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What is Data Science?

The wide application of Information Technology and Computer Science has given rise to so many new fields in the corporate sector which have enormous potentials and possibilities. One of the fastest growing careers among them is Data Science, which has become extremely popular among youth because of its exciting nature of work and new newness. Professionals who do this job are known as Data Scientists.

The demand for Data Scientists is increasing all over the world, and the world’s reputed organizations have listed this profession as the best profession of the 21st century. And the fact that there aren’t enough Data Scientists currently implies great opportunities for the newcomers in this field.

What Is Data Science All About?

We live in an age where we are surrounded by data. Communicating with all this data is a challenging task. Earlier, industries relied on simple tools such as BI for Data Mining, but with the advent of technical statistics and computer science, this has evolved to be known as Data Science. The introduction of Big Data and its relation to Information Technology led to its grand scale rise.

With Data Science, we can learn the hidden information in the data, and by applying analytics, we can use this information to solve business problems, foresee future trends and understand certain patterns which would be very difficult to be performed just by applying human intellect. But only mining some diverse set of data is not enough. The success depends on building programs based on the data which enhances the industrial results, and that’s what Data Scientists are expert in.

It is actually a platform where Business, Computer Science and Statistics come together. The job of a Data Scientist includes:

  1. Organizing: This refers to the collection of data through open source software frameworks such as Hadoop and SAS.
  2. Modeling: This is where a Data Scientist transforms, integrates and refines the data in order to understand it and create statistical models which can be applied to solve the problem at hand.
  3. Delivering: After a model is constructed, the Data Scientist explains the model to the customer and other members.

Benefits of Applying Data Science in Industry

All major companies have reported excellent improvement in their business results by applying Data Science to exploit their user’s data and understand how to improve their products. The prime reason why companies like Google, Amazon and Apple are so ahead of their competitors is that they’re making excellent use of Data Science to track each and every user and draw inferences from their behavior and shopping pattern. This can be seen easily in the preciseness of Google searches, Facebook’s newsfeed recommendation and Amazon’s products suggestions. But this is not the end because Data Science is also creating milestones in industries like Medical Science, Banking and Finance, Online Education, Security Systems, Aviation Industry… and this list can go on and on.

Your Way to Become a Data Scientist

Data Scientists are the professionals who have a good knowledge of programming, statistics, mathematics and computers. They can deal with any type of data and process it to make it meaningful. The online course is designed to prepare students experts in all the concepts and tools which are employed in Data Science.

Skills That Are Essential For A Data Scientist

Being a Data Scientist is a position of great esteem. It is held in high regards, the sky-high pay is also one of the reasons that makes it so in demand. However, there is a scarcity in the number of data scientists available in the nation. If you are planning to make a career out of Data Science, then read on.

Starting with the fundamentals, one has to have the knowledge of Algebraic functions and matrices. Along with this, relational algebra, binary tree and hash functions are to be learned. Other topics are inclusive of Business Intelligence vs. Reporting vs. Analytics. Extract Trans form Load (ETL) is also included in the fundamentals category.

Then comes statistics, this includes the Bayes theorem, probability theorem, outliers and percentiles, exploratory analysis of the data, random variables and CDF (Cumulative Distribution Function), and skewness. Other fundamentals of statistics are also included here.

In case of Programming, the essential languages to be learned are ‘Python’ and ‘R’.

For Machine Learning, one should possess the understanding of concepts such as unsupervised learning, supervised learning and reinforcement learning. Under the algorithms of unsupervised and supervised learning, one should understand clustering, random forest, logistic regression, linear regression, decision tree and K nearest neighbour.

When it comes to Data Visualization, one should have a hands-on knowledge about the visualization tools such as Google Charts, Kibana, Tableau, and Datawrapper.

We all know that Big data can be found everywhere and anywhere. Data is being generated every second, and therefore there is a need for the storage and collection of this data. Data analytics has become a crucial tool for business companies as well as organizations, because of the fear that they might lose out on something important. In the long run, there is a need for this to keep up as well as surpass the competition. The tools that are important for learning the framework of Big Data are Spark and Hadoop respectively.

