Category: Analytics

Social media platforms like Facebook, Twitter, Instagram, Snapchat have revolutionised the way we communicate. Social media has also become a tool for us to document our daily lives. Social media has penetrated most aspects of our lives in a very real way, where others can peek into and see what we are doing, our likes and dislikes, and a great deal about the kind of person we are is out in the open for others to see. All age groups are sort of addicted to putting up their lives on the social media, from a six years old to a seventy years old, you will find all your family members waving out on the social media.
People use the social media platform to stay connected with each other, Businesses use it for networking, Media uses it to publish news to keep people updated about current events, similarly, law enforcement is also using the power of social media to investigate the crime.
Observing social media interaction and listening to it has helped the law enforcement a great deal in solving crimes faster. A specific social media monitoring platform is able to help the force identify patterns, behaviours, identify sentiment, understand tonality and language that can tie the culprit to the crime. In fact, social media analytics can not only help the law enforcement to detect crime but also prevent it by exposing illegal activities.
Law enforcement, in recent times, use social media analytics to track and conduct, sting operations, tracking criminal geolocation, gathering evidence, for community outreach, alerting the public, recruitment. According to a survey conducted a few years ago,

80% of the contacted respondents said that they use social media to conduct investigations, 60% said social media analytics helps solve crimes more quickly, 10% of those said they have received formal training in social media analytics, thus making social media analytics as their prime route while investigating crime.

Hence social media and crime solving become an ideal match, but there are some factors that need to be monitored.
As far as social media is being used for investigation and tracking criminal incidents it’s excellent. However, along with the positives, there is also a slight flip side where investigations can get corrupted on social media analytics. Social media can also become a tool for victim blaming, it is also a tool through which the friends and family of the victim can also be subject to public outrage. Trials by social media is another menace that comes in the way of those working in the criminal justice system. However so far the benefits of using social media analytics for investigations, outweigh the negatives. And as far as law enforcement is using social media to solve crimes as well as build rapport with the public they can continue to do so.
In future, as platforms evolve, there will be new benefits and also challenges in social media that will come up. We need to understand that social media is here to stay and we need to be innovative in our thoughts to understand this phenomenon, capitalise on its benefits and prevent or minimise the ill effects and take advantage of the platform.
To learn more about the Analytics watch this space until next week for the big news!

Business Analytics is the systematic computational analysis of data. In the simplest sense, Analytics is Applied Business Statistics. Statistics is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions to understand underlying macro trends.

Applied statisticians apply their knowledge of statistical methods to a variety of subject areas, such as biology, economics, engineering, medicine, public health, psychology, marketing, and education.

A Brief History of Analytics:

Availability and accuracy of data is the key to decision making. As such, Analytics in some form or the other has been part of business decision making.

There are numerous drivers of production and the supply chain, and there are several processes under each driver. These processes are associated with high overheads and offer opportunities for cost reduction.

Cost reduction requires a complete knowledge and mapping of all costs, cycle times, purchases, inventories, suppliers, customers, logistics, and other service providers throughout the supply chain.

Just-in-Time and Six Sigma are two very popular statistics-driven management areas which have been around since the early 1900s.From then on wards, there has been a steady integration of Statistics into business decision making.

By 2005 most banks, insurance companies, drug and pharma companies, casinos, telecom companies and internet companies had started using data to make critical decisions. These days, data scientist has become a ‘cool’ word to refer to Analysts.

Critical Competencies for a Data Analyst:

A Data Analyst needs to have three critical skills:

1. Understanding the business – to define the problem in statistical terms and, after the analysis, to summarize and suggest solutions which the business can implement
2. Understanding statistical techniques – to decide which technique can be used to solve which problem
3. Understanding of Analytics software like SAS, R , SPSS, Excel Analysis Toolpak etc. – To be able to implement the statistical techniques on the data

Future and Careers in Business Analytics

With the explosion in the quantum of data (mobile, internet, application software, sensors) and reduction in costs for storing data, there is a huge demand for Analysts – who are seen as people who can make “sense” of data.

• Data scientists are high in demand — according to one estimate, job postings increased by 15,000% between 2011 and 2012.
• Between 2010 and 2020, the data scientist career path is projected to increase by 18.7 percent, beat only by video game designers.
• There’s a global talent shortage, which means that data scientists are in high demand but short supply. As a result, average salaries are trending higher and higher — the average data science salary in the U.S., according to Glassdoor, is $117,500. Not surprisingly, those with PhDs and experience can earn salaries as high as $650,000, based on a Reuters peHUB article.

