10 Most Popular Analytics Tools In Business

The increasing importance and demand for data analytics have opened up new potential in the market. Each year, new tools and programming languages are being launched aimed at easing up the process of analyzing and visualizing the data.

While many such advanced business intelligence tools come up in paid versions, there are great free and open-source data analytics courses and tools available in the market too. Read on to find out about the 10 best and most popular data analytics tool for business right now.

1. R Programming
R is the most popular programming language cum tool widely used by experts for the purpose of data analytics and visualization. The tool is free and open-source in nature and allows the users to alter its code set for clearing bugs and updating the software on their own.
2. PYTHON
Python is an open-source and free OOP based scripting language popular in the data analytics market since the start of the 90s. Python supports both structured and functional programming methods and is very easy to learn and operate upon. Python is expert in handling text-based data.
3. Tableau Public
Tableau Public is another free software and business intelligence tool which is capable of connecting all kinds of data source be it Excel-based data, Data Warehouse or web-based data. Tableau creates maps, graphs and dashboards with real-time updates presenting on the web. The data can be shared over social networks too.
4. SAS
Sas is a leading analytics tool and programming language specifically developed for the purpose of interacting with and manipulating data by the SAS institute in 1966 with updates presented during the 80s and 90s. Data present in SAS can be accessed, analyzed and managed easily from any sources and is capable of predicting behaviors of customers and prospects along with recommending optimized communication models.
5. Excel
One of the most popular and underrated data analytics and visualization tool in the market, Excel was developed by Microsoft as part of their MS Office and is one of the most widely used tools in the industry. All kinds of data analytics tools still require Excel to work in some kind of way and it is very easy to be learnt and operated.
6. KNIME
KNIME is a leading open source and integrated analytics tool developed by a team of software engineers from the University of Konstanz in January 2004. KNIME allows the users to analyze and model the data through visual programming integrating components of data mining and machine learning via its modular data pipelining concept.
7. Apache Spark
Developed in 2006 by the Berkeley’s AMP Lab of University of California, Apache is a fast large-scale data processing, analysis, and visualization tool capable of executing applications around 100 times faster in memory and 10 times faster on disk. It is popular for data pipelining and machine learning models development allowing it to double up as business intelligence tool.
8. RapidMiner
RapidMiner is another powerful data analytics tool which can double up as business intelligence tool owing to its capability to perform predictive analysis, behavioral analysis, data mining, etc. The tool can incorporate with any other data source types such as Excel, Microsoft SQL, ACCESS, Oracle, Ingres, IBM SPSS, Dbase, etc.
9. Google Analytics
A freemium and widely recommended product for data analytics, Google Analytics is a perfect offering from Google for the Small and Medium-scale enterprises who don’t possess the technical knowledge or the means to gather that knowledge in the present course.
10. Splunk
Splunk is an analytics tool mostly directed to searching and analyzing machine-generated data. The tool pulls up all text-based log data and provides the means to search through it for gathering any relevant or required data.

Data Science Resume: A Complete Guide!

‘Information Scientist’ is at the first spot on the list of the best positions in 2019. It compensates fairly and furthermore offers an exceptionally difficult and remunerating vocation way. In that capacity, the quantity of information science positions have expanded thus have the quantity of candidates.

Regardless of whether you overlook the opposition, you actually need to demonstrate that you have what it takes to be a piece of the organization. Anyway, what is the initial step to sacking the information science position you had always wanted? A heavenly and all around created continue with Certifications.

Indeed, even before you meet the employing administrator, they will have shaped an assessment on you through your resume. Thus, it should be eye catching and lead them to call you for a meeting.

Procure Data Science Certifications – Click Here

The Basics

Most applicants commit the huge error of setting one up resume and sending it off every single possible manager (and generally erroneously cc-ing them all). This is an extremely unfruitful practice; it will not get you the outcomes you need.

Along these lines, if an organization puts out a promotion for an information researcher whose essential ability is Python and you send them a resume clarifying how you are King of R, then, at that point sorry; it won’t work.

Every one of your resumes ought to be custom-made to the position and opportunity you are applying for. A similar resume can be conveyed to a couple of various managers, yet and still, at the end of the day minor changes should be made.

Likewise, remember the accompanying pointers as you start making your information science continue:

Keep the resume one page long. Until and except if you have 15+ pertinent involvement with the field, don’t go more than one page.

Use whitespace liberally.

Use headings and subheadings where suitable. It makes the resume more discernible. Highlighting does as well.

Utilize clear text styles. Most up-and-comers trying to be extravagant, utilize cursive textual styles (like Lobster). Or on the other hand they take it to the next outrageous and utilize relaxed ones (like Caveat). Keep away from these limits. Keep it utilitarian and expert. Use text styles like Arial, Times New Roman, and Proxima Nova.

Try not to exaggerate the tones.

