online certification Archives - Page 2 of 15 - DexLab Analytics | Big Data Hadoop SAS R Analytics Predictive Modeling & Excel VBA

The Boons of Technology: How Excel Has Changed Workplace Dynamics

Microsoft Excel is a smorgasbord of information – it is a staple technology tool in any business environment. Whether you are crunching business data, organizing client sales inventory or planning an office event, Excel is arguably the most powerful tool entrenched across multiple business domains worldwide.

 
The Boons of Technology: How Excel Has Changed Workplace Dynamics
 

Aspiring professionals contemplating to make an entry into the workplace are required to excel on Excel tricks – Excel Dashboards Training Pune from DexLab Analytics is a promising gateway to your dreams!

Continue reading “The Boons of Technology: How Excel Has Changed Workplace Dynamics”

Why Data Science Matters More Than Data Scientists?

More is always better, isn’t it? But does it always holds true, especially when it comes to customer data? Maybe not, because business is all about extracting meaningful insights from data, and if that cannot be acted upon then it is of no good.

 
Why Data Science Matters More Than Data Scientists?
 

Recently, Accenture concluded that one of the greatest challenges that marketers face nowadays is to discover the right ways to turn data into productive insights and then into action. For that, you would need analytics professionals who do know how to collect, store and integrate information, while mastering the technology aspect.

Continue reading “Why Data Science Matters More Than Data Scientists?”

Risk Analytics Market: Serious Growth Rate Projection for 2017-2021

Want to get to the core of understanding risk within various business frameworks? The answer is Risk Analytics. This new breed of data analytics facilitates organizations in precisely defining, recognizing and managing their risk, and its need is going to increase in the coming few years. New developments in risk analytics are gaining limelight and bringing a notable transformation in the market, while enhancing its overall capability.

 
Risk Analytics Market: Serious Growth Rate Projection for 2017-2021
 

Recently, a team of analysts had eureka moment – they introduced a new concept of real-time risk analytics – it is nothing but a modern, more advanced version of traditional risk analytics methods. Here, the prediction is based on real-time data – it processes, examines and determines risk all on a real-time basis – hence top notch financial institutions are putting real-time risk analytics to best use to manage and mitigate associated risks. Several asset management, portfolio management and hedge fund firms, and investment banks are relying on this mode of risk analytics to modify their operating principles to play in accordance with investment and market shifts.

Continue reading “Risk Analytics Market: Serious Growth Rate Projection for 2017-2021”

Credit Risk Modelling: A Basic Overview

Credit Risk Modelling: A Basic Overview

HISTORICAL BACKGROUND

The root cause for the Financial Crisis which stormed the globe in 2008 was the Sub-prime crisis which appeared in USA during late 2006. A sub-prime lending practice started in USA during 2003-2006. During the later parts of 2003, the housing sector started expanding and housing prices also increased. It has been shown that the housing prices were growing exponentially at that time. As a result, the housing prices followed a super-exponential or hyperbolic growth path. Such super-exponential paths for asset prices are termed as ‘bubbles’ So USA was riding a Housing price bubble. Now the bankers, started giving loans to the sub-prime segments. This segment comprised of customers who hardly had the eligibility to pay back the loans. However, since the loans were backed by mortgages bankers believed that with housing price increases the they could not only recover the loans but earn profits by selling off the houses. The expectations made by the bankers that asset prices always would ride the rising curve was erroneous. Hence, when the housing prices crashed the loans were not recoverable. Many banks sold off these loans to the investment banks who converted the loans into asset based securities. These assets based securities were disbursed all over the globe by the investments banks, the largest being done by Lehmann Brothers. When the underlying assets went valueless and the investors lost their investments, many of the investment banks collapsed. This caused the Financial Crisis and a huge loss of investors and tax-payers wealth. The involvement of Systematically Important Financial Institutions (SIFIs) and Globally Systematically Important Financial Institutions (G-SIFIs) into the frivolous lending process had amplified the intensity and the exposure of the crisis.

Understanding Credit Risk Management With Modelling and Validation – @Dexlabanalytics.

SYSTEMATICALLY IMPORTANT FINANCIAL INSTITUTIONS AND THEIR ROLE IN SYSTEMIC STABILITY

A Systematically Important Financial Institution (SIFI) is a bank, insurance company, or other financial institutions whose failure might trigger a financial crisis.

