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Tag: Data Analytics certification

Data Analysis Techniques: Trend Analysis and Time Series Analysis

Posted on by Imarticus Learning

In today’s data-driven world, extracting meaningful information from vast data is crucial. It serves a huge purpose in almost every sphere.

Whether it is a business looking to optimise operations or a financial analyst predicting market trends, data analysis is a definitive tool for unlocking hidden insights.

Among various techniques from a data analyst’s toolkit, two of the most powerful ones are trend analysis and time series analysis. It helps in identifying patterns, making informed decisions, and even predicting future outcomes.

We will dive deep into understanding these two effective techniques of data analysis. So, whether you’re a seasoned data analyst or beginning a career in data science, this blog will equip you with knowledge about trends and time series data.

Data Analysis and its Techniques

Data analysis is the systematic process of cleaning, transforming, interpreting, and extracting actionable information from the data. It encompasses a range of methods and tools to scrutinise, process, and extract insights from data. Some of these techniques are statistical analysis, machine learning, data mining, and visualisation methods.

Apart from these, there are two prominent data analysis techniques that one must master if considering a data science course in the future. These are trend analysis and time series analysis. These approaches empower individuals to discover meaningful patterns, correlations, insights, and trends to help make effective decisions across various industries and domains through data science training.

Trend Analysis

This effective data analysis technique deciphers long-term patterns and directional changes hidden within datasets. It involves examining data points over time to identify consistent trends.

To perform trend analysis, data analysts employ statistical methods and data visualisation tools, foundational knowledge in any data science course. These tools help reveal recurring patterns, growth or decline trends, and fluctuations within the data. And from these insights, businesses can adapt their strategies, optimise their operations and even anticipate market shifts.

Examples of Trend Analysis

There are several domains where one can make the most out of trend analysis, making it a crucial component of any data analytics course. Some of the examples are given below:

  • In financial markets, trend analysis assists in predicting stock price movements and assessing investment opportunities.
  • The marketing industry identifies its consumer’s preferences and emerging market trends with the help of trend analysis.
  • In the wide field of epidemiology, this data analysis technique tracks disease outbreaks.
  • The field of climate science benefits from this as it is helpful for studying long-term weather patterns.

Types of Trend Analysis

Trend analysis encompasses various approach various approaches to interpreting data patterns over time. Here are some types that help extract actionable insights from time series data:

  • Linear- Identifies consistent upward or downward trends, facilitating predictions based on historical patterns.
  • Seasonal- Captures recurring patterns within fixed time intervals, enabling businesses to adapt to marketing trends.
  • Exponential- Focuses on exponential growth or decay patterns, valuable for highly volatile scenarios.
  • Cyclical- Uncovers long-term cycles of ups and downs, crucial to understanding market fluctuations.
  • Breakpoint- Identifies abrupt changes, vital for recognising shifts in data patterns
  • Non-linear- Applies advanced mathematical functions to accommodate non-linear data behaviours.

Time Series Analysis

In this specific technique of data analysis, analysts record data points at consistent intervals over a set period of time. But this isn’t just an act of collecting data over time, it is much more than that.

The time series analysis shows how variables change over time, along with providing additional sources of information and a set order of dependencies between the data.

This analysis typically requires an extensive data set to ensure consistency and reliability. Its analysis can cut through noisy data, assuring that any trends or patterns discovered are not outliers and can account for seasonal variance.

Organisations use time series analysis to dig deep into why a particular trend or pattern has occurred. And when they analyse data over consistent intervals, they can even use predictive analytics to forecast future events better. However, apart from forecasting, some other key types of time series analysis are:

  • Classification- Assigning categories to data.
  • Descriptive Analysis- Identifying patterns like trends, cycles, or seasonal variations.
  • Intervention Analysis- Talks about the impact of an event on data.
  • Exploratory Analysis- Highlight the main characteristic of time series data.
  • Segmentation- Splits the data into segments and shows the underlying properties.
  • Curve Fitting- Studies the relation of variables within the data.
  • Explanative Analysis- Understand the cause-and-effect relationship within it.