According to a report by McKinsey Global Institute, “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

Imarticus offers an extensive certification course in Business Analytics for freshers and working professionals :

Data Science Prodegree: This program is co-created with Genpact as Knowledge Partner. This program helps you with the deep understanding of Data Analysis and Statistics, along with business perspectives and cutting-edge practices using SAS, R, Python, Hive, Spark and Tableau.

Post Graduate Program in Data Analytics: This program helps you to understand foundational concepts and hands-on learning of leading analytical tools, such as SAS, R, Python, Hive, Spark and Tableau as well as functional analytics across many domains.

To know more, please refer to our website: https://imarticus.org/post-graduate-program-in-data-analytics/
Source: Analyticsindiamag

The process of assessing data using analytical and logical rationale, to inspect each detail and component of the data, can be described as data analysis. For example, you get a jigsaw puzzle and sort it in groups, it then becomes important that you analyze how the pieces in the group fit and progress towards the larger picture.

Data analysis is the process of breaking down data, evaluating it for trends over a time period, comparing it with other sectors. Visualizing the data in different perspectives and intuitively accessing it, are all efforts put into data analysis.

Data analysis starts from asking the right questions, about the past, the present, and where do you want to be. Data analysis thus can also be described as the primary element of data crunching, data mining and business intelligence, as it becomes a process of acquiring insight that initiates business solutions and decisions.

Data analysis as a process might differ from company to company, however, it does have some steps which remain common.

In data analysis the objective has to be set by the data science team, then they need to identify the levers and metrics that need to be evaluated, methods of data collection along with the channel of data collection should then be finalized. To improve the quality of the data and to avoid making incorrect conclusions, the data should be checked for any inconsistencies.

And lastly, the data science team will apply advanced analytics, where large-scale data analysis platforms are created to automate and optimise the process for future purposes.

Through data analysis, organizations get better insights and gain information that helps them in making informed decisions, to better serve their customers, predict customer behaviour, and relatively increase productivity and overall revenue of the organization.

By now we all understand that companies need data analysis to help them make better decisions and the person of importance in this process is the Data Analyst. after all, they are the people who give meaning to values and numbers, So, in short, a data analyst’s job is to take the data and help companies make better decisions, but how do they do that???

As you see there are many types of data analyst, they work in a wide range of areas, like, Business Intelligence, Data Assurance, Data Quality, Finance, Higher Education, Marketing, Sales etc…, they can be involved in many parts of the analytics process.

Essentially they will be involved in building systems for collecting data and compiling their findings into reports and dashboard for better navigation of the organization.

Main responsibilities of a data analyst include……

• Producing Reports
• Spotting Trends
• Inter-Process Collaboration
• Collecting Data and Setting up Infrastructure for the same

A toolset for a data analyst would comprise of, strong working knowledge of Excel, SQL, Google Analytics, Visual Optimizer, Tableau, Knowledge in programming languages like R, Python etc…,

Data that is collected and not evaluated it worthless. A data analyst adds value to the data so that the organization or the client can move their business in the right direction based on their strategic goals.

Dubai is known for its wealth, and also is referred to as one of the most populated cities in the United Arab Emirates. Dubai is synonymous with new technology, innovations, and is considered as one of the richest countries in the world. If you have the right skill sets, then there are many opportunities that you will be able to bank on.
In the age of the internet of things, where technology is fast integrating with people’s lives, proficiency in computer science, software programming, data science and data analytics are skills which are really sought after. Such skill sets become crucial if you are seeking to build your career based on current and future trends. Dubai targets on becoming a global innovation yardstick.
Due to its high tech infrastructure and implementations of several smart initiatives, Dubai has been declared as the Middle East’s leading smart city, by Huawei and Navigant, who studied the strategy and implementation of smart city campaigns in the region. According to a research based on UAE’s employers, 88% of them indicate that they need specific skill sets in the Analytics domain, showing an incline by 47% from the year 2014.
Since UAE has great plans of becoming a smart hub by driving digital transformations, with specific requirements in skill sets like coding and analytics, which are directly connected with digitizing industry verticals, will be high in demand. Due to the rise of Big Data, huge volumes of information can be collected from various channels.
Accurate analysis and interpretation of trends and insights from this data can benefit almost any organisation. Specifically, in Dubai, Big data is ever-present, especially since the technology to store this data is evolving and can be seen covering a wide range from collecting Salik (Toll payments) tags data, to online shopping trends.
Dubai faces the same challenges that other technology-driven institutions face, that of analysing the data in the most efficient and accurate manner, correct identification of areas of improvement and then selecting or prioritising, based on the substantial value and understanding, requires expertise. There is a dearth of data analysts in Dubai and hence jobs in this domain are great in demand there.
Dubai is also known to be a good paymaster, a junior analyst’s position can start from AED
20K per month and go up to 30 or even 40K AED.
Some specific skills along with job titles for IT-related roles in Dubai are:

