In the past couple of years, the field of data science seems to have rapidly shot to fame. The main reason for this is ‘data’. In our information-driven world, all of us play quite a crucial role. Since the moment we wake up and glance at our phones, to the last moment of the day, we are all digital labourers, trying to generate the very data that acts as a fodder material for the companies over at Silicon Valley.
All of these high tech companies today are all for the increasing demands of data scientists and data analysts. Jobs in this sphere have been steadily increasing and have taken up permanent residence at the top job search engines all over the web. The various titles that beckon data aspirants are the likes of Data Scientists, Data Analysts, Data Engineers and many others. While the prefix of all of these job titles may lead you to believe that all of these professionals carry out the same functions, it is not really so. As data science happens to be a vast field with so many diverse verticals and untapped areas, there is always something new to do with someone new.
Coming back to how these similar sounding positions are actually quite different. Let us start with Data Scientists, these professionals are popularly known as the rock stars of the Information Technology industry. They are usually in charge of making accurate predictions, which help the businesses take most lucrative decisions. These individuals have a treasure trove of educational qualifications and experience.
They usually belong to a background of computer science applications, modelling, statistics or math. They have an ‘IT’ factor in the form of a combination of brilliant business skills and excellent communication skills that set them apart from the general public involved in the industry. Further division of roles for a Data Scientist could be becoming a Data Researcher, Data Developer, Data Creatives and even Data Businessmen.
Apart from Data Scientists, there is another career option which is called as Data Analyst. These professionals perform a wider spectrum of functions like collecting, organizing and analysing of data in order to derive important information from the same. They are also known as Data Visualizers as they are supposed to present this collected and processed data in the form of charts, graphs and tables and go on to build other related databases for their firms. They could diversify their careers by going into roles like Data Architect, Database Administrators, Analytics Engineers, and Operations and so on.
The major differences between these two positions are that a Data Scientist usually is required to be familiar with database systems like MySQL as well as Java, Python and so on. Whereas a data analyst must be familiar with other data warehousing and business intelligence concepts and must have an in-depth knowledge of SQL and analytics.
If we put the differences between the two aside, then we would infer that both the positions require a professional to do a thorough course in programming tools like Python, Big Data Hadoop, SAS Programming, and R Programming and so on. While these tools could be learnt by self-study, but most prefer institutes like Imarticus Learning to help them along their journey.
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Times are changing, from a generation that was addicted to television, we are now in the age with extreme dependency on devices. It is not only for pleasure but also convenience. Using e-commerce websites to shop, or apps to remind you about your medicine intake, to having an interactive house where appliances communicate with you to keep things in order when you are back from a long day at work, all this is possible due to our own data and data connected devices. Institutions have recognised that they can be integral and add great business value by applying Data Analytics. Huge investments have been made by organisations in Data Analytics, specifically in creating data products which will make our lives simpler.
The way the world is progressing there is a huge demand for professionals in the Big Data Analytics field. Many job profiles and roles have come up, Data Scientist, Data Architect, Data Analyst, Big Data Engineer etc…., are being headhunted by recruiters and talent management professionals. There are many perks attached to a role in Big data analytics, not only are you involved in a satisfying role, genuinely impacting the way the business is performed, but also since there is a high demand in the comparatively new line of business, the supply of individuals with the right talent is scarce, hence the big advantage is the high pay scale, in comparison with other traditional professions.
According to a survey conducted by the Analytics Association, many data science professions were spoken to in a matter of 12 months to gauge the analytics and data science industry. The survey was focused on the Indian trends.
It is important to be aware of the salary trends here, as, after the US, India has the largest demand of analytics, and IT professionals with experience or fresher’s will get a clarity on the skill to pick up and make an informed decision on the role to pursue.
Salary Trends in Data Analytics
- Fresher’s in Analytics get paid more than then any other field, they can be paid up-to 6-7 Lakhs per annum (LPA) minus any experience, 3-7 years experienced professional can expect around 10-11 LPA and anyone with more than 7-10 years can expect, 20-30 LPA.
- Opportunities in tier 2 cities can be higher, but the pay-scale of Tier 1 cities is much higher.
- E-commerce is the most rewarding career with great pay-scale especially for Fresher’s, offering close to 7-8 LPA, while Analytics service provider offers the lowest packages, 6 LPA.