Examples of Time Series Analysis

To understand time series analysis better, we must know the places it is applied in. There are several industries including finance, retail, and economics where this crucial technique from data analytics certification course is highly used as currency and sales are always changing.

Some other examples of time series in action are weather data, stock prices, rainfall measurements, quarterly sales, brain monitoring, industry forecasts, and many others.

Component of Time Series Analysis

The force behind changing the attributes of a time series is its components. There are four main key components of time series analysis:

  • Trends- Represents long-term patterns to indicate the variable’s change in direction over time.
  • Seasonal Variations- Recurring patterns that occur at fixed intervals within a time series.
  • Cyclical Variations- These represent longer-term oscillations in time series data, typically not fixed to specific time intervals, discussed in advanced data science certification.
  • Irregular or Random Movements- Unpredictable fluctuations in time series data, often caused by unforeseen events.

Conclusion

In today’s digital age, data analysis has become a cornerstone of informed decision-making across industries. One can draw out the benefits of data analysis in many ways. It plays a pivotal role in offering a career in data analytics and eventually helps organisations make data-driven decisions and drive innovation.

To decipher data’s hidden narratives, there are two data analysis techniques that stood out as potent tools. From predicting market trends to tracking disease outbreaks, trend analysis, and time-series analysis offer invaluable insights.

Trend analysis reveals long-term patterns and directional shifts, while time series analysis dives deep into data collected over consistent intervals. Both approaches empower organisations to harness data for strategic advantage, adapt to changing circumstances, and forecast future outcomes.

If you aspire to master these data analysis techniques and become a data analyst, consider Imarticus Learning’s data science course with placement assistance . This comprehensive program equips you with the skills and knowledge needed to excel in the dynamic world of data analytics, ensuring you’re well-prepared to tackle real-world data challenges and drive innovation in your chosen field.

What Are The New Advancements In Data Analytics?

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Data analytics is a field which witnesses a continuous revolution. Since data is becoming increasingly valuable with each passing time, it has been treated with great care and concern. New tools, techniques, theories, and trends are always introduced in the data analytics sector to cope with the constant changes in the industries and societies. You can opt for a sought-after data analytics course to get a deeper understanding.

In this article, we will go through some of the latest data analytics opportunities which have come up in the industry.

The Intelligent Data Mesh

The intelligent data mesh has been termed the next revolution in healthcare and medical diagnostics systems in the coming years by the Gartner Top 10 Strategic Technology Trends in 2018.

The “Intelligent Data Mesh” has been described by Gartner as a meeting point of the physical and digital worlds where humans, machines, data, and services have been entwined together into a mesh.

The purpose is to gather the benefits offered by all these individual entities into a single unit to find solutions to complex issues thought to be insolvable until now.

One major industry expected to benefit most from this system is the healthcare industry where Intelligent Data Mesh is being hailed as a game-changer in enhancing patient care.

Blockchain

Blockchain continues to be an exciting technology even in 2018 and is expected to remain so for at least another decade. New advancements are being made almost daily regarding this technology as blockchain finds wider uses in various industries with time.

It will not be wrong to describe blockchain as one of the greatest data analytics opportunities. The concept of blockchain started with the idea of a decentralized digital currency which came to be known as Bitcoin in the market.

However, even though the controversy regarding the currency rose the concept of a decentralized and open-source peer-to-peer modeled technology for storing and analysing data. The concept of blockchain is now applied in a wide range of industries with its use predicted to keep rising soon.

Artificial Intelligence

Artificial intelligence is one such data analytics opportunity which is finding widespread adoption in all businesses and decision-making applications. As per Gartner 2018, as much as 41 percent of organizations have already adopted AI into some aspect of their functioning while the remaining 59 percent are striving hard to do the same.

There is considerable research going on at present to incorporate artificial intelligence into the field of data science too. With data becoming larger and more complex with each passing minute, management of such data is getting out of manual capacities very soon. Scholars have now turned to AI for storing, handling, manipulating and managing larger chunks of data in a safe environment.