If a SIFI has the capacity to bring in a recession across the globe then it is known as a Globally Systematically Important Financial Institution (G-SIFI). The Basel Committee follows an indicator based approach for assessing the systematic importance of the G-SIFIs. The basic tenets of this approach are:

  1. The BASEL committee is of the view that the global systemic importance should be measured in terms of the impact that a failure of a bank can have on the global financial system and wider economy rather than the risk that the failure can occur. So, the concept is more of a global, system wide, loss given default (LGD) concept rather than a probability of default (PD) problem.
  2. The indicators reflect the following metrics: size of banks, their interconnectedness, the lack of availability of substitutable or financial institution infrastructure for provided services, their global activity, their complexity etc. Each of these are defined as:

(i) Cross-Jurisdiction: The indicator captures the global footprints of the banks. This indicator is divided into two activities: Cross Jurisdictional claims and Cross Jurisdictional liabilities. These two indicators measure the banks activities outside its home relative to overall activity of other banks’ in the sample. The greater the global reach of the bank, the more difficult is it to coordinate its resolution and the more widespread the spill over effects from its failure.

(ii) Size: Size of a bank is measured using the total exposure that it has globally. This is the exposure measure used to calculate Leverage ratio. BASEL III paragraph 157 uses a particular definition of exposure for this purpose. The score of each bank for this criterion is calculated as its amount of total exposure divided by the sum of total exposures of all banks in the sample.

(iii) Interconnectedness: Financial distress at one institution can materially raise the likelihood of distress at other institutions given the contractual obligations in which the firms operate. Interconnectedness is defined in terms of the following parameters: (a) Inter-financial system assets (b) Inter-financial system liabilities (c) The degree to which a bank funds itself from the other financial systems.

(iv) Complexity: The systemic impact of a bank’s distress or failure is expected to be positively related to its overall complexity. Complexity includes: business, structural and operational complexity. The more complex the bank is the greater are the costs and time needed to resolve the banks.

Given these characteristics, it was important to apply different restrictions to keep the lending practices of the banks under control. Frivolous lending done by such SIFIs had resulted in the financial crisis 2008-09. Post the crisis, regulators became more vigilant about maintaining appropriate reserves for banks to survive macroeconomic stress scenarios. Three major sources of risks to which banks are exposed to are: 1. Credit Risk 2. Market Risk 3. Operational Risk. Several regulations

have been imposed on banks to ensure that they are adequately capitalised. The major regulatory requirements to which banks need to be compliant with are:

  1. BASEL 2. Dodd Frank Act Stress Testing 3. Comprehensive Capital Adequacy Review.

Before looking into the Regulatory frameworks and their impact on the Credit Risk modelling, let us form an understanding of the framework of the Bank Capital.

Risk Management in a Commercial Lending Portfolio with Time Series and Small Datasets – @Dexlabanalytics.

CAPITAL STRUCTURE OF BANKS

The bank’s capital structure is comprised of two main components: 1. Equity Capital of Banks 2. Supplementary capital of banks. The Equity capital of banks are the purest form of banking capital. This is true or the actual capital that a bank has and it has been raised from the shareholders. The supplementary capital of banks comprises of estimated capital such as allowances, provisions etc. This portion of the capital can easily be tampered by the management to meet undue shareholders expectations or unnecessarily over reserve capital. Thus, there are strong capital norms and regulations around the supplementary capital. The two tiers of capital are: Tier1 and Tier2 capital. Tier1 capital is also decomposed into two parts: Tier1 Common capital and Tier1 capital.

 

Tier1 common capital = Common shareholder’s equity-goodwill-Intangibles. Goodwill and intangibles are no physical capital. In scenarios, where the goodwill and intangible assets are stressed, the capital in the banks would deteriorate. Therefore, they cannot be added to the company’s tier1 capital. Only the core or the physical amount of capital present in the bank account is the capital.

Tier1 Capital = Total Shareholders’ equity (Common + Preffered stocks) -goodwill -intangibles + Hybrid securities.

Tier 1 is the core equity capital for the bank. The components of Tier1 capital are common across all geographies for the banking system. Equity capital includes issued and fully paid equities. This is the purest form of capital that the bank has.

Tier2 Capital: tier 2 capital comprises of estimated reserves and provisions. This is the part of capital which is used to cushion against expected losses. Tier 2 capital has the following composition:Tier 2 = Subordinated debts +Allowances for Loans and lease losses + Provisions for bad debts -> This portion of the capital is reserved out of profits. Hence,

managers always try to under report these parameters to meet shareholder’s expectations. However, under reserving often poses the chances of bankruptcies or regulatory penalties. Total Capital of a Bank = Tier 1 capital + Tier 2 Capital

Explaining the Everlasting Bond between Data and Risk Analytics – @Dexlabanalytics.