Edit and language structure check your resume consistently. Run it through Grammarly or have a companion take a gander at it. Indeed, even one spelling misstep can destroy your impression.

Get Certifications – Click Here

Segments to remember for your information science continue

Here are the essential areas to be incorporated. You can add and preclude as you wish, however these exemplify the fundamental subtleties that an employing supervisor would have to know. The request can likewise be as you wish.

Resume objective/outline

Work insight

Key/center abilities

Instruction and certificates (assuming any)

Any activities or distributions

Essential data about you

Interests area (or one that shows your character like ‘generally pleased with’)

Become familiar with the IT Skills Here

What to remember for each segment

Resume objective/rundown

This is the principal area that the selection representative’s eyes will fall upon. It is an extremely vital segment since it will assist you with securing your opportunity and propel the spotter to peruse the remainder of your resume where you elucidate upon your accomplishments.

All in all, which one do you compose? Level headed or outline?

Assuming you are a new alumni or a fresher in this field, you compose a resume objective. In the event that you have applicable experience and results in the field, you compose a synopsis.

Here’s the way to compose a resume objective

Ongoing alumni from XYZ University with a Bachelors’ in Computer Science. Applied my insightful and vital abilities in building projects that won me the Global Data Science Challenge in 2018. Anxious to apply my abilities to tackle genuine issues now.

Intriguing. You’d need to peruse further, no?

Here’s the point at which you would not have any desire to peruse further

Ongoing alumni from XYZ University with a Bachelors’ in Computing and IT. Hoping to learn information science innovations and become gifted at them.

Oopsies. That one gets thrown in the receptacle. Notice your abilities, any accomplishments on the off chance that you have them, and how you can help the business rather than the opposite way around. Then, here’s the means by which to compose a resume outline:

Aspiring information science engineer with 5+ long periods of involvement. Spend significant time in utilizing Tableau to make clearness creating information models that distil a lot of information into effectively got perceptions. Victor of the Annual Tableau Challenge.

Here’s the way to not compose it

Information science engineer with broad experience can do factual examination, information cleaning, information perception and furthermore lead groups.

End: stay away from obscure cases. Incorporate hard realities and numbers to make your ability more unmistakable.

Work insight

Notice your work insight in switch sequential request. This will permit you in the first place the most great focuses since your obligations and results would have increased since your vocation started. Then, pick your best undertakings to incorporate. No compelling reason to make reference to each project you’ve chipped away at under the sun.

At long last and above all, focus on sway. Each datum science resume will specify measurable examination, information perception, and information mining. In any case, the effect that you would’ve made would be novel to you. So incorporate hard realities and numbers about how your endeavors and abilities assisted the organization with developing.

Here’s a potential arrangement :

Position and friends name

Worked from ____-____

Area

Key accomplishments

<Here you talk about the effect you have made through your obligations and any critical honors that you may have won>

Here’s a guide to make it more clear:

Information researcher at Goldman Sachs

Jan 2015-October 2019

Bangalore, India

Key accomplishments

Made and carried out models for anticipating advance benefit. Accomplished a 20% improvement rate in the nature of credits supported.

Driven an information representation group of 20 to work on the nature of factual revealing.

Won the Global GS Data Science Competition 3 quarters in succession.

Once more, keep away from unclearness. Backing your cases with statistical data points.

Key/center abilities : If the construction of your resume permits it, partition your abilities into hard abilities and delicate abilities.

Hard abilities in information science incorporate : Python, R, SQL, APIs, Data Cleaning, Data Manipulation, Command Line, and so forth

Delicate abilities incorporate : administration, logical reasoning, key reasoning, innovativeness, collaboration, and so forth

Training and confirmations

The vast majority incorporate this segment before the work experience area. Be that as it may, the last is more pertinent to the recruiting cycle, particularly in the event that you have been in the business for somewhere around 2 years. Thus, place it in like manner.

In the event that you’ve passed college, there’s no compelling reason to incorporate your tutoring. Likewise, follow a converse sequential request wherein you notice your latest degree first. Notice any intriguing ventures or grants you won during your program or any numerical/figuring clubs/social orders you were a piece of.

In the event that you have any certificates, incorporate those also. For instance, when you are going after an information science related position, an affirmation of information science from a rumored foundation would assist you with getting the meeting call.

IIT Bhilai Data Science Program

Fundamental data

This incorporates your name, city, state (and nation on the off chance that you are going after an abroad position). Additionally, incorporate your dynamic email address, phone, connection to your LinkedIn profile, and blog interface in the event that you have one. Since you are going after an information science job, enrollment specialists will need to see which projects you have worked on or are at present chipping away at. In this way, incorporate a GitHub interface also.

Wrapping Up

These will assist with directing you in making your information science continue. It is pretty much as significant as some other part of the recruiting interaction. In this way, make a point to give it your best by following the above tips and rules. We’ll see you on the opposite side of being recruited!