Augmented Reality

Augmented Reality is an interesting new technology coming up in recent years. As a source which facilitates the interaction between machine and humans in a unique manner, AR has the potential to be a game-changer in the field of data sciences making it another top data analytics opportunity in the future.
best big data analytics course

AR can provide for the development of simpler UIs for the man-machine interaction when merged with AI allowing the users to store and interact with data in a completely new manner.

Imagine going to an island where all your data is stored in different treasure chests and you are provided with customised keys to access the chests with your data in it. These things may be possible in the future because of the use of AR in data analytics. 

Imarticus Learning offers select best data analytics courses that not only boosts your skillset but also your career as a whole. 

Frequently Asked Questions

What is a data analytics course?

Data analytics involves examining raw data to extract valuable and actionable insights. These insights, once gleaned, serve as the basis for informed and strategic business decisions, contributing to the enhancement of overall business intelligence.

Do data analysts require coding?

Certainly, coding is a fundamental requirement when undertaking an online Data Analytics Degree. While highly advanced programming skills may be optional, mastering the basics of R and Python is crucial. Additionally, a solid command of querying languages such as SQL is indispensable for a comprehensive understanding of data analytics.

Is Python a mandate for data analysts?

Possessing a thorough grasp of Python programming proves highly advantageous for data analysts. Employers commonly anticipate proficiency in utilizing Python libraries to streamline various data-related tasks. Consequently, acquiring skills in Python emerges as a prudent and strategic career decision for aspiring data analysts.

Why problem-solving using Data Analytics needs new thinking?

Posted on by Imarticus Learning

Data analytics helps organizations examine all sorts of data, be its real-time, historical, unstructured, structured, or qualitative data to uncover trends and develop insights that may be used to guide and, in some circumstances, automate decisions, thus connecting intelligence and action.

best data analytics certification courses in IndiaThe best solutions now enable the entire analytical process, from data access, preparation, and analysis through analytics operationalization and evaluating results. Hence, there is a rise in data analytics courses in India.

Data analytics enables firms to digitally transform their company and culture, allowing them to make more innovative and forward-thinking decisions. Algorithm-driven firms are the emerging innovators and business leaders, going beyond typical KPI monitoring and reporting to uncover hidden patterns in data.

In the present scenario, many firms have had to adjust their emphasis, review their existing timeframes, and rethink how they function. Companies that prioritized the digitalization of their business, particularly investments in analytics and automation, have fared the best during this crisis. Nonetheless, AI continues to be a source of consternation for many business leaders across industries.

Why is data analytics used to solve business problems?

Businesses sometimes overlook the fact that the most complicated business issues can be solved using analytics by utilizing the most recent data analytics solutions. It is critical to understand that the analyst spends approximately 70% – 80% of his or her time generating analytical files.

Only the remaining 20-30% is spent on developing a solution. Predictive models or simple business rules are used in this approach to solution creation. A corporation can use analytics tools to extract simple structured data and reduce its effort in retrieving unstructured and semi-structured information.

The rising need for an analytical solution has compelled businesses to invest in analytics solutions that will allow business users and employees across the enterprise to get the answers they need. Businesses can use these powerful tools to perform complex analytics without the need for programming assistance from data scientists. As a result, businesses will gain a new competitive advantage and the ability to unearth previously undetected patterns, propelling them to a position of influence.

Today’s problem solving necessitates a new outlook in order to obtain these insights quickly and efficiently, and there is a need for a cohesive technological change to displace the conventional data analytics tools and dissolve the walls between business groups.

Technology behemoths have demonstrated what can be accomplished when data and analytics are at the core of a business model. Needless to mention, the world’s most successful firms are all data-driven, with their focus on leveraging data to understand, market to, and increase income from their customers. This cultural shift of democratization of access to data and analytics across an organization has allowed these organizations to capitalize on the data economy and drive digital transformation.

Analytic Process Automation, or APA, is a new category that could be the key to capturing the best of man and machine at scale. APA automates business processes and provides immediate self-service access to business-critical data insights to even the most inexperienced knowledge workers. In reality, this implies that more employees will be able to use as well as gain from data with hardly any training.

