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Category: Analytics

What is the difference between data science and data analytics?

Posted on by Imarticus Learning

 

One of the biggest jobs in the technological center is working with big data. There are plenty of roles within this sector and two of the most popular ones include data science and analytics. While a lot of companies tend to hire similar candidates for these roles, there is still a difference between the two.

It is important that you understand the two roles before you choose a career path in either. If you’re looking to kickstart a career in the field of big data, then knowing the difference between data science and analytics is a good pointer to keep in mind.

What is data science?

Data science is a broader term for different methods and models used to get information. Under data science are the statistics, scientific methods, and math along with other tools which can be used to manipulate and analyze data. If there is a process or a tool that can be used on data to analyze it and extract information from the same, then it falls under the umbrella of data science.

As a practitioner of data science, you’ll have to connect data points and information to figure out connections which can be more useful for a business. It requires you to explore the unknown and find newer patterns or insights which can then be turned into actionable decisions from a business perspective. Data science attempts to delve into connections and figure out methodologies which work for the betterment of a business.

What is data analytics?

Data analytics is more concentrated and specific than data science. It is focused on achieving a specific goal by sorting through large data sets and looking for ways to support the same. Analytics are more automated as they can help in providing better insights in certain areas. Data analysis also involves analyzing large data sets to find smaller, more useful pieces of information to fulfill an organization’s goals.

Analytics basically sorts data into things that an organization knows or doesn’t know and can be used to measure any event in the present, past or even future. It moves from insights to impact and connects patterns and trends along with the true goals of a company, keeping the business aspect in mind.

Knowing the difference:

Data analysts and scientists perform different roles and companies must know exactly what they’re looking for. Data analytics usage in industries such as travel, gaming or healthcare, where analysts can extract specific data to improve business, data science is used in more broader categories such as digital advertising or internet searches.

Data science also plays a role in developing machine learning and Artificial Intelligence. Companies are looking at systems which allow computers to go through large amounts of data. They then formulate algorithms, developed by analysts which can sift through the same and find connections which can help them reach their objectives and thus, bring in more revenue.

Imarticus provides the best data analytics course to make it easier for anybody looking to enter the world of big data science and kickstart their career. 

For more details regarding this, you can directly visit – Imarticus Learning and can drop your query by filling up a simple form through 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, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad. 

What are the best practices for training machine learning models?

Posted on by Imarticus Learning

As we all know, Machine learning is a popular way of learning at your own pace. Machine learning also facilitates learning based on your likes and interests. For example, you are a person who is interested in space and astronomy, a machine learning driven course to learn mathematics for you, will first ask you few basic questions about your interest.

Once it establishes your interest, it will give examples of mathematical calculations using objects of space to keep you engaged. So, how are these machines able to establish your interest? What are the best practices for training machine learning models is something that we will see in this article.

Machine learning is based on three important basics.
• Model: A Model is responsible for identifying relationship between variables and to make logical conclusion.
• Parameters: Parameters are the input information that is given to Model to make logical decisions.
• Learner: Learner is responsible for comparing all the Parameters given and deriving the conclusion for a given scenario.

Using these three modules, machine is trained to handle and process different information. But it is not always easy to train the machine. We need to adopt best practices for training machines for accurate predictions.

• Right Metrics: Always start the machine learning training or practice with a problem. We need to establish success metrics and prepare a path to execute them. This is possible when we ensure that the success metrics that have been established are the right ones.

• Gathering Training Data: The quality and quantity of data used is of utmost importance. The training data should include all possible parameters to avoid misclassifications. Insufficient data might lead to miscalculated results. The quantity of data also matters. Exposing the algorithms to a small set of humongous data can make them responsive to a specific kind of information again leading to inaccurate results when exposed to something other than the test data.

• Negative sampling: It is very important to understand what is categorized as negative sampling. For example, if you are training your data for a Binary classification model, include data that requires other models like multi class classification model. By this, you can train the Machine to handle negative sampling too.

