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How To Become a Data Analyst

With the increase in the generation of data, Data Analysis has become one of the major functions in any organization. Since the past few years, the job of a ‘Data Analyst’ has evolved immensely and is considered to be one of the most sought-after roles after ‘Data Scientist’.  However, there are several questions that you as an aspiring data analyst may have. What is Data Analytics? What are the roles and responsibilities of a Data Analyst? How does one become a Data Analyst and what are the skills required? And many more. Our primary focus, in this article, will be to answer all these questions and take you a step further towards your dream of becoming a Data Analyst.Let us first get a clear understanding of what Data Analytics is.What is Data Analytics?In layman’s terms, the term ‘analyze’ means to examine something in a systematic manner to gain meaningful insights from it. Data Analytics or Data Analysis refers to the process of analyzing the raw data to gather useful information. This data can be in the form of some corporate information, product innovations, or market trends.Let us understand this more precisely with an example. We can compare Data Analytics with a jigsaw puzzle. The first thing is to collect all the pieces of the puzzle and fit them correctly to reveal the final picture. Similarly, in the process of data analytics, we collect the raw data from several sources, analyze it and transform it into some meaningful information that can be interpreted by humans.  Thus, we can define Data Analysis as the process that helps to discover new and significant patterns by cleaning, summarizing, transforming, and modeling data which later can be used to make informative decisions. The collected data can be of any of the three forms – structured, semi-structured, or unstructured and can be represented visually in the form of graphs and charts. The visualized information enhances the precision and allows an individual to have a clearer view of the final analysis.Organizations sniff around to recruit individuals who can perform the task of converting raw data into useful information which in turn helps in their business growth. There are numerous job roles in this field and out of all these, a Data Analyst’s career journey is the most satisfying and amazing.Now, let us understand the role and responsibilities of a Data Analyst in the field of Data Science.What is the role of a Data Analyst?Organizations, in recent times, with the help of huge chunks of data often try to optimize their strategies for efficient business growth. In order to derive useful information from this massive collection of data, they require a highly qualified professional who can make sense of the data and help others understand. That is where a Data Analyst comes in.  A Data Analyst collects, processes, and performs analysis of these large amounts of data. Every business organization, be it small or big generates and collects data which can be in the form of accounts, logistics, marketing research, customer feedback, etc.  A Data Analyst processes the data and generates significant indicators useful for decision making depending upon the customers, or the products, or the performance of the company. These indicators help companies to decide what products should be offered to their customers, what type of marketing strategy is to be implemented, how to reduce transportation costs, or what changes are to be made to enhance the process of production.Mostly data analysts collaborate with IT teams, the management, or data scientists to mine, clean, and then analyze and interpret information with the help of statistical tools. Their prime focus is to determine trends, correlations, and patterns in large and complex data sets which in turn allows companies to identify new ways for process improvement.  Let us now understand the basic responsibilities of a Data Analyst.What are the responsibilities of a Data Analyst?The first step towards becoming a Data Analyst is to understand the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:Understanding the GoalThe first and foremost task of a Data Analyst is to identify the goal of the organization by evaluating the resources, understanding the business problem, and then collecting the proper data.QueryingData Analysts also write SQL queries in order to collect, store, and derive information from databases like Microsoft SQL Server, Oracle, and MySQL.Data MiningData Analysts mine data from multiple sources and structure them to build data models which help in enhancing the system’s efficiency.Data TestingWith the help of analytical and statistical techniques, Data Analysts perform a logical examination of data.Interpretation of Data TrendsUsing different libraries and packages, Data Analysts identify trends and patterns from complex data thereby providing important insights to the organization.Preparation of ReportsThe leading teams of the organization are able to make timely decisions with the help of the summary reports prepared by Data Analysts. They perform this task using Data Visualization tools like Tableau or Google Charts, etc.Let us now take a look at the most popular industries that hire Data Analysts.What are the top industries hiring Data Analysts?There are around 82 thousand job openings worldwide in 2021 that require skills in data analysis, but there is a huge shortage of data talent. Almost every industry requires data to be analyzed and data jobs are diverging into a variety of fields.  The top industries that hire data analyst are as follows:Business Intelligence It is one of the leading industries that hire data analysts and according to a survey by Indeed, 20% of Data Analysts are from this sector. The most posted job vacancies for data analysts in the US and Europe are primarily from the Business Intelligence sector.Finance It is one of the earliest industries to be associated with data science and takes advantage of big data to make business ventures more efficient. Finance organizations such as investment banks, consumer banks, capital firms are responsible for generating a large number of data analytics jobs.Healthcare It is one of those industries that were dependent on paper data systems for many centuries. However, the importance of Data Analysis in this field is growing at a good pace and it is certain that they benefit the most. Most, Data Analysts in this sector are termed healthcare data analysts.Sharing-based economyThis industry has blossomed in recent years. Nearly every urban area be it small or big hires data talent. eBay is considered to be the first global marketplace that successfully launched the service economy services. Data analytics can be a game changer in this field.EntertainmentThis sector is evolving very fast. Global streaming networks like Netflix and Amazon are major players in this market and are using data insights to boost their growth. Data Analysts along with messaging analytics engineers or marketing intelligence analysts are very common positions in this industry. What are the technical skills required to master as a Data AnalystsThe most essential task of the data analyst is to parse through a good quantity of raw information and then develop meaningful insights in the entire process. The other tasks also include removing corrupted data, understanding the quality of data, and preparing various reports. All of these tasks involve knowledge of certain technologies and technical skills. Let us focus on a few of them.1. Data VisualizationData Visualizations revolve around a person’s ability to present data findings via graphics or other illustrations. It allows a data analyst to understand data-driven insights and helps the business decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance.Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” Get yourself enrolled for the Data Visualization course offered by KnowledgeHut.2. Data CleaningIt is believed that cleaning is an invaluable part of achieving success. Similarly, data cleaning is one of the most critical steps in assembling a functional machine learning model and consumes a good amount of time in a data analyst’s day. Any uncleaned data may result in misleading patterns and incorrect conclusions. However, a thoroughly cleaned dataset is capable of generating remarkable insights. Data Analysts should necessarily have proper data cleaning skills.3. RR is one of the most pervasive and well-used languages in data analytics. The structure and syntax were specifically created in order to support analytical work. It comprises several built-in, easy-to-use commands. R can easily handle large and complex quantities of data. As an aspiring data analyst, considering the popularity and functionality of R, it is very essential to learn R. Learn more about R programming language from the course offered by KnowledgeHut.4. PythonPython is among the most popular programming languages for data analysis. It is an essential language to be learnt by would-be analysts. It offers a large number of specialized libraries and built-in functions.  