- It is advised to combine your skills to attract better packages, skills such as SAS, R Python, or any open source tools, offers around 13 LPA.
- Machine Learning is the new entrant in analytics field, attracting better packages when compared to the skills of big data, however for a significant leverage, acquiring the skill sets of both Big Data and Machine Learning will fetch you a starting salary of around 13 LPA.
- Combination of knowledge and skills makes you unique in the job market and hence attracts high pay packages.
- Picking up the top five tools of big data analytics, like R, Python, SAS, Tableau, Spark along with popular Machine Learning Algorithms, NoSQL Databases, Data Visualisation, will make you irresistible for any talent hunter, where you can demand a high pay package.
- As a professional, you can upscale your salary by upskilling in the analytics field.
This is the age of the analytics specialist and not anyone with general skills in big data. As the field is evolving, the expectations are getting more specific. Applicants or professionals working in the field should have the ‘Subject Matter Expertise of Analytics’, and ‘The Understanding of the Business Vertical’ they wish to work in so that they have a good combination of skills and the Business Context to apply those skills.
We are an award-winning institute do offer certification and PG courses for various big data analytics tools such as R, SAS, Python, Big Data and Hadoop. To grab the opportunities in the data analytics field feel free to join our data analytics courses
Let’s go back to the time when we all had no idea whatsoever about social media or the internet of things. This was the time when the concept of a personal computer, did not really exist. All these desktops were supposed to do, was to store information, to help in some calculations as a part of other related activities. This was also the time when normal storage devices, were not really normal. They used to be sold at very exorbitant prices and would set you back by more than just a few thousand rupees. Anyone who has an idea about this situation, may be pleasantly surprised of how cheap these storage devices have become lately. If you happen to be an individual with very less requirement for storage space, you might as well not buy a storage device for all intents and purposes. Meaning, why buy a hard disk, when you have free space available to you to store your things.
Today there are so many applications online, for instance Google or the famous Drop Box, these applications are providing users with up to 15 gigabytes of free storage space, to store everything they want to. With the advent of mobile phones with great storage capacities, the need to buy a pen drive has almost become negligible. Now one would wonder, how exactly would it benefit these companies to lower their storage costs. One thing is definitely sure, these companies benefit by having all of this data, in order to enhance their very service offerings. In more technical terms, it allows these companies to generate big data and thus go ahead and use it for their profit. Today, with the great advancement in every single field, due to the massive explosion of developing technology, has also led to a tremendous generation of data. In the field of Data Science, the jargon they use for this process is a person’s digital footprint. Think of it as a carbon footprint, but devoid of the negative connotations. This digital footprint of every individual consists of the data they generate, by their various dealings online, which in turn are used by companies to enhance their chances of success.
Have you every stopped and wondered, how when you open any page on your browser, you have ads of the exact things, that you were looking for about two days before. This is exactly the power of Big Data. The whole idea here is trying to map someone’s digital identity get all the information regarding the person’s likes, dislikes and then present that person with every kind of enhancement possible, from targeted recommendations, to even someday finding out if we could make clones of that person, based on their social media activities. This concept of big data is gradually, yet very effectively changing the way the world works, making it smarter, more efficient and faster. There are many theories that in the future, we would be even able to develop robots, create our own personas in the virtual world, have the most heightened artificial intelligence technologies and basically be able to harness our data a rich surroundings to their optimum potential.
Machine Learning (ML) or Artificial Intelligence (AI), are almost used as synonyms for each other and are perhaps the most talked about topics in recent times. What the internet did to our lives, emerging as one of the game changers of the time, AI and ML are expected to do the same, by impacting and transforming our lives in ways we cannot fathom or imagine. It will change the way we shop, communicate, travel, conduct our daily lives and do our business.
So what is Artificial Intelligence and Machine Learning? AI is kind of a problem-solving process, which enables software to perform tasks without specifically being programmed. It includes Neural Networks and Deep Learning concepts, a way through which machines can perform tasks which are equivalent to the way a human mind would. ML is an application of AI which to put it simply, enables the machine to access data and learn to perform tasks by the use of algorithms, and further allows the system to find insights, and at times fix anomalies, without any external human programming intervention.