One comes across the feature selection while in the process of performing data analysis, this is before they have applied the analytical model to data. Therefore one can say that the activity performed so that the raw data is free of any impurities before input into the analytical algorithm is known as data munging. For this process of data munging, one can make use of either ‘Python’ or ‘R’ packages. For a person that deals with data, one should know the concepts and features regarding this important process, along with this data scientists should also be able to recognize their dependent label or variable. The process of Data Munging is also called as Data Wrangling.

Finally, the tool box. One shouldn’t take this lightly, as it is quite crucial and comes in handy at all times. A data scientist should possess hands-on good knowledge on the tools such as Python and R along with Spark, Tableau, and MS Excel. They should also have knowledge of high-speed tools such as Hadoop.

How Data Science Is Reforming Human Resources

Data Science and Human Resources

We live in a world flooding with data, and the rate at which this data is exploding can be felt by the fact that the entire amount of data in the world gets doubled every two years. That’s so much data, and it’s beyond any human’s capacity to store and manage them. Thanks to Data Science which has emerged as the strongest technique to exploit all this data and manipulate it in order to solve business problems.

Human resources have been a little slower to embrace it as compared to other industries. But now the corporations have realized that this is the best assistance they can ever have in order to spend less time and resources on hiring decisions and analyzing employees’ performance and engagement.

Applications of Data Science in Human Resources

  • Managing Data: To run a company efficiently, the top officials such as CEOs, Managers, Executives and Leaders need to know what is happening in and outside of the company. They need information in order to guide their employees in the right direction. Moreover, sales data, KPIs etc keep changing and are always uncertain. Data Science and Machine learning can help them draw better and faster insights and inferences that can solve problems and foresee future events.
  • Hiring: Every year millions and people, including freshers and experienced, apply for new jobs. Major companies receive tens of thousands of resumes. Companies have to keep track of all the data present in the resumes and with the help of Data Science they can create a base for identifying best prospects among all the applicants. Companies can also discover other information such as which job portal is bringing the best type of applicants. This makes hiring effective to a level which would be impossible to achieve without it.
  • Employee Engagement: Every company wants to retain its fruitful employees. Also, studies show that keeping employees happy in the workplace helps the company in the long run. This makes it important to use Machine Learning algorithms to identify trends and figure out ways to keep their employees happy and increasing their productivity. Analytics can help them calculate the right bonuses and perks for individual employees.
  • Employee Performance: One of the key features of Data Science in Human Resources is analysis of employees’ performance. Using Data Science, HR team can get real-time data on each employee and categorize them on the basis of their performance.
  • Chat bots: Chat bots are new computer programs based on AI which can answer frequently asked questions stored in the databases. This not only helps in reducing communication overhead, but also monitors prevalent issues among the employees.

How to Become a Data Scientist in Human Resources

Use of Data Science in Human Resources is all about analytics and Machine learning. So, it is imperative to gain expertise in Statistics and Machine learning algorithms in order to become a Data Scientist. The online training in Data Science not only provides in-depth preparation in the above-mentioned fields, but all the important concepts of tools used in Data Science. With regular assessments, webinars, and live projects, the course is designed so as to make students experienced in all the popular jobs relevant to Data Industry.

The Many Accomplishments Of Data Science

Search Engines

Data science definitely plays a larger part in our everyday life today. In fact, it has deliberately made our lives easier than in the past. For example, when someone does not possess knowledge of a particular word or topic, the first thing they do is turn to a search engine. ‘Google’ being the most commonly used search engine of all times. This has become an everyday thing, and it couldn’t be made possible without the support of Data Science in the respective field. Not only Google, but also other search engines, namely – AOL, Ask, Yahoo and Bing implement the usage of the algorithms of data science for dishing out the best possible results in a matter of milliseconds. It is estimated that data of about 20 petabytes is processed by Google on a daily basis.

Detection of Fraudulent Activities

Debts as well as losses occur in large numbers in companies each year. However, with the implementation of Data science in the finance sector, the losses and debts have been reduced to some extent. The banking companies have come to learn over time to divide the data as well acquire it through the past expenditures, the customer’s profiling, etc. along with other variables necessary for analyzation of the chances of defaults and risks. It has also aided them in pushing their products of banking in accordance with the purchasing power of the customer(s).

Medicinal / Drug Discovery.

