Contact Us
×

Tag: data analytics courses

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However, if the data is outdated or irrelevant, it serves no purpose.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When large amounts of data are processed for interpretation chances are all of it is not relevant or outdated. Although data wrangling is a tedious process, conducting it will ensure that the data secured is not outdated or irrelevant.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.

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.


Importance of Data Analysis and why you should learn it?

Posted on by Imarticus Learning

Inspection,cleansing, transformation and modelling of data in order to achieve information that further suggests conclusions and assists with decision making is what data analysis is all about. It’s a rapidly booming field of study for the youth, and companies are always on the hunt to find people who are masters at this procedure so as to increase their growth.

Analytical and logical tools are used to determine and accurately learn data analysis. These skills need to be learnt and honed over time in order to land yourself a good position in this field.              

Analyzing data is important for any business, old or new. It provides a clear understanding of customer behavior and much more essential business intelligence to promote growth and rectify mistakes if any. The first step in this huge process is defining an objective, without which the purpose of the study is lost.

Posing questions is the next step, after which comes data collection through various online and offline tools and techniques. This is the most crucial part of the process, as you need to define your objectives to learn data analysis as accurately as possible.

Learn data analysis by learning the essential tools and the most basic ones used in this line of work. One of the most widely used programs for data analysis is Excel. The other ones are Python, SQL & R. It is easy to get defocused with so many programming languages available and not knowing which one to learn first.A road map always helps while learning something new. R is a good place to start in terms of programming language. R Studio is an essential program to have to learn data analysis.   

If you want to learn data analysis, do not get intimidated by the courses available. You can look up educational websites and just by investing a few bucks, and you can know all there is to it. The most important part to remember before starting is having a fair idea of which software or program does what. It is always better to practice till you’re perfect, rather than spend time only on reading about it. There are also a lot of offline courses available to keen learners in order to learn data analysis.

If you’re sure about pursuing this field, then investing in a good college, institute, or course can help bring out the best in you. While there are many crash courses for the same, not many degree courses are available to learn data analysis. Interning under data analysts in your city of choice and your company of choice will also contribute largely towards technical and practical knowledge. Companies generally welcome promising interns and are willing to work towards their progress as a professional while seeking a fresh approach to business from them in return.        

The best way to excel in this line of work is choosing a specific skill you want to take forward professionally. It is the best bet for making the most of there sources available to you to learn data analysis.             

We offer data analytics courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.                                     

Top tips on how to apply data analytics in your project

Posted on by Imarticus Learning

Data science and analytics are two extremely useful tools that can give accuracy to your project and help automate repetitive tasks. With the demand and scope of data analytics growing with each passing day, companies are trying to integrate everything and get as much information on it as they can.

Data science techniques and analysis are quite helpful because they can be used to enhance the decision-making capacity of your manager, predict future revenues, understand market segments, and produce better content. In the healthcare sector, this technology can be used to diagnose patients correctly.

But how do you integrate data analytics into your professional projects? For that, a sound knowledge of the same is required. Even if you learn the basics of data analytics, it will give a major boost to your career. The entire world is moving towards digitization, and so data analytics is required to gather, analyse, and make sense of the data in front of you.

In order to become an expert in data analytics, and incorporate it seamlessly into your project, you need to have a data analytics training.There are many data analytics courses that you can take for a better understanding of data science and analysis. Here is a list of some of the best data analytics courses available online.

  • Introduction to Data Science

This data analytics training course requires a basic understanding of R programming language and provides an in-depth insight into the necessary tools and concepts used in the data science industry. They also work with powerful techniques for analyzing data and use real-world examples to help you gain clarity over the concepts.

  • Applied Data Science with Python

It is being offered by the University of Michigan. It aims to introduce learners to the specialized version of data science through Python. It is for learners with an understanding of Python, and want to expand their knowledge by incorporating the essentials of statistical,machine learning, information visualization, text analysis, and social network analysis techniques into their projects.

  • The Python Mega Course: Build 10 Real World Applications

This data analytics training is aimed at people with no background of Python, but are interested in learning basic as well as advanced skills of Python and data analysis. It is for people with no previous or little programming experience.