‘Information Scientist’ is at the first spot on the list of the best positions in 2019. It compensates fairly and furthermore offers an exceptionally difficult and remunerating vocation way. In that capacity, the quantity of information science positions have expanded thus have the quantity of candidates.

Regardless of whether you overlook the opposition, you actually need to demonstrate that you have what it takes to be a piece of the organization. Anyway, what is the initial step to sacking the information science position you had always wanted? A heavenly and all around created continue with Certifications.

Indeed, even before you meet the employing administrator, they will have shaped an assessment on you through your resume. Thus, it should be eye catching and lead them to call you for a meeting.

Procure Data Science Certifications – Click Here

The Basics

Most applicants commit the huge error of setting one up resume and sending it off every single possible manager (and generally erroneously cc-ing them all). This is an extremely unfruitful practice; it will not get you the outcomes you need.

Along these lines, if an organization puts out a promotion for an information researcher whose essential ability is Python and you send them a resume clarifying how you are King of R, then, at that point sorry; it won’t work.

Every one of your resumes ought to be custom-made to the position and opportunity you are applying for. A similar resume can be conveyed to a couple of various managers, yet and still, at the end of the day minor changes should be made.

Likewise, remember the accompanying pointers as you start making your information science continue:

Keep the resume one page long. Until and except if you have 15+ pertinent involvement with the field, don’t go more than one page.

Use whitespace liberally.

Use headings and subheadings where suitable. It makes the resume more discernible. Highlighting does as well.

Utilize clear text styles. Most up-and-comers trying to be extravagant, utilize cursive textual styles (like Lobster). Or on the other hand they take it to the next outrageous and utilize relaxed ones (like Caveat). Keep away from these limits. Keep it utilitarian and expert. Use text styles like Arial, Times New Roman, and Proxima Nova.

Try not to exaggerate the tones.

Edit and language structure check your resume consistently. Run it through Grammarly or have a companion take a gander at it. Indeed, even one spelling misstep can destroy your impression.

Get Certifications – Click Here

Segments to remember for your information science continue

Here are the essential areas to be incorporated. You can add and preclude as you wish, however these exemplify the fundamental subtleties that an employing supervisor would have to know. The request can likewise be as you wish.

Resume objective/outline

Work insight

Key/center abilities

Instruction and certificates (assuming any)

Any activities or distributions

Essential data about you

Interests area (or one that shows your character like ‘generally pleased with’)

Become familiar with the IT Skills Here

What to remember for each segment

Resume objective/rundown

This is the principal area that the selection representative’s eyes will fall upon. It is an extremely vital segment since it will assist you with securing your opportunity and propel the spotter to peruse the remainder of your resume where you elucidate upon your accomplishments.

All in all, which one do you compose? Level headed or outline?

Assuming you are a new alumni or a fresher in this field, you compose a resume objective. In the event that you have applicable experience and results in the field, you compose a synopsis.

Here’s the way to compose a resume objective

Ongoing alumni from XYZ University with a Bachelors’ in Computer Science. Applied my insightful and vital abilities in building projects that won me the Global Data Science Challenge in 2018. Anxious to apply my abilities to tackle genuine issues now.

Intriguing. You’d need to peruse further, no?

Here’s the point at which you would not have any desire to peruse further

Ongoing alumni from XYZ University with a Bachelors’ in Computing and IT. Hoping to learn information science innovations and become gifted at them.

Oopsies. That one gets thrown in the receptacle. Notice your abilities, any accomplishments on the off chance that you have them, and how you can help the business rather than the opposite way around. Then, here’s the means by which to compose a resume outline:

Aspiring information science engineer with 5+ long periods of involvement. Spend significant time in utilizing Tableau to make clearness creating information models that distil a lot of information into effectively got perceptions. Victor of the Annual Tableau Challenge.

Here’s the way to not compose it

Information science engineer with broad experience can do factual examination, information cleaning, information perception and furthermore lead groups.

End: stay away from obscure cases. Incorporate hard realities and numbers to make your ability more unmistakable.

Work insight

Notice your work insight in switch sequential request. This will permit you in the first place the most great focuses since your obligations and results would have increased since your vocation started. Then, pick your best undertakings to incorporate. No compelling reason to make reference to each project you’ve chipped away at under the sun.

At long last and above all, focus on sway. Each datum science resume will specify measurable examination, information perception, and information mining. In any case, the effect that you would’ve made would be novel to you. So incorporate hard realities and numbers about how your endeavors and abilities assisted the organization with developing.

Here’s a potential arrangement :

Position and friends name

Worked from ____-____

Area

Key accomplishments

<Here you talk about the effect you have made through your obligations and any critical honors that you may have won>

Here’s a guide to make it more clear:

Information researcher at Goldman Sachs

Jan 2015-October 2019

Bangalore, India

Key accomplishments

Made and carried out models for anticipating advance benefit. Accomplished a 20% improvement rate in the nature of credits supported.