• Take the algorithm to the database: We usually take the data out from the database and run the algorithm. This takes lot of effort and time. A good practice would be to run the training algorithm on the database and train it for the desired output. When we run the equation through the kernel instead of exporting the data, we not only save hours of time but we also prevent duplication of data.

• Do not drop Data: We always create pipelines by copying an existing pipeline. But what happens in the background is, the old data gets dropped many a times to provide place for the fresh data. This can lead to incorrect sampling. Data dropping should be effectively handled.

• Repetition is the key: The Learner is capable of making very minute adjustments for refining the model to obtain the desired output. To achieve this, the training cycle must be repeated again and again until the desired Model is obtained.

• Test your data before actual launch: Once the Model is ready test the data in a separate test environment till you obtain the desired results. If your data sample is all the data up to a particular date for which you have all predictions, the test should be conducted on upcoming data to test the predictions.

Finally, it is also important to review the specifications of the Model from time to time to test the validity of the sample. You may have to upgrade it after a considerable amount of time depending on the type of model.

There is a lot to learn about ML(Machine Learning) that cannot be explained in a simple article like this. The Machine learning future in India is very bright. If you have the desired machine learning skills and need to pursue big data and machine learning courses in India, learn from pioneers like Imarticus.

Is Data Analytics An Interesting Career Field?

Posted on by Imarticus Learning

One of the biggest job sectors of the last few years, data analytics is seen as one of the most lucrative career options today. In the United States, an estimated 2.7 million jobs are predicted to be taken by data science and analytics by 2020. The value that big data analytics can bring companies is being noticed and companies are looking for talented individuals who can unearth patterns, spot opportunities and create valuable insights.

If you’re someone who’s good at coding and looking to make the next jump from a career perspective, then data science could be your calling. Here are a few reasons you should look out for a career in data analytics:

Higher Demand, Less Skill:
India has the highest concentration of data scientists globally, and there is a shortage of skilled data scientists. According to a McKinsey study, the United States will have 190,000 data scientist jobs vacant due to a lack of talent, by 2019. This opens the door for a good data analyst not just to make money, but own the space.

Good data analysts can take complete control of their work without having to worry about interference. As long as you can provide crucial insights which contribute to the company’s business, you’ll find yourself moving up the ladder faster than expected.

Top Priority in Big Companies:
Big data analytics is seen as a top priority in a lot of companies, with a study showing that at least 60% of businesses depend on it to boost their social media marketing ability. Companies vouch by Apache Hadoop and its framework capabilities to provide them data which can be used to improve business.

Analytics is being seen as a massive factor in shaping a lot of decisions taken by companies, with at least 49% believing that it can aid in better decision making. Others feel that apart from key decisions, big data analytics can enable Key Strategic Initiatives among other benefits.

Big Data Is Used Almost Everywhere:
Another great reason to opt for big data or data analytics as a career option is because they are used pretty much everywhere! With the highest adopters of the technology being banking, other sectors which depend on big data include technology, manufacturing, consumer, energy and healthcare among others.

This makes big data an almost bulletproof option because of the wide range of applications it can be used for.

Most Disruptive Technology In The Next Few Years:
Data analytics is also considered as one of the most disruptive technologies to influence the market in the next few years. According to the IDC, the big data analytics sector is touted to grow to up to $200 billion by 2024.

Thus, big data analytics is going to be the future of computing and technology. The sector is seeing massive growth and a lot of demand. The more you’re able to provide insights that can make a difference in this sector, the higher are your chances of getting a lucrative job.
Whether it’s a data analytics course in Bangalore or any other city, Imarticus will be able to provide you with the right kind of training and knowledge with data analytics courses to help your career soar.

How AI is Helping the Financial Sector Cover Regulatory and Compliance?

Posted on by Imarticus Learning

Synopsis

Artificial intelligence (AI) is here and is making waves in the financial industry. From sales management to compliance and protection against cybercrime, here is everything you need to know about AI 

On any given day, you as a consumer can carry out transactions online without having to worry about security and if your payment goes through or not. How is this possible?  From shopping online to overseas transfer, emerging tech such as Artificial Intelligence, Blockchain, Cloud has revolutionized the way the Financial industry works. In the past decade, Fintech has seen new dawn with many organizations heavily investing in Artificial Intelligence.

