institute can accelerate your career in the right direction. Innovative teaching methods, modern and relevant course material along with ample opportunities to interact with leading industry personals offers the relevant exposure and training to succeed in the job market.