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Category: Analytics

What Are The Machine Learning Interview Questions?

Posted on by Imarticus Learning

 

It is not surprising that machines are an integral part of our eco-system driven by technology. Reaching a point in technical pinnacle was made easier from the time machine started learning and reasoning even without the intervention of a human being. The world is changing from the models developed by machine learning, artificial intelligence and deep learning which adapt themselves independently to a given scenario. Data being the lifeline of businesses obtaining machine learning training helps in better decision-making for the company to stay ahead of the competition.

Machine learning interview questions may pop up from any part of the subject like it may be about algorithms and the theory that works behind it, your programming skills and the ability to work over those algorithms and theory or about your general insights about machine learning and its applicability.

Here is a collection of a comprehensive set of interview questions about machine learning and guidelines for the answers:

1. What are the different types of machine learning?

Machines learn in the following ways:

Supervised learning: A supervised learning essentially needs a labeled data which are pre-defined data set using which machines provide a result when new data is introduced.

Unsupervised learning: Here machines learn through observation and defines structures through data as these models do not require labeled data.

Reinforcement learning: Here there is an agent and reward which can meet by trial and error method. Machine tries to figure out ways to maximize rewards by taking favorable action.

2. How does machine learning differ from deep learning?

Machine learning essentially uses algorithms to parse data, learn from them and makes informed decisions based on the learnings. Whereas, deep learning structures different algorithms and gimmicks an artificial neural system to make intelligent decisions by learning on its own.

3. Having too many False positives or False negatives which one is better? Explain

It completely depends on the question and domain for which we are figuring out a solution. For a medical domain showing false negatives may prove risky as it may show up no health problems when the patients are actually sick. If spam detection is the domain then false positives may categorize an important email as spam.

4. What is your idea about Google training data for self-driving cars?

Google uses Recaptcha to sense labeled data from storefronts and traffic signals from its eight sensors interpreted by Google’s software. Creator of Google’s self-driving car Sebastian Thrun’s insights is used to build a training data.

5. Your thoughts on data visualization tools and which data visualization libraries do you use?

You may explain your insights data visualization and your preferred tools. Some of the popular tools include R’sggplot, Python’s seaborn, Matplotlib, Plot.ly, and tableau.

6. Explain about a hash table?

In computing, a hash table is a data structure which that implements an associative array. It uses a hash function using which a key is mapped to certain values.

7. Explain the confusion matrix?

Confusion matrix or error matrix essentially visualizes the performance of algorithms in machine learning. In the below table TN= True negative, FN=False negative, TP=True Positive and FP=False positive.

8. Write pseudo-code for a parallel implementation by choosing an algorithm

Enlighten your knowledge about pseudo-code frameworks such as Peril-L and some visualization tools like Web sequence diagram to aid you in showcasing your talent to write a code that reflects parallelism well.

9. How do you handle missing or corrupted data in a dataset efficiently?

You could identify missing or corrupted data in a dataset and ideally drop them or replace them with another value. In pandas isnull() and dropna() are two useful methods which can be used to identify columns of missing or corrupted data and drop them or replace an invalid value with a placeholder value like fillna().

10. Difference between a linked list and an array?

An array consists of an ordered collection of objects wherein it assumes that every object has the same size. A linked list, on the other hand, is a series of objects with directions as to sequentially process them which helps a linked list to grow organically than an array.

Conclusion

For becoming a successful machine learning engineer, you could join Machine learning certification training to make yourself proficient in various topics of machine learning and its algorithms. From this curated list of interview questions, you would have understood that machine learning is an internal part of data science. Use these sample questions to broaden your knowledge about the questions that may pop up in your interview and be ready to spellbind the interviewer with your swift answers.

For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon, and Ahmedabad.

Statistics For Data science

Posted on by Imarticus Learning

Data Science is the effective extraction of insights and data information. It is the science of going beyond numbers to find real-world applications and meanings in the data. To extract the information embedded in complex datasets, Data Scientists use myriad techniques and tools in modelling, data exploration, and visualization.

The most important mathematical tool of statistics brings in a variety of validated tools for such data exploration. Statistics is an application of mathematics that provides for mathematical concrete data summarization. Rather than use one or all data points, it renders a data point that can be effectively used to describe the properties of the point regarding its make-up, structure and so on.

Here are the most basic techniques of statistics most popularly used and very effective in Data Science and its practical applications.

(1) Central Tendency

This feature is the typical variable value of the dataset. When a normal distribution is x-y centered at (110, 110) it means the distribution contains the typical central tendency (110, 110) and that this value is chosen as the typical summarizing value of the data set. This also provides us with the biasing information of the set.

