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We Take Immense Pride in Sponsoring the Ultimate CMO Challenge – Atharva’17

We are back again with some exciting news for you! We, a team of consultants of DexLab Analytics are sponsoring Atharva – the Ultimate CMO Challenge 2017, which is to be held at the Delhi School of Economics, today.

 
We Take Immense Pride in Sponsoring the Ultimate CMO Challenge – Atharva’17
 

For detailed information, click on this link. DexLab Analytics is sponsoring “The Ultimate CMO challenge” by the Delhi School of Economics

 

The first round was held on 13th February, 2017, where an Initial Case Study was needed to be submitted online and a brief for solutions, in the form of 3-4 slides or 2-3 pages write-up was to be submitted by 19th February, 2017. The candidates who got selected were declared as shortlisted by 21st February, 2017. And within 27th February 2017, final solutions in the form of PPT (with maximum 15 slides) were submitted.

Continue reading “We Take Immense Pride in Sponsoring the Ultimate CMO Challenge – Atharva’17”

2.5 Quintillion Bytes of Data are Being Created Everyday

Astounding amounts of 2.5 quintillion bytes of data are created everyday today. Attached with this post is an informative infographic made by our team of trainers at DexLab Analytics.

 

The process of Big Data accumulation is best described as the collection of complex and large datasets of such an amount that it becomes difficult to process, capture, store, analyze and search them with the use of conventional data base systems.

 

It requires the use of more advanced mechanisms to do the same. Currently the use of Big Data is shaping the world around us, offering a deeper qualitative insight within our daily lives.

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You Must Put These Data Analytics Books in Your Reading List This Year

To be a successful data analyst, you must share two very important attributes that you must possess:

 

  1. You must be a voracious reader in order to keep up with the developments in the industry
  2. You must be willing to share your knowledge with the people in a simplified manner, so that everyone around you also gets access to this knowledge
     
    You Must Put These Data Analytics Books in Your Reading List This Year

 

That is because the universe around us deals in the common currency of information and wisdom, which should flow freely without any price tags on it.

Continue reading “You Must Put These Data Analytics Books in Your Reading List This Year”

You Must Know These 7 Data Analytics Job Titles

You Must Know These 7 Data Analytics Job Titles

These days leveraging data be it big or small has become a powerful tool for all enterprises. IT firms are successfully transitioning to digital businesses and opportunities within the companies themselves are increasing to fulfil the growing demands.

So, if you want to join this megatrend in the job market, read on to find out the most in-demand data analytics job titles for today’s professionals:

Data scientist:

This job title has been getting a lot of attention since the past few years now. So much so, that even Glassdoor named it as the best career choice for optimum work/life balance. Their salaries are also comparatively higher.

But the field is still cloudy in terms of the job functions. So, let us understand what it actually means to be a data scientist.

According to Burch Works data scientists are people who “apply sophisticated quantitative measures and computer skills to both structure and analyze the massive amount of unstructured data sets or stream data continuously with an intention to derive information and prescribe action.

The executive recruiting firm says that the coding skills of these professionals are the main distinguishing factor that separates them from other predictive analytics professionals and allows them to exploit data regardless of its size, source and format.

These data professionals often have a master’s degree or a PhD in quantitative disciplines, such as applied math or statistics. They have expert skills and knowledge in statistical and machine learning methods and know tools like SAS, R etc. they are also proficient in other Big Data software like Hadoop and Spark.

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Advanced analytics professional:

The professionals with this job role perform predictive analysis, prescriptive analysis, simulations, and all other forms of advanced analytics. Their role is however, significantly different from data scientists as they do not work with very large data sets and also not with unstructured data.

Data analyst:

A gamut of responsibilities fall under the job listings of a data analyst. They include ensuring data quality and governance, building different systems that enable businesses to gain user insights, performing actual data analysis and much more. However, the skill sets are similar and typically these professionals fit into the same category as advanced analytics professionals and data scientists, because they all can analyze data. But despite such similarities data analysts may be considered as more junior-level employees who are still in a way generalists and can fit into several different job roles within the organization.

Data engineers:

These are the wizards who work behind the scenes to make the jobs of data analysts and data scientists easier. They are technical professionals who have a deep understanding of Hadoop and other Big Data technologies like MapReduce, Hive, SQL and Pig, NoSQL technologies and other data warehousing systems.

Their primary job role is to construct the plumbing, build the data pipelines that clean, collect and aggregate data, organize it from different sources and then load them in data warehouses and databases.

Note that data engineers do not analyze data, but in other words keep the data flowing for processing so that other professionals can analyze them.

Business Analyst:

Business analysts can perform all the tasks that are almost the same for those who perform data analysis. However, business analysts generally have specialized knowledge of their specific business domain and then they apply that knowledge and analysis specifically for the business operations. For example, they may use their analytical skills to recommend improvement suggestions for the business.

Database Administrator:

These professionals are responsible for all things relevant to the operations, monitoring, and maintenance of the databases, often SQL or other relational database management systems also form their jurisdiction. Their tasks include installation, configuration, schemas definition, user training, and maintaining documents.

The database vendors like IBM, Oracle, Microsoft and others often offer certifications specific to their own proprietary technologies for such pros.

Business Intelligence professional:

BI professionals are responsible for adapting themselves with OLAP tools, reports and other data dashboards for looking at historical trends within data sets. Business Intelligence can have data visualization, and also include popular business intelligence platforms like Qlik, Tableau and Microsoft Power BI.

These were the most in-demand job titles in the data analysis industry, to help turn your career into the right direction take a look at our Big Data courses and have a job that you would thoroughly enjoy.