Driven an information representation group of 20 to work on the nature of factual revealing.

Won the Global GS Data Science Competition 3 quarters in succession.

Once more, keep away from unclearness. Backing your cases with statistical data points.

Key/center abilities : If the construction of your resume permits it, partition your abilities into hard abilities and delicate abilities.

Hard abilities in information science incorporate : Python, R, SQL, APIs, Data Cleaning, Data Manipulation, Command Line, and so forth

Delicate abilities incorporate : administration, logical reasoning, key reasoning, innovativeness, collaboration, and so forth

Training and confirmations

The vast majority incorporate this segment before the work experience area. Be that as it may, the last is more pertinent to the recruiting cycle, particularly in the event that you have been in the business for somewhere around 2 years. Thus, place it in like manner.

In the event that you’ve passed college, there’s no compelling reason to incorporate your tutoring. Likewise, follow a converse sequential request wherein you notice your latest degree first. Notice any intriguing ventures or grants you won during your program or any numerical/figuring clubs/social orders you were a piece of.

In the event that you have any certificates, incorporate those also. For instance, when you are going after an information science related position, an affirmation of information science from a rumored foundation would assist you with getting the meeting call.

IIT Bhilai Data Science Program

Fundamental data

This incorporates your name, city, state (and nation on the off chance that you are going after an abroad position). Additionally, incorporate your dynamic email address, phone, connection to your LinkedIn profile, and blog interface in the event that you have one. Since you are going after an information science job, enrollment specialists will need to see which projects you have worked on or are at present chipping away at. In this way, incorporate a GitHub interface also.

Wrapping Up

These will assist with directing you in making your information science continue. It is pretty much as significant as some other part of the recruiting interaction. In this way, make a point to give it your best by following the above tips and rules. We’ll see you on the opposite side of being recruited!

Why Indian Businesses are adopting Fast Oracles Self-driving Database?

The era of automation and cloud is here. In order to drive success and delight customers, Indian companies are looking to Fast Oracle Self driving databases. 

Data drives the world. Be it consumer insights such as shopping behavior, music preference or payment preferences. Today companies are leveraging the power of technology to crunch large amounts of data. Digital transformation has become an integral factor behind a company’s growth. When we take a closer look at how large amounts of data is processed and stored, we know that the possibilities are endless thanks to automation and cloud.

Companies such as Oracle have been leaders in the space for decades are now combining technology pillars such as machine learning, automation and cloud to provide solutions such as ‘self-drive database’. In simple terms, what it means with minimal human intervention, the power of data, businesses are able to achieve high performance at a lower cost.

Take for example, a clothing brand that wants to improve their point of sale interactions to enhance customer experience. How can they do it? By using the ‘Autonomous Cloud Service’ by Oracle, the brand is able to extract and manage relevant data which can support the end customer experience. All this can be done in an Agile business courses enterprise by unlocking deep insights from large amounts of data.

Furthermore, services such as Autonomous Database not only automate the whole process but also take into consideration data privacy, protection against cyber-attacks, data thefts and storage. Organisations that undergo Agile business training can unlock the true potential of autonomous services.

Some of the key attributes which can help companies invest in such services are:

Automation of Management Processes

Oracle Cloud Infrastructure and Autonomous Database provide companies with integrated solutions such as data management, repair, tuning and upgrade to ensure business continuity and growth. 

Reduces Cost of Operations

Due to minimal or zero human intervention, businesses can focus on leveraging key customer insights derived from data thereby reducing operational costs. 

Data Privacy

Data Privacy has become a top priority for most organisations today and implementing a software database storage solution provides them with the opportunity to safeguard data on cloud against cyber-crimes.

Take Strategic Decisions Fast

In an agile world, a key factor that comes into play is when businesses are provided with an opportunity to take quick, decisions. From a strategy perspective, Autonomous Data Warehouses offer this as a part of the solution.
Conclusion
Using a traditional database is time-consuming, and Indian businesses are catching on. Due to the exponential value that self drive databases provide Indian companies are adopting this in order to accelerate growth.

Spark Vs MapReduce

Spark and Hadoop MapReduce are both open-source frameworks from the Apache stable of Software. Since 2013 when Spark was released it has literally overtaken and acquired more than twice the number of Hadoop’s customers. And this lead is growing.

However, big-data frameworks are directly linked to the customer’s need for a particular framework and its uses. Therefore a literal comparison is difficult and we need to discuss what Spark and MapReduce are used for and their differences to evaluate their performance.

The performance differences between Spark and MapReduce:

The main differences between the two are that is that MapReduce processing involves, reading from data and then writing it to the disk, whereas Spark process data within its memory. This feature makes Spark very fast at processing data.