So, what is Artificial Intelligence? Simply put Artificial Intelligence courses are the ability of a machine to learn and process data for insights that impact the business.

It means that a machine is capable of learning on its own and arriving at solutions that can reduce cost and improve the efficiency of any business. In the financial sector, artificial intelligence is involved in every component today. From regulatory compliance to consumer insights, AI is changing the way the Fintech industry functions.

One of the most important aspects of the financial industry is regulatory compliance and cybersecurity. Another facet of this is sales management. As there is a shift in the way things work, it is important for the leaders of organizations to take stock of the benefits and consequences of deploying AI in their company.

Here are the top things that one must be prudent of while hailing in this new technology

Regulatory Compliance

Before Artificial Intelligence, the burden of compliance and authority rested with individuals and professionals who were trained in the field.  This also accounted for human errors, incorrect processing of data, and took a longer
duration of time. With AI, there is minimal human intervention when it comes to regulatory compliance and the machine also takes less time when it comes to analyzing the right data and arriving at a solution.

This will also impact the business drastically and reduce costs. In the financial sector, compliance is something that cannot be compromised on, and thereby use of AI will have a positive impact.

How Big Data is Powering The Internet of Things Revolution?

Posted on by Imarticus Learning

Big Data and IoT cannot exist without one another in today’s digital era. The two technologies are pushing the technological revolution across the world in a big way and here’s how.  
Today you can simply go for a run or a walk and your wearable gadget will not only tell you how many steps you have taken but also take your calls, turn off the lights in your house in case you have forgotten. This is the power of Internet of Things or IoT.
A lot of devices such as smartphones, smart-homes , DHL’s tracking and monitoring systems, smart security are run on IoT. So what is IoT? Simply put IoT is the ability of a device to communicate with another device over the internet.  These devices or networks are enabled by IoT which gives them the option to connect, communicate, send and receive and store data.
By 2020, the IoT industry is set to grow by 330 million dollars according to a Gartner study.  When combined with the power of data analytics or big data, IoT will disrupt the way industries function. Big data means the ability to analyze large volumes of data at a great velocity and provide valuable insights.
This can be both unstructured and structured data that is dense and can be stored.  The sheer volume of data that is processed at an incredible speed gives big data its name. Big Data courses provides industries with valuable insights and information on their customers, behaviors, spending habits which in turn can help enhance customer experience.
Now that we have a better understanding of Big Data and IoT, here are the ways in which both technologies are complimenting each other and driving digital transformation
Storage of Data
Today there is an abundance of data which is processed on a day to day basis. From videos watched on Youtube to messages sent over the internet, data is created, stored and proceeded at an unprecedented rate.  This means that large scale digital centers need to be set up to store data load.
Hence organizations are using IoT based infrastructure to move into a platform as a service model or a cloud solution to store data.  These systems provide flexibility and scalability of data storage.
Data Security 
The vast of amounts of IoT data processed will also contain a lot of secure information that cannot be stored on public networks or devices.  There needs to be well established protocols in place to combat theft of data and other fraudulent crimes. A lot of organisations are using programming languages such as Hadoop or Hive to store data with proper protocols in place.
Gearing for the future
Once a proper data storage system has been set in place, there needs to be enough infrastructure to support growth and performance. This means that there will also be new job opportunities created in the IoT space to maintain and process analytics.
Conclusion
IoT is remarkable in many ways, and when it combines with the forces of data, it is able to manipulate the data and provide valuable solutions to organisations. They both are closely connected have enabled the growth and transformation of many businesses today.

Top Features of Amazon Sagemaker AI Service

Posted on by Imarticus Learning

 

Amazon Sagemaker is the latest service that has changed the programming world and provided numerous benefits to machine learning and AI. Here’s how:

The Amazon Sagemaker or the AWS as its popularly known as has many benefits to organisations. It can scale large amounts of data in a short span of time, thereby reducing the overall cost of data maintenance.  Amazon Sagemaker provides data scientists with the right data to make independent strategic decisions without human intervention. It helps to prepare and label data, pick up an algorithm, train an algorithm and optimise it for deployment. All this is achieved at a significantly low cost.