 

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Understanding the Difference Between Factor and Cluster Analysis

Understanding the Difference Between Factor and Cluster Analysis

Cluster analysis and factor analysis are two different statistical methods in data analytics which are used heavily in analytical methods of subjects like natural sciences and behavioural sciences. The names of these analytical methods are so because both these methods allow the users to divide the data into either clusters or into factors.

Most newly established data analysts have this common confusion that both these methods are almost similar. But while these two methods may look similar on the surface but they differ in several ways including their applications and objectives.

Difference in objectives between cluster analysis and factor analysis:

One key difference between cluster analysis and factor analysis is the fact that they have distinguished objectives. For factor analysis the usual objective is to explain the correlation with a data set and understand how the variables relate to each other. But on the other hand the objective of cluster analysis is to address the heterogeneity in the individual data sets.

Put in simpler words the spirit of cluster analysis is to help in categorization but that of factor analysis are a form of simplification.

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Difference is solutions:

This is not an easy section for drawing a line of separation in between cluster and factor analysis. That is because the results or solutions obtainable from both these analysis is subjective to their application. But still one could say that with factor analysis provides in a way the ‘best’ solutions to the researcher. This best solution is in the sense that the researcher can optimize a certain aspect of the solution this is known as orthogonality which offers ease of interpretation for the analysts.

But in case of cluster analysis this is not the case. The reasons behind that being all algorithms which can yield the best solutions for cluster analysis are usually computationally incompetent. Thus, researchers cannot trust this method of cluster analysis as it does not guarantee an optimal solution.

Difference in applications:

Cluster analysis and factor analysis differ in how they are applied to data, especially when it comes to applying them to real data. This is because factor analysis can reduce the unwieldy variables sets and boil them down to a smaller set of factors. This makes it suitable for simplifying otherwise complex models of analysis. Moreover, factor analysis also comes with a sort of confirmatory use researchers can use this method to develop a set of hypotheses based on how the variables in the data set are related.  After that the researcher can run a factor analysis to further confirm these hypotheses.

But cluster analysis on the other hand is suitable only for categorizing objects as per certain predetermined criteria. In cluster analysis a researcher can measure selected aspects of say a group of newly discovered plants and then place these plants into categories of species grouped by employing cluster analysis.

Here is an infographic to better explain the difference between cluster analysis and factor analysis: 

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
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To learn more about Machine Learning Using Python and Spark – Enrol Now.
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Aspiring Data Analysts Must Know the Answer to These Interview Questions

Aspiring data analysts must know the answer to these interview questions

You have recently completed Data analyst certification and are hunting vigorously for a job as a data scientist. But the prospect of sitting for such an important job role at a corporate firm in front of a room full of C-suite interviewers is an intimidating prospect. But fear not as we at DexLab Analytics have got you covered both inside the class room as well out.

This megatrend on Big Data analysts started first in 2013, when the leading universities of the world began to realize the gap in between the demand and supply of Big Data professionals. And soon several , Data analyst training institutes cropped up here and there and rooms transformed into classrooms with several students being keen to learn about the steps to handle Big Data  and to join the ranks of data scientists which is a highly sought after profession of these days. Continue reading “Aspiring Data Analysts Must Know the Answer to These Interview Questions”

Trending Data Job Role: Chief Data Officer

Trending data job role: Chief Data Officer

Financial firms are going berserk in order to employ the best Chief Data Officers from around the world. This is the new hype in the C-suite world who wants to manage risks associated with data and also grasp its opportunities for conducting better business.

These days all financial firms are sincerely focused on maintaining their data and governing them to comply with the latest rules and regulations. They want to comply with customer demands to maintain their competitive edge and stay on top of the game. And in order to maintain this, the financial services teams are on a hyper drive in hiring the C-suite role of a Chief Data Officer i.e. CDO.

Recent developments in the regulatory mandates of Volcker Rule of the Dodd-Frank Act in relation to capital planning have made it difficult for financial organizations to aggregate and manage their data. In a recent stress test a large number of major US corporate banks and other financial institutions have failed as the quality of their data was not up to scratch.

But expert data analyst and scientists state that only regulatory compliance is not the main issue at hand. Effective risk management goes hand-in-hand with efficient data management. And firms are lacking that front as they do not manage their data effectively and are simply gambling with chances of a hug penalty at the risk of losing customers and acquiring a bad name in the business.

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The opportunities in this position of Chief Data Officer:

While the aspects of regulatory compliance and risk management are becoming more and more complex every day, but that is not the only reason to move up information management positions and invite them into the boardroom. That is why as most financial organizations know that good governance requires strong data management skills with good understanding of architecture and analytics. Companies have come to realize that this kind of information can prove to be effective and provide them with competitive advantage in terms of reaching out to customers and protecting them with the offering of innovative products and services.

According to latest research, experts predicted that 25 percent of every financial organization will have employed a Chief Data Officer by the end of 2015. The job responsibility of this role is still clouded and most organizations are trying to refine and boil it down, but as of now three main roles have been identified – data governance, data analysis and data architecture and technology. While according to this survey 77 percent of the CDOs will remain focused in governance focused but their responsibilities are likely to grow into other areas as well. The main objective behind data architecture is to oversee how data is sourced, integrated and then consumed in the global organizations. The way to lead efficiencies in this respect is to consider this aspect in depth. Thus, it can be concluded that data analytics has the most potential.

For more details on Online Certificate in Business Analytics, visit DexLab Analytics. Their online courses in data science are up to the mark as per industry standards. Check out the course module today.

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics has started a new admission drive for prospective students interested in big data and data science certification. Enroll in #BigDataIngestion and enjoy 10% off on in-demand courses, including data science, machine learning, hadoop and business analytics.

 

Interested in a career in Data Analyst?

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To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

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