CALCULATION OF CAPITAL RATIOS

Every bank faces three main types of risks: 1. Credit risk 2. Market Risk 3. Operational risk. Credit Risk is the risk that arises from lending out funds to borrowers, given their chances of defaulting on loans. Market Risk is the risk that the bank faces due to market fluctuations like stock price changes, interest rate risk and price level fluctuation etc. Operational risk occurs as a failure of the operational processes. The exposure of the banks to these risks differ from bank to bank. So the capital that they to set aside would differ based on the exposure to risk. Therefore, regulators have defined a metric called Risk Weighted Assets (RWA) to identify the exposure of the bank’s assets to risk. Every bank must keep aside their capital relative to the exposure of their asset to risk. The biggest advantage of RWAs is that they not only include On-balance sheet items but off-balance sheet items as well. Banks need to maintain their Tier1 common capital, tier1 capital and tier2 capital relative to their RWAs. Thus, arises the Capital ratios.

 

Total RWA = RWA for Credit Risk + RWA for Market Risk + RWA for Operational Risk

Tier1 Common Capital Ratio = tier1 common capital / RWA (CR + MR + OR)

Tier1 Capital Ratio = Tier1 Capital / RWA (CR+MR+OR)

Total Capital Ratio = Total capital/ RWA(CR+MR+OR)

Leverage Ratio = Tier1 Capital / Firms consolidated assets

Regulators require some critical cut-offs for each of these ratios:

Tier1 Common Capital Ratio > = 2% all times

Tier1 Ratio >= 4% all times

Tier 2 capital cannot exceed Tier1 capital

Leverage ratio > = 3% of all times.

 

In the next blog we explore how the credit risk models help in ensuring the capital adequacy of the banks and in the business risk management.

 

Looking for credit risk analysis course online? Drop by DexLab Analytics – it offers excellent credit risk analysis course at affordable rates.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

10 Amazing Excel Templates to Help Maintain an Organized Budget

10 Amazing Excel Templates to Help Maintain an Organized Budget

Managing a proper budget is an essential task. Professionals working in enterprises are somehow involved in performing this task to some degree. Fortunately, a handful number of budgeting Excel templates is available online – check out some of our favorites:

Back to the basics, budget is something more than an educative, future projection of what should happen to a company’s financial position over a specific course of time, based on the latest trends, current data and previous history. The accuracy of the entire forecast depends on the quality of financial data accumulated during the budget period – it is here that a robust set of data collection tools saves the day.

Here’s a roundup of some of the most useful and easy-to-use budget-related MS Excel templates – most of them are available and are for free. Let’s check them out:

Microsoft Office Templates

When it comes to software, Microsoft remains unbeatable. It offers few applaud-worthy customizable office templates that help you become more productive in implementing programs, perform tasks and track financial budgets. Some of them are below:

Business Expense Report

Business travel expense report is one of the most popular budget reporting activities. For reimbursement of travel expenses, the employee needs to present detailed accounts of expenditures. The Excel template in here is easy to fill up, less time-consuming and removes the frustration caused due to tracking expenses in a business trip.

Project Budget

Project Budget template helps you evaluate your project’s design, development and delivery costs so as to monitor actual expenses that occur thereafter.

 

bfreeexcelbudget

Profit and Loss Statement

An all-inclusive profit and loss statement is fundamental for any enterprise. Whether it’s on a departmental level or encompassing an entire organization, determining profit and loss in the financial statement is important to put into focus the problematic areas of your business. The Excel template helps in calculating both the actual and budget revenues and expenses over a pre-determined period of time.

Balance Sheet

Balance Sheet is an effective financial budgetary document that enlists all the assets and liabilities, while evaluating their budgetary values, depreciation rates and amortization records, and the job gets easier when it’s done on an Excel template.

Activity-based Cost Tracker

The activity-based cost tracker template provides you a visual representation of standard, administrative, direct and indirect costs in relation to production. It also helps in monitoring the cost of each unit, while helping you manipulate your budget plan, if other factors, including costs fluctuate.

Small Business Cash Flow Projection

Managing cash flow is more significant than determining profit and loss and balance sheet and by properly using the Excel template small businesses will do well for themselves anytime.