The tool was designed to ensure that companies have minimum issues while scaling up when it comes to machine learning.  The most common programming language used for AI programs Python and also Jupyter Notebook is in-built into the Amazon Sagemaker.

You can start by hosting all your data on Amazon Sagemaker’s Jupyter Notebook and then allow it to process that information, post which the machine will begin the learning process.

One of the best features of Amazon Sagemaker is the ability to deploy a model which can be a tricky business. Apart from this, we have listed down the top features of Amazon Sagemaker below.

Build the Algorithm

The Sagemaker allows organisations to build accurate and relevant data sets in less time by using algorithms that support artificial intelligence and machine learning courses.  It becomes extremely easy to train machines using this service as they are given easy access to relevant data sources in order to arrive at correct decisions. It has the ability to automatically configure frameworks such as  Apache, SparkML, TensorFlow and more thereby making it easier to scale up.

Testing can be done locally

When there are broad open source frameworks such as Tensorflow and Apache MXNet, it becomes easy to download the right environment and locally test the prototype of what you have built. This reduces cost significantly and does not remove the machine from the environment it is supposed to function in.

Training

Training on Amazon Sage Maker is easy as the instructions for the same are specific and clear. Amazon SageMaker provides end to end solution to the training that is there is a setup of computer distributed cluster, and then the training occurs and when results are generated the cluster is torn down.

Deployment

Amazon Sagemaker has the feature of deploying on one click once the model production is complete and the testing is done.  It also has the capacity to do A/B testing to help you test the best version of the model, before deploying it. This ensures that you have the best results for the program itself.  This will have a direct impact on reduced cost due to continuous testing and monitoring.

Conclusion

Amazon Sagemaker service provides many benefits to companies who are heavily invested in deep learning and AI. These enable data scientists to extract useful data and provide business insights to organisations.

What is Data Wrangling and Why is it Important?

Posted on by Imarticus Learning

Data has changed the digital landscape drastically in the past few decades. From analyzing and providing insights real-time to enhance one’s life, data is integral to everything we do. 

It is impossible today to live in a world where we do not encounter data. Whether it is watching recipes on YouTube to adding friends on social networking sites, data is everywhere. Due to the abundance of data, there is also an abundance of knowledge and insights which we never had before.

However, if the data is outdated or irrelevant, it serves no purpose.  This means that there is a real need today for data wrangling. Data wrangling is the art of providing the right information to business analysts to make the right decision on time. It aids organisations by sorting through data and access them for further processing and analytics.

Apart from this data wrangling also involves removing unnecessary data, organising them in a consumable fashion.
Data wrangling also provides organisations with the right information in a short span of time to access the right information thereby helping make strategic decisions for the business. It also helps business perform all these tasks at a reduced cost and more efficiently with minimal human intervention.

Here are the top reasons why data wrangling should be everyone’s priority

Credibility of data
When large amounts of data are processed for interpretation chances are all of it is not relevant or outdated. Although data wrangling is a tedious process, conducting it will ensure that the data secured is not outdated or irrelevant.  Therefore, data wrangling provides credibility to data analytics courses. It picks the right data required in order to provide the necessary solutions to a problem. 

Build trust amongst stakeholders
When valuable information is extracted and presented to the stakeholders involved it build trust. Data should not only be presented in a simple format, but it also must add value to the circumstances. This means that any data that is extracted must be able to benefit the organisation or individual one way or another. This can be achieved through data wrangling, making it an important activity to carry out in an organisation.

Aid Machine Learning
Machines of today have the ability to create, process and understand data to arrive at plausible solutions thereby aiding a company’s overall growth and success. In order to optimise the vast volumes of data obtained from various sources, data wrangling becomes an important task.