AI and ML concepts have been around for decades, but it is only recently, that huge volumes of data that is gathered from distributed sources, which was not possible in the past, is making a difference to this concept. Data is important as it offers that many reference points, for example, if a machine stores the case history of cancer patients across large hospitals, from different continents, then while choosing a treatment path, an oncologist can view the data across and suggest the most effective and preferred treatment. Besides healthcare, if we take an example of education, this concept can even predict who will drop out from school by studying the sentiment before dropping out, or if we look at fashion, then it can predict the style or maybe even the colour of the next season. ML has already been adopted by the Retail and Transportation (read Airline specifically) and Financial Services Industries.
India has also recognised the power of AI and ML and is transforming from the base with regards to this, the learning switch is triggered, many new aspirants are looking at ML and AI as new career opportunities rather than the traditional roles in IT. Many organisations have also realised the power of data, as they were already sitting on gold mines of data, so the cloud is getting the computing power to the existing data, and ML is creating an impact in decision making by providing actionable intelligence.
According to a recent report by Accenture, AI has the potential to add close to $957 Billion to the Indian economy by changing the nature of work, to create better outcomes for business and society. The report called ‘Rewire for Growth’ predicts that AI intervention could increase India’s annual growth rate of gross value added (GVA) by 1.3 % points, thus lifting the country’s income by 15% in 2035.
Definitely for AI to reach its full potential, India will need to develop a vision, which in the long run will have an action plan to collaborate the ecosystem and manage technical and ethical questions as they ascend. An increased spending on AI will facilitate this, and of course, the big businesses will also play a key role in the unlocking the economic value of AI. Also as found in the research by Accenture, more than 88% of businesses in India are expected to make moderate to extensive investments in AI related technologies over the next three years.
Picking up Machine Learning skills is the best advice one can give to anyone looking to pursue a career in the analytics field. As the demand for ML Analyst is only anticipated to grow in the coming years across all domains within the country and across the globe.
The era of top-down information flow or Intuition Driven decision making is a thing of the past. Business leaders and professionals, on the whole, are beginning to favour the data-driven or analytics-driven decision-making techniques. People from across the organisation are making business intelligence and business analytics a part of their daily lives. Of course, their expectations with an analytics-driven approach is that the solution will be quick and will work on any kind of data. However, we know that might not be the case always, there are a few challenges, like building a vast interconnected analytics ecosystem. There is a great amount of refining and recognising the right data links between different departments of a business. One also has to ensure that the data focused resource is effectively and efficiently used.
Almost every organisation is using data analytics or business analytics in some capacity. The big question is, ‘How Effectively?’ It is evident that every business head acknowledges how business analytics if used accurately and effectively can impact two areas positively. i.e.
(1) Revenue Increase &
(2) Reduction in the Cost.
According to a survey conducted by Deloitte, a majority of respondents are said to have commented, that in the market-place related areas, the most significant use of data analytics was in identifying methods of increasing sales, by understanding customer behaviour, and hence targeting products and services to segmented audience.
One notices that the awareness is there amongst professionals on the benefits associated with Business Analytics, however the impact on application is not always optimum, every organisation is unable to get as much out of analytics application as it could, let us read on to understand how can we maximise the impact of application, let us uncover the unknown facts of business analytics solution.
Goal – Have a clear strategy on why and How you are going to initiate an Analytics Solution. Have the What, Why and How in place, along with the Stakeholders.
Data – Use an analytics tool to find anomalies in the data, and engage people who own that data to fix the process. You can never wait for the perfect data set.
Visualization– Most of the times the story is nice but not presented well, hence is not impactful. It is the same with data, the way users visualize the data, also influences the way they understand, data should be engaging and visually appealing. Hence a good designer who can create effective dashboards should be engaged.
Promote Analytics – While software project is mandatory, adoption of analytics is usually voluntary, hence promote and sell analytics, be the pioneer of business analytics across the organisation. Find a success story within the organisation and promote the same within.
Beyond Reports – Most business analytics or business intelligence arrangements speak through the age-old reports. On the other hand, diagnostics discovery that is finding the why and not just the what rebalances, the focus on exploration capabilities.