Python is a cross-functional, maximally interpreted language that has lots of advantages to offer. It is easy to learn, well supported, flexible- a fantastic option for data processing, scalable, and has a huge collection of libraries. Python Certification course offered by KnowledgeHut will assist you in mastering the concepts of Python and its libraries like SciPy, Matplotlib, Scikit-Learn, Pandas, NumPy, Lambda functions, and Web Scraping. You will also learn how to write Python Programming for Data Analytics.5. Linear Algebra and CalculusIn data analytics, one thing that is non-negotiable is having advanced mathematical skills. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice!  Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.  6. Microsoft ExcelWhile Excel is a great application to learn, it must be noted that the operations Excel can perform, other programming languages like R and Python can perform much faster. Excel is clunky in comparison to other platforms. However, Spreadsheets are still relevant and a great tool to learn about data. While it’s not the only or most fitting solution for all data projects, but it remains a reliable and affordable tool for analytics. It’s a foundational structure for intelligent data because it deepens your understanding of the analytics process. Many industries and businesses continue to emphasize the importance of Excel skills because it remains an intelligent way to extract actionable insights. Revenue patterns, operations, marketing trends, and more can be analyzed through Excel spreadsheets, but the real advantage is the process.7. Critical ThinkingIt’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.8. CommunicationAt the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.  Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.What are the Data Analysts’ salaries around the world?According to a report by Forbes, around 92 percent of organizations worldwide gain effective marketing insights by analyzing data. As an individual in the technical field, becoming a Data Analyst is a pretty amazing career opportunity.  In recent times, every business organization extracts information from sales or marketing campaigns and uses this data to gather insights. These insights allow the business to answer questions like what worked well, what did not, and what to do differently in the future. Thus, businesses can make more informed decisions with the right and organized data.  The salaries of Data Analysts depend on several factors like which industry they are working in, how many years of experience they have, what is the size of the organization, and so on. However, one big advantage of being a Data Analyst is they are always in demand globally and if you get bored of working in a particular city or a particular country, you always have the option of moving somewhere else because of the freedom and flexibility that this role offers.  Let us now look at the highest paying countries and their average annual salary of a Data Analyst:CountryAverage Annual SalaryIndiaThe average annual Data Analyst salary in India is over INR 4,45,000USAThe average annual Data Analyst salary in the USA is around USD 65,000GermanyThe average annual Data Analyst salary in Germany is around €44,330United KingdomThe average annual Data Analyst salary in the UK is around £26933CanadaThe average annual Data Analyst salary in Canada is around CAD 55000AustraliaThe average annual Data Analyst salary in Australia is over AUD 82,000DenmarkThe average annual Data Analyst salary in Denmark is around DKK 881,794SingaporeThe average annual Data Analyst salary in Singapore is around SGD 55,000What factors affect the salary of a Data Analyst in India?According to Payscale, around 78 percent of Data Analysts in India have a salary ranging between 0 – 6 Lakhs. A Data Analyst in India with experience between 1 – 4 years has net earnings of around 3,96,125 INR. On the other hand, an individual with experience of 5 – 9 years makes up to 6,00,000 INR per annum and someone with more experience than that can earn up to 9 Lakhs INR per annum. However, there are several factors that are also associated while deciding the salary of a Data Analyst.Every company, big or small, around the world now considers data analytics as an important sector and looks upon its potential to change the market trends. The decision-making authorities of the companies are focusing more on technology and people.  Now, let us understand what are the significant factors that affect the salary of a Data Analyst in India.1. Based on ExperienceAccording to a survey by Zippia, an entry-level Data Analyst in the USA having a bachelor’s degree and 2 years of experience, has an average annual salary of $54,000. A couple more years of experience can help them earn up to $70,000. A senior analyst gets an annual salary of $88,000 with experience of 6 years. However, someone with a specialization in the field can get a salary of around $100,000.Let’s see how experience affects the salary of a Data Analyst in India:The average annual salary of an Entry-Level Data Analyst in India is ₹325,616.The average annual salary of a mid-Level Data Analyst in India is ₹635,379The average annual salary of an experienced Data Analyst in India is ₹852,516.2. Based on IndustrySince every industry around the world recruits Data Analysts, there has been a significant increase in individuals who are choosing this career path, which in turn adds a lot of value to this field.  In an organization, the Data Analysts are directly responsible for some of the decision-making process and they perform this task with the help of the analyzed data using statistical tools like Excel, Tableau, and SQL. The progress impacts the salaries of these Data Analysts, which range between $54,000 to $70,000 for entry-level professionals.  Financial accounting companies hire financial analysts to predict the company’s performance and study the macro and microeconomic trends. The analysts in this industry are responsible for creating economic models and forecasts using the data. In 2017, Robert Half made a survey on the salary of entry-level financial analysts. The survey showed that their average annual salary ranges between $52,700 to $66,000.Source: Data Analysts  Salary Trends in India By IndustryMarketing research analysts use sales data, customer surveys, and competitor research to optimize the targeting and positioning efforts of their products. This industry has a pay scale ranging from $51,000 to $65,000 at the entry-level.Similarly, the Data Analysts working in the healthcare industry whose job is to maintain the daily administrative advancements and operations get an average annual salary of $46,000 to $80,000.3. Based on LocationThe number of Data Analysts and the average annual data salary in India is the highest in the Silicon Valley of India, that is Bangalore.Source: Data Analysts  Salary Trends in India By LocationBangalore, Pune, and Gurgaon offer 19.2%, 9.8%, and 9.5% more than the average annual salary in India respectively. On the other hand, Data Analysts working in Mumbai get 5.2% lesser than the national average. Hyderabad and New Delhi receive 4.85 and 2.8% lesser than the national average respectively.4. Based on CompanyThe top recruiters of Data Analysts in India are tech giants like Tata Consultancy Services, Accenture, and Earnest & Young whereas, according to reports, salaries offered are highest at HSBC which is around 7 Lakhs.Source: Data Analyst Salary Based on Company5. Based on SkillsSkill is an important factor while deciding the salary of a Data Analyst in India. You need to go beyond the qualifications of a Master’s degree and gather more knowledge of the respective languages and software. Some useful insights are as follows:The most important skill is to have a clear understanding of Python. A python programmer in India alone earns around 10 Lakhs per annum.  There is an increase of around 25 percent in the salary of a Data Analyst in India when you get familiar with Big Data and Data Science.  Experts in Statistical Package for Social Sciences or SPSS get an average salary of 7.3  Lakhs whereas experts in Statistical Analysis Software or SAS have an earning of around 9 Lakhs to 10.8 Lakhs.A Machine Learning expert in India alone can earn around 17 Lakhs per year. Along with being a Data Analyst, if you also have Machine Learning and Python skills, you can reach the highest pay in this field.How KnowledgeHut can helpAll these free resources are a great place to start your Data Analytics journey. Beside these there are many other free resources on the internet, but they may not be organized and may not have a structured approach.  This is where KnowledgeHut can make a difference and serve as a one stop shop alternative with its comprehensive Instructor-led live classes. The courses are taught by Industry experts and are perfect for aspirants who wish to become Data Analyst.Links for some of the popular courses by KnowledgeHut are appended below-Big Data Analytics CertificationR Programming Language TrainingPython Certification TrainingData Visualization with Tableau TrainingIn this article we attempt to understand about Data Analytics and the major roles of a Data Analyst. We also learnt about the responsibilities of a Data Analyst, the various industries offering jobs to Data Analysts and also the technical skills required to master to be a Data Analyst.  If you are inspired by the opportunities provided by Data Analytics, enroll in our  Data Analytics Courses for more lucrative career options in this field.