This, in turn, alleviates the long-standing friction between data specialists and business managers, the latter of whom has relied on the former’s access to critical information. APA de-emphasizes data analytics at a level that the business sector has never seen before.

Conclusion

Data Science as we all know is emerging as an extremely important profession in the present scenario and it is important to have a data analytics certification to be an expert in this field. There are good data analytics courses in India that provide data analytics certification and will also increase your career prospects if you wish to pursue it further.

Predictions For The Future Of Big Data

Posted on by Imarticus Learning

While a lot of experts believe that there’s some great stuff in store for the future of big data, it is also true that technology will be greatly advancing throughout 2017. This is why there are a number of complex facets of big data, which are increasing by the day. Various attributes of big data, such as artificial intelligence and cloud computing, are believed to have a huge impact on big data analytics. There are a number of factors that exhibit the potential to change or more likely determine the direction at which big data is moving. For instance, there will soon be a number of customers who would replace the businesses, in demanding various amounts of data, to look for the cheapest hotels and understanding climate issues and similar concepts. There is a very acceptable idea today, of a reality where it would be the customers, the common man, if you may, who would be demanding personalized, tailored artificial intelligence technology, to suit their particular needs and demands.
While these seem like mere examples, with a tinge of realism, there are absolute chances of these becoming a reality very soon.
Ten years ago, all the data that was ever generated and accumulated, made up the highest denominations of storage space, which was namely Gigabytes or Terra-bytes, but the recent few years have made an explosion of data, into what is known as exabytes; this term roughly refers to billions of Gigabytes of data. This is where we derive the term ‘big data’ from, it is to denote the humongous amounts of data that has been generated, all over the world, in such a short amount of time. Regardless of whatever happens in other aspects of this field, one thing we can be absolutely certain of. That is, that data will be continuously growing, which means that soon there will come a time, when we will be talking about Zettabytes, which roughly amounts for a trillion Gigabytes.
Artificial Intelligence began its advent, as just a buzzword which was continuously used by sci-fi movie enthusiast and was mainly used to refer to technology only seen in sci-fi movies and the likes. Today, this term is no longer reserved for those, who are obsessed with technological gizmos, or those involved in science. It has very well become a part of our everyday lives, through various examples, like Google’s Allo, Microsoft’s Cortana and Apple’s Siri. There are absolute indicators that AI has full potential of transforming, from something nice to have to very essential technology to have. There are so many changes and futuristic developments that big data can make today, as well as in the future.
One of the biggest prediction is the fact that big data can result in various advanced applications for fields of national security, customer behavior tracking, weather forecasting, HR, sports, health and so on.

One prediction is definitely going to happen, which is that big data will have a better, smarter and a huge impacting role to play in the future.

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Top Big Data Analytics Challenges in Health Insurance!

Posted on by Imarticus Learning

Have you ever wondered that by the end of 2025 there will be more than 200 Zettabytes of data available in global cloud storage?

This ever-increasing data is either available in an unstructured or semi-structured form. The health insurance sector is one of the major contributors to this global data.

The rapid digital transformation of the insurance sector is powered by artificial intelligence, machine learning and predictive analysis. Big data in the field of health insurance has started playing a crucial role.

In order to transform the unstructured data into a structured one, organizations need detailed algorithms. Trained professionals from the field of data analytics can build and apply these algorithms in a strategic way to make the best use of the data.

Big Data Analytics Courses in India

There are no two ways that data analytics is transforming the insurance sector at a much faster pace, yet the unique nature of the health insurance market poses many challenges to meeting the requirements. If you are looking to make your career as a data analyst in the health insurance sector, you should first understand some major data-related challenges existing in the health insurance sector.

In order to facilitate flawless services, two major challenges faced by the health insurance sector are Regulatory compliance and data integrity.

Regulatory Compliance

Most of the challenges in any process which is governed by rules and regulations majorly set by the state are the matter of regulatory compliance. Even the slight shift in the set of the state and the federal regulators may result in a major shift in terms of execution and thus always having a close eye on the latest developments has become the need of the hour.