• Statistical Analysis and Data Mining
• Algorithm Design
• Web Architecture and Development Framework
• Middleware and Integration Software
• Data Scientist / Data Analytics / Data Architecture
• User Interface Design
• To touch on these professions, and skills sets, it is essential to have a perspective and knowledge of SAS, Python, R, Tableau, deep understanding of Data Analysis, Statistics, etc…,

Keeping the trends and requirement in sight, Imarticus Learning, in association with Genpact as a knowledge partner, co-created a cutting-edge industry-aligned curriculum, an online training, using a practical hands-on learning methodology.
The course is called ‘Data Science Prodegree’. It is a 180 hours course, which includes hands-on training with 6 industry projects, covering the essential requirement in the field of data science. The course also offers career assistance, which will make you job ready, by providing mentorship, in resume building and mock interviews by industry experts.
So if you wish to make a career as a Data Scientist in Dubai, consider doing a course with Imarticus Learning, known as industry stalwarts for skill enhancement in the data science circuit.
We also offer online Data Science Prodegree courses in Sharjah and Abu Dhabi

Python is soon gaining a lot of popularity as a tool for data analysis. Python has evolved as a more mature tool offering flexibility, which one can enjoy without really sacrificing the functionality of older programs. Python is considered as the language for people working in the field of data science more specifically for data analysis. The objective of initiating any data analysis project is to create the highest quality of data, in the shortest possible time. On understanding the basic concepts behind what you are doing, anyone can perform the data analysis by almost using any language, however, the USP of python is that it is one of the best options available for beginners.
For data analysis the two most popular languages of choice are ‘R’ and ‘Python’, both of them are easy and simple to install, are available for free, and to an extent are easy to get started. However, there are a few variables where Python takes over as the most preferred language, to begin with.
Python is a general-purpose programming language, which translates into the fact that it can be used for almost all purposes like, data munging, data engineering, data wrangling, also for web scraping, web crawling, to build a web-based application and many more uses. If you have any prior experience in object-based languages like Java or C++ then, it will be easier to gain flexibility in Python than in R.
Python is again preferred, as because it is an object-based language it is easier to write codes, especially in scenarios where one has to write large scale codes, which are sustainable and strong. One can duplicate the prototype code in python from your private computer, to be used as the production code if needed.
Python might not have libraries or an all-inclusive set of data, as compared to what other programming languages have to offer. Nevertheless, one can use python in collaboration with tools like Pandas or Numpy etc.., to get you the desired results.
Data Wrangling with Pandas:
Pandas is one of the most popular python libraries for data wrangling, which is used to deal with some of the most common data formats and their transformations.
Data wrangling is an essential part of any data analysis. Before any algorithms are applied to the data set it is crucial that the data is checked and ready for consumption. For example, if your data set is incomplete, or has null values, the analysis will not be complete and correct.
Data Wrangling with Pandas will help you drop Null Values, Filter Data.
Data wrangling with pandas can also be used for Grouping data, which is slightly more challenging than filtering data. Through Grouping one can find ways to correlate the data and discover trends. If you are working with financial data or weather data, with Pandas Time Series Analysis Tool, one could analyse events by hours or even by the minutes
Lastly, you can Export cleaned and filtered data to Excel or another format, basically share the data and present it in the best possible format.
So preparing the data is the first and most crucial task for data analysis, data wrangling with Pandas assures that any treatment that is applied to the data set will be effective.

When an organisation formulates major business goals and initiatives by the top management and decides on the implementation of the said devised strategy, it is participating in the strategic analysis initiative. There are as usual many definitions available that explain strategy analysis, but to simply put it, the Strategic analysis is a method to facilitate, research, analyse, and map a company’s ability to achieve the future target or threshold based on current reality and resources. This can be achieved by bridging gaps that exist between the strategic, and operational features of the organisation.

There are many tools and techniques that can be applied to the strategic analysis. As the strategic analysis is a process of conducting research on the current business environment so that a sought-after business strategy can be formulated. There are certain factors which will help you in picking up or determining the right technique to get to the desired goal effectively. Analytical tools and techniques are key in formulating a strategic plan. There are many important considerations that you need to be aware of to make an informed decision.
Simple yet important factors to consider would be……

• Does the tool assist you in asking and more importantly answering the right questions aligned to the need of the organisation?
• Is the expected benefit of using the tool defined? Will the tool be capable of assisting you with actionable insights? Absolute clarity at this stage will guarantee that the analysis is successful.
• Applying a choice of different tools and techniques to analyse a current situation is also an option.
• Does the tool help you in understanding, where you are today, where do you wish to go, and how do you get there?