It does not rely on a lot of theoretical teaching but focuses instead on giving problems to the students that they can solve by doing. This course uses video, quizzes, real-world examples to familiarize learners with Python in the beginning and then enhance their skills later.

  • Social Media Data Analytics

This is one of the best data analytics courses available online that especially caters to social media. It is for people who want to use their data analysis skills to get the best out of social media.This course involves giving assignments and mini-projects, which would require you to use your data analytics skills to leverage your social media presence.

Will Data Analytics Ever Rule the World?

Posted on by Imarticus Learning

Of late, there has been a sudden surge of data analytics in the world. This will undoubtedly change the way people live and trade in the market. The use of data analysis tools is increasingly used in different technology devices for carrying out several day-to-day decisions in professional lives. It helps people to drive the business smoothly by identifying waste and blank spots seeking the help of different data analytic tools.

Although the companies are finding crunch in leveraging the ideas of this field, yet several global surveys reveal that it has the capacity of make the impossible possible, and it is still in the early stage of the data age. Today, most of the companies are investing in data analytics capacities by creating data analyst jobs are merely to remain in the competition. Data analytics have a great future, and it has the potential to rule the world.

Data Analytics- The Present and the Future
The data analytics development cycle can be defined in different stages. It starts from Descriptive to Diagnostic stage – The former deals with what happened, while the latter explains why did it happen? Then comes the stage of discovery followed by predictive. The former deals with everything that helps us to learn from and the latter talks about the things those are likely to happen.

Lastly, the prescriptive analytics that deals with what kind of action is to be taken. Generally speaking, the organizations today are in the first stage (diagnostic and discovery stages).

In order words, the data analyst jobs are simply helping companies to make informed and better decisions than before. With proper use of data analysis tools, it has become simple to blend a number of multiple data sources giving away the insights.     Thus experts feel that it would be the backbone of a decision-making process, which will end up in producing a better outcome. The Google Car is the classic example of it.

The impact on Business
There will be a radical change in business with the use of data analytics. More and more new data analyst jobs will be created and the job profiles would change with the growth of the market by unleashing the power of this field. With the passage of time, the number of data analysis tools will keep on adding new capabilities, which will help in managing and storing the data effectively.

Also, there will be newer methods of analyzing the data will emerge seeking the help of cognitive analytics and machine learning ideas. This will further help in giving few professions. Currently, IBM Watson and MS Cortana are among the forerunners in this domain. So, the days of asking what is data analytics, are now gone as the world are in the transition phase and soon would have data analytics dominating everywhere.

The Opportunities
The modern day smart devices are easily able to share data with the Internet of Things and are able to deliver massive amounts of data. These include the sensor data including location, health weather, machine data, and error messages to name a few. This will help in honing diagnostic and predictive analytics capabilities. Things would turn inexpensive, as people will be able to exchange the supplies even when it is not required, however, with this you can boost up the uptime.

Also, the coming time will make things simple and user-friendly to connect all types of data from numerous sources to each other. This will end up giving the insights in real time. You will be able to solve all your issues in minimal time duration, which will further settle down the challenges of business and IT alignment. These challenges will not be seen in the coming years with the advancements in data analytics courses and technologies.

Wrapping up
Needless to say that data analytics will rule the world. Currently, the world is passing through the transition as data analytics remain in the nascent stage. However, with ongoing research and development in this field, the data analyst jobs with better insight and capacities will increase and change the phase of the world. So, if you are planning to join any data analytics course, it’s the right time to invest.