One such regulatory Act in the health insurance sector is the Health Insurance Probability and Accountability Act (HIPAA). Despite the understanding of HIPPA’s privacy policies, very few insurers are aware of its data security and protection.

For example, e-PHI contains the electronic records of personal health information as guided by HIPAA’s security rule book. These guidelines ensure the insurer will maintain the confidentiality of the data they receive through e-PHI.

In order to safeguard crucial and confidential data, insurers need to identify and protect the data from potential threats and need to ensure that the entire workforce during execution follows all the compliance.

Data Integrity

Data integrity is not a very new challenge, many solutions to it exist, but the lower standards in terms of quality can cause major issues.

The main challenges related to data integrity lie in the health reports of patients. To deal with these challenges, special data understanding is required. In addition to this, the nature and scope of the patient-provider relationship lie in precisely capturing the events such as illness, diagnosis, prescription, claims, etc.

The problem lies in identifying the policyholders who are not in active engagement with the insurers. Another related problem lies in identifying the policyholders who stop filing prescription-related claims.

What would a Data Analyst do to overcome these challenges?

Big Data Analytics Course in IndiaIf you are looking for some data analytics courses in India, to build your career as a data analyst in the health insurance sector, you can contribute at every stage, right from data mining to data architecting to statistics.

Data analysts design the required infrastructure that suits the organizational requirement of data integrity and compliance dynamics. Data analysts play a crucial role in designing independent systems which help them analyze the data, engineer the data and eventually get the best out of the data.

To get a clear sense of what data analysts do, we should see data analysts as data architects, data scientists, data engineers, and statisticians at different phases of the project.

If all this information regarding big data in health insurance has piqued your interest, you must research more about the data analytics courses in India which would provide you with the next steps to get that much closer to becoming a full-fledged data analyst yourself.

Join the latest webinar on this for FREE

https://imarticus.org/Learn-Data-Analytics/

Top 7 Reasons to Convince You To Take on that Data Analytics Job

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It’s more than just a buzzword, it’s a revolution– data analytics is here and here to stay. For four years in a row, data analytics was ranked the best job in the U.S. alone by Glassdoor in 2019. The data fever is catching on in other parts of the world too, as global economies become more interdependent and related.

More and more companies and industries are embracing data analytics, not least because it’s a science that delivers valuable insights applicable across all plans including business and marketing.

If you’re still hesitating about whether to go for a career in data analytics, allow these top 7 reasons to convince you:

#1: It’s in demand

Data analytics is one of the most in-demand jobs in the world today. This is because all industries need data-driven insights to make even changes, be it to pick a marketing option during A/B testing or rolling out new products. Data analytics is a high-skills, high-stakes job, which is why companies are ready to hire those willing to think creatively and derive data-based solutions to business problems.

#2: It’s easy to start

Educational institutions and course providers have sat up and taken notice of the demand for data analysts, leading them to introduce related training courses. Regardless of whether you’re a fresher or a professional in the tech field, data analytics training can help you start from scratch and build a portfolio of projects to showcase your skills These courses also provide tutorials in essential data analytics software such as Hadoop, Sisense and IBM Watson.

#3: There are plenty of job roles

Within the data analytics field, there are job roles that span academic divisions and aren’t restricted to engineering or software alone. Data scientists, systems analysts and data engineers will benefit from a background in the aforementioned academic fields. However, statisticians and digital marketing executives can look into roles such as quantitative analysts, data analytics consultants and digital marketing managers to put their skills to good use.

#4: The pay is good

The average salary in the data analytics field is US$122,000– a testament to how in-demand the profession is and how in dire need companies are of skilled employees. The figures vary depending on the role and job description but suffice to say that the pay is often much better than other technical jobs that people still seem to hover to by default. It’s also dependent on what industry you will work for, in what capacity and towards which goals.

#5: Growth opportunities abound

Technology is a dynamic field and with new changes come the chance to upskill, pick up new software and contribute to futuristic projects. Data analytics professionals can find themselves growing through roles and projects, oftentimes being tasked to lead a team or be the sole owner of a large-scale project.