It is not possible for a machine to scale and learn from new information if the data itself is corrupt or unnecessary.  Data which is historic in nature which allows the machine to learn and adapt can only be procured through data wrangling. If the quality of data that is fed into an AI is useless, the results which it will produce will also be irrelevant.

Conclusion
Data wrangling is extremely relevant today due to the large amounts of data that gets proceeded every day.  We will not be able to do thorough analytics if we do not have a strong infrastructure of data storage and hence companies are investing heavily in data wrangling tools.

What Do You Need To Know For AI

Posted on by Imarticus Learning

In a world where technology is developing at a rapid rate, fields that focus on automation and artificial intelligence are becoming the most lucrative. With many artificial intelligence courses available, it is important to remember that a strong base is fundamental.

So, the question is – What do you need to know before you can venture into a field like artificial intelligence?

Before heading to the pre-requisites, it is important to understand that AI is a field that is multi-dimensional. It can be used for anything from medicine to education. This also means programming AI is diverse, akin to law, where you constantly need to educate yourself on the updates of the technologies available in your field of AI.

Finally, the different fields of AI can have specific requirements, but on a broader scale, most AI in any field requires strong foundations that are basically the same. Here are a few things you need to know before studying about artificial intelligence.

Numbers Are Key
A strong understanding of mathematics is a must when venturing into artificial intelligence. The key here isn’t just knowing basic math. If you hope to venture into artificial intelligence, a deep understanding of discrete mathematics is part of the core foundation of the field.

Most artificial intelligence is based on various algorithms, and an understanding of these algorithms, as well as the ability to mathematically analyse them for errors and solutions, are considered the most basic requirement for AI.

Programming
 Much like math, programming is an essential part of artificial intelligence, implementing the mathematical data into code in a manner where you can not only develop but maintain and enhance machine learning is also part of the core foundation of AI. This means you must be able to code at a high level and find a way to be creative with code to improve the functions of a developing AI system.

In-depth knowledge of Python is often considered a mandatory pre-requisite to learning artificial intelligence as this open sourced programming language is currently the most popular and widely used.

Analyzing Data
 While programming and math are the foundations, the ability to analyse and interpret data is considered a cornerstone for anyone involved with developing AI. This skill is important as this is where the error guidance and solution base of AI stems from. Imagine a world where you create an algorithm and program that algorithm into a robot to vacuum.

This works successfully as a single task. Imagine now that you integrate another code into the same robot to do the dishes. The robot accidentally breaks the dishes or uses bleach to wash the dishes. This error is because the codes can overlap and create a fault or a bug. Data interpretation is essential to identify faults and bugs in order to rectify them.

Conclusion
While the three pre-requisites mentioned above are core tools for those studying AI, they aren’t the only ones you need. The field of AI you venture into may require knowledge of the field itself. An example of this is medical AI, where you will need an in-depth knowledge of medicine and how medicine functions. AI is ever growing, and its complexities are deep.

No matter the type of AI you choose to learn, a strong understanding of math, programming and the ability to analyse data accurately are a must.

Getting on the Right Artificial Intelligence Path

Posted on by Imarticus Learning

 

Are you looking to expand your current skill-sets? Does Artificial Intelligence pique your interest? Artificial Intelligence uses software or machines to use intelligence similar to that of humans. Even the humble calculator is an example of artificial intelligence. The field of AI is currently focusing on creating systems that can reason, learn, present knowledge, plan, and understand natural language amongst many others.

If you want to jump into this new and exciting field of innovation, you might want to make sure that you have your basics covered. There are several artificial intelligence courses in India that you can enrol in. However, if you are looking to explore on your own, you can follow the path given below to give you an understanding of how AI functions.

Brush Up On Your Math
A strong understanding of mathematics is key to your ability to move forward in the field of AI. Knowing as much math as you can will definitely help you later, but at the start, you can focus on statistics, calculus, and optimization. There are several resources available online for these topics, and you can also brush the dust off your old math textbooks.