How Data Sciences Principles Play an Important Role in Search Engines

Posted on by Imarticus Learning

Organisations today have started using data at an unprecedented rate for any and everything. Hence, it is mandatory that any organisation that has adopted data will need to analyse the data. Here is the real job of a search engine which can search and get results back in milliseconds.
The notion where people believe search engine is only used for text search is completely wrong as search engines can find structured content in an enhanced way than relational databases. Users can also check on portions of fields, such as names, addresses at a much quicker pace and enhanced manner. Another advantage of search engines is that they are scalable and can handle tons of data in the most easier and faster manner.
Few of the benefits of using search engine tools for data science tasks which are taught in big data analytics courses include:
Exploring Data in Minutes: Datasets need to be loaded to search engines, and the first cut of analysis are ready within minutes sans codes. This is the blessing of modern search engines that can deal with all content types including XML, PDF, Office Docs to name a few. Although data can be dense or scarce, the ingestion is faster and flexible. Once loaded the search engines through their flexible query language can support querying and the ability to present larger result sets.
Data splits are Easier to Produce: Some firms use search engines as a more flexible way to store data sets to be ingested by deep learning systems. This is because most drivers have built-in support for complex joins across multiple datasets as well as a natural selection of particular rows and columns.
Reduction of Data: Modern search engines come with an array of tools for mapping a plethora of content which includes text, numeric, spatial, categorical, custom into a vector space and consist of a large set of tools for constructing weights, capturing metadata, handling null, imputing values and individually shaping data according to the users will.
However, there is always room to grow there is an instance where modern search engines are not ready for data science and still evolving. These areas include analysing graphs, iterative computation tasks, few deep learning systems and lagging behind search support for images and audio files. There is still room for improvement and data scientists are working towards closing in on this gap.

Importance of Data Analysis in India

Posted on by Imarticus Learning

The importance of data in the world of today can not overstate. Though data has formed the backbone of all research for centuries, today, its use has spread to businesses – both online and offline, governments, think tanks which help in policy formulation, and professionals.
With the surge is collection and dissemination of data, the importance of data analysis has grown as well. While data collation is vital, it is just the first step in the process of using it. The ultimate use of data is to draw meaningful insights from which can then be put to use to practice. Data analysis helps in doing this by transforming raw data into a human or machine-usable format from which information is being drawn.
Also Read: What is Data Analysis and Who Are Data Analysts?
Data AnalyticsSome ways in which data analysis can be distinguished are as follows:

  • Organizing data: Raw data collected from single or multiple sources may be disorganized, or present in different formats. Data analysis helps in providing a form and structure to data and makes it useful so that other tools can be used to arrive at findings and interpret the results.

  • Breaking down a problem into segments: Working on data collection from an extensive survey or transaction and consumer behavior data can become very challenging due to the sheer volume of data involved. Data analysis techniques can help segment the data thereby reducing a massive, seemingly insurmountable problem, into smaller parts which can be relatively easily tackled.
  • Drawing insights and decision-making: This is the aspect which is most readily associated with data analysis. Tools and techniques from the field applied to pre-organized and segmented data assist in drawing meaningful insights which can either help in concluding a research project or support business in understanding consumer behavior towards their products better.

Further, through data analysis in itself is not a decision-making process, it certainly does help policymakers and businesses make decisions based on insights, information, and conclusions drawn while researching and analyzing data.

  • Presenting unbiased analysis: The use of data analysis techniques helps ensure that unwarranted biases – human or statistical – are reduced at least or eliminated at best. It helps ensure that top quality insights can be extracted from the data set which can help in taking effective policy actions or decisions.

Some people misconstrue data analysis to be just the presentation of numbers in a report based on which researchers support their thesis or managers take decisions. This is far from being true. More than merely data collection, data analysis helps in cleaning raw data, dissecting it, and analyzing it. It can also assist in presenting the insights drawn or information received from this exercise in a format which is compact and easy to understand.
In companies, there are data analysts and data scientists who are responsible for conducting data analysis. They can play a crucial role in harvesting information and insights from the data collection and study cause and effect relationships by understanding the meaning behind figures in light of business objectives. They are trained to process technical information and convert it into an easily understandable format for management.
Some data analysis methods that they use include:

  • Data mining: This studies patterns in large data sets – also known as big data – by applying statistical, machine learning, and artificial intelligence methods.
  • Text analytics: It processes unstructured information in text format and derives meaningful information from it. It also converts this information into the digital format for use by machine learning algorithms.
  • Business intelligence: This method draws insights from data and converts it into actionable information which is used by management for strategic business decisions.
  • Data visualization: This method uses data analysis tools to present trends and insights visually, thus making data more palatable.