#6: Industries are interwoven

With other tech fields, you might be restricted in your tasks or limited to a company. In data analytics, however, you get to pick and choose the fields you want, whether pure tech or even retail. Data analytics is in use across most industries so, once you find your niche, you’re ready to start dabbling in the industry of your choice.

#7: Influences decision-making

If you’ve ever wanted to be part of the larger organizational or business structure and contribute positively, chances are data analytics might be the niche for you. The insights that emerge from analyses of data can power strategies and create new business plans. This way, your contribution leads to progress on an organizational scale and your work can make or break a business.

Data analytics gives you the opportunity to become a more active stakeholder and contributor to any business regardless of the industry, so take the leap today.

With Jobs at Risk, can a Career in Big Data Keep You Safe?

Posted on by Imarticus Learning

Data powers the information economy just like oil powers industrial economy. No wonder they say, “data is the new oil”. A critical asset to many industries, data science and AI changed the way information is gathered and processed. Even when COVID-19 hit the global economy, leading to job cuts and hiring freeze, data science remained unaffected.

While companies do not debate on the importance of data science, collecting and storing the huge volume of data was a big challenge. With limited capabilities, companies had a big struggle to maintain and process data. However, AI and cloud-based technologies provide a solution to this problem. These technologies have created better job opportunities for data professionals than ever before. If you are aspiring for a data analyst career, there isn’t a better time than this.

Why Big Data?

The world is consumer-centric and will remain so despite the hard hits on the economy. Consumerism is the driving force that creates revenue, and job opportunities. From healthcare to e-commerce, all industries are data-driven. The data requirement changes from one business to another, from one company to another. But the enormous amount of unstructured data can be collected using various tools and techniques, organized and structured according to the business needs.

No matter the business is consumer data is vital to all businesses. The tech giants like Google, Amazon etc, and the social media giants like Facebook have been using the potential of data to achieve a competitive advantage over their rivals. And the result is pretty much evident. They are far ahead of their competitors.

What is common among all of them is that they collect large swathes of data regarding their customers – right from what products they buy, which products they ditched after adding to the cart, which posts get better engagement, how long does a person spend time on their webpages – every single move of their customer after arriving on their website is tracked, processed and analyzed to make better business decisions.

The global health crisis saw the extensive application of data, how it can be used to manage a crisis better. From contact tracing, health screening and mitigating the spread. Many apps were developed to help contain the spread, leveraging the GPS to identify the COVID-19 hotspots.

The Increasing Demand for Data Scientists

COVID-19 has indeed changed the way the world functions. With more people staying indoors, individuals flocking the internet also increased. From work to shopping, everything is being done online. And this has increased the requirements for data scientists. While many companies struggle to acclimatize and manage their current employees logging in from a remote place, Tech firms are out with a pressing need to recruit more talents.

With more students and professionals active online, the need for online tools and platforms is growing, and this has led to the demand for an intense expansion of their talent pool.

AI and cybersecurity talents are the most coveted as many companies need technical support in digitizing their businesses. This calls for the improvement of data security measures and to enhance automation to reduce the on-site manpower.

Firms that rely on AI-powered software and those which provide such platforms are on a lookout for technical talents including software engineers and data analysts. Furthermore, financial services companies are also gearing up to become market-ready when the economy reopens. They have started headhunting for people with risk management and data analytics skills to cater to the recent spike in digital banking and online payments activities.

Data Science Online CourseData science is one of those areas not affected by COVID-19. In fact, the pandemic and the enforced stay-ins have resulted in an increased demand for data scientists. If you are a new graduate, take this opportunity to make the most out of the current market situation.

Enrolling in a Big Data Analytics Course could help you land on a lucrative career in data analytics and big data.

How Businesses Are Building Futures With Data Analytics?

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Data analytics handles the raw data from the resources using technology, algorithms, and mechanics into a simpler and human-friendly version to help data businesses and organizations. It is now an important factor that drives the business as well as the decision-making process in everyday lives.