It is always a good practice to ponder on these points before you embark on your strategic analysis journey

• Always involve a broad group in strategic planning so that the responsibility for successful implementation is for everyone.
• Irrespective of the tool you use, understand the value, vision, and mission and develop clear objectives on what you desire to achieve.
• Have a mixed approach, just applying a top-down approach to strategic planning might not always be successful.
• Account for the tangibles and non-tangibles, such as company culture and quality of management, especially while assessing strengths.
• While identifying threats, it is always good to look for internal and external threats along with future possibilities that might work against your objective.
• Lastly, it is always important to devise a plan which can be measured, is broken down into small actionable goals, eventually working towards the larger plan. Clearly, defining the action plans and performance metrics.

Here are a few popular tools
SWOT Analysis is a strategic method that will help you identify the Strength, Weakness, Opportunity, and Threat to your business or a project venture.

PEST is a well-known macro framework that analysis the Political, Economic, Social and Technological developments. This model can be further expanded to include Legal and Environmental Concerns as well.

Root Cause Analysis, is a recommended tool, which can be used in isolation and with a combination of other tools as well, whenever a requirement arises to dig deeper into a situation. If you have identified the problem correctly, this technique will then come handy as a systematic approach to identifying the cause and resolving the situation.

There are many tools and techniques available, this article does not even touch the tip of the iceberg. An individual or organisation, faced with a challenge or with the intention to achieve evolved goals in the future will be the best judge on the tool to use.

In simple words, sentiment analysis is nothing but a fancy term for opinions, it is the analysis of feelings, attitudes, and opinions. It is a systematic way of reading between the lines. Opinions are mined behind specific words, using tools like Natural Language Processing (NLP). So essentially not only are we looking at your likes and comments but are making a systematic effort in ‘Understanding’ your reaction, like does a certain behaviour look sarcastic? Or Negative? Or is it indeed positive?

Being able to accurately capture this information is very vital for marketers, as they get an accurate response to their social content, and estimate if it is hitting the accurate target or getting the correct response, having access to this information can help us course-correct our interventions to resonate on the targeted touch points, with our end users. Unlike measuring quantifiable metrics, sentiment analysis measure what matters.

It measures quality metrics like opinions, feelings, satisfaction ratings and most importantly ‘Quality of Engagement’ over time. So say I viewed a particular add but was I stationary or toggling between screens? And if toggling, at what points? Such feedback is priceless.

There are many monitoring tools which can be used in combination or individually to analyse user sentiment.
At Imarticus we offer a wide range of hybrid courses, with online and classroom engagement. While offering online courses we have to be very conscious especially while designing the course content, as the social and online learning environment is very different from the traditional ones.

In an online course, the interaction is only possible via webinars, discussions, hence it is driven by online participation. This can be both positive and negative. Hence in some way, as providers if we can increase online participation, it will have a direct impact on overall satisfaction, the results will be better participation, improved return on investment, better retention, and overall satisfaction and reduce the number of midterm dropouts.

There is a lot of data that gets collected with the online activity of the participants, hence it directly gives us the option of analysing online engagement, this trend is gaining popularity especially for online educational courses.

In fact, in some scenarios research has proven that with an application of techniques like ‘Learning Analytics’ and ‘Educational Data Mining’, one is able to predict participant behaviour and alert a potential dropout pattern.

Applying the capability of the monitoring tools available in sentiment analysis, one can possibly find a link between the sentiment tonality of learners using the online platform for education. These findings are never definite and can be tested with many other available metrics to reach a conclusion.

