abhi Oct 19, 2022 No Comments
In the HR (Human resource) niche, decision-making is changing. At a time when the traditional ways of operating HR are no longer sufficient to keep pace with the new technologies and competition, the field is at crossroads. This is a perfect case study to find out the effectiveness of analytics in HR.
When we talk about analytics in HR there are many facets that come into play. HR analytics aims to offer insight into how effectively to manage employees and attain business goals. Because so much data is accessible, it is crucial for HR teams to initially identify which data is most relevant, along with how to use it for optimum ROI.
Modern talent analytics mix data from HR and other business operations to address challenges related to:
So, a leading Multinational Professional Service Company reached Ivy Professional School for upskilling of their HR department to obtain optimum benefit from their operations.
Upskilling as the name suggests implies taking your skill to a next level. This has various benefits for any organization and the individual as well. Upskilling is very crucial as it:
Each employee searches for a purpose in their work, and innovation comes its way when the goal of the organization aligns with individual career aims.
When an employee leaves an organization, you must fill that position, which again starts the hiring and recruiting processes.
Along with upskilling, this analytics program is aimed at creating domain knowledge among the employees in the HR department. Domain knowledge is basically the knowledge of a specific, specialized discipline or niche, in contrast to general (or domain-independent) knowledge.
Considering the characteristics of the job profile and the expectations set by the company, a special curriculum was created.
Analytics in HR is reaching new horizons now. By using people analytics you don’t have to depend on gut feeling anymore. So now many organizations are inclining towards upskilling their employees in the HR department so that they get a good domain knowledge and become a more valuable resource of their company.
You can also reach out to us if you want us to organize similar analytical programs for your organization. Please email us your requirement at firstname.lastname@example.org
abhi Oct 13, 2022 No Comments
Data science and data analytics are the trending words of this decade. For people who are looking for a long-term career option, data science and big data jobs have been a safe bet. This trend is likely to continue but before you think further about this career option, you should know about the difference between data analytics and data science. Let us begin with the comparison of data science vs analytics.
Presently, data is the primary object for the swift operation of any business to gather important insights and enhance business performance to evolve in the market. So without any further delay, let us begin with the difference between data science and data analytics with example.
Before we get into data science vs analytics, let us gather some ideas about these topics separately. Data analytics aims at performing and processing statistical analysis of prevailing datasets. Analytics emphasizes formulating methods to process, capture, and organize data to unravel actionable insights for present issues and formulating ideal ways to offer this data. More conveniently, the area of data and analytics is directed towards solving issues for questions we know we don’t know the answers to. More crucially, it is based on producing outcomes that can lead to instant enhancements.
Data analytics also revolves around a few varied branches of broader statistics and analysis which help to mix diverse sources of data and locate connections while easing the outcomes.
Data science is a multiskilled field aimed at finding actionable insights from big sets of structured and raw data. The field basically fixates on unraveling answers to the things we are unaware of. Data science professionals use many varied techniques to get answers, including computer science, predictive analytics, machine learning, and statistics to examine huge datasets in an attempt to establish solutions that haven’t been thought of yet.
The primary aim of a data scientist is to ask questions and find out potential routes of study, with less concern for prominent answers and more focus placed on finding the appropriate question to ask. Experts attain this by anticipating potential trends, exploring disconnected and disparate data sources, and finding better avenues to analyze data.
While there are many who use these terms interchangeably, big data and data science are unique fields, with the major variation being the scope. This is an umbrella term for a group of fields that are used for mining large datasets. Data analytics software is a more niched version of this and can even be considered part of the bigger process. Analytics is inclined to identify actionable insights that can be levied immediately based on prevailing queries.
Another prominent difference between the two areas is the question of exploration. Data science isn’t concerned with answering major queries, instead analyzing via huge datasets in sometimes unstructured ways to expose insights. Data analysis operates better when it is aimed, at having questions in the mind that require answers based on the prevailing data. Data science offers wider insight that focuses on which questions should be asked, while big data analytics focuses on discovering answers to questions being asked.
More crucially, data science is more worried about asking questions in comparison to finding specific answers. The niche is aimed at creating potential trends based on prevailing data along with realising better ways to evaluate and model data.
|Data Science||Data Analytics|
|Goal||To ask the right questions||To find actionable data|
|Major Fields||Machine learning, AI, search engine engineering, corporate analytics||Healthcare, gaming, travel, industries with immediate data needs|
|Using Big Data||Yes||Yes|
The two fields can be considered varied sides of the same coin, and their operations are highly interconnected. Data science lays the foundations and analyses big datasets to formulate initial observations, potential insights, and future trends that can be crucial. This data by itself is useful for some niche, especially modelling, enhancing machine learning, and improving AI algorithms as it can enhance how data is evaluated and sorted. However, data science asks crucial questions that we didn’t know about before while offering little in the way of hard answers. By adding data analytics into the mix, we can turn those things we know we don’t know into actionable insights along with practical applications.