However, MapReduce has a far greater potential for processing data compared to Spark. Spark is faster by a 100-fold increase in speed and its ability to process data within the memory has scored with its customers preferring it over MapReduce.

Where MapReduce is useful:

As pointed out above the potential for data processing is high in MapReduce. It  is useful in applications using:

  • Large data sets linear-processing:

Hadoop-MapReduce enables very large data sets to be processed in a parallel fashion. It uses the simple technique of dividing the data into smaller sets processed on different nodes while gathering the results from these multi-nodes to produce a single set of results. When the resultant data set produced is bigger than the RAM capacity Spark will falter whereas MapReduce performance is better.

  • The solution is not for speedy processing: 

Where processing speed is not critically important Hadoop MapReduce is a viable and economical answer. Ex: If data can be processed at nights.

Where Spark is useful:

  • Rapid processing of data: 

Spark’s processing speeds are within the memory and about 10 fold better in terms of storage data and a 100 fold in terms of RAM data.

  • Repetitive data processing:

Spark’s RDDs allow it to map all operations with the memory. MapReduce will read and write the resultant set to the disk.

  • Instantaneous processing:

Spark enables such processing if instantaneous decision-making is required.

  • Processing of Graphs:

Spark scores in repetitive iterative tasks as in graphs because of its inbuilt API GraphX.

  • Machine learning:

Unlike MapReduce, Spark has an inbuilt ML library. MapReduce needs an ML library to be provided by an outside source to execute the same task. The library has many innovative algorithms that both Spark and MapReduce use while computing.

  • Combining datasets:

Spark is speedier and can combine data sets at high speeds. In comparison, MapReduce is better at combining very big data sets albeit slower than Spark.

Conclusion:

Spark outperforms Hadoop with real-time iterative data processing in memory in

  • Segmentation of customers demonstrating similar patterns of behavior thus providing better customer experiences.
  • Management of risks in decision-making processes.
  • Detection of fraud in real-time is possible due to its ML library of algorithms being trained on data that is historical and inbuilt. 
  • Analysis of industrial big-data analysis in machinery breakdown is a plus feature of Spark.
  • It is compatible with Hive, RDDs and other Hadoop features.  

AI and Machine Learning in Robotics!

Artificial intelligence (AI) has to transform almost every industry we can imagine, and industrial robotics is no different. The powerful combination of robotics and artificial intelligence or machine learning opens the door to completely new automation possibilities.

Artificial intelligence courses and machine learning are currently only used to a limited extent and are increasing the capabilities of industrial robot systems. We have not yet reached the full potential of robotics and machine learning, but the current applications are very promising.

4 Basics of Artificial Intelligence and Machine Learning in Robotics
There are four areas of robotic processes that influence AI and machine learning to make today’s applications more efficient and profitable. The scope of AI in robotics includes:

Vision – AI helps robots see elements they’ve never seen before and see objects in greater detail.
Reaching – Robots also capture objects they’ve never seen before using AI and machine learning to help them determine the best position and orientation to pick up objects.
Motion Control – Machine Learning helps robots interact dynamically and avoid obstacles to maintain productivity.
Data – AI and machine learning help robots understand physical and logistical data models in order to be active and act accordingly.
AI and machine learning are still in their early stages for robotic applications, but they are already having an important impact.
Two types of applications for industrial robots using artificial intelligence and machine learning
Supply chain and logistics applications see some of the first implementations of AI and machine learning in robotics.

In one example, a robotic arm is responsible for handling frozen food crates that are closed cold. Frost causes the shape of objects to change – the robot not only displays different parts from time to time, but also constantly displays parts of different shapes. AI helps robots to recognize and capture these objects even though they are different shapes.

Another great example of machine learning is selecting and storing over 90,000 types of parts. This number of types of parts would be useless without machine learning, but now engineers can regularly send images of new parts to the robot, and the robot can then successfully capture those parts.

AI and machine learning will have a transformative impact on industrial robots. While these technologies are still in their infancy, they will continue to push the boundaries of what is possible with industrial robotic automation for decades to come.

The State of Analytics Firms in India

Big Data and analytics is here to stay and provides tangible value to thousands of organizations around the world. India also seems to play an important role in propagating analytics and data science as disciplines around the world. What we are seeing right now is faster adoption of analytics and consequently, a huge demand for companies that provide these services.

Here is a quick snapshot of the analytics industry in India.

Growth Rate of Analytics Companies in India: 27% in 2016

While that sounds great, it is only the established players who contributed significantly to the India growth story. The percentage increase in the number of analytics companies seems abysmally small compared to 2011-2014, when number of analytics companies more than doubled each year.

Average Employee Strength: 162 employees

On average, Indian Analytics companies have 160 employees on their payroll as of 2016, according to Analytics magazine. While this isn’t a huge number, it is a healthy jump from the average of 115 employees since last year. However, compared to global standards for Analytics firms, India is above average – almost 73% of analytics companies in India have less than 50 employees, compared to 86% globally.