Simple Invoice

In the business circle, an Excel template on invoice gains a lot of accolades. This kind of Excel template automatically calculates the totals and subtotals so that the businesses don’t have to lose any of its valuable time.

cfreeexcelbudget

Beyond the jurisdiction of Microsoft, here are few other budget-related Excel templates:

Year-round IT Budget Template

From the curators of Tech Pro Research team, this cutting edge Excel template helps you track all your spending, administer unplanned expenditure, classify expenses, and highlight key data, like cost of hiring new recruits, contract end dates and recurring payments.

 

afreeexcelbudget

Systems Downtime Expense Calculator

Taking care of an enterprise network is the most demanding job role for any IT administrator. Nothing in this wide world will be more traumatic than network downtime. Hence, this Excel template assist you place a dollar amount on your network downtime.

Computer Hardware Depreciation Calculator

This Excel worksheet helps you determine depreciation for your equipment – finding the rate of depreciation is not a piece of cake – there are many methods of deprecation to take into account.

Do you want to know more about how to use MS Excel for better budgeting for your business? Advanced Excel course by DexLab Analytics would be perfect for you. Excel certification in Delhi helps you grow in your career, so enroll today!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

The Future of Risk Management: Triggering a Technology Dividend

The Future of Risk Management: Triggering a Technology Dividend

Many factors are constantly shaping and reshaping the structure of risk management today – including global geopolitical inconsistency, macroeconomic headwinds and increasing number of cyber activities – which is extensively damaging and recurring. All this is leading to elevated risk perceptions.

The nature of risks has changed over the years too, as well as the manner of addressing them. Today, to mitigate risk issues, technology plays a crucial role. Headwinds like global and Asian accelerating debt levels, lower projection of productivity growth, increasing levels of policy uncertainty and constant increase of US interest have created a lot of prominent macroeconomic challenges, especially in export-oriented Asian economies. Topping that, budding risks from technological advancements are on the rise, exposing industries to newer challenges like cybersecurity and data fraud.

Explaining the Everlasting Bond between Data and Risk Analytics – @Dexlabanalytics.

As a result, the regulatory scenario of the world is also changing, especially after the global financial crisis. With a wide array of regulations introduced, the issue of risk management has started getting the desired prominence. These increasing regulations have compelled banks to accelerate their compliance activities, while giving increasing pressure on risk-management policymaking. The risk management teams now need to be constantly on a lookout for newer uncertainties – the key to address this concern remains productivity gains, but for that technology needs to be employed to the vast extent.

Cyber Value-at-Risk Model: Quantifying the Value-at-Risk – @Dexlabanalytics.

Hitting a technology dividend

Advanced data analytics, contemporary data and NLP coupled with process digitization offers new robust opportunities for effective market risk management. The technological opportunities can be realized throughout various key functions and levels, but it is the duty of the risk professionals to chalk out a more affordable and fruitful approach to address risk-related issues.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling – @Dexlabanalytics.

Check out these 3 principal levers to nab potential opportunities:

Data – Data is the new powerful combat weapon. Financial institutions consist of huge piles of data, where internal and external sources of data continuously pour in at an accelerating rate.  Data, in every form – including transaction, social media, and other sources helps discover real-time customer insights and generate dividends thereafter.

Analytics – Nowadays, machine learning, NLP, advanced analytics and self-learning algorithms are widely available and at achievable prices. The best example to show how advanced analytics is boosting risk management is improving debt collection.

As per conventional debt repayment collection procedure, a lot many calls were asked to make, out of which very few turned out to be successful. But now, with advanced analytics, a set of high-end predictive models are developed to fire up decision-making process. After this, an improved insight about customers can be curated, which can further be developed with better prediction quality.

Processes – With digitization, one gets the opportunity to automate and design risk-monitoring processes to mitigate emerging risks. Nowadays, several financial institutions are implementing machine learning and transaction data to automate monitoring of conduct risk.

Subject to the extent of digitization, the change in factors for risk organization is proposed – in the beginning of digitization, one expects 15-20 percent efficiency gains, while a 60-70% improvement is to be expected in case of a fully digitized risk function, which is quite a show!

Market Risk Analytics: What It is All About – @Dexlabanalytics.

Do you want to know more about market risk modelling techniques? Drop by DexLab Analytics; being a one-stop-destination for Market Risk Modelling using SAS, it boasts of superior training and well-researched study materials.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Cyber Value-at-Risk Model: Quantifying the Value-at-Risk

Cyber Value-at-Risk Model: Quantifying the Value-at-Risk

Cybersecurity attacks are the new potent threat to businesses. Diligent professionals and big mouth board members have started reviewing their company’s cybersecurity frameworks, while establishing better security controls and discerning deeper insights about the business impact of cybersecurity attacks: what kind of risks are they exposed to? Are they expending too much and need to curtail down? What amount of risk can be reduced using the proposed info security budget? Cyber-insurance, will it fetch better results?