Learn A Language
No, we don’t mean French. You need to learn the right programming languages in order to be able to delve into Artificial Intelligence. Focus your time on learning Python, C, and C++. These languages come with well-stocked toolkits and libraries which will help you navigate your future projects. Each of these languages has their own benefits and limitations, but starting with Python is a good bet. Look up artificial intelligence online courses offered by Imarticus Learning.

Solve a Problem you Know
One of the best ways to get started on AI is to practice with a problem you know and are interested in. It will keep you motivated as you continue to delve deeper into the intricacies of AI. The problem should interest you and must come with ready to access data that can be worked with a single machine. You could also start with the Titanic Competition that is tailormade for beginners like you.

Make Your Own Bot
A BOT is a type of weaker AI that can complete automated tasks. Try your hand at building your very own Chatbot. An example of an advanced chatbot is the Google Search Engine. It has three basic components – input text, send button, and output text. You can explore open source platforms like XPath and Regex in order to build your very own chatbot. This chatbot can be complex or funny and helpful. You can choose what your bot does for you.

Participate in an Actual Kaggle Competition
Kaggle has many real-time competitions that see hundreds of enthusiasts try to solve a problem. You can test out your knowledge and also learn where you need to explore more. This opportunity also allows you to connect to other AI enthusiasts. The forums are a rich resource on problem-solving and debugging.

Free Resources
There are many places on the internet and artificial intelligence courses which will help you expand your knowledge of AI one skill at a time. A great free resource is the Intel AI Academy which provides much-needed support, tech, and other tools for beginners like yourself.

Popular Tools to Analyze Data

Posted on by Imarticus Learning

Big Data is now an inevitable part of how many companies operate. While we all leave our footprint on the internet, companies ranging from IT to manufacturing firms are reaping the benefits of data analytics.

Knowing how to extract the information and trends, you require from the vast pool of data is imperative. Data analytics courses lets companies leverage this information for creating new plans, products, trends, offers, and more.

There are many tools that can be used effectively for analyzing data. Each of these tools has their own benefits and strengths. Once you are familiar with the capabilities of these tools, you will be able to employ the right tool for the right analysis. Tools for data analysis can be categorized into three main types.

  • Open Source Tools

KNIME

KNIME Analytics Platform is one of the most popular choices available to data scientists. It lets you model and manipulates data with more than 1000 modules, ready-to-run examples, a comprehensive set of integrated tools, and a large collection of advanced algorithms.

RapidMine

This tool is similar to KNIME in that it is a visual program. This tool has a unified environment making it easy to run through the entire gamut of the analytical workflow. You can use this tool for everything from data prep to machine learning to model validation to deployment.

  • Tools for Data Visualizations

Datawrapper

This is an effective tool used by news rooms around the world to create easy understand graphics and interactive charts. During elections, for example, newsrooms will plug in data collected by various resources and journalists on the ground to create charts that the layman can use.

The data can be populated according to race, ethnicity, age, gender, qualification, and more in order to understand the trend of the elections. Politicians in turn can use the same data to understand where they have popularity and with whom their ideologies resonate.

Google Fusion Tables

This is an amped up version of Spreadsheets backed by the powerful mapping tools of Google. You can use preexisting tables and combine two or more tables to create a visualization for both sets of data. You can choose to map, graph, chart the data which can then be shared or embedded into any page. This tool is great for collaboration as all the data organisation is saved on Google Drive.

  • Sentiment Tools

SAS Sentiment Analysis

Going back to the elections example, sentiment techniques can be used to assess sentiments in real time. The SAS tool extracts and interprets sentiments in real time or over a time period that you can specify. The tool features natural language processing and statistical modelling. The language processing is rule-based, and so you can choose the specific trend or emerging topic. This tool can be used to find the current feeling a population has towards a particular electoral candidate. This can be further developed to reflect the sentiments based on age, employment, gender, and sexual orientation.

Opinion Crawl

This is a great data analytics tool for all data scientists. It allows you to get sentiment analysis based on topic. This could be a person, a real-time event, a company, a product, or more. This tool provides the data in the form of a pie chart representing the real-time sentiment of the topic along with related images, a few headlines, and, most importantly, key semantic concepts related to the topic according to the public.