The process of discovery of drugs is full of complications and is inclusive of several disciplines. It takes long lengths, extending up to even decades of testing and then the discovery of a particular drug for a particular disease / ailment. However, since the arrival and use of data science this process has been reduced in length. Along with this, the expenditure has also gone down and a lot of time is also saved which would otherwise be wasted.

Through the implementation of learning algorithms and data science, one can introduce a perspective for each step starting from the beginning screening for drug compounds to predict the success rate based upon the biological factors.

The purpose of these algorithms is to predict what kinds of effect will the respective compound cause in the body, with the help of advanced level of mathematical simulation and modeling rather than the lengthy experiments carried out in the lab. Computer models and simulations of them are created in the role of a biologically network that makes the predictions of the future easier to make along with more accuracy in the outcomes.

Customer Support

With the implementation of data science, we can help promote healthier lifestyles for patients, encouraging them to make healthy decisions. Along with this, it enables the doctors to concentrate their focus to the cases that are more critical. By simply describing one’s symptoms, and asking questions they can receive the key information regarding their medical condition. With the help of apps, one can be reminded of taking medicines on time and these also provide healthcare support to patients.

Top Reasons Why Human Resource Industry Is Turning to Data Science

The Advent of Data Science

Data science is the technical method of drawing information from big and raw data in order to use it for enhanced decision-making. The rate of creation of big data around us is so immense that basic human analytics and typical software cannot deal with it, and this has increased the importance of its implementation in every field.

Trends of Data Science in

  • Algorithms: Algorithms are the well defined computational processes which take some data as input and process it in order to produce the desired output. It is a way of dealing with the data and helping programmers design programs to perform various functions.
  • Artificial Intelligence: It has revolutionized the way we understand and manipulate data. Artificial Intelligence is the most prominent trend among all the other applications, and it focuses on building machines which have the ability to work and think like human beings.
  • Predictive Analysis: Analytics makes it easier to predict a specific outcome. Utilizing its techniques can help in assessing future trends in Human Resource industries. It can be used to forecast the creativity of a particular candidate, behaviors of employees, their success metrics etc based on the data present in the databases.

Uses of Data Science in Human Resource Industries

With Data Science, thousands of resumes can be studied in a short time while Machine learning algorithms can enhance the accuracy of selection. It helps companies identify most appropriate employee among thousands of applicants and can ensure fairness, since computers don’t tend to bias.

By applying its techniques, companies can keep a better record of employees’ engagement and appraise their performances. They can forecast employees’ behavior and calculate their lifetime value. Human resource is a field which undergoes continuous changing and it has emerged as the key through which can design robust methods of dealing with the varying environments.

It has proved to be effective in Decision-making processes. It gives Human resource managers the ability to analyze real-time information and understand the situation within the organizational framework. It can prioritize tasks according to their importance and enable evidence-based solutions to HR planning and industrial strategy.

Further Scopes in Human Resources

Human Resource is actually a new field which has opened itself to embrace Data Science, but with the advancement of technology and a large influx of data within the organization had made it crucial to apply its techniques to the management. It has proved to be the best solution for excellent decision-making and a better understanding of both external and internal environment.

How Can You Become a Human Resource Data Scientist?

Industries need skilled Data Scientists who are creative, have good communication skills and can work expertly with Data Science algorithms and computing tools. The study of Data Science includes a thorough understanding of statistics, ML algorithms for tasks like classifying and regression, programming languages such as Python, R, SQL etc, and data modeling software like SAS, Hadoop, Minitab etc. The course provides detailed training on all the concepts of Data Science and gives students multiple chances to work on live projects in order to gain real-world experience. And after the training is over, they are assisted to find the job of the Data Scientist in leading business corporations.

An Exciting Career Option

Data science is a multidisciplinary approach towards gaining valuable insights from Big data. These insights help an organization in improving its operations and making efficient and intelligent decisions. It makes use of techniques like Machine learning, Cluster analysis, Data mining, Visualization and employs the field of Mathematics and statistics.

Why Data Science?

With every passing day, the amount of Big Data is increasing giving rise to the requirement of management and processing of this Big data and Data science gains its importance from this requirement. With 2.5 quintillion bytes being produced every day, the skill of organizing this set gives one an added advantage. Companies like Google, Facebook, Microsoft are looking for experts, increasing the job vacancies in the field. The influence of Data science is across all major industries like healthcare, finance, retail, chemical, agriculture, media etc.