In one way or the other, business and organizations depend on data analysis to improve their trade. It wouldn’t be wrong to say that business analytics and Artificial Intelligence have both a major role in building the future everywhere.

  • General analysis: A business needs everyday updates to keep up with the market and trends. It is necessary to identify the low and high points to work on these areas to make the necessary changes. Such random analysis can find new opportunities and can predict the capability of the new strategies.

    This is highly important for all types of businesses in any field. Whether it is the retail, healthcare, medical, technology, food industry, online industries, etc. They all rely on big data to help improve their services.

  • Business improvement: The pandemic has brought the world to a standstill for a while. It is now slowly improving its pace but there are several companies and businesses that suffered a huge loss. In order to get their business back on track, they can rely on data analysis. Finding what is trending or what the public is expecting is the key to a restart. Identifying the trend allows planning and strategy to make necessary changes in the projects to make it plausible for the future and improve the business.
  • Automation: Automation is something the general public does not think about too much but most of them are using it every single day. The use of AI and Machine Learning has integrated to come up with ideas such as Alexa, Siri, Google voice, etc.

    Apart from these, voice-controlled automobiles, electric devices, etc are all part of this. These are definitely in the initial stages right now and have better prospects for the future where they can be applied in many more domains and areas. It is even possible to attract more customers and urge them to use the same.

  • Managing data: The biggest huddle in data analysis is managing the raw data. With the accumulation of data growing in every department of life, it is significant to have proper usage of the stored data. Data analytics Certification could be used to find ideal solutions for the problems in each department.

    Data is useful for every business and department and it is up to the data scientists to find the use of these pieces of information. This is exactly why data scientists are the hottest job profiles in the current scenario. They are needed by every business to build their future by creating strategies for success and predicting their prospects. Since people provide their data in one form or the other through various mediums, managing this data holds a key role in the future of businesses and public lives.

Conclusion
Data analytics was started as a part of technology is now an integral part of lives that has an impact on almost all levels and departments of life. Such reliance on data increases the competition between businesses and it can be healthier when they can identify their key to success from the data available.

But ultimately, it all comes down to how each of them identifies and interprets the same pool of data with their insights and implements them along with the same pulse of the public. After all, going with the trend is more reliable than finding something new and making it successful.

What Are The Ways Big Data is Being Used To Create The Next Generation of Mobile Apps?

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Big data tops the charts when it comes to providing a considerably better user experience by increasing app engagement and optimizing resources correctly. While it not only makes content for users more relevant but also personalized content and when analyzed from a business point of view, it improves conversion rates. To put it in simpler words, the future of big data is the gold mine that app developers need in providing information and creating apps that users want.

Here is a breakdown of the various ways in which big data is being used to create the next generation of mobile apps:

Seamless and easy to use UX
Big data is incredible in providing insights that help tract every movement of a user by crunching numbers to improve overall user experience. Additionally, it also helps in signaling developers when apps do not meet either the design standards or the UX. Studies suggest that most app users stick to or delete an app based on its user-friendly quotient, which is the ease of use. This kind of information helps big data constantly make improvements for user interface and reduce friction.

Machine learning and artificial intelligence

With the help of machine learning and usage of artificial intelligence, big data can recognize failure patterns if any and suggest improvements.  Also, this helps understand any glitches that might be acting as slowdowns, including loading time for a website or a page.

Predictive analytics and customization
Big data helps customize the user experience and deliver content based on previous usage patterns. This is where predictive analytics come into play by suggesting what you should buy or what you should watch. This gets increasingly better as you consistently use a particular service.

Widely used by companies like Netflix and Amazon, predictive analytics shows up an image or shows pricing options based on user data buying patterns and more. Basics of predictive analytics are taught during a Data Analytics Course.

Increase app engagement

Users often get more engaged with a particular app and keep returning to it frequently. A term referred to as- app stickiness, this actively engages customers more than its competitors and factors like duration session, the flow of content on the screen and churn tracking help in contributing to stickiness.