Value the Data – It could be possible that the data present could hold immense potential, it is always good to think of creative uses of monetising the data already available.
While business analytics holds answers and insights that could benefit the organisation, there are certain barriers that need to be worked. Engaging analytics to decision making, Acquiring the right talent, Strategizing accurately, Creating a more central co-ordination for analytics, and Building a smart and modern analytics foundation are steps that can be adapted to plan for this evolution in advance.
The world of Data analytics is constantly evolving, almost all manual repetitive tasks are being automated, and some complex ones too. If you are in the profession of big data, a data scientist, or from the field of machine learning, understanding the functions of these algorithms would be of great advantage.
A continuation of the earlier blog, mentioned below are a few popular algorithms commonly used by the data scientists and the machine learning enthusiast. The headings might differ slightly in terms of the nomenclature of the algorithms, but here we have tried to capture the essence of the model and technique.
- Linear Regression
Imagine you have many logs to stack together from the lightest to the heaviest, however you cannot weigh each log, you need to do this on appearances, the height and the girth of the log, only using the parameters of the visual analysis, you should arrange them. This, in other words, is Linear Regression, where a relationship is established between independent and dependent variable by arranging them to a line. Another example would be modelling the BMI of individuals using weight. You should use linear regression if there is a possible relationship or some sort of association between variables, if not then applying this algorithm will not provide a useful model.
- Logistic Regression
Just like any other regression, logistic regression is a technique used to find an association between a definite set of input variables and an output variable. But in this case, the output variable would be a binary outcome, i.e. 0/1, Yes/No, for e.g., if you want to assess, will there be traffic at Colaba, the output will be a specific Yes or No. The probability of traffic jam in Colaba will be dependent on, time, day, week, season etc…, through this technique you can find the best fitting model that will help you understand the relationship between independent attributes and traffic jam, incidence rates, and the likelihood of an actual jam.
This is a sought of unsupervised learning algorithm where a data set is clustered into unique groups. So if you have a database of 100 customers, you can internally group them into different clusters or segments based on variables. If it’s a customer database that you are working on, then you can cluster them basis, gender, demographics, purchasing behaviour etc…, This is unsupervised as the outcome is unknown to the analyst. The algorithm is deciding the outcome, and an analyst is not training the algorithm on any past input. There is no right or wrong solution in this technique, business usability decides the best solution. There are two types of clustering techniques, Hierarchical, and Partitional. Clustering is also referred to by some as Unsupervised Classification
- Decision Trees
As the name suggests, decision trees is a visual representation of a tree-shaped visual, which one can use to reach to a desired or a particular decision, by simply laying down all possible routes and their consequence or occurrences. Like a flow chart for every action, one can interpret what would the reaction be for selecting the said option.
- K-Nearest Neighbours
The data science community essentially uses this algorithm to solve classification problems, although it can be used to solve regression problems as well. This algorithm is very simple, it stores all available cases, and then classifies any new cases by taking a vote from its K-Neighbours. The new case is then assigned to the class with the most common attributes. An analogy to understand this would be, the background checks performed on individuals to gather relevant information.
The main objective of the Principal Component Analysis is to analyse the data to identify patterns and find patterns, to basically reduce the dimensions of the dataset with minimal loss of information. The aim is to detect the correlation between variables. This linear transformation technique is common and used in numerous applications =, like in stock market predictions.
- Random Forest
In the random forest, there is a collection of decision trees, hence the term ‘Forest’, here to classify a new object based on attributes, each tree gives a classification and that tree votes for that class. And overall the forest chooses the classification having the most votes, so in the true sense every tree votes for a classification.
- Time Series / Sequencing
Time series is an algorithm which provides regression algorithms that are further optimized for forecasting of continuous values, like for example, the product sales report, over a period of time. This model can predict trends based on the original dataset which was used to create the model. To add new data to the model, you need to make a prediction and automatically integrate the new data in the trend analysis.
- Text Mining
The objective of the text mining algorithm is to derive high-quality information from the text. It is a broad term which covers a variety of techniques to extract information from unstructured data. There are many text mining algorithms available to choose from based on the requirements. For example, first is the Named Entity Recognition, in which you have the Rule-Based Approach, and the Statistical Learning Approach. Second is the Relation Extraction, which further has, Feature Based Classification, Kernel Method.