Company Type

  • 37% – of analytics providers in India are essentially full-service outsourcing firms in IT or processes that have separate offerings for analytics or big data.
  • 47% – analytics providers are boutique analytics firms, with majority focus on providing analytics as a service.
  • 47% – analytics providers are boutique analytics firms, with majority focus on providing analytics as a service.
  • 4% – training & education firms and 1% staffing firms in analytics space

 

The Big Players

The 10 largest IT/ BPO service providers with Analytics capabilities (Based on employee strength in analytics are:

  • TCS
  • Accenture
  • Cognizant
  • IBM
  • Genpact
  • Infosys
  • Wipro
  • Capgemini
  • Deloitte
  • HCL

 

The 10 largest Boutique analytics firms in India

  • Mu Sigma
  • Fractal Analytics
  • Manthan
  • Axtria
  • Latentview analytics
  • Absolutdata
  • Blueocean Market Intelligence
  • Analytics Quotient
  • Ugam Solutions
  • Bridgei2i

 

Analytics Hubs in India

Bangalore continues to lead the most number of analytics firms in India, i.e. almost 30%, with Delhi-NCR catching up fast at 26% of all Analytics firms being based there, and Mumbai with 17%.
Analytics firms in Mumbai have the highest employee strength on average (311 employees per company), followed by Bangalore (192) & Chennai (191).

Zenobia Sethna has over 12 years of content and writing experience at leading Indian eLearning companies. She currently works as AVP – Learning Design and Effectiveness at Imarticus Learning, where she spends her free time writing blog articles on Soft Skills, Finance and Analytics.

Analytics interview questions

1. What is the importance of validation of data?
From a business perspective, at any stage, data validation is a very important tool since it
ensures reliability and accuracy. It is also to ensure that the data stored in your system is
accurate, clean and useful. Improper validation or incorrect data has a direct impact on sales,
revenue numbers and the overall economy.

2. What are the various approaches to dealing with missing values?
Missing values or missing data can be dealt with by taking the following approaches-
● Encoding NAs- this used to be a very common method initially when working with
machine learning and algorithms was not very common
● Deleting missing data casewise- this method works well for large datasets with very few
missing values
● Using mean/median value to replace missing values- this method works very well for
numerical features
● Run predictive models to impute missing values- this is highly effective as it works best
with the final model
● Linear regression- works well to provide good estimates for missing values

3. How do you know if a developed data model is good or bad?
A developed data model should fulfil the following criteria to qualify as a good model-
● Whether the data is the model can be easily consumed
● If the model is scalable in spite of good data changes
● Whether performance can be predicted or not
● How good and fast can a model adapt to changes

4. What are some of the challenges I can face if I were to perform a data analysis?
Performing data analysis may involve the following challenges-
● Too much data collection which can often overwhelm data analysts or employees
● Differentiation between meaningful and useless data
● Incoherent visual representation of data
● Collating and analyzing data from multiple sources
● Storing massive amounts of generated data
● Ensuring and restoring both security and privacy of stored data as well as generated
data
● Inadequate experts or lack of industry professionals who understand big data in depth
● Exposure to poor quality or inaccurate data

5. Explain the method of KNN imputation.
The term imputation means replacing the missing values in a data set with some other possible
values. Using KNN imputation in data analysis helps in dealing with missing data by matching a
particular point with its nearest K neighbours assuming that it is a multi-dimensional space. This
has been a highly popular method in pattern recognition and statistical estimation since the
beginning of the 1970s.

6. What does transforming data mean?
Data transformation involves the process of converting data or information from a different
format into the required format in a system. While mostly transforming data involves the
conversion of documents, occasionally it also means conversion of a program from one
computer language to another in a format that is readable by the system.
Data transformation comprises of two key phases, data mapping to ensure smooth
transformation, and code generation, for the actual transformation to happen and run on
computer systems.

7. State the difference between null and alternative hypothesis.
It is a null hypothesis when there is no key significance or relationship between two variables
and is something that the researcher is trying to disprove. No effects are observed as a result of
null hypothesis and neither are there any changes in actions or opinions. The observations of
the researcher are a plain result of chance.
An alternative hypothesis on the other hand is just the opposite of a null hypothesis and has a
significant relationship between two measured and verified phenomena. Some effects are
observed as a result of an alternative hypothesis; and since this is something the researcher is
trying to prove, some amount of changes in opinions and actions are involved. An alternative
hypothesis is a result of a real effect.

8. What would you mean by principal component analysis?
Principal component analysis is a method used to reduce large data sets in dimension by
transforming larger sets of variables into smaller ones, while retaining the principal information.
This is majorly done with the intent of improving accuracy since smaller data sets are easier to
explore, as a result of which data analysis gets faster and quicker for machine learning.