Real-time analytics

Real-time analytics help an app developer to analyze data related to that app and make dynamic changes based on the present situation. The mobile app market in itself is a pretty dynamic one, where things significantly change every minute. Organizations are using real-time analytics o predict patterns that include flying for airlines when visibility is good, avoiding certain roads to get rid of traffic, avoiding extreme weather conditions, sharing driver and customer live locations, estimates fares at a given point in the day and more.

Evolve marketing strategies

Big data can help make better marketing strategies, by capturing user data that helps app developers understand the kind of people their users are. Existing strategies are reworked on to reach out to new users and rearrange older users. Study of user demographics, buying patterns social behavior of apps, posts liked, websites visited, all of which can be used to build individual user personas which are then used to strategize marketing strategies.

Considerable cost reduction

Lastly, big data helps understand and predict app development costs, since building a standard app might often be time-consuming and quite expensive. This not only includes the app development process costs but also calculates the number of developers, designers, testers and more will be needed to have an app up and running. Additionally, the longer time it takes to build the app, the higher the cost graph goes.

What Are The Tips To Prepare For a Hadoop Interview?

Posted on by Team Imarticus

The popularity of big data has been growing at an immense rate opening the doorway to a spectrum of jobs that require skilled professionals. Noteworthy among these is the job of a Hadoop developer; challenging, technical and well paid, Hadoop is known to be one of the best segmentation of big data and analysis and a developing platform for candidates interested in a career in data science.
Learn Hadoop to pursue a career as a Hadoop analyst, Hadoop developer, or a Hadoop Architect, Hadoop tester among other job roles on the Hadoop platform. If you are looking for a career in this domain, it is highly essential to understand that a Hadoop developer not just created codes in programming but is also expected to have an expertise of multitasking while as his job, which includes programming in Java, writing scripts, reviewing log files, scheduling jobs across clusters on Hadoop amongst others.
Basic skill set for a Hadoop interview
Hadoop works with a number of other software like Ambari, HBase, Hive, Pig and more, therefore, knowledge of technologies is essential. While it is important to also have an idea about other visualization and ETL tools, SQL, gateway and edge nodes, basic cloud computing, some of the must-have skills an interviewee needs to possess during Hadoop training include JAVA, Hadoop Framework, Pig, HDFS, MapReduce, and Floop.
Tips to prepare for a Hadoop interview
Cracking a successful Hadoop interview does not essentially mean having specified skillsets but also ensuring that all of the interviewee’s questions are addressed. While Hadoop in big data is a relatively new concept, here are a couple of tips to help you prepare better for an upcoming Hadoop interview.
Knowledge of Programming Languages
Java experience is as important as it can since Hadoop is a software-based on Java. If your career path monitors progress from C++ to Java, nothing like it. Knowledge of other programming languages like OOAD, JS, Node.js, and HDFS only add to your skillset and make your resume stand out from the rest of the candidates.
Big Data experience
If you have experience working with big data, a Hadoop interview would be fairly easy to crack, since Hadoop is mostly built for the working of big data.
Technical Expertise
To crack a Hadoop interview, you not just need hard skills for Hadoop but also various other technologies that include Flume, Sqoop, Hive, Pig and more. These technologies often seem smaller, however, they make data processing easier on Hadoop.
Interview domains that are essential to prepare for
Along with a good grasp of relevant skill sets, listed below a couple of interview domains every interviewee needs to prepare for-
Practical experience
Theoretical knowledge is important, however, most interviewees are tested on practical knowledge. Expertise in the practical field subjects candidates to various degrees of exposure otherwise impossible by merely learning theories.
Communication Skills
Hadoop experts have to communicate with people in various other job roles, that often include engineers, analysts or even architects. In cases like these, good communication goes a long way.
Knowledge of domain
The interviewee is expected to know the A-Z of Hadoop along with its basic functionalities. You may be expected to back your interview answers with sufficient theoretical or analytical examples.
Conclusion
Big data is growing at an immense rate and more professionals are getting enthusiastic to work in the field. An extensive Hadoop training can go a long way in helping a big data enthusiast to master the best skills in the market and make it big as a professional.
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