When considering two disciplines, it is crucial to forget about seeing them as data science vs analytics. Instead, we should see them as parts of a complete system that are crucial to evaluating not just the data we have, but how to better evaluate and analyse it.
As stated above, the career prospects for data science and data analytics are huge and if you want to land your career in the same, then it is important that you get a certification from a reputed institution. The best we can think of is Ivy Professional School. They offer specially curated courses taught by industry experts.
abhi Oct 11, 2022 No Comments
Data engineers are IT professionals, and as such, they are expected to gain expertise in various processes and applications. By learning and developing these data engineering skills, one can become an efficient data engineer and a more eligible candidate.
In this article, we are going to have a look at the various data engineering skills that can be expected from a data engineer and will also have a look at the steps required to start your career in the same.
Data engineers formulate and maintain the architecture that is employed in several data science projects. They are liable for assuring that the data flow between applications and servers is uninterrupted.
Data engineering mixes elements of data science and software engineering. Some of the basic operations of a data engineer include things such as developing data collection processes, integrating new data management technologies and software into a prevailing system, and streamlining the prevailing foundational process for data use and collection.
For performing their duties effectively and efficiently, data engineers must have the following soft and technical skills:
Coding is the most important skill that a data engineer should master to flourish in their career. This is needed in the majority of data engineering positions. Many employers want the candidate to have primary knowledge of programming languages such as:
Data engineers are charged with analyzing and storing a good amount of data. That is the reason for experience and familiarity using data warehousing solutions like Panopoly or Redshift is imperative in a data engineering role.
As a data engineer, developing an in-depth evaluation of the OS such as Apple macOS, Linux, Microsoft Windows, UNIX, and Solaris is very important.
It is crucial for data engineers to have a deep understanding of database management. Since SQL (Structured Query Language) is thought to be the most broadly used solution, gathering an in-depth idea of it is highly valuable in this niche. SQL is a database coding language that gathers and manages data stored in tables. Other than that there are other database solutions such as Bigtable or Cassandra that one should learn as well, mainly if they are planning on doing freelance data engineering.
Most employers want data engineering candidates to have a strong knowledge of analytic software, mainly Apache Hadoop-based solutions such as MapReduce, Pig, Hive, and HBase.
Data engineers should be able to understand problems and then develop solutions that are both effective and creative. Since there are times when you might need to create a solution that does not exist yet, the potential to think critically is key.
Even though ML is basically the aim of data scientists, it can be helpful for data engineers to have at least a basic understanding of using this form of data. Constructing your knowledge of data modeling and statistical analysis can help you formulate solutions that can be used by peers and set you apart as an incredible asset to any company.
As a data engineer, you have to be able to collaborate with colleagues with and without any technical expertise, which is the reason gaining great communication skills is so crucial. Even though you often work with other data experts like data scientists and also data architects, you typically have to share all your findings and also suggestions with peers without any technical background.
The following steps will help you get an idea of how you can become a data engineer along with data engineering skills.
Even though there are various components that are equally as crucial as formal education when getting into this profession, most employers need data engineers to hold at least a bachelor’s degree.
You should hold a degree in something like computer science, computer engineering, information technology, software engineering, applied math, statistics, physics, or a related area. If you plan to pursue a degree beyond one of these majors, you should aim at taking courses in coding, algorithms, database management, or data structure.
Internships are seldom a great way to evolve your skill set and gain valuable experience, but you can also take on personal projects that enable you to increase your expertise in the niche and develop your expertise with crucial solutions and programming languages like Python and SQL. Make sure that you include these experiences in your portfolio so that you can portray to future employers what you are capable of.
Though finding an entry-level job in data engineering is chosen, any IT-associated position can offer a great source of experience and will supply you with exposure to managing issues in a data company. Apart from allowing you to develop your critical thinking and also problem-solving skills, an entry-level job enables you to evaluate the varied aspects of this industry, how it operates and just how collaborative it is.
For instance, data engineers operate with data architects, data scientists, and also the management to collect, evaluate, and use data.
In the last few years, the demand for data engineer roles has increased astronomically. Organizations are actively searching for data engineers to address their data agonies. These data engineering skills are in demand, and it is far from being oversaturated like other fields. Those who have these skills have the chance to make high salaries. For this reason, the right certification can turn out to be quite useful.