9. Define the term – logistic regression.
Logistic regression is a form of predictive analysis in machine learning that attempts to identify
relationships between variables. It is used to explain the relationship between a binary variable
and one or multiple nominal, ordinal, interval or ratio-level variables, while also describing the
data. Logistic regression is used for categorical dependent variables.

10. How can I deal with multi-source problems?
Storing the same data can often cause quality hindrances in analytics. Depending on what the
magnitude of the issues are, a complete data management system needs to be put in place.
Data reconciliation, elaborate and informative databases and pooling in segmented data can
help in deal with multi-source problems. Aggregation and data integration is also helpful while
dealing with multi-source data.

11. List the most important types of clustering algorithms.
The most important types of clustering algorithms are-
● Connectivity models- based on the idea that farther data points from each other exhibit
less similarity when compared to closer data points in data space
● Centroid models- the closeness of a data point to the cluster centroid derives the notion
of similarity for this model
● Distribution models- based on the probability that all data points in the same cluster are
part of the same distribution
● Density models- search for varied density areas of data points in the data space

12. Why do we scale data?
Scaling is important because sometimes your data set will have a set of features that completely
or partially vary in terms of units, range and magnitude. While certain algorithms have minimum
or zero effects, scaling can actually have positive impacts on the data. It is an important step of
data preprocessing that also helps to normalise data within a given range. Scaling of data also
often helps in speeding up algorithm calculations.

All you Need to Know about Python and being a Certified Professional!

Programming has always been the core of computer science and Information Technology. Every year millions of programmers graduate with degrees to look for employment opportunities. Therefore, the demand for programmers has grown exponentially, and the trend will not be out anytime soon.

Python is one of the most familiarly used programming languages and was released by Python Software Foundation in 1991. In a fraction of years, it gained popularity and was started being used as a programming language in various disciplines.

Python Programming Defined:

Python is an interpreted, general-purpose, and high-level programming language developed by Guido Van Rossum. Today, companies use Python for GUI and CLI-based software development, web development (server-side), data science, machine learning, drone systems, AI, robotics, developing cyber-security tools, mathematics, system scripting, etc.

Python ranks second among other programming languages. Imarticus Learning has some fascinating advanced-level courses on Python and data science, covering Machine Learning and Artificial Intelligence using Python. With expertise in python programming, candidates can start learning advanced-level Python libraries and modules such as Pandas, SciPy, NumPy, Matplotlib, etc.

Python Programming Career Options:

Python programming coursesAfter a course in applied data science with python specialization, you can choose several career paths. Some are stated below:

Data Visualization with Python and Matplotlib: The profile is linked with extensive data analysis, which is a future for the IT industry.

Web Programming: As you know, python is a concise language; many things can help you build a career as a web programmer.

Developing Games: If you are passionate about gaming and wish to develop games as a career someday, you need to put in efforts to learn Python and how to develop games.

Analyzing Data with Python and Pandas: This allows you to pivot into data science.

Why Python for Data Science?

The first benefit of data science using python is its simplicity. While data scientists come from a computer science background or know other programming languages, many belong to backgrounds with statistics, mathematics, and other technical fields. They may lack coding experience when they enter the field of data science. Python is easy to follow and write, making it a simple programable language to start and learn quickly.

There are numerous free resources available online let you learn Python and get help from communities. Python is an open-source language and is beneficial for data scientists looking to learn a new language because there is no up-front cost involved. This also means that many data scientists are already using Python, so there is a strong community for better guidance.

Python is especially popular among data scientists. There are many python tutorials and python classes where the world comes together to share knowledge and connect. Countless libraries like Pandas, NumPy, and Matplotlib available in Python for data cleaning, data visualization, data analysis, and machine learning make tasks easy.

Build Career in Data Science with Imarticus Learning:

Python programming course

Imarticus Learning offers some best data science courses in India, ideal for fresh graduates, professionals, and executives. If you wish to fast-track your Data Science career with guaranteed placement opportunities, Imarticus learning is the place you need to head for right away!

Industry experts design the programs to help you learn real-world data science applications and build robust models to generate valuable business data. Students go through rigorous exercises, hands-on projects, boot camps, hackathon, and personalized Capstone project, which prepares them to start a career in Data Analytics. Send an inquiry through the Live Chat Support System and request virtual guidance to commence the transforming journey!

What are the Steps to Transition into Data Analytics?

One can always migrate to data analytics regardless of his/her field and educational background. But people often find the transition to be confusing. If you are also looking to change your career into data analytics, this article will help you in getting an understanding as to what to do and how. Many companies hire fresh graduates from the college and provide them in-house data analytics training at their cost.

As they are looking for new and unbiased opinions regarding their business problems as well as its solutions. Being a fresher relieves you from any baggage and allows you to mould your career in the field any way you want.