How To Become a Data Analyst
Priyankur

How To Become a Data Analyst

With the increase in the generation of data, Data Analysis has become one of the major functions in any organization. Since the past few years, the job of a ‘Data Analyst’ has evolved immensely and is considered to be one of the most sought-after roles after ‘Data Scientist’.  However, there are several questions that you as an aspiring data analyst may have. What is Data Analytics? What are the roles and responsibilities of a Data Analyst? How does one become a Data Analyst and what are the skills required? And many more. Our primary focus, in this article, will be to answer all these questions and take you a step further towards your dream of becoming a Data Analyst.Let us first get a clear understanding of what Data Analytics is.What is Data Analytics?In layman’s terms, the term ‘analyze’ means to examine something in a systematic manner to gain meaningful insights from it. Data Analytics or Data Analysis refers to the process of analyzing the raw data to gather useful information. This data can be in the form of some corporate information, product innovations, or market trends.Let us understand this more precisely with an example. We can compare Data Analytics with a jigsaw puzzle. The first thing is to collect all the pieces of the puzzle and fit them correctly to reveal the final picture. Similarly, in the process of data analytics, we collect the raw data from several sources, analyze it and transform it into some meaningful information that can be interpreted by humans.  Thus, we can define Data Analysis as the process that helps to discover new and significant patterns by cleaning, summarizing, transforming, and modeling data which later can be used to make informative decisions. The collected data can be of any of the three forms – structured, semi-structured, or unstructured and can be represented visually in the form of graphs and charts. The visualized information enhances the precision and allows an individual to have a clearer view of the final analysis.Organizations sniff around to recruit individuals who can perform the task of converting raw data into useful information which in turn helps in their business growth. There are numerous job roles in this field and out of all these, a Data Analyst’s career journey is the most satisfying and amazing.Now, let us understand the role and responsibilities of a Data Analyst in the field of Data Science.What is the role of a Data Analyst?Organizations, in recent times, with the help of huge chunks of data often try to optimize their strategies for efficient business growth. In order to derive useful information from this massive collection of data, they require a highly qualified professional who can make sense of the data and help others understand. That is where a Data Analyst comes in.  A Data Analyst collects, processes, and performs analysis of these large amounts of data. Every business organization, be it small or big generates and collects data which can be in the form of accounts, logistics, marketing research, customer feedback, etc.  A Data Analyst processes the data and generates significant indicators useful for decision making depending upon the customers, or the products, or the performance of the company. These indicators help companies to decide what products should be offered to their customers, what type of marketing strategy is to be implemented, how to reduce transportation costs, or what changes are to be made to enhance the process of production.Mostly data analysts collaborate with IT teams, the management, or data scientists to mine, clean, and then analyze and interpret information with the help of statistical tools. Their prime focus is to determine trends, correlations, and patterns in large and complex data sets which in turn allows companies to identify new ways for process improvement.  Let us now understand the basic responsibilities of a Data Analyst.What are the responsibilities of a Data Analyst?The first step towards becoming a Data Analyst is to understand the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:Understanding the GoalThe first and foremost task of a Data Analyst is to identify the goal of the organization by evaluating the resources, understanding the business problem, and then collecting the proper data.QueryingData Analysts also write SQL queries in order to collect, store, and derive information from databases like Microsoft SQL Server, Oracle, and MySQL.Data MiningData Analysts mine data from multiple sources and structure them to build data models which help in enhancing the system’s efficiency.Data TestingWith the help of analytical and statistical techniques, Data Analysts perform a logical examination of data.Interpretation of Data TrendsUsing different libraries and packages, Data Analysts identify trends and patterns from complex data thereby providing important insights to the organization.Preparation of ReportsThe leading teams of the organization are able to make timely decisions with the help of the summary reports prepared by Data Analysts. They perform this task using Data Visualization tools like Tableau or Google Charts, etc.Let us now take a look at the most popular industries that hire Data Analysts.What are the top industries hiring Data Analysts?There are around 82 thousand job openings worldwide in 2021 that require skills in data analysis, but there is a huge shortage of data talent. Almost every industry requires data to be analyzed and data jobs are diverging into a variety of fields.  The top industries that hire data analyst are as follows:Business Intelligence It is one of the leading industries that hire data analysts and according to a survey by Indeed, 20% of Data Analysts are from this sector. The most posted job vacancies for data analysts in the US and Europe are primarily from the Business Intelligence sector.Finance It is one of the earliest industries to be associated with data science and takes advantage of big data to make business ventures more efficient. Finance organizations such as investment banks, consumer banks, capital firms are responsible for generating a large number of data analytics jobs.Healthcare It is one of those industries that were dependent on paper data systems for many centuries. However, the importance of Data Analysis in this field is growing at a good pace and it is certain that they benefit the most. Most, Data Analysts in this sector are termed healthcare data analysts.Sharing-based economyThis industry has blossomed in recent years. Nearly every urban area be it small or big hires data talent. eBay is considered to be the first global marketplace that successfully launched the service economy services. Data analytics can be a game changer in this field.EntertainmentThis sector is evolving very fast. Global streaming networks like Netflix and Amazon are major players in this market and are using data insights to boost their growth. Data Analysts along with messaging analytics engineers or marketing intelligence analysts are very common positions in this industry. What are the technical skills required to master as a Data AnalystsThe most essential task of the data analyst is to parse through a good quantity of raw information and then develop meaningful insights in the entire process. The other tasks also include removing corrupted data, understanding the quality of data, and preparing various reports. All of these tasks involve knowledge of certain technologies and technical skills. Let us focus on a few of them.1. Data VisualizationData Visualizations revolve around a person’s ability to present data findings via graphics or other illustrations. It allows a data analyst to understand data-driven insights and helps the business decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance.Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” Get yourself enrolled for the Data Visualization course offered by KnowledgeHut.2. Data CleaningIt is believed that cleaning is an invaluable part of achieving success. Similarly, data cleaning is one of the most critical steps in assembling a functional machine learning model and consumes a good amount of time in a data analyst’s day. Any uncleaned data may result in misleading patterns and incorrect conclusions. However, a thoroughly cleaned dataset is capable of generating remarkable insights. Data Analysts should necessarily have proper data cleaning skills.3. RR is one of the most pervasive and well-used languages in data analytics. The structure and syntax were specifically created in order to support analytical work. It comprises several built-in, easy-to-use commands. R can easily handle large and complex quantities of data. As an aspiring data analyst, considering the popularity and functionality of R, it is very essential to learn R. Learn more about R programming language from the course offered by KnowledgeHut.4. PythonPython is among the most popular programming languages for data analysis. It is an essential language to be learnt by would-be analysts. It offers a large number of specialized libraries and built-in functions.  