Companies like Amazon and Google have made pioneering efforts in using data analysis by applying machine learning and artificial intelligence to create end-user experience better. Given that we are living in the information technology age, the use of data analysis is expected to increase manifold in the future and enhance its scope.
Also Read:

The Importance of Big Data Analytics in The Banking and Financial Services Industry

Posted on by Imarticus Learning

In this data-driven world, Data Analytics has become vital in the decision making processes in the Banking and Financial Services Industry. In Investment banking, volume, as well as the velocity of data, has become very important factors. Big Data Analytics comes into the picture in cases like this when the sheer volume and size of the data is beyond the capability of traditional databases to collect.
Today, data analytics practices have made the monitoring and evaluation of vast amounts of client data including personal and security informant data-driven and other financial organizations much simpler.
There are several use cases in which Big Data Analytics has contributed significantly to ensure the effective use of data. This data opens up new and exciting opportunities for customer service that can help defend battlegrounds like payments and open up new service and revenue opportunities.
For example, in October 2106, Lloyds Banking Group had become the first European bank to implement Pindrop’s PhoneprintingTM technology for detecting fraud. Their technology used AI to create an ‘audio fingerprint’ of every call by analyzing over 1300 unique call features – such as location, background noise, number history, and call type – the o highlight unusual activity, and identify potential fraud.
It cracks down on tactics like caller ID spoofing, voice distortion, and social engineering without any need for customers to provide additional information. Subsequently, Lloyds Banking Group went on to win the Gold Award for ‘best risk and fraud management program’ at the European Contact Centre & Customer Service Awards 2017.
Danske Bank uses its in-house start-up, advanced analytics to evaluate customer behavior and determine preferences, as well as to better identify fraud while reducing false positives.
JPMorgan Chase also developed a proprietary Machine Learning algorithm called Contract Intelligence or COiN for analyzing various documentations and extracting important information from them.
Big Data is also used for personalized marketing, which targets customers based on the analysis of their individual buying habits. Here, financial services firms can collect data from customers’ social media profiles to figure out their needs through sentiment analysis and then create a credit risk assessment. This can also help establish an automated, accurate and highly personalized customer support service. Big Data also helps in Human Resources management by implementing incentive optimization, attrition modeling, and salary optimization.
The list of use cases implemented in the workflows of the Banking and Financial sector is growing day by day. The huge increase in the amount of data to be analyzed and acted upon in the Banking and Financial Sector has made it essential to incorporate increase the implementation of Big Data Analytics.
Knowing the importance of data science is crucial in these sectors and should be integrated into all decision-making processes based on actionable insights from customer data. Big Data is the next step in ensuring highly personalized and secure banking and financial services to improve customer satisfaction.

Career Opportunity in Data Analytics

Posted on by Imarticus Learning

We are in a technology-driven age and are ever managing the growing needs of the companies and consumers with regards to the same. In such a scenario the role of data analysts becomes very perilous to manage the demands. A data analyst is someone who is in charge of collecting and analyzing the data, responsible for performing statistical analysis on the data. It is not essential that the skills of a data analyst are as evolved as a data scientist, a data analyst can or cannot create algorithms. Although they share the same goal of discovering insights from the data and strategically use them to create solutions.
Usually, data scientist works with the IT teams, data scientist or the management, to define organizational goals, data mining, identifying new trends and opportunities, designing and creating databases. Now, these skills come handy when considered as a base to progress in diverse directions in the analytics field.
There are various professional possibilities that can be easily handled by a data analytics professional. If you are a newcomer in this filed, or are trying to explore the field of data analytics, and are wondering about the future options for either career progression, or any alternatives to the analytics job, then this article will help you gauge the opportunities in a Data Analytics field.

Data Management Professional

Affiliated with the role of a database administrator, this role is a possibility but has nothing in common with the data analyst role. One does not need proficiency in programming languages like R or Python. SQL orientation is, however, a plus. This is an IT role, where the person manages data and the infrastructure that manages IT.