Skills Required

Data scientist requires mastering certain skills to excel in the field. These include R programming, Python Coding, Hadoop Platform, SQL Database/Coding, Machine learning. Besides these technical skills, Data scientist needs some soft skills: Analytical ability, Exceptional communication skills, Visualization and presentation skills, Able to work within a team, Strategic Acumen, Problem-solving skills.

Job titles offered by Data Science

Some distinguished job titles offered by Data science are:

-Data Scientist

The role of a data scientist is to handle raw data using suitable techniques. They are required to be well versed in the programming language of R, SAS, Python, SQL, MATLAB, Hive, Pig, Spark. Data scientists are skilled in Distributed Computing, Predictive modeling, Math and Machine learning. Data Scientists are employed by Adobe, Google, and Microsoft.


Their role is to develop, construct, test, and maintain the architecture (such as databases and large-scale processing systems). Data engineers are skilled in Database systems (SQL & NoSQL based), modeling and warehousing solutions. They are employed by Facebook, Amazon, and Spotify.


They create blueprints for a data management system to integrate, centralize, protect and maintain data sources. They are skilled in data warehousing solutions, In-depth knowledge of database architecture, data modeling, System development, Extraction Transformation. They are hired by VISA, Logitech.


They ensure that the database is available to all relevant users, is performing properly and is being kept safe. They are skilled in Data modeling and design, Distributed Computing, Database systems (SQL & NoSQL based), Security, and Business Knowledge. They are employed by Twitter, Reddit.

What Data Science has to offer as a career?

Being the “Sexiest Job of the 21st Century”, Data science promises a bright career to its professionals. A report by Glassdoor says that Data scientist leads for the best job in America with a median salary of $116,000 and 1,736 job openings for Data Scientists. It offers a career full of achievements and admiration.

Applications and Role of Data Science

A company has to deal with a huge amount of data like salaries, employee’s data, customer’s data, customer’s feedbacks, etc. This data can be both in unstructured and structured form. A company would always want this data to be simple and comprehensive so they can make better, precise decisions and future policies. This is when data science comes handy.

Data science helps the clients to take right decisions from right information fetched out of an enormous amount of messy data. Data scientists use their formidable skills in mathematics, business, programming and statistics to clean and organize data into useful information and reveal hidden patterns, trends and correlations.

Applications of data science

It has now become an inevitable and integral part of industries like risk management, market analytics, market optimization, fraud detection and public policies amongst others. Data science by using statics, machine learning and predictive modelling helps industries to resolve various issues and attain quantifiable benefits. There are tons of reasons to opt for a data course, as a career option. Following applications help us to understand it better:

  1. It helps companies to understand customer behavior and inclinations in a much-empowered manner. It helps them connect to the customers in a more personalized manner and ensure better services to customers.
  2. It helps brands to use the data in a comprehensive manner to communicate their message in an engaging and convincing manner with the target audience.
  3. The results and findings of data science can be implemented in almost all sectors like healthcare, education and travel, among others, helping them to address the challenges in their field in a more effective fashion.
  4. Big Data is a recently emerged field and is helping organizations to tackle problems in Human resources, resource management and IT in a strategic manner by using material and non-material resources.

Data scientist is one of the prime positions in an organization. They open new grounds of experimentations and research to the organization. Some of the direct roles of a data scientist are:

  • To link the new data with the previous one to offer new products that satisfy the aspirations of the target audience.
  • To interpret weather conditions and accordingly reroute the supply chain.
  • To enhance the speed of data set assessment and integration.
  • To reveal anomalies and frauds in the market.

An insight into the Data Science Course
Data science course is 160+ hours training with an experienced faculty working in top organisations to keep you abreast with recent technologies. An overview of the course is as follows:

  • Mathematics and statistics: This is an integral subject of data science course and includes integration, differentiation, differential equations, etc. Statistics covers inferential statistics, descriptive statistics, chi-squared tests, regression analysis, etc.
  • Programming Language: One can select from an array of programming languages like Python, C++, Matlab, Hadoop, etc.
  • Data wrangling and Data Management: This part deals with data mining, cleaning and management using MySQL, NoSQL, Cassandra, etc.
  • Machine learning: This includes supervised and unsupervised learning, testing, reinforcement learning, evaluation of models and their validation.
  • Data Analysis and Data Visualisation: This part teaches using the plotting libraries for programming languages like seaborn in python, plotly, ggplot2 in R, matplotlib, etc. It also involves using Excel, Tableau and D3.js for data visualisation.