People who wish to embark or make a career shift in coding, or have a passion for coding always find themselves standing at crossroads with the dilemma of choosing the most appropriate programming language. Coding is an essential skill set for any data scientist or a professional working in the data analysis field. SAS often comes as the most preferred option in programming languages. Specially, if you are beginning your career, then this is the most obvious and logical thought to have.
Is SAS the right language to learn? Is knowledge in SAS enough to start your career in data science and coding? SAS v/s R or Python which is best? few questions you should have answers to.
As a general answer, there is no language in programming that can be termed as ‘Best’ purely because it is knowledge that you are acquiring, the same knowledge can be transferred.
All programming languages are good and developmental. Learning a programing language can be compared to driving, if you take the analogy further, you learn driving a particular car, but later you can apply the same skill while driving a truck, or a tractor, or an automatic vehicle, left hand or a right-hand drive. In the same way, all programming languages implement, Input, Output, Variables, Loops, Conditions, and Functions.
Learning one language will make learning the other one simpler, it is majorly only a different Syntax.
SAS is popular and can be considered as the undisputed market leader in the enterprise analytics scenario, it has a good GUI hence learning becomes fairly structured and easier. It has a good array of statistical functions and also offers great technical support.
SAS is majorly popular in established organisations because they are synonymous with great customer support and service. SAS is an expensive tool, therefore in the financial sector, where a budget is not a concern, it is usually the preferred option.
For these reasons SAS is still considered a leader in the coding sector by dominating 80% of market and R and Python together at 20%, they are open source languages.
However, on conducting a survey with over 1000 quantitative professionals, on mapping their preference in programing language, only 39% of them voted for SAS, while 42% for R and the rest 20% for Python.
Retail, Marketing and majorly Healthcare, Pharma and Financial services are loyal to SAS, while Telecom and the Corporate Start-up sector, swings towards R and Python. These sectors have large volumes of unstructured data, machine learning techniques need to be applied, for which R and Python are more suitable.
Cost of learning is also one of the factors while making an informed decision. SAS is very expensive when compared to other languages, so unless your company is assisting you in training, on an individual level it’s a costly affair. Although expensive SAS is fairly easy to learn, plus you do not need any prior knowledge in programming, basic SQL knowledge is good enough.
With SAS it will be easy for you to get a job, it is a fourth generation language, it relies on user-written programs, that when requested know what to do. Based on your needs and interests, it can be said that SAS is versatile and flexible with a variety of input and output formats. SAS has an electronic network where resources are available and one can get connected to share knowledge.
One needs to base their decision on personal factors, if your dream is to join a start-up or the telecom sector, then perhaps R or Python should be your choice. If you want to join the financial sector or venture in healthcare, then maybe SAS should be your first preference before learning any other languages.
 

Everyone wants to become a data scientist, after all, it is considered and labelled as the ‘sexiest job of the 21^st century!’ There is a lot of mystic in the tile ‘Data Scientist’. The data scientist is considered as a wizard, with mystical skills, which when applied can get great insights from the existing data, that holds the key to the future success.

But as much as we would like to believe otherwise, it is the way the data is interpreted, analyzed and methods of data science applied by the data scientist that gets us great results. In this blog let’s understand “What Does a Data Scientist Do?”

The power of data comes from great knowledge of Business Domain, Statistics, Algorithms, Computer Science or Programming skills and exceptional Communication Skills. These become the four pillars of data science. When these skills are applied in a harmonious and synchronized manner, the true essence of data science is discovered.

With the arrival of Big Data, comes the age of large datasets which cannot be managed by the traditional data processing applications. This has opened a way for skilled professionals who can do the job. A data scientist takes the raw data and analysis it in a way that makes it accessible and valuable for the organization to make strategic decisions.

So, in short, they analyze data to get actionable insights from it. A data scientist’s level of experience and knowledge would range from nascent to expert levels, and their degree of work will change with time and business requirements.

Precise tasks performed by the data scientist would be……

• Applying knowledge in identifying the best data analytics program suitable for the organization to optimise opportunities.
• Collecting and analysing large sets of data, in structured, unstructured and semi structured format.
• A major role of the data scientist would include cleaning, and mining and validating the sets of data to ensure that it is accurate, complete, correct and that it is uniform in nature.
• A data scientist has to ensure that the correct dataset and variables are defined.
• A data scientist would need to dedicate a great deal of time in applying models and algorithms to mine the stores of big data for insights.
• One of the key tasks of a data scientist is to identify the patterns and trends in the data set and communicate the same so that strategic decisions can be based on it.
• As much as they are required to identify trends, a data scientist jobs also require finding hidden solutions and opportunities from the data set.
• Lastly, use their communication and visualisations skills to translate the finding or while making suggestions to the stakeholders.

To perform the above tasks, it is vital to have knowledge in maths or statistics, an inquisitive mindset with critical thinking capabilities is an added advantage. A knack for connecting the dots, correlating findings from the data to business advantage is a must.

If you have a background in computer programing, it will be easier for you to devise the algorithms for excavating the stores of big data. R, Python, SAS, Hadoop are a few preferences. A business acumen, that like an entrepreneur will be a big advantage, as you need to be a leader in devising your own methods and building your own infrastructure so that you can treat the data in your own methods to get new discoveries from the same data sets.

Lastly, you need to be confident, understand the business domain of the institution you are working with. Possess exceptional communications skills to that you can explain technical findings to the non-technical stakeholders’.

Harvard correctly states Data scientist as the trendiest job, it is an extremely important role, high in demand and has a significant impact on business’s ability to achieve its goals irrespective of its nature.