Python is a cross-functional, maximally interpreted language that has lots of advantages to offer. It is easy to learn, well supported, flexible- a fantastic option for data processing, scalable, and has a huge collection of libraries. Python Certification course offered by KnowledgeHut will assist you in mastering the concepts of Python and its libraries like SciPy, Matplotlib, Scikit-Learn, Pandas, NumPy, Lambda functions, and Web Scraping. You will also learn how to write Python Programming for Data Analytics.5. Linear Algebra and CalculusIn data analytics, one thing that is non-negotiable is having advanced mathematical skills. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice!  Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.  6. Microsoft ExcelWhile Excel is a great application to learn, it must be noted that the operations Excel can perform, other programming languages like R and Python can perform much faster. Excel is clunky in comparison to other platforms. However, Spreadsheets are still relevant and a great tool to learn about data. While it’s not the only or most fitting solution for all data projects, but it remains a reliable and affordable tool for analytics. It’s a foundational structure for intelligent data because it deepens your understanding of the analytics process. Many industries and businesses continue to emphasize the importance of Excel skills because it remains an intelligent way to extract actionable insights. Revenue patterns, operations, marketing trends, and more can be analyzed through Excel spreadsheets, but the real advantage is the process.7. Critical ThinkingIt’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.8. CommunicationAt the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.  Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.What are the Data Analysts’ salaries around the world?According to a report by Forbes, around 92 percent of organizations worldwide gain effective marketing insights by analyzing data. As an individual in the technical field, becoming a Data Analyst is a pretty amazing career opportunity.  In recent times, every business organization extracts information from sales or marketing campaigns and uses this data to gather insights. These insights allow the business to answer questions like what worked well, what did not, and what to do differently in the future. Thus, businesses can make more informed decisions with the right and organized data.  The salaries of Data Analysts depend on several factors like which industry they are working in, how many years of experience they have, what is the size of the organization, and so on. However, one big advantage of being a Data Analyst is they are always in demand globally and if you get bored of working in a particular city or a particular country, you always have the option of moving somewhere else because of the freedom and flexibility that this role offers.  Let us now look at the highest paying countries and their average annual salary of a Data Analyst:CountryAverage Annual SalaryIndiaThe average annual Data Analyst salary in India is over INR 4,45,000USAThe average annual Data Analyst salary in the USA is around USD 65,000GermanyThe average annual Data Analyst salary in Germany is around €44,330United KingdomThe average annual Data Analyst salary in the UK is around £26933CanadaThe average annual Data Analyst salary in Canada is around CAD 55000AustraliaThe average annual Data Analyst salary in Australia is over AUD 82,000DenmarkThe average annual Data Analyst salary in Denmark is around DKK 881,794SingaporeThe average annual Data Analyst salary in Singapore is around SGD 55,000What factors affect the salary of a Data Analyst in India?According to Payscale, around 78 percent of Data Analysts in India have a salary ranging between 0 – 6 Lakhs. A Data Analyst in India with experience between 1 – 4 years has net earnings of around 3,96,125 INR. On the other hand, an individual with experience of 5 – 9 years makes up to 6,00,000 INR per annum and someone with more experience than that can earn up to 9 Lakhs INR per annum. However, there are several factors that are also associated while deciding the salary of a Data Analyst.Every company, big or small, around the world now considers data analytics as an important sector and looks upon its potential to change the market trends. The decision-making authorities of the companies are focusing more on technology and people.  Now, let us understand what are the significant factors that affect the salary of a Data Analyst in India.1. Based on ExperienceAccording to a survey by Zippia, an entry-level Data Analyst in the USA having a bachelor’s degree and 2 years of experience, has an average annual salary of $54,000. A couple more years of experience can help them earn up to $70,000. A senior analyst gets an annual salary of $88,000 with experience of 6 years. However, someone with a specialization in the field can get a salary of around $100,000.Let’s see how experience affects the salary of a Data Analyst in India:The average annual salary of an Entry-Level Data Analyst in India is ₹325,616.The average annual salary of a mid-Level Data Analyst in India is ₹635,379The average annual salary of an experienced Data Analyst in India is ₹852,516.2. Based on IndustrySince every industry around the world recruits Data Analysts, there has been a significant increase in individuals who are choosing this career path, which in turn adds a lot of value to this field.  In an organization, the Data Analysts are directly responsible for some of the decision-making process and they perform this task with the help of the analyzed data using statistical tools like Excel, Tableau, and SQL. The progress impacts the salaries of these Data Analysts, which range between $54,000 to $70,000 for entry-level professionals.  Financial accounting companies hire financial analysts to predict the company’s performance and study the macro and microeconomic trends. The analysts in this industry are responsible for creating economic models and forecasts using the data. In 2017, Robert Half made a survey on the salary of entry-level financial analysts. The survey showed that their average annual salary ranges between $52,700 to $66,000.Source: Data Analysts  Salary Trends in India By IndustryMarketing research analysts use sales data, customer surveys, and competitor research to optimize the targeting and positioning efforts of their products. This industry has a pay scale ranging from $51,000 to $65,000 at the entry-level.Similarly, the Data Analysts working in the healthcare industry whose job is to maintain the daily administrative advancements and operations get an average annual salary of $46,000 to $80,000.3. Based on LocationThe number of Data Analysts and the average annual data salary in India is the highest in the Silicon Valley of India, that is Bangalore.Source: Data Analysts  Salary Trends in India By LocationBangalore, Pune, and Gurgaon offer 19.2%, 9.8%, and 9.5% more than the average annual salary in India respectively. On the other hand, Data Analysts working in Mumbai get 5.2% lesser than the national average. Hyderabad and New Delhi receive 4.85 and 2.8% lesser than the national average respectively.4. Based on CompanyThe top recruiters of Data Analysts in India are tech giants like Tata Consultancy Services, Accenture, and Earnest & Young whereas, according to reports, salaries offered are highest at HSBC which is around 7 Lakhs.Source: Data Analyst Salary Based on Company5. Based on SkillsSkill is an important factor while deciding the salary of a Data Analyst in India. You need to go beyond the qualifications of a Master’s degree and gather more knowledge of the respective languages and software. Some useful insights are as follows:The most important skill is to have a clear understanding of Python. A python programmer in India alone earns around 10 Lakhs per annum.  There is an increase of around 25 percent in the salary of a Data Analyst in India when you get familiar with Big Data and Data Science.  Experts in Statistical Package for Social Sciences or SPSS get an average salary of 7.3  Lakhs whereas experts in Statistical Analysis Software or SAS have an earning of around 9 Lakhs to 10.8 Lakhs.A Machine Learning expert in India alone can earn around 17 Lakhs per year. Along with being a Data Analyst, if you also have Machine Learning and Python skills, you can reach the highest pay in this field.How KnowledgeHut can helpAll these free resources are a great place to start your Data Analytics journey. Beside these there are many other free resources on the internet, but they may not be organized and may not have a structured approach.  This is where KnowledgeHut can make a difference and serve as a one stop shop alternative with its comprehensive Instructor-led live classes. The courses are taught by Industry experts and are perfect for aspirants who wish to become Data Analyst.Links for some of the popular courses by KnowledgeHut are appended below-Big Data Analytics CertificationR Programming Language TrainingPython Certification TrainingData Visualization with Tableau TrainingIn this article we attempt to understand about Data Analytics and the major roles of a Data Analyst. We also learnt about the responsibilities of a Data Analyst, the various industries offering jobs to Data Analysts and also the technical skills required to master to be a Data Analyst.  If you are inspired by the opportunities provided by Data Analytics, enroll in our  Data Analytics Courses for more lucrative career options in this field.