One-Way-Analysis of Variance is used to analyse if the mean of more than two groups of the dataset is significantly different from each other. For example, if a marketing campaign is rolled out on 5 different groups, where an equal number of customers are present within the same group, it is important for the campaign manager to know how differently the customer sets are responding so that they can make amends and optimize the intervention by creating the right campaign. The Analysis Of Variance works by analysing the variance between the group to variance within the group.
Optimise your knowledge by understanding these algorithms intensely if you wish to flourish in the field of data science.
If you are a big data enthusiast and want to enter the field of big data, or if you are employing a development team to handle your big data requirements, you would find yourself pondering over this question many times. Is Python the best choice over the many other programming languages available? Which language should you train yourself or your staff in? Python or R or Hadoop? well, an article cannot solve your quandary, but read on if you need to find out what Python has to offer.
Python is an open source programing language, which is most popularly used in big data. Python Language is synonymous with flexibility, powerful yet easy to use features. Python has its USP in the rich set of utilities and the libraries it offers for analytics and data processing tasks. So, all in all, it is a given fact that among other options available Python maintains its popularity essentially because of it’s easy to use features, which supports big data processing.
Python was developed with the philosophy to bring coding to an open platform, where coding becomes easy, more readable, where one can write less number of lines and yet get the desired results. Keeping the objective in mind, a standard library was introduced, which contained ready to use tools for performing various tasks.
These features make Python the most preferred choice for software development, and mostly so for Artificial intelligence and Machine Learning.
To put it shortly you need to learn Python because……
- It offers a speedy learning curve and reduced development time, the syntax in Python is much cleaner and neater in comparison to other languages. It is easy to debug due to shorter codes. The modular architecture makes it easy to merely import and use a module rather than writing a large block of code. Great choice for beginners. Shorter and quicker codes reduce the development time drastically.
- You can automate the repetitive tasks, for lesser cognitively demanding tasks, tasks that need little decision making can be automatically programmed by writing a script in Python.
- It is the most common choice for data scientist and analytics because of the convenience of feature-rich modules in Python which makes it easy to conduct data analytics in an efficient manner.
- Python is an object-oriented language, so if you learn Python it will make it easy for you to switch to any other object-oriented language. You will only need to learn the syntax of the other language.
- It is the future for Artificial Intelligence and Machine Learning, which will be integrated in most functions in the very near future. Python becomes the premium choice for Machine learning algorithms mainly because of the portable extendable and scalable features of the language.
The field of data science and analytics, more specifically artificial intelligence and machine learning will only continue to flourish in the coming years. If you are looking to take a plunge in this field, then fluency in Python can be considered a prerequisite. Learning Python has minimal investment and maximum benefits, it then surely becomes an advantage to learn.
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.
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 organisation 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 skills 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 recruitments approach, Chief Data Officer will be a position that will open up in most organisations.
Whatever the debate on the future application of data analytics might be, one thing is clear, analytics has the capability of impacting profitability and productivity of a business colossally. Hence, there is no doubt in stating that the ‘Future is in Analytics’.
Data Analytics, Big Data, Data Scientist, these are no longer big terms from a far away profession, these words or rather roles are becoming catalysts, impacting the growth of our businesses and enhancing the overall experience we get in doing our daily tasks.
Our online presence is not a matter of choice anymore; we often find ourselves using online portals to shop, connect with a doctor, research, basically from going on a vacation to preparing for motherhood, marriages, and dating, to banking, and even school and college admissions, all of these are done online, we even use social networking to express ourselves, through tweets, posts etc…,
Excessive usage of the internet creates online activity logs that contain humongous amounts of data.
Now imagine the camera’s mounted almost on every corner of the street and satellite based observations like the google map and google earth, they also collect data in large numbers on how people conduct themselves.
This data that is generated is being collected in large numbers around the clock, in real time and historic, this data further needs to be extracted, however, it is easier said than done, data is huge and extraction and explanation of the same cannot be done effortlessly. Most of the data collected is unstructured and not authentic, so you need to be wise to catch the correct characteristics at the right time.
People who can perform this extraction in a functional manner and make sense of it are called, Data Scientist or Data Analysts. The competencies that