Benefits of Data Science Course

Data analysts and data scientists are the most sought after by companies like LinkedIn, Facebook, Groupon and Amazon. These companies have to deal with enormous amount of raw data and seek the high-tech experts to simplify the job for them. Other industries are also hiring these big-data, scientists like government agencies, big retailers, social-networking sites and even defense forces.

Data scientists and analysts have a substantial career growth and there prevails a huge gap between talent and hiring, meaning that there are more job opportunities than the qualified data scientists to occupy them.

Database management specialists, who can effectively use DBMS software like Oracle, SQL, are in constant demand by companies etc. The business analytics and intelligence sector has an unlimited job opportunities and earning potential. It is one of the top salary providing fields with job profiles like Business Analysts, Business Intelligence Analysts, SAS Data Analysts, Big Data Scientists, IBM Data Analysts, Data Mining Engineer, Enterprise Data Architect, Hadoop Engineer, Senior Data Scientist, Data Warehouse Architect, Senior Big Data Analysts, etc. They earn $250,000 per annum on an average as salary plus other allowances and incentives. A data scientist can also work as a freelancer and earn up to $30 – 80 per hour depending on his skills, expertise, project size and requirements.

About the course:
The candidates get an electronically sharable certificate on successfully completing the course and clearing an online test. They can mention this certificate in their resume to weightage to it. The candidates have to complete the assignments and projects assigned in line with the syllabus in order to earn eligibility for the online exam. On successfully clearing the course, the candidates become market-ready and the institute provides placement assistance in all sorts of industries like banking and finance, insurance, travel and transportation, and health care industry, to name a few. The candidates must have a good command of mathematics and statistics to comprehend huge figures.

The course covers a number of subjects and tools that acts as body and soul in database management like basic Statistics, Hypothesis Testing, Data Mining and Clearing, Machine Learning, Data Forecasting, Data Visualization, Programming Languages like Mattlab, C++, Hadoop, Plotting Libraries Like Python, Plotly, Matplotlib, etc.

Benefits of the course:
This is a holistic industry-centered certificate course that makes you ready for the ocean of opportunities by teaching the latest trends in the industry to deal with the gigantic amount of data with their specially designed curriculum, practical knowledge of analytics tools, projects and case-studies along with real-time data analysis. The course provides you with industry connections and networking opportunities. This program, centered around business intelligence and analytical skills helps bridge the gap between talent demand and supply by giving or providing the talented professional an atmosphere where they can continuously learn and equip themselves with management-skills, design thinking, problem-solving and collaboration.

If you too are blessed with good programming knowledge or analytical skills and want your knowledge to be used to the best and wish to stand out against the competition, get yourself enrolled to The Best Institute of Dubai offering data science Certificate Course and get an edge over others.

How Can Data Science Training Get You Data Science Jobs?

Now, as the century is moving towards the age of Big data, the requirement for storage of data arises. Data storage was a big problem concerning industrial enterprises until a few days back when Hadoop and other such frameworks came to the rescue. After this problem was solved, focus shifted to the problem of the processing of this stored data. Data science is a solution to this problem of data processing.

Data science in its very brief form is the science of drawing out insights and information out of raw data using a mixture of various tools, algorithms, and machine learning principles. This art of driving out insights from raw data has been flourishing since ancient times when the Egyptians used census data to increase tax collection efficiency and predict the flooding of the Nile river every year. The difference is, with time, data got big and this Big data needs Data science to draw meaning and uncover patterns out of it. This Big data acquires its importance in this modern era from its potential of helping companies in improving their operations and making much faster and intelligent decisions.