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Overview of Deploying Machine Learning Models

Machine Learning is no longer just the latest buzzword. In fact, it has permeated every facet of our everyday lives. Most of the applications across the world are built using Machine Learning and their applications extend further when they are combined with other cutting-edge technologies like Deep Learning and Artificial Intelligence. These latest technologies are a boon to mankind, as they simplify tasks, helping to complete work in lesser time. They boost the growth and profitability of industries and organizations across sectors, which in turn helps in the growth of the economy and generates jobs.What are the fields that machine learning extends into?Machine Learning now finds applications across sectors and industries including fields like Healthcare, defense, insurance, government sectors, automobile, manufacturing, retail and more. ML gives great insights to businesses in gaining and retaining customer loyalty, enhances efficiency, minimizes the time consumption, optimizes resource allocation and decreases the cost of labor for a specific task.What is Model Deployment?It’s well established that ML has a lot of applications in the real world. But how exactly do these models work to solve our problems? And how can it be made available for a large user base? The answer is that we have to deploy the trained machine learning model into the web, so that it can be available for many users.When a model is deployed, it is fully equipped with training and it knows what are the inputs to be taken by the model and what are the outputs given out in return. This strategy is used to advantage in real world applications. Deployment is a tricky task and is the last stage of our ML project.Generally, the deployment will take place on a web server or a cloud for further use, and we can modify the content based on the user requirements and update the model. Deployment makes it easier to interact with the applications and share the benefits to the applications with others.With the process of Model Deployment, we can overcome problems like Portability, which means shifting of software from one machine to the other and Scalability, which is the capacity to be changed on a scale and the ability of the computation process to be used in a wide range of capabilities.Installing Flask on your MachineFlask is a web application framework in Python. It is a lightweight Web Server Gateway Interface (WSGI) web framework. It consists of many modules, and contains different types of tools and libraries which helps a web developer to write and implement many useful web applications.Installing Flask on our machine is simple. But before that, please ensure you have installed Python in your system because Flask runs using Python.In Windows: Open command prompt and write the following code:a) Initially make the virtual environment using pip -- pip install virtualenv And then write mkvirtualenv HelloWorldb) Connect to the project – Create a folder dev, then mkdir Helloworld for creating a directory; then type in cd HelloWorld to go the file location.c) Set Project Directory – Use setprojectdir in order to connect our virtual environment to the current working directory. Now further when we activate the environment, we will directly move into this directory.d) Deactivate – On using the command called deactivate, the virtual environment of hello world present in parenthesis will disappear, and we can activate our process directly in later steps.e) Workon – When we have some work to do with the project, we write the command  “workon HelloWorld” to activate the virtual environment directly in the command prompt.The above is the set of Virtual Environment commands for running our programs in Flask. This virtual environment helps and makes the work easier as it doesn’t disturb the normal environment of the system. The actions we perform will reside in the created virtual environment and facilitate the users with better features.f) Flask Installation – Now you install flask on the virtual environment using command pip install flaskUnderstanding the Problem StatementFor example, let us try a Face Recognition problem using opencv. Here, we work on haarcascades dataset. Our goal is to detect the eyes and face using opencv. We have an xml file that contains the values of face and eyes that were stored. This xml file will help us to identify the face and eyes when we look into the camera.The xml data for face recognition is available online, and we can try this project on our own after reading this blog. For every problem that we solve using Machine Learning, we require a dataset, which is the basic building block for the Model development in ML. You can generate interesting outcomes at the end like detecting the face and eyes with a bounding rectangular box. Machine learning beginners can use these examples and create a mini project which will help them to know much about the core of ML and other technologies associated with it.Workflow of the ProjectModel Building: We build a Machine Learning model to detect the face of the human present in front of the camera. We use the technology of Opencv to perform this action which is the library of Computer Vision.Here our focus is to understand how the model is working and how it is deployed on server using Flask. Accuracy is not the main objective, but we will learn how the developed ML model is deployed.Face app: We will create a face app that detects your face and implements the model application. This establishes the connection between Python script and the webpage template.Camera.py: This is the Python script file where we import the necessary libraries and datasets required for our model and we write the actual logic for the model to exhibit its functionality.Webpage Template: Here, we will design a user interface where the user can experience live detection of his face and eyes in the camera. We provide a button on a webpage, to experience the results.Getting the output screen: when the user clicks the button, the camera will open directly and we can get the result of the machine learning model deployed on the server. In the output screen you can see your face. Storage: This section is totally optional for users, and it is based on the users’ choice of storing and maintaining the data. After getting the outputs on the webpage screen, you can store the outputs in a folder on your computer. This helps us to see how the images are captured and stored locally in our system. You can add a file path in the code, that can store the images locally on your system if necessary.This application can be further extended to a major project of “Attendance taking using Face Recognition Technique”, which can be used in colleges and schools, and can potentially replace normal handwritten Attendance logs. This is an example of a smart application that can be used to make our work simple.Diagrammatic Representation of the steps for the projectBuilding our Machine Learning ModelWe have the XML data for recognizing face and eyes respectively. Now we will write the machine learning code, that implements the technique of face and eyes detection using opencv. Before that, we import some necessary libraries required for our project, in the file named camera.py # import cv2 # import numpy as np # import scipy.ndimage # import pyzbar.pyzbar as pyzbar # from PIL import Image Now, we load the dataset into some variables in order to access them further. Haarcascades is the file name where all the xml files containing the values of face, eye, nose etc are stored. # defining face detector# face_cascade = cv2.CascadeClassifier("haarcascades/haarcascade_frontalface_default.xml") # eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye.xml')The xml data required for our project is represented as shown below, and mostly consists of numbers.Now we write the code for opening the camera, and releasing of camera in a class file. The “def” keyword is the name of the function in Python. The functions in Python are declared using the keyword “def”.The function named “def __init__” initiates the task of opening camera for live streaming of the video. The “def __del__” function closes the camera upon termination of the window.# class VideoCamera(object):#    def __init__(self):        # capturing video#       self.video = cv2.VideoCapture(0) #  def __del__(self):#        # releasing camera#        self.video.release()Next, we build up the actual logic for face and eyes detect using opencv in Python script as follows. This function is a part of class named videocamera.