Data Engineer

While as a data management professional you will manage data infrastructure, as a data engineer, you will design and implement the data infrastructure. A step up in complexity from the data management professional, a data engineer is a non-analytical big data career opportunity. You cannot say one of the two is superior, it is your knowledge, skill, and preference that should be the deciding factor. Both these roles are similar in the technologies and skills to an extent. However, the application and complexity of the same are different.

Business Analyst

If you thrive when working with big data frameworks, analysis and presentation, creating dashboards, querying of databases is your forte, then this is the perfect career opportunity for you. The above two options will help you manage data and designing data, the role of a business analyst will be extracting information from the data other than what it already says superficially. There are unique skills requirements which can be learned if you wish to pursue in this field.

Machine Learning

Investigating data is the base of a role as a practitioner in machine learning, in addition to this capability you will also need to be hands-on with proficiency in statistics, writing machine learning algorithms, etc…, this is where big data becomes sophisticated, insightful, where tools and experience are used together to leverage data. Therefore, statistics and programming both become essential assets for a machine learning professional, if those are your interests then go for it as machine learning integration in technologies is going to be huge over the next couple of years.

Data Scientist

This term means nothing specific in general but uses all the roles and technologies listed above. From fluency in programming languages to querying and statistical capabilities, to extracting, managing and designing, and conducting initial exploratory analysis, and deciding which machine learning algorithm to use to perform predictive analysis, from visualizing the results to giving the presentation to the management with the end result, all comes under the job responsibilities of this role in addition to having the domain knowledge.
The options mentioned above are only a few of the possibilities but will serve as a good starting point for anyone exploring to understand options available to a data analyst.

What is the Scope of Analytics?

Posted on by Imarticus Learning

The word analytics has come into focus over the last couple of years. Analytics is considered to be pivotal especially in an era where internet and technology have taken centre stage in our daily lives. Analytics is essentially a field which brings together, Data, Information Technology, Statistical Analysis, Quantitative Methods and Computer-Based Models to one platform.

All this put together to form data, that is accumulated through various ever growing channels, due to the integration of technology in our daily lives, from phones to applications to online movement, any traction on the internet creates data. Analytics done on this data gives decision makers information on which to base their informed decisions.

Data Science Course

In recent times, with changing business dynamics, organisations are looking for innovative methods through which they can enhance productivity and cut costs. Companies have large volumes of data being created from almost every area of function.

Performing Descriptive, Predictive or Prescriptive Analytics on this data will assist the organization to identify potential risk areas, understand which areas need intervention and strategy reformation, and with the application of Computer-Based Models also run a simulation, on performance based on the said strategy, and gauge application based on the results.

Hence, the application of analytics in businesses is very vast, if applied with the right vision and strategy, the possibilities are limitless. Analytics can be applied to Customer Service, Acquisition and Retention, Financial Management of an Institution, Supply Chain Management, Human Resource, Government functions, Sports, Marketing, to name a few.

The scope and use of data analytics is not only a global phenomenon, but as it is turning out, India is being considered as a big market for data analytical skill sets. A career in business analytics is very fulfilling and is one of the fastest-paced developments in the current market scenario. India is hence fast becoming the most preferred destination for offshoring data analytics capabilities.

In India, the development or the use and scope of analytics is massive and noteworthy mainly in Media Communications, Outsourcing Companies, Internet business Companies, etc…,

Looking at these trends it is only obvious that the future of analytics will only continue to grow upward.

Outlined below are a few future opportunities in Analytics,

  • Since data is expected to grow exponentially in the future, the application of analytics will only increase in businesses.
  • Nevertheless, there will be a development of the tools used for data analysis, an example could be ‘Spark’
  • One will see an integration of Prescriptive Analytics in the Business Analytics Tool.
  • Going forward people will be able to see real-time insights in data and will be able to make real-time decisions.
  • Moving forward, Machine Learning will be a necessary element for data preparation and Predictive Analysis for businesses.
  • There will be Big Data staffing shortages, but the crunch might ease when companies start using internal training and innovative recruitment approach, Chief Data Officer will be a position that will open up in most organizations.

Whatever the debate on the future application of data analytics might be, one thing is clear, analytics has the capability of impacting the profitability and productivity of a business colossally. Hence, there is no doubt in stating that the ‘Future is in Analytics’.