Big data is on the rise and so is the requirement for professionals with skills. The training enables folk to pursue an interesting career as a Data Scientist. To analyze largely complicate data requires training in the use of sophisticated data analysis tools, like SQL or Python or R. The training empowers the individual in data management technologies like Hadoop, R, Flume, Sqoop, Machine learning, Mahout Etc and prepares them for the growing demand of Big data skills and technologies. Expertise in skills like Programming Skills: R/Python, Java, Statistics and Applied Mathematics, Working Knowledge of Hadoop and Spark, Databases: SQL and NoSQL, Machine learning and Neutral networks, Proficiency in deep learning frameworks: TensorFlow, Keras, Pytorch, and Creative Thinking & Industry Knowledge ensures one a better and competitive career.

Job opportunities created by Big data does not only pay handsome salaries when compared to other IT jobs, but are spread across leading industries of the world. The training allows you to apply for various data science job titles like Administrators, Architects, Visualizers, Engineers, Ecologists and the exciting salary these titles offer.

The market for Data jobs is growing with Top Fortune Companies like Facebook, Apple, Microsoft, Google, Amazon, eBay, StumbleUpon, PayPal, to name a few, looking for Data science experts. The training armors you with the required skills and knowledge in a career with a huge job vacancy. Big Data is a road on which we are still nowhere near the end, which guarantees a long and successful career in Big data.

The various educational institute offers training programs and certifications in Data Science courses allowing individuals to pursue a bright career as a Data Science expert. With the ever-growing influence of Big data across all industries, the sky is the limit for these professionals. Healthcare, Banking & Finance, Retail, Chemical, Agriculture, Media, E-Commerce, Manufacturing are among the few industries which are now demanding Data Science experts.

Data scientists are being labeled the “Sexiest Job people of the 21st century” and the “Rock Stars of the IT world”. It becomes the most sought-after profile, gifting individuals with the career of admiration and achievement and a handsome pay package. Join ExcelR and get data science certification to get your dream data science job.

Science of Something New

Quite often I find myself confused regarding the career my friends are in pursuit of- data science. The steady growth of vacancies for data scientists, data analysts, data engineers and of the sort fuelled the flame of curiosity in me.

From what I have gathered data science promptly interprets data of a seemingly infinite amount and can be used for uncountable purposes, both in the business sector as well as various organizations. Living in the period of the fourth industrial revolution, it is practically impossible to think about conducting a business without data.


Today, 90% of the calling is for data analysts and data scientists. According to NASCOM more than 1 lakh job opportunities are of the said field. If you are interested to mark your place in the sector let me tell you, this is the right time. Surf up the window, followed by the required training and you can roll up your sleeves to tackle the chase.


Data scientists require a certain skill set; particularly in the three major areas- mathematics, technology, and business acumen.

Preceeding the above mentioned requirements, education holds the primary position. The first step is earning a Bachelor’s degree in either subject-

• Computer science

• Mathematics

• Engineering

• Statistics.

Tailed by a Master’s degree in

• Mathematics

• Data science

• Statistics, etc.,

Ending with a PhD.


Possessing any amount of degrees would be considered less significant if there is a lack of technical skill. Data scientists have to have an ardent grasp of computer skills, which includes:

Data Mining: it falls under the category of mathematical expertise. Mining is to retrieve data from the data warehouse. Several business moguls require a quantitative analysis of data and build an analytical model model.

R programming: this language is a noteworthy resource for the aspiring data scientists. In order to have an in depth grasp of data analytics, R programming is the solution.

Python: it is the most versatile programming language and can be used in almost every data science process.

Machine learning: it is an aspect of the artificial intelligence. Machine learning aims at programming computers to learn from data without human interference

AI: presumably most of the young adults has an idea of what Artificial intelligence is, owing to Tony Stark (MCU character). Undoubtedly, AI is the future of the world, leading to human robotics. Research suggests eventually this will create an impact on the upcoming generations.


There is a glaring misconception of data scientists and data analysts being synonymous. A data analyst is given the responsibility of solving the given problems, analyzing, and sorting the data and interpreting it into a comprehensive list. While a data scientist formulates answers which will be beneficial for the respective sector.

The digitization of our world consequentially led to an ardent search for a lucrative career. Data scientists are taking the lead in the top ten hottest callings. Gone are the days where business was only characterized by the physical quid pro quo. Now, the whole kit and caboodle is computerized.

From image recognition in Facebook and suggestions on various eCommerce platforms (Amazon, Flipkart etc.) to voice recognition in Siri and Cortana and operational efficiency in logistics data science is the basic requirement.