# class VideoCamera(object):#    def __init__(self):#        # capturing video#        self.video = cv2.VideoCapture(0)#    def __del__(self):#        # releasing camera#        self.video.release()#    def face_eyes_detect(self):#        ret, frame = self.video.read()#        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#        faces = face_cascade.detectMultiScale(gray, 1.3, 5)#        c=0#        for (x,y,w,h) in faces:#            cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 2)#            roi_gray = gray[y:y+h, x:x+w]#            roi_color = frame[y:y+h, x:x+w]#            eyes = eye_cascade.detectMultiScale(roi_gray)#            for (ex,ey,ew,eh) in eyes:#                cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)#            while True:#                k = cv2.waitKey(1000) & 0xFF#                print("Image "+str(c)+" saved")#                file = 'C:/Users/user/dev/HelloWorld/images/'+str(c)+'.jpg'#                cv2.imwrite(file, frame)#                c += 1            # encode Opencv raw frame to jpg and display it#        ret, jpeg = cv2.imencode('.jpg', frame)#        return jpeg.tobytes()The first line in the function “ret, frame” reads the data of live streaming video. The ret takes the value “1”, when the camera is open, else it takes “0” as input. The frame captures the live streaming video from time to time. In the 2nd line, we are changing the color of image from RGB to Grayscale, i.e., we are changing the values of pixels. And then we are applying some inbuilt functions to detect faces. The for loop, illustrates that it is having some fixed dimensions to draw a bounding rectangular box around the face and eyes, when it is detected. If you want to store the captured images after detecting face and eyes, we can add the code of while loop, and we can give the location to store the captured images. When an image is captured, it is saved as Image 1, Image 2 saved, etc., for confirmation.All the images will be saved in jpg format. We can mention the type of format in which the images should be stored. The method named cv2.imwrite stores the frame in a particular file location.Finally, after capturing the detected picture of face and eyes, it displays the result at the user end. Creating a WebpageWe will create a webpage, in order to implement the functionality of the developed machine learning model after deployment using Flask. Here is the design of our webpage.The above picture represents a small webpage demonstrating “Video Streaming Demonstration” and a link “face-eyes-detect”. When we click the button on the screen, the camera gets opened and the functionality will be displayed to the users who are facing the camera.The code for creating a webpage is as follows:If the project contains only one single html file, it should be necessarily saved with the name of index. The above code should be saved as “index.html” in a folder named “templates” in the project folder named “HelloWorld”, that we have created in the virtual environment earlier. This is the actual format we need to follow while designing a webpage using Flask framework.Connecting Webpage to our ModelTill now we have developed two separate files, one for developing the machine learning model for the problem statement and the other for creating a webpage, where we can access the functionality of the model. Now we will try to see how we can connect both of them.This is the Python script with the file name saved as “app.py”. Initially we import the necessary libraries to it, and create a variable that stores the Flask app. We then guide the code to which location it needs to be redirected, when the Python scripts are executed in our system. The redirection is done through “@app.route” followed by a function named “home”. Then we include the functionality of model named “face_eyes_detect” to the camera followed by the function definition named “gen”. After adding the functionality, we display the response of the deployed model on to the web browser. The outcome of the functionality is the detection of face and eyes in the live streaming camera and the frames are stored in the folder named images. We put the debug mode to False. # from flask import Flask, render_template, Response,url_for, redirect, request.# from flask import Flask, render_template, Response,url_for, redirect, request  # from camera import VideoCamera  # import cv2  # import time  # app = Flask(__name__)  # @app.route("/")  # def home():  #     # rendering web page  #     return render_template('index.html')  # def gen(camera):  #     while True:  #         # get camera frame  #         frame = camera.face_eyes_detect()  #         yield(b'--frame\r\n'  #                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')  # @app.route("/video_feed")  # def video_feed():  #     return Response(gen(VideoCamera()),  #           mimetype='multipart/x-mixed-replace; boundary=frame')  # if __name__ == '__main__':  #     # defining server ip address and port  #     app.run(debug=False)Before running the Python scripts, we need to install the libraries like opencv, flask, scipy, numpy, PIL, pyzbar etc., using the command prompt with the command named “pip install library_name” like “pip install opencv-python”, ”pip install flask”, “pip install scipy” etc.When you have installed all the libraries in your system, now open the python script “app.py” and run it using the command “f5”. The output is as follows:Image: Output obtained when we run app.py fileNow we need to copy the server address http://127.0.0.1:5000/ and paste it on the web browser, and we will get the output screen as follows:Now when we click the link “face-eyes-detect”, we will get the functionality of detecting the face and eyes of a user, and it is seen as follows:Without SpectaclesWith SpectaclesOne eye closed by handone eye closedWhen these detected frames are generated, they are similarly stored in a specified location of folder named “images”. And in the Python shell we can observe, the sequence of images is saved in the folder, and looks as follows:In the above format, we get the outcomes of images stored in our folder.Now we will see how the images were stored in the previously created folder named “images” present in the project folder of “HelloWorld.”Now we can use the deployed model in real time. With the help of this application, we can try some other new applications of Opencv and we can deploy it in the flask server accordingly.  You can find all the above code with the files in the following github repository, and you can make further changes to extend this project application to some other level.Github Link.ConclusionIn this blog, we learnt how to deploy a model using flask server and how to connect the Machine Learning Model with the Webpage using Flask. The example project of face-eyes detection using opencv is a pretty common application in the present world. Deployment using flask is easy and simple.  We can use the Flask Framework for deployment of ML models as it is a light weight framework. In the real-world scenario, Flask may not be the most suitable framework for bigger applications as it is a minimalist framework and works well only for lighter applications.
Overview of Deploying Machine Learning Models
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Overview of Deploying Machine Learning Models

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Top In-demand Jobs During Coronavirus Pandemic

With the global positive cases for the COVID-19 reaching over two crores globally, and over 281,000 jobs lost in the US alone, the impact of the coronavirus pandemic already has been catastrophic for workers worldwide. While tourism and the supply chain industries are the hardest hit, the healthcare and transportation sectors have faced less severe heat. According to a Goldman Sachs report, the number of unemployed individuals in the US can climb up to 2.25 million. However, despite these alarming figures, the NBC News states that this is merely 20% of the total unemployment rate of the US. Job portals like LinkedIn, Shine, and Monster are also witnessing continued hiring for specific roles. So, what are these roles defining the pandemic job sector? Top In-demand Jobs During Coronavirus Pandemic Healthcare specialist For obvious reasons, the demand for healthcare specialists has spiked up globally. This includes doctors, nurses, surgical technologists, virologists, diagnostic technicians, pharmacists, and medical equipment providers. Logistics personnel This largely involves shipping and delivery companies that include a broad profile of employees, right from warehouse managers, transportation-oriented job roles, and packaging and fulfillment jobs. Presently, Amazon is hiring over 1,00,000 workers for its operations while making amends in the salaries and timings to accommodate the situation.  Online learning companies Teaching and learning are at the forefront of the current global scenario. With most of the individuals either working from home or anticipating a loss of a job, several of them are resorting to upskilling or attaining new skills to embrace broader job roles. The demand for teachers or trainers for these courses and academic counselors has also shot up. Remote learning facilities and online upskilling have made these courses much more accessible to individuals as well.  Remote meeting and communication companies The entirety of remote working is heavily dependant on communication and meeting tools such as Zoom, Slack, and Microsoft teams. The efficiency of these tools and the effectivity of managing projects with remote communication has enabled several industries to sustain global pandemic. Even project management is taking an all-new shape thanks to these modern tools. Moreover, several schools are also relying on these tools to continue education through online classes.  Psychologists/Mental health-related businesses Many companies and individuals are seeking help to cope up with the undercurrent. This has created a surge in the demand for psychologists. Businesses like PwC and Starbucks have introduced/enhanced their mental health coaching. Mental health and wellness apps like Headspace have seen a 400% increase in the demand from top companies like Adobe and GE.  Data analysts Hiring companies like Shine have seen a surge in the hiring of data analysts. The simple reason being that there is a constant demand for information about the coronavirus, its status, its impact on the global economy, different markets, and many other industries. Companies are also hiring data analysts rapidly to study current customer behavior and reach out to public sentiments.  How to find a job during the coronavirus pandemicWhether you are looking for a job change, have already faced the heat of the coronavirus, or are at the risk of losing your job, here are some ways to stay afloat despite the trying times.  Be proactive on job portals, especially professional networking sites like LinkedIn to expand your network Practise phone and video job interviews Expand your work portfolio by on-boarding more freelance projects Pick up new skills by leveraging on the online courses available  Stay focused on your current job even in uncertain times Job security is of paramount importance during a global crisis like this. Andrew Seaman, an editor at LinkedIn notes that recruiters are going by the ‘business as usual approach’, despite concerns about COVID-19. The only change, he remarks, is that the interviews may be conducted over a video call, rather than in person. If the outbreak is not contained soon enough though, hiring may eventually take a hit. 