What objectives to secure with Cyber value-at-risk models?

This is the epic question that has triggered the development of Value-at-risk models, especially in the domain of information security. Also known as Cyber VaR, these models are a game-changer. They offer a sound base for quantification of information risk coupled with infusing discipline into the whole process.

Market Risk Analytics: What It is All About – @Dexlabanalytics.

The objective of VaR is:

  • To help risk professionals formulate the notion of cyber risk in plain financial language without using any technical jargons.
  • To enable business professionals achieve a standard balance between safeguarding an organization and running the business by making cost-effective decisions.

Enterprises powered by VaR models for cybersecurity make complicated decision-making as easy as pie. They trigger risk-related discussions, where risks become more consistent, and business-goal driven.

A New Course Alert! DexLab Analytics Launches Market Risk Analytics and Modelling – @Dexlabanalytics.

What exactly is cyber VaR?

In the world of finance, value-at-risk modeling is the statistical methodology to appraise the level of financial risk that a firm is exposed to over a specific period of time.

The VaR is ascertained using these three variables:

  • The amount of conjectured loss
  • The probability of that amount of loss
  • The time frame

Probabilities are effective to evaluate likely losses from the cyber attacks during a specific time period. Top notch global organizations, like World Economic Forum and several regulatory bodies, like The Open Group are revolutionizing the concept of cyber VaR models.

fi1-2

What is its benefit?

VaR was initially developed in 1990’s to boost the investment banking sector, wherein managers were to identify the risks that popped up daily in multiple market reports. From the name itself, you can understand, it is more likely a measurement tool to analyze the financial impact of risky events within a particular time frame.

The most beneficial effect of VaR is that it not only quantifies risk but also pens it down in economic terms that are easily understood by all. It also assists in mitigating long-term challenges by aggregating cyberrisk with various other operational risks within an enterprise risk management system.

Here’s All You Need to Know about DexLab Analytics’ Market Risk Modelling Live Demo Session – @Dexlabanalytics.

How to determine the value of cyber VaR?

 CISOs, Chief information security officers decipher what exactly VaR offers in terms of cyberrisk management. This hi-tech concept is too good to help with crucial decision-making, like addressing cyberrisk appetite and defining the optimal allocation of cyber risk management resources.

Market risk analytics is a new concept in the make. Many organizations have realized its crucial importance, while many are yet to decipher. For the best enterprise risk management certification, drop by DexLab Analytics. They are a leading economic capital model training institute offering state-of-the-art courses to the candidates.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Incredible Tech Transformation: How Machine Learning is changing the Scope of Business

Incredible Tech Transformation: How Machine Learning is changing the Scope of Business

Machine Learning coupled with data analytics is modifying the norms of how business handles crucial data. Insights into ML and AI is already reaping benefits in transforming vast pools of data – curated by dexterous data pundits into meaningful, relevant analytic results that would have escaped clumsy human analysis, previously.

Today, the combat weapon of Machine Learning has started to influence the entire business world. While many organizations have grasped the bounties of this hi-tech tool of learning, few are left to fathom how it would affect the way they do business. The automation process is a completely data-driven task – ideal to change enterprises into vendors – by turning lessons learnt into advanced algorithm programs worthy of licensing to software and service providers for good money.

2

Nevertheless, a lot of all that depends on how machine learning is going to evolve in the coming five to ten years and what implications it would bring into the hiring or recruitment strategies in the long run. And the best area to start off this discussion is unsupervised machine learning, where intricate frameworks are allotted large datasets and asked to draw patterns without human help to figure out what the software needs. With minimum human interference, the scalability of this mode of ML is the highest.

How to Assess Clustering Tendency: Unsupervised Machine Learning – @Dexlabanalytics.

Supervised or Unsupervised? Which is better?

Supervised ML needs human help to develop large sets of training data and corroborate the results of the training. Speech Recognition is the perfect example of such ML. But it is challenging to procure and classify vast data for supervised training. As a result, unsupervised ML is the key to the future – it reduces such interaction to a large extent. The minimum involvement of human beings suffices to be a boon – but take a note, a data scientist is required to select the data that is to be evaluated.