Also Read – What are the Salary Trends in Data Analytics?
Here are the steps to follow to transition into data analytics –

Identify Your Interest and Ideal Job

The first thing you need to do when you are changing your field of data analytics is to identify the perfect place for you to be here. There is a lot of scope in data analytics as you can choose to be a traditional data analyst or try some more exciting options such as data scientist, data engineer and so on. Conduct proper and thorough research into the field at first to have a clear basic understanding regarding it. You can do this while still at your current job and give yourself a head start for the transition as you won’t have to sit idle after leaving your post.

Acquire Proper Skill-set and Training

Now that you have settled down or are close to settling down on the ideal job option for you in data analytics, it is time to start training and acquiring the skill-sets needed to survive and thrive in the field. You have to brush up and strengthen your knowledge and understanding of mathematics and especially statistics as it is the essential requirement of the area. Then, you have to acquire analytics skills, tools skills, problem-solving ability and much more. It is better to join a professional data analytics training course for this as they will provide you with the all-round training required to prosper in the industry as a data analyst.

Get Data Analytics Certification

Although you may have acquired some or all of the skill-sets to be a useful data analyst, possessing a data analytics certification will boost your chances of getting into the field as a fresher especially since you are jumping in from a different one. Getting a certification will make it easier for you to start your data analyst career as companies tend to hire a certified professional as they come with a reputation attached to them.

You can get your certification by giving any recognised data analytics test which takes place both online and offline all the time. You must choose the examination carefully though as some of them are designed to provide certification for a particular job option only.

Get into a Company and Start Your Career

Once you have acquired certification for data analytics, you are now eligible to sit in the interviews organised by the companies and organisations to fill up the positions of data analyst in their ecosystem. Once again, you have to be careful regarding deciding as to what area you want to apply for. Companies may announce vacancies for data scientists, data engineers or other related job posts too. You would want to take up the position you were preparing for since the beginning. Although, it is always possible for you to change course mid-way. You must never take your job lightly though as there is a lot of stress coming with a=data analyst responsibilities and thus you have to prepare yourself vigorously.

Related Article :

What Is Virtualization In Cloud Computing?

Virtualization is one of the most important aspects of cloud computing. It allows multiple virtual machines to run on a single physical server, and it also helps reduce costs.

Cloud computing is just the latest buzzword for this technology, but many people don’t know that it has been around since the 1960s when IBM released its first mainframe computer and started to think about how to make more efficient use of hardware.

What is virtualization in cloud computing?

Virtualization in cloud computing can be described as running multiple operating systems simultaneously on a single computer with their own set of resources allocated to them. This makes use of resources more efficiently and reduces the cost for users who want to access these services.

The system allows sharing a single physical instance of resource to multiple users. Cloud Virtualization manages workload by transforming traditional computing and making it more scalable, economical, and efficient.

The benefits are many- everything from increased security, better back-ups, lower power consumption, and easier management.

How Virtualization Works?

Virtualization in Cloud Computing training provides a virtual environment in the cloud that can be software hardware or anything. In virtualization, server and software application are required by the cloud providers for which they pay nominal fees to the third party.

With the help of Hypervisor, which is software, the cloud customer can access the server. A hypervisor connects the server and the virtual environment and distributes the resources between different virtual environments.

Types of Virtualizations in Cloud Computing

Operating System Virtualization: In operating system virtualization, the virtual machine software is installed in the host’s operating system rather than directly on the hardware system. Its most important use is for testing applications on various platforms or operating systems.

Server Virtualization: In server virtualization, the software is directly installed on the server system. A physical server can be divided into many servers depending on the need and balance load. This software helps the server administrator to divide one physical server into multiple servers.

Hardware Virtualization: It is used in server platforms due to its flexibility. In hardware virtualizations, virtual machine software is installed in the hardware system. It comprises a hypervisor to control and monitor the process, memory, and other hardware resources.

Storage Virtualization: This process groups physical storage from multiple network storage devices to make a single storage device. Storage virtualization is implemented by using software applications and is mainly done for backup and recovery purposes.

Explore New-Age Careers with Imarticus Learning:

To gain insights into the technical aspects of virtualization and how it impacts organizations and their operations, one needs to take an in-depth study into it. Students opt for online distance MBA courses to learn how technology drives the industries differently.

Others opt for online MBA courses and undergo structured learning. Imarticus Learning delivers career-defining professional education while partnering with global leaders. The unique Ed-Tech expertise, industry insights, market acumen, operational excellence, the sprawling network has an extensive impact on learners.

Imarticus Learning offers the best online MBA courses in various streams and provides students and professionals an edge over the competition. The programs give you access to limitless opportunities related to career and networking that no other courses offer!

Since technology has taken organizations by storm, career landscapes have changed for professionals, and Imarticus Learning prepares candidates for the same!

Send us an inquiry now through our 24×7 Live Chat Support System and request virtual guidance from experts!