Top In-demand Jobs During Coronavirus Pandemic
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Top In-demand Jobs During Coronavirus Pandemic

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5 Big Data Challenges in 2021

The year 2019 saw some enthralling changes in volume and variety of data across businesses, worldwide. The surge in data generation is only going to continue. Foresighted enterprises are the ones who will be able to leverage this data for maximum profitability through data processing and handling techniques. With the rise in opportunities related to Big Data, challenges are also bound to increase.Below are the 5 major Big Data challenges that enterprises face in 2020:1. The Need for More Trained ProfessionalsResearch shows that since 2018, 2.5 quintillion bytes (or 2.5 exabytes) of information is being generated every day. The previous two years have seen significantly more noteworthy increments in the quantity of streams, posts, searches and writings, which have cumulatively produced an enormous amount of data. Additionally, this number is only growing by the day. A study has predicted that by 2025, each person will be making a bewildering 463 exabytes of information every day.A report by Indeed, showed a 29 percent surge in the demand for data scientists yearly and a 344 percent increase since 2013 till date. However, the searches by job seekers skilled in data science continue to grow at a snail’s pace at 14 percent. In August 2018, LinkedIn reported claimed that US alone needs 151,717 professionals with data science skills. This along with a 15 percent discrepancy between job postings and job searches on Indeed, makes it quite evident that the demand for data scientists outstrips supply. The greatest data processing challenge of 2020 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data.2. Inability to process large volumes of dataOut of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it. A major portion of raw data is usually irrelevant. And about 43 percent companies still struggle or aren’t fully satisfied with the filtered data. 3. Syncing Across Data SourcesOnce you import data into Big Data platforms you may also realize that data copies migrated from a wide range of sources on different rates and schedules can rapidly get out of the synchronization with the originating system. This implies two things, one, the data coming from one source is out of date when compared to another source. Two, it creates a commonality of data definitions, concepts, metadata and the like. The traditional data management and data warehouses, and the sequence of data transformation, extraction and migration- all arise a situation in which there are risks for data to become unsynchronized.4. Lack of adequate data governanceData collected from multiple sources should have some correlation to each other so that it can be considered usable by enterprises. In a recent Big Data Maturity Survey, the lack of stringent data governance was recognized the fastest-growing area of concern. Organizations often have to setup the right personnel, policies and technology to ensure that data governance is achieved. This itself could be a challenge for a lot of enterprises.5. Threat of compromised data securityWhile Big Data opens plenty of opportunities for organizations to grow their businesses, there’s an inherent risk of data security. Some of the biggest cyber threats to big players like Panera Bread, Facebook, Equifax and Marriot have brought to light the fact that literally no one is immune to cyberattacks. As far as Big Data is concerned, data security should be high on their priorities as most modern businesses are vulnerable to fake data generation, especially if cybercriminals have access to the database of a business. However, regulating access is one of the primary challenges for companies who frequently work with large sets of data. Even the way Big Data is designed makes it harder for enterprises to ensure data security. Working with data distributed across multiple systems makes it both cumbersome and risky.Overcoming Big Data challenges in 2020Whether it’s ensuring data governance and security or hiring skilled professionals, enterprises should leave no stone unturned when it comes to overcoming the above Big Data challenges. Several courses and online certifications are available to specialize in tackling each of these challenges in Big Data. Training existing personnel with the analytical tools of Big Data will help businesses unearth insightful data about customer. Frameworks related to Big Data can help in qualitative analysis of the raw information.
5 Big Data Challenges in 2021
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How Big is ‘Big Data’, Anyway?

When I got introduced to the data-world with my first corporate induction training, about 10 years ago. I was then still processing the difference between Data and Information. The following helped me understand the same:Data: It is raw information (unprocessed facts and figures) without any context for e.g. Number 20Information: structured Data grouped together which can have interpretation. E.g $20 for a toy.Knowledge: combination of information, experience and insight that may benefit the individual for the organisation. E.g. $20 for a toy in Black Friday Sale in a mall.Wisdom: Knowledge becomes wisdom when one can assimilate and apply this knowledge to make the right decisions. E.g. One who wants to buy a toy will wait for the Black Friday Sale to get it at a cheaper price.By the time I started understanding above differences, ‘Big data’ term was already making it big and then the obvious question in mind was,” When to call ‘data’ à ‘ Big data’? “I then made an attempt to understand ‘how big is a data to be called big data?’ and here, I have a big revelation to make, for all of you reading this article, that ‘Big Data’ is actually misleading term and it is irrelevant with “Bigness of data” but it is to be used in relevance. In fact, it is a term which needs to be understood, only in perspective.The simplest one I could find relevant is, Big data is the data that cannot be stored with traditional storages, cannot be processed with traditional methods/ways and within a short period of time (and these references would still be valid as time advances.) but this is not textbook or only definition of big data. Interestingly, One who finds one set of data as big data can be traditional data for others so truly it cannot be bounded in words but loosely can be described through numerous examples. I am sure by the end of the article you will be able to answer the question for yourself. Let’s start.Do you know? - NASA researchers Michael Cox and David Ellsworth use the term “big data” for the first time to describe a familiar challenge in the 1990s supercomputers generating massive amounts of information - in Cox and Ellsworth’s case, simulations of airflow around aircraft - that cannot be processed and visualized.If you go through a brief history of big data, you would know data which is not fitting into memory or disk was called ‘Big data problem’ back in 1997.As the years passed by innovations were on rising and disruptions were made so the data universe is growing all the time. Let’s understand a few widely available and stated statistics for ‘big data’ (Collected around 2017 or before) >>On average, people send about 500 million tweets per day.Snapchat users share 527,760 photos in a minute Instagram users post 46,740 photos in a minute More than 120 professionals join LinkedIn in a minute Users watch 4,146,600 YouTube videos in a minuteThe average U.S. customer uses 1.8 gigabytes of data per month on his or her cell phone plan.Amazon sells 600 items per second.On average, each person who uses email receives 88 emails per day and send 34. That adds up to more than 200 billion emails each day.MasterCard processes 74 billion transactions per year.Commercial airlines make about 5,800 flights per day.You might be interested to read through Domo’s Data Never Sleeps 5.0 report, for the numbers generated every minute of the day.Understanding that the above stats are probably about 1.5-2 years older and data is ever-growing, it helps to establish the fact that ‘big data‘ is a moving target and…. In short,“Today’s big data is tomorrow’s small data.”Now that we have some knowledge about transactions/tweets/snaps in a day, Let’s also understand how much data, all these “One-minute Quickies” are generating. Let’s talk about some volumes too. Afterall volumes are one of the characteristics of big data but mind you, not only characteristic of big data. It is believed that, In a single day, the world produces 2.5 quintillion bytes (2.3 trillion gigabytes) of data, in layman's terms, this is the equivalent of everyone in the world downloading 60 episodes of Breaking Bad, in HD, 20 times! [Source: VCloud 2012] and According to estimates, the volume of data worldwide doubles every 1.2 years.IDC predicts that the collective sum of the world's data will grow from 33 zettabytes this year to a 175ZB by 2025, for a compounded annual growth rate of 61 per cent. The 175ZB figure represents a 9 per cent increase over last year's prediction of data growth by 2025 – As per the report published in Dec’2018.But, do you know: how much would be 1 zettabyte of data? Let’s understand. One zettabyte is equal to one sextillion bytes or 1021 (1,000,000,000,000,000,000,000) bytes or, one zettabyte is roughly equal to a trillion gigabytes.Fun Fact: There is a legit term coined as The Zettabyte Era (Today’s Era).The Zettabyte Era can also be understood as an age of growth of all forms of digital data that exist in the world which includes the public Internet, but also all other forms of digital data such as stored data from security cameras or voice data from cell-phone calls.You must check out this infographic by economywatch (taken from SearchEngineJournal) to understand how much data zettabyte consists of, putting it into context with current data storage capabilities and usage.Today’s ‘big data’ is generated from majority 3 sources i.e.People Generated: Social media uploads, Mails etc. Machine Generated: M2M (machine to machine) interactions, IOT devices etc. Business Generated: Data generated and stored into today’s OLTPs, OLAPs, Data warehouses, data marts, reports, operational data throughout the enterprise/organization.Various analytics tools available in the market today, help in solving big data challenges by providing ways for storing this data, process this data and make valuable insights from this data.As we discussed, big data is moving target as time advances, it is also interesting to know even today, data which is not of huge size but is difficult to process and of relatively smaller volume would still be categorized as Big Data. For example, unstructured data in emails, from social media platforms, data which is required to process with real-time/near real-time etc. all the examples we have seen so far, all of it is big data.   But, It would be a mistake to assume that, Big Data only as data that is analyzed using Hadoop, Spark or another complex analytics platform. As big data is moving the target and it’s ever-growing, also with various disruptive sources of data are being introduced every day, to process this data newer tools would be invented, and hence big data cannot just remain a function of tools being used to analyze it. To conclude, as discussed at the starting of the article, it would still be appropriate and reasonable to say, this moving target of big data which would always be challenged for storage, processing methods and process it within a short period as well. So big data is a function of volume and/or time and/or storage and/or variety. It was fun and exciting to know what different aspects are hidden in ‘BIG DATA’ word and I thoroughly enjoyed solving this mystery.Did you enjoy solving it too?Do let us know how was experience through comments below.Happy Learning!!!