Unsupervised learning also needs a human touch to assign values to data structures and clusters. Hence, we cannot say for sure they are totally human-error free. Instead, we should focus more to ace up the performance of humans in tackling data for own interests.

In this context, “I think, right now, that people are jumping to automation when they should be focused on augmenting their existing decision process,” says David Dittman, director of business intelligence and analytics services at Procter & Gamble. “Five years from now, we’ll have the proper data assets and then you’ll want more automation and less augmentation. But not yet. Today, there is a lack of usable data for machine learning. It’s not granular enough, not broad enough.”

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

How to become a vendor from a consumer

A portion of what drives an incessant demand for data scientists is the pressing need for data to turn ML more productive. Mike Gualtieri, Forrester Research’s vice president and principal analyst for advanced analytics and machine learning thinks that some organizations, exactly five years from now might turn into vendors -“Boeing may decide to be that provider of domain-specific machine learning and sell [those modules] to suppliers who could then become customers,” he says. Like him, Dittman also sees the thriving combination of Data and ML code as being a highly sellable product, more so a potent new source of revenue for organizations – “Companies are going to start monetizing their data,” he explains. “The data industry is going to explode. Data is absolutely exploding, but there is a lack of a data strategy. Getting the right data that you need for your business case, that tends to be the challenge.”

Irrespective of what the future holds, technology is grooming to become an extravagant revolving door of striking innovation, and the only way to nab this technology is by making ourselves technology-friendly. For excellent business analytics course in Delhi, DexLab Analytics provides the perfect platform to deliver student-friendly education on data analytics at affordable prices. Dig into our data analyst course by clicking on our homepage.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Discover the Best Industries to Have a Career in Data Science

Discover-the-Best-Industries-to-Have-a-Career-in-Data-Science

Data fires up everything, nowadays. And data science is gaining exceptional traction in the job world, as data analytics, machine learning, big data, and data mining are fetching relevance in the mainstream tech world. By 2025, it is being expected that data science industry will reach $16 billion in value – this is why landing a job in data science domain is the next big thing!

The skills you will imbibe as a data scientist would be incredible, powerful and extremely valuable. You can easily a bag a dream job in corporate moguls, like Coca-Cola, Uber, Ford Motors and IBM, as well as play a significant role in any pro-social or philanthropic endeavors to make this world a better place to live in.

Check out these extremely interesting fields you could start your career in data science:

Biotechnology

No wonder, science and medicine are intricately related to each other. As the technology pushes boundaries, more and more companies are recommitting themselves towards a better public health by nabbing biotechnology. Being a data scientist, you would help in unraveling newer ways of studying large amounts of data – including machine learning, semantic and interactive technologies. Eventually, they would influence treatments, drugs-usage, testing procedures and much more.

Untitled

Energy

Power industry functions on data – and tons of it. Whether it’s about extracting mineral wealth from the earth’s crust or transporting crude oil or planning better storage facilities, the demand for data scientists is on the rise. Just as expanding oil fields ask for humongous amounts of data study, installing and refining cleaner energy production facilities relies on data about the natural environment and ways of modern construction. Data scientists are often given a ring to enhance safety standards and help companies recommit themselves towards better safety and environmental regulations.

Transportation

Recently, transportation is undergoing a robust change. For example, Tesla paved a new road of development and turned countless heads by unveiling a long-haul truck that could drive on its own. Though it’s not the first time, they are prone to lead the change.

Beyond self-driving vehicle technology, the transportation industry is looking for more efficient ways to preserve and transport energy. These advancements in technology works wonders when combined with better battery technology development – in simple terms, every individual field in transportation industry is believed to benefit from a motley team of data scientists.

jpg

Telecommunications

The internet is not only about tubes, but all about data. The future of the internet is here, with ever-increasing networks of satellites and user devices establishing communication through blockchain. Though they are yet to be used on large-scale, they have started making news. In situations like this, it would be difficult not to highlight the importance of data science and data architecture as they are becoming major influencers in the internet world. Whenever there is a dire need to make the public aware of a new product, we rely on user data – hence the role of data scientists is the key to a better future.

Today, data science is an interesting field to explore, and it is going to play an integral role as the stride in technology and globalization keeps expanding its base. If you have a keen eye for numbers, charts, patterns and analytics, this niche is perfectly suitable for you.

DexLab Analytics is a prime Data Science training institute Delhi that excels in offering advanced business analyst training courses in Gurgaon. Visit our official site for more information and make a mark in data analytics!

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Call us to know more