There are 2 methods commonly used to select central tendency.

Mean:

The average value is the mid-point around which data is distributed. Given 5 numbers here is how you calculate the Mean. Ex: There are five numbers

Mean= (188 2 63 13 52) / 5 = 65.6 aka mathematical average value used in Numpy and other Python libraries.

Median:

Median is the true middle value of the dataset when it is sorted and may not be equal to the mean value. The Median for the sample set requires sorting and is:

[2, 13, 52, 63, 188] → 52

The median and mean can be calculated using simple numpy Python one-liners:

numpy.median(array)

numpy.mean(array)

(2) Spread

The spread of data shows whether the data is around a single value or spread out across a range. If we treat the distributions as a Gaussian probability figure of a real-world dataset, the blue curve has a small spread with data points close to a narrow range. The red line curve has the largest spread. The figure also shows the curves SD-standard deviation values.

Standard Deviation:

This quantifies the spread of data and involves these 5 steps:

1. Calculate mean.

2. For each value calculate the square of its distance from the mean value.

3. Add all the values from Step 2.

4. Divide by the number of data points.

5. Calculate the square root.

Made with https://www.mathcha.io/editor

Bigger values indicate greater spread. Smaller values mean the data is concentrated around mean value.

In Numpy SD is calculated as

numpy.std(array)

(3) Percentiles

The percentile shows the exact data point position in the range of values and if it is low or high.

By saying the pth percentile one means there is p% of data in the lower part and the remaining in the upper part of the range.

Take the set of 11 numbers below and arrange them in ascending values.

3, 1, 5, 9, 7, 11, 15,13, 19, 17, 21. Here 15 is at the 70th percentile dividing the set at this number. 70% lies below 15 and the rest above it.

The 50th percentile in Numpy is calculated as

numpy.percentile(array, 50)

(4) Skewness

The Skewness or data asymmetry with a positive value means the values are to the left and concentrated while negative means a right concentration of the data points.

Skewness is calculated as

Skewness informs us about data distribution is Gaussian. The higher the skewness, the further away from being a Gaussian distribution the dataset is.

Here’s how we can compute the Skewness in Scipy code:

scipy.stats.skew(array)

(5) Covariance and Correlation

Covariance

The covariance indicates if the two variables are “related” or not. The positive covariance means if one value increases so do the other and a negative covariance means when one increases the other decreases.

Correlation

Correlation values lie between -1 and 1 and are calculated as the covariance divided by the product of SD of the two variables. When 1 it has perfect values and one increase leads to the other moving in the same direction. When less than one and negative the increase in one leads to a decline in the other.

Conclusion: 

When doing PCA-Principal Component Analysis knowing the above 5 concepts is useful and can explain data effectively and helps summarize the dataset in terms like correlation in techniques like Dimensionality Reduction. Thus when more data can be defined by a median or mean values the remaining data can be ignored. If you want to learn data science, try the Imarticus Learning Academy where careers in data science are made.

How Criminals Are Using AI And Exploiting It To Further Crime?

Posted on by Imarticus Learning

AI can use the swarm technology of clusters of malware taking down multiple devices and victims. AI applications have been used in robotic devices and drone technology too. Even Google’s reCAPTCHA according to the reports of “I am Robot” can be successfully hacked 98% of the time.

It is everyone’s fear that the AI tutorials, sources, and tools which are freely available in the public domain will be more prevalent in creating hack ware than for any gainful purpose.

Here are the broad areas where hackers operate which are briefly discussed.

1. Affecting the data sources of the AI System:

ML poisoning uses studying the ML process and exploiting the spotted vulnerabilities by poisoning the data pool used for MLS algorithmic learning by. Former Deputy CIO for the White House and Xerox’s CISO Dr. Alissa Johnson talking to SecurityWeek commented that the AI output is only as good as its data source.

Autonomous vehicles and image recognition using CNNs and the working of these require resources to train them through third-parties or on cloud platforms where cyberattacks evade validation testing and are hard to detect. Another technique called “perturbation” uses a misplaced pattern of white pixel noises that can lead the bot to identify objects wrongly.

2. Chatbot Cybercrimes:

Kaspersky reports on Twitter confirm that 65 percent of the people prefer to text rather than use the phone.  The bots used for nearly every app serve as perfect conduits for hackers and cyber attacks.

Ex: The 2016 attack on Facebook tricked 10,000 users where a bot presented as a friend to get them to install malware.

Chatbots used commercially do not support the https protocol or TLA. Assistants from Amazon and Google are in constant listen-mode endangering private conversations. These are just the tip of the iceberg of malpractices on the IoT.

3. Ransomware:

AI-based chatbots can be used through ML tweaking to automate ransomware. They communicate with the targets for paying ransom easily and use the encrypted data to ensure the ransom amount is based on the bills generated.

4. Malware:

The very process of creating malware is simplified from manual to automatic by AI. Now the Cybercriminals can use rootkits, write Trojan codes, use password scrapers, etc with ease.

5. Identity Theft and Fraud:

The generation of synthetic text, images, audio, etc of AI can easily be exploited by the hackers. Ex: “Deepfake” pornographic videos that have surfaced online.

6. Intelligence garnering vulnerabilities:

Revealing new developments in AI causes the hackers to scale up the time and efforts involved in hacking by providing them almost simultaneously to cyber malware that can easily identify targets, vulnerability intelligence, and spear such attacks through phishing.

7. Whaling and Phishing:

ML and AI together can increase the bulk phishing attacks as also the targeted whaling attacks on individuals within a company specifically. McAfee Labs’ 2017 predictions state ML can be used to harness stolen records to create specific phishing emails. ZeroFOX in 2016 established that when compared to the manual process if one uses AI a 30 to 60 percent increase can be got in phishing tweets.

8. Repeated Attacks:

The ‘noise floor’ levels are used by malware to force the targeted ML to recalibrate due to repeated false positives. Then the malware in it attacks the system using the AI of the ML algorithm with the new calibrations.

9. The exploitation of Cyberspace:

Automated AI tools can lie incubating inside the software and weaken the immunity systems keeping the cyberspace environment ready for attacks at will.

10. Distributed Denial-of-Service (DDoS) Attacks

Successful strains of malware like the Mirai malware are copycat versions of successful software using AI that can affect the ARC-based processors used by IoT devices. Ex: The Dyn Systems DNS servers were hacked into on 21st October 2016, and the DDoS attack affected several big websites like Spotify, Reddit, Twitter, Netflix, etc.

CEO and founder of Space X and Tesla Elon Musk commented that AI was susceptible to finding complex optimal solutions like the Mirai DDoS malware. Read with the Deloitte’s warning that DDoS attacks are expected to reach one Tbit/sec and Fortinet predictions that “hivenets” capable of acting and self-learning without the botnet herder’s instructions would peak in 2018 means that AI’s capabilities have an urgent need for being restricted to gainful applications and not for attacks by cyberhackers.

Concluding notes:

AI has the potential to be used by hackers and cybercriminals using evolved AI techniques. The field of Cybersecurity is dynamic and uses the very same AI developments providing the ill-intentioned knowledge on how to hack into it. Is AI defense the best solution then for defense against the AIs growth and popularity?

To learn all about AI, ML and cybersecurity try the courses at Imarticus Learning where they enable you to be career-ready in these fields.

Should You Start With Big Data Training or Learn Data Analytics First?

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Should you start with big data training or learn data analytics? Which one should I start first?

We live in a highly interconnected and dependent world of technology wherein the amount of technology we use is like a drop in the ocean. At the pace we are traveling in this digital world approximately 2.5 quintillion bytes of data is generated on a daily basis. Yes, it is indeed a staggering amount of data that is being produced.

So, the application of analytics in Big Data has various merits for businesses. Hence, businesses look forward to using this to gain a competitive edge over their competitors in the foreseen future. Though businesses understand the prominence of big data they are unaware of using big data to achieve the desired success.

So, ideally, there is a huge scope for talented who are capable of stimulating the business in the route of success using big data. For data science aspirants it is a wise choice to start with big data training than Data Analytics Course. Read on to know more about it!!

Difference between big data and data analytics

The primary difference between the two is that big data is centered around figuring out meaningful insights within a large pile-up of either structured or unstructured data whereas data analytics is more focused and looks out through relevant data to solve business problems.  Big data training consists of complex skills which will be a great addition on top of your knowledge in statistics, database topics, and programming languages. You can also see that most companies are dependant on Hadoop training which essentially helps you to assimilate the huge data using programming languages like Java, C, Python, Swift, etc.

On the other end, Data Analytics Training on operational insights of the business by making predictive models using programming languages and uses manipulative techniques for understanding the trends. Understanding historical data and extracts interferences from it to solve complex business issues.

Tools and skill sets that differentiate the two courses

Typically having good insights about databases, programming languages, frameworks like Apache and Hadoop and coding would help you positively for big data training. Basic knowledge of statistics and mathematics is essential along with creativity to filter a large database. Knowledge about statistics and mathematics along with data wrangling is required to become an expert in data analytics.  Big data utilizes complex technological tools whereas data analytics uses statistical and straightforward tools.

Various tools like Hadoop, Tableau, NoSQL, R and many more are used to draw interference from big data to get desirable graphics, statistical data, and visualization. Learning R programming language is essential to learn Data Analytics due to its widespread use of tools to deal with statistical and analytical data. So, R developers have an edge over others in learning data analytics. Whereas Big data efficiently uses MapReduce, a programming model for processing huge amount of data.  When MapReduce is coupled with Hadoop Distributed File System (HDFS) for its efficient use in Big Data.

What should you do to master big data?

In the bustling world of digital technology, we have access to any information presented by experts in the field of data. Enrolling in either big data training or data analytics courses will definitely be useful to fill the gap between the demand and supply in big data. Having a diversified skill set will give you an edge over your competitors. Big data training and data analytics classes are available online in reputed institutes who provide hands-on training to understand and interpret the concepts in real-time business situations. Look out for training institutes who provide comprehensive insights and training in big data for gaining proficiency in the subject right now.

Conclusion

With an acute shortage of skilled in the field of big data, its demand is set to increase and is deemed to be a long-term growth-oriented career option. As you can understand one course is used to manage large sets of a database, on the other hand, another uses such a database to gain meaningful insights. Learning Big data training may be a smart option for landing in your dream job, it is your call to take up either of the courses first. Take a step forward right now to taste success in the near future!!!

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a form from the website or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi,  Gurgaon, and Ahmedabad.

Good Ways to Learn Data Science Algorithms, if Not From IT background?

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At the beginning of your career in data sciences, algorithms are hugely over-rated. Every routine task, every subroutine, every strategy or method you do or write is because of an effective algorithm. In essence, all programs are formed of algorithms and you implement them with every line of code you write! Even in real life, you are executing tasks by algorithms formulated in your brain and just remember that all algorithms are simulations of how the human brain works.
Just as you begin with baby-steps and then worry about speed and efficiency it is a good routine to start your Data science career with the algorithms if you are not from a computer science background. And there are hordes of resources online that you can start with. Some people prefer the Youtube tutorials to reading books or even a tandem process including texts and videos which is fine.
As a beginner of a Data Science Career, your focus should be on making your algorithm work. Scalability comes much later when you integrate writing programs for large databases. Start with simple tasks. You will need to learn by practice and with determination laced with dedication. Don’t give up, as you never did, when you started walking or talking in English!
At the onset of learning, you will need to:

  • Understand and develop algorithms.
  • Understand how the computer processes and accesses information.
  • What limitations does the computer face when executing the task on hand?

Here’s an example of how algorithms work. Though huge amounts of data are stored and processed almost instantly, it can process/access only one/two pieces of information every time. This is the basis that algorithms use for simple tasks like finding the lowest/ highest number. An algorithm is essentially a series of sequential steps that helps the computer perform a task.
Starting with very basic algorithms for finding maximum/ minimum numbers, identifying prime numbers, sorting a list, etc will help understand and move to more complex algorithms. Modern times computer scientists use the suite and libraries of optimized and developed algorithms for both basic and complicated tasks.
For one who is not from a computer science background here are the basic steps to learn algorithm writing.

  • Begin with basic mathematics needed for algorithmic complexity analysis and proofs.
  • Learn a basic computer language like the C Suite.
  • Read about data science topics and the best programming practices:
  • Study algorithms and data structures
  • Learn about data analytics, databases and how the algorithms in CLRS work.

Learning algorithms and mathematics:
All algorithms for a  data science career requires proficiency in the three topics of Linear Algebra, Probability Theory, and Multivariate Calculus.
Some of the many reasons why mathematics is crucial in learning about algorithms are: 

  1. Selecting the apt algorithm with a mix of parameters including accuracy, model complexity, training time, number of features, number of parameters and such.
  2. Selecting the validation of strategies and parameter-settings.
  3. Using the tradeoff of Bias-Variance in identifying under or overfitting.
  4. Estimating uncertainty and confidence intervals.

Can you learn Math for data science quickly? The answer is that it is not required for you to be an expert. Rather understand the concepts and applications of the math to algorithms.
Doing math and learning algorithms through self-learning is time-consuming and laborious. But, there is no easy way out. If you want to quicken the process there are short and intensive training institutes to help.
While there may be any number of resources online, mathematics and algorithms are best learned by solving problems and doing! You must undertake homework, assignments and regular tests of your knowledge.
One way of getting there quickly and easily is to do a Data Science Course with a bootcamp for mathematics at Imarticus Learning. This will ensure the smooth transition of math and algorithmic data science applications. At the end of this course, you can build your algorithms and experiment with them in your projects.
Conclusion:
Algorithms and Mathematics are all about practice and more practice. However, it is crucial in today’s modern world where data sciences, AI, ML, VR, AR, and CS rule.
These sectors are where most career aspirants are seeking to make their careers because of the ever-increasing demand for professionals and the fact that with an increase in data and development of these core sectors, there are plentiful opportunities to land the well-paid jobs.
At the Imarticus Learning, Data Science career course, you will find a variety of courses on offer for both the newbie and tech-geek wanting to go ahead in his/her career.
For more details, you can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.
Start today if you want to do a course in the algorithms used in data sciences. Happy coding!

NLP vs NLU- From Understanding A Language To Its Processing!

Posted on by Imarticus Learning

Today’s world is full of talking assistants and voice alerts for every little task we do. , Conversational interfaces and chatbots have seen wide acceptance in technologies and devices.

Their seamless human-like interactions are driven by two branches of the machine learning (ML) technology underpinning them. They are the NLG- Natural Language Generation and the NLP- Natural Language Processing.

These two languages allow intelligent human-like interactions on the chatbot or smartphone assistant. They aid human intelligence and hone their capabilities to have a conversation with devices that have advanced capabilities in executing tasks like data analytics, artificial intelligence, Deep Learning, and neural networking.

Let us then explore the NLP/NLG processes from understanding a language to its processing.

The differences:

NLP:
NLP is popularly defined as the process by which the computer understands the language used when structured data results from transforming the text input to the computer. In other words, it is the language reading capability of the computer.

NLP thus takes in the input data text, understands it, breaks it down into language it understands, analyses it, finds the needed solution or action to be taken, and responds appropriately in a human language.

NLP includes a complex combination of computer linguistics, data science, and Artificial Intelligence in its processing of understanding and responding to human commands much in the same way that the human brain does while responding to such situations.

NLG:
NLG is the “writing language” of the computer whereby the structured data is transformed into text in the form of an understandable answer in human language.

The NLG uses the basis of ‘data-in’ inhuman text form and ‘data-out’ in the form of reports and narratives which answer and summarize the input data to the NLG software system.

The solutions are most times insights that are data-rich and use form-to-text data produced by the NLG system.

Chatbot Working and languages:

Let us take the example of a chatbot. They follow the same route as the two-way interactions and communications used in human conversations. The main difference is that in reality, you are talking to a machine and the channel of your communication with machines.NLG is a subset of the NLP system.

This is how the chatbot processes the command.

  • A question or message query is asked of the chatbot.
  • The bot uses speech recognition to pick up the query in the human language. They use HMMs-Hidden Markov Models for speech recognition to understand the query.
  • It uses NLP in the machine’s NLP processor to convert the text to commands that are ML codified for its understanding and decision making.
  • The codified data is sent to the ML decision engine where it is processed. The process is broken into tiny parts like understanding the subject, analyzing the data, producing the insights, and then transforming the ML into text information or output as your answer to the query.
  • The bot processes the information data and presents you a question/ query after converting the codified text into the human language.
  • During its analysis, the bot uses various parameters to analyze the question/query based on its inbuilt pre-fed database and outputs the same as an answer or further query to the user.
  • In the entire process, the computer is converting natural language into a language that computer understands and transforming it into processes that answer with human languages, not machine language.

The NLU- Natural Language Understanding is a critical subset of NLP used by the bot to understand the meaning and context of the text form. NLU is used to scour grammar, vocabulary, and such information databases. The algorithms of NLP run on statistical ML as they apply their decision-making rules to the natural-language to decide what was said.

The NLG system leverages and makes effective use of computational linguistics and AI as it translates audible inputs through text-to-speech processing. The NLP system, however, determines the information to be translated while organizing the text-structure of how to achieve this. It then uses grammar rules to say it while the NLG system answers in complete sentences.

A few examples:

Smartphones, digital assistants like Google, Amazon, etc, and chatbots used in customer automated service lines are just a few of NLP applications that are popular. It is also used in online content’s sentiment analysis.NLP has found application in writing white papers, cybersecurity, improved customer satisfaction, the Gmail talk-back apps, and creating narratives using charts, graphs, and company data.

Parting Notes:

NLG and NLP are not completely unrelated. The entire process of writing, reading, and talk-back of most applications use both the inter-related NLG and NLP. Want to learn more about such applications of NLP and NLG? Try the Imarticus Learning courses to get you career-ready in this field. Hurry!

What is Main Role of Business Analyst

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Business Analyst plays a crucial role from start to finish in the process of data analytics and extracting information from databases for the valuable data-driven insights into the business. Also, take into account that data is an organizational asset today and is constantly growing by the nanosecond causing technology to change with it.

Since data is here for the long haul, it creates a job market driven by a huge need for professionals in the analytics process. Though reputed training institutes like Imarticus Learning produce excellent career aspirants the supply positions are dismally low. Business analyst has no shortage of jobs in the next decade at the very least!

The process of data analytics carried out by the business analyst is quite comprehensive and begins with cleaning and aligning the multiple sources of data. He then uses his Business Analyst training and tool kit comprising of several suites of languages specifically designed for extracting trends, forecasts, and insights which he reports in his presentations to the business decision makers.

The decision makers then use these actionable insights to make strategic business decisions which impact the organization’s profitability, productivity, and efficiency.

Being a key player in the organization means he is a master of the web with a repertoire of programming languages and techniques. This also means he has done Business Analyst training and has a certification in business data analytics. No wonder he gets a handsome salary and is always in demand.

Education Required For Business Analyst

There is no minimum formal education required to undertake Business Analyst training. However, the curriculum lends itself to graduates in subjects like economics, business management, finance, mathematics and statistics. Classroom mode of learning can effectively help augment both practices of technical and non-technical skills required for this job role.

Business skills:
Business Analyst will need to have
• Data Analysis and Modelling
• Business Acumen
• Conceptual Thinking
• Inquisitiveness, visualization best practices and detail-oriented mindset.
• Self-discipline, ethics and maturity
• Excellent emotional IQ.

Technical skills:
Business analyst’s technical skills should have at least the following. The more suites you know the better and the quicker you can adapt to technological changes the faster your career progresses.

You will need:

• The fundamentals of computer science
• Python suite and R programming
• Proficiency in suites like Spark, NoSQL, Hive, Pig, SQL, Hadoop, MapReduce, Apache Spark, and more.
• Adept at ML, AI, handling of data that is unstructured
• Microsoft Excel
• Visualization techniques of data like charts, graphs, tables etc.
• Cloud data-storage techniques.

Personal Skills:
Business Analyst will need all of these skills which are never taught in formal college courses and yet are crucial to the job role. To spot the trends and insights calls for great quantitative problem-solving skills. The BA has to use inferential logic well to make the presentations interesting and understandable even by the layman.

His interpersonal, communicative and reporting skills are presented as nuggets of insights into the data. With fantastic reporting skills and keen business acumen, he reads piles of data to present decision-making data-driven actionable insights. Obviously, he ought to be an excellent team player too if he has to place his insights into the right hands at the right time.

Did you know that at the Imarticus Business Analyst training the focus to produce well-rounded and equipped BAs includes personality development, resume writing, interview preparation, and soft-skills training?

Job Scope and payouts:

According to Glassdoor, the median salary base is 489,641Rs for entry-level data analysts in India. In the US it was an impressive116,000 USD in 2018. Recent reports by Payscale suggest that the technology field offers 41per cent of all data science jobs.

The median salary of 53,000 to 69,000 USD for BAs in a larger enterprise is handsome when compared to other jobs. You can add on with generous commission that can double your total take-home according to the Accounting and Finance Salary Guide by Accounting and Finance Salary Guide undertaken in 2017.

Parting notes:
If you invest in yourself, you get to learn the latest BABOK V3 techniques and specifications for BAs. Become a business analyst in the fast-tracked course today.

The course for Business Analyst training with IIBA endorsement can get you assured placements in a dream career and well-paying job. Certification helps by endorsing your practical and technical skills.

All the best in your business analyst career!

Why Should You Enroll in a Business Analyst Certification Program

Posted on by Imarticus Learning

Businesses and the data they produce and require to thrive are growing, expanding fast and need efficient analysts to use the data for growing. Data has turned out to be the most valuable asset in any organization to help measure efficiency, success and production in all organizational tasks. Technology too has kept pace with these rapid developments and the BA is a career that tops any career aspirants list. Especially software related projects need to clean and use data from multiple sources to gain foresight and insights into the databases to help the BA analyze and use the data for predictive analysis.

Why Do Business Analyst Certifications

Organizational business analytics is of paramount importance and helps determine trends, future plans, goal setting, budgeting and many other indices for sustained growth. An approximate half of IT spends are on such initiatives which have resulted in unprecedented growth and demand for BAs.
Business Analyst Certification can help in many ways in a market where making a career in business and data analytics for business purposes is well-paid and demand booming. One of the major benefits of such certifications is that they are an essential spend whether you wish to make a career, are switching jobs or aspiring for a promotion. All organizations want to know before recruiting that the aspirant has sufficient experience and certification in business analytics practice. This is where they need a measurable endorsement and validation of your practical skills and application of learning from a reputed institution like Imarticus Learning. And, certification provides them with exactly this information. Certifications besides being a goal achieved can also provide you with a number of other benefits like
• Builds your confidence while adding to your resume.
• Visibility in a pool of aspirants in the demand-driven job-market.
• Salaries that are probably better and account for the better experience in BA practices under skilled and industry-relevant mentors.
• The credibility that you know, implement and practice business and data analysis techniques on the latest technological suites and frameworks.
• Suit and match market and technological trends in business analytics that are current and in demand.
Thus from the above diagram, you will see that this certification in business analytics is a right career step that can enhance your resume, build your confidence and help with landing those BA jobs with the best payouts. The McKinsey study reports that 490,000 data science jobs are vacant and have only less than 200,000 certified and qualified professionals to fill them. TechCrunch reports claim worldwide demand for data analysts will grow by 50% and fuel better payouts while increasing the demand-supply gap.
The payouts:
According to Glassdoor.com the median salary of a US business analyst is 77,712 USD per annum. The US Bureau for Census reports that median salaries for BA’s are on an average of 34,940 USD per annum and clearly indicates that most BAs draw more than this figure. Of course, this is a huge plus when making a career or changing jobs or roles. Business2Wirereports show that a certified professional earns 20 to 40% more than the rest.
Qualifications Needed
Can you believe that with such great pay packages all you could possibly need to launch your career is finding the right training course and partner? A basic sound degree in finance, economics, business administration etc definitely helps though not mandatory. Along with the boom in data technologies, the demand for training institutes to cater to the huge number of trained personnel has also seen tremendous growth. That is exactly why finding a reputed training partner like Imarticus makes a huge difference. Their curriculum is sound, practically-oriented and has industry acceptance globally. The certification is widely accepted and even has modules on soft-skill development, resume writing and such important topics. Getting into the right job is also easy with assured placements and effective mentorship from certified industry-drawn instructors and mentors.
Parting Thoughts:
Data analytics is a practice- intensive course best learnt from a reputed training institute like Imarticus Learning. Certification enables smart career choices and normally achieves a better pay-package and learning retention. Choose your vertical and ensure you get lots of practice then. All the best then and do not forget to call Imarticus today.

What Do Experienced Data Scientist Know That Beginner Data Scientist Don’t Know?

Posted on by Imarticus Learning

The one thing that sets the experienced data scientist from the beginner’s data scientist career is that 99 percent of data sciences lies in the effective use of story-telling!

At the start of one data scientist career, most have the same skill-set as the top scientists with many years of experience and are job-prepared. The best of them learn to use their tools and techniques gained with practice and expertise to become excellent at using data to tell a compelling user-story. A data scientist in the early stages of the career is actually practicing as an analyst of data and probably comes from any of these fields. Namely,

  • Data analysis and wanting to pursue academics.
  • Analysts on the business intelligence side.
  • With computer science, statistics or mathematics expertise.

Large doses of the previous job role are normally used at the beginning of the data scientist’s jump into this field. That is being job prepared! And it will not be uncommon if the analysts are the busy rattling of their insights on blodgets and widgets, the business intelligence or business analysts present information in complex tables and graphs, and the group of CS, mathematicians, and statisticians write code the whole day. But that is not what a data scientist’s role is about especially in this role.

Whether you have deep learning knowledge, can crack ML algorithms, or write compelling codes for vector classifiers the skills you will need to be an excellent data scientist are not the same as the skills you landed the job with.

Your job is to use the data to tell the most compelling story while using your skills, tools and techniques learned to graphically illustrate your narration. Compare your story to a thrilling novel that you can’t put down till the last page. Your tale has to be anchored to the data and last till the final calculations are presented.

Story-telling skills:

For this, you will need the following skills they did not teach you in college and comes with aptitude, practice, and experience in a Data Scientist Career. Let us explore these attributes.

  • Structure: This is the manner of presenting data and information in an easily comprehended, logical, no-nonsense and understandable way that any reader or user can relate to. That’s precisely why most storybooks introduce their characters in the first few chapters itself. Most people err in not defining the issue and pitching its solution at the very start of writing.
  • Theory of the narrative: Good stories sustain interest till the very end and that is the essence of the narration. Keep your lines tight and use your data findings to get the story across cogently.
  • Expressive writing: This is the essential glue that holds the interest, tells the narrative and proves your point clearly and without ambiguity. Your grammar, sentence construction and choice of apt terms and words will go a long way and comes only by practice. Whether it be an email, a press note or internal communication remember that it may land on the table of the management head or your juniors.You wouldn’t want spelling and syntax errors in your calculations or writing style. Avoid ambiguous terms, technical jargon, and irrelevant information. At the beginning all tasks are difficult. They do ease out with regular practice and learning the right way to do things.
  • Presenting complex information: Being a data scientist isn’t totally about writing those accurate reports. As you move up the ladder you will be asked for your views, suggestions, and assessment. These are of a highly complex and technical nature and you need to train yourself to present your views without compromising accuracy, truth or the crucial data supporting your premise.This needs a lot of practice in all the above attributes to reach a level of credibility coupled with all the essentials and ingredients of the story. If you fail here you are possibly doomed to remain in those middle rungs of your career and can never rise to the top. Wisdom and skill are not gained by the number of years you spend on the job. They are learned on the job with regular and dedicated practice.

Conclusion:

The difference between the artist and artisan is the situation that occurs in the Data Scientist Career. No matter what your background is, excellence at the data scientist’s job comes from practice and learning from experiences. In data sciences, you will not only have to acquire the right tools of the trade, but you will also have to excel at wielding them artistically to tell the story WITH data. Not tell the story OF data.

At Imarticus Learning the data scientist learns this during the training in the soft-skills and personality development modules. Begin your story-telling today!

How Can You Learn Deep Learning Quickly?

Posted on by Imarticus Learning

 

Why is Deep Learning important to learn in today’s world of ever-changing technologies? Human capabilities to do tasks especially on very large volumes of data are limited. AI stepped in to help train computers and other devices to aid our tasks. And how does it do so? The evolved devices use ML to learn by themselves recognizing data patterns and arriving at predictions and forecasts very much like the human brain. Hence one would need to learn all of the above-mentioned concepts to even reach the deep-learning possibility.

In order to learn ML, one would need to have knowledge of Java, R or Python and suites like DL4J, Keras, and TensorFlow among others depending on the areas you are interested in. It is also important to have the Machine Learning Course before one delves into deep-learning. And yes there is a lot of statistics, probability theory, mathematics and algebra involved which you will have to revise and learn to apply.

 

If you are interested in learning Deep Learning quickly, here are the top four ways to do so.

A. Do a course: One of the best ways is to scour the net for the best top free MOOC courses or do a completely paid but skill oriented course. Many are online courses and there are classroom courses as well. For the working professional course from a reputed training partner like Imarticus Learning makes perfect sense. Just remember that to learn Deep learning you will need to have access to the best industry-relevant solutions and resources like mentoring, assured placements, certification and of course practical learning.

B. Use Deep Learning videos: This is a good resource for those with some knowledge of machine learning and can help tweak your performance. Some of the best resources of such videos are ML for Neural Networks by the Toronto University, the tutorials of Stanford University on Deep Learning, ConvNet resources on Github, and videos by Virginia Tech, E and CE, the Youtube, etc.

C. Community Learning: There are communities available online like the Deep Learning community and r-learning communities from Quora, Reddit, etc. Such communities can be of immense help once you have a firm grasp of the subject and need to resolve or are practicing your skills.

D. DIY books: There is a wealth of books available to learn Deep Learning and understand the subject better. Do some research on the best deep-learning resources, the limits of it, differences between ML and deep-learning, and such topics. DIY books are easy to read and hard to practice with. Some excellent books are the TensorFlow-Deep Learning, Nielsen’s Neural Networks-and-Deep Learning, and Chollet’s Python and Deep Learning.

The Disadvantages:

  1. Rote knowledge is never really helpful and the syllabus is very vast and full of complicated subjects.
  2. The practice is the key is only acquired through constantly doing relevant tasks on relevant and industry-standard technology.
  3. Mentorship is very important to learn the current best practices.
  4. Time is a constraint, especially for working professionals.
  5. The best value courses are often paid-for courses.
  6. DIY is bereft of certification and hence a measure of your skills.
  7. The DIY approach may also never train you for the certification exams.
  8. Assured placements in the paid for courses are a huge draw for freshers making a career in deep-learning.
  9. There are non-transferable soft-skills that you require and do not find in the packages.
  10. Industry acceptance is often sadly lacking for the self-learning candidates.

Conclusion:

Learning is always a process where reinforcement and practice scores. Though there are many options available to do deep-learning for free and on one’s own, the route is never easy. Thus it seems the paid courses, like the one at Imarticus Learning, is definitely a better bet. Especially if the course is combined with mentorship of certified trainers, assured placements, widely accepted certification, personalized personality-development modules and a skill-oriented approach with tons of practice as the one at Imarticus is.

The Imarticus Learning courses deliver well-rounded and skilled personnel and offer a variety of latest technology courses which are based on industry demand.

Given the above information, the quickest way to master deep-learning definitely appears to be doing a course at Imarticus. If you want to be job-ready from day one, then don’t wait. Hurry and enroll. We have multiple centers in India – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon and Ahmedabad. So you can consider as per your need!!