How Big is ‘Big Data’, Anyway?
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14251
How Big is ‘Big Data’, Anyway?

When I got introduced to the data-world with my fi... Read More

Apache Spark Pros and Cons

Apache Spark:  The New ‘King’ of Big DataApache Spark is a lightning-fast unified analytics engine for big data and machine learning. It is the largest open-source project in data processing. Since its release, it has met the enterprise’s expectations in a better way in regards to querying, data processing and moreover generating analytics reports in a better and faster way. Internet substations like Yahoo, Netflix, and eBay, etc have used Spark at large scale. Apache Spark is considered as the future of Big Data Platform.Pros and Cons of Apache SparkApache SparkAdvantagesDisadvantagesSpeedNo automatic optimization processEase of UseFile Management SystemAdvanced AnalyticsFewer AlgorithmsDynamic in NatureSmall Files IssueMultilingualWindow CriteriaApache Spark is powerfulDoesn’t suit for a multi-user environmentIncreased access to Big data-Demand for Spark Developers-Apache Spark has transformed the world of Big Data. It is the most active big data tool reshaping the big data market. This open-source distributed computing platform offers more powerful advantages than any other proprietary solutions. The diverse advantages of Apache Spark make it a very attractive big data framework. Apache Spark has huge potential to contribute to the big data-related business in the industry. Let’s now have a look at some of the common benefits of Apache Spark:Benefits of Apache Spark:SpeedEase of UseAdvanced AnalyticsDynamic in NatureMultilingualApache Spark is powerfulIncreased access to Big dataDemand for Spark DevelopersOpen-source community1. Speed:When comes to Big Data, processing speed always matters. Apache Spark is wildly popular with data scientists because of its speed. Spark is 100x faster than Hadoop for large scale data processing. Apache Spark uses in-memory(RAM) computing system whereas Hadoop uses local memory space to store data. Spark can handle multiple petabytes of clustered data of more than 8000 nodes at a time. 2. Ease of Use:Apache Spark carries easy-to-use APIs for operating on large datasets. It offers over 80 high-level operators that make it easy to build parallel apps.The below pictorial representation will help you understand the importance of Apache Spark.3. Advanced Analytics:Spark not only supports ‘MAP’ and ‘reduce’. It also supports Machine learning (ML), Graph algorithms, Streaming data, SQL queries, etc.4. Dynamic in Nature:With Apache Spark, you can easily develop parallel applications. Spark offers you over 80 high-level operators.5. Multilingual:Apache Spark supports many languages for code writing such as Python, Java, Scala, etc.6. Apache Spark is powerful:Apache Spark can handle many analytics challenges because of its low-latency in-memory data processing capability. It has well-built libraries for graph analytics algorithms and machine learning.7. Increased access to Big data:Apache Spark is opening up various opportunities for big data and making As per the recent survey conducted by IBM’s announced that it will educate more than 1 million data engineers and data scientists on Apache Spark. 8. Demand for Spark Developers:Apache Spark not only benefits your organization but you as well. Spark developers are so in-demand that companies offering attractive benefits and providing flexible work timings just to hire experts skilled in Apache Spark. As per PayScale the average salary for  Data Engineer with Apache Spark skills is $100,362. For people who want to make a career in the big data, technology can learn Apache Spark. You will find various ways to bridge the skills gap for getting data-related jobs, but the best way is to take formal training which will provide you hands-on work experience and also learn through hands-on projects.9. Open-source community:The best thing about Apache Spark is, it has a massive Open-source community behind it. Apache Spark is Great, but it’s not perfect - How?Apache Spark is a lightning-fast cluster computer computing technology designed for fast computation and also being widely used by industries. But on the other side, it also has some ugly aspects. Here are some challenges related to Apache Spark that developers face when working on Big data with Apache Spark.Let’s read out the following limitations of Apache Spark in detail so that you can make an informed decision whether this platform will be the right choice for your upcoming big data project.No automatic optimization processFile Management SystemFewer AlgorithmsSmall Files IssueWindow CriteriaDoesn’t suit for a multi-user environment1. No automatic optimization process:In the case of Apache Spark, you need to optimize the code manually since it doesn’t have any automatic code optimization process. This will turn into a disadvantage when all the other technologies and platforms are moving towards automation.2. File Management System:Apache Spark doesn’t come with its own file management system. It depends on some other platforms like Hadoop or other cloud-based platforms.3. Fewer Algorithms:There are fewer algorithms present in the case of Apache Spark Machine Learning Spark MLlib. It lags behind in terms of a number of available algorithms.4. Small Files Issue:One more reason to blame Apache Spark is the issue with small files. Developers come across issues of small files when using Apache Spark along with Hadoop. Hadoop Distributed File System (HDFS) provides a limited number of large files instead of a large number of small files.5. Window Criteria:Data in Apache Spark divides into small batches of a predefined time interval. So Apache won't support record-based window criteria. Rather, it offers time-based window criteria.6. Doesn’t suit for a multi-user environment:Yes, Apache Spark doesn’t fit for a multi-user environment. It is not capable of handling more users concurrency.Conclusion:To sum up, in light of the good, the bad and the ugly, Spark is a conquering tool when we view it from outside. We have seen a drastic change in the performance and decrease in the failures across various projects executed in Spark. Many applications are being moved to Spark for the efficiency it offers to developers. Using Apache Spark can give any business a boost and help foster its growth. It is sure that you will also have a bright future!
Apache Spark Pros and Cons
Blogs
9785
Apache Spark Pros and Cons

Apache Spark:  The New ‘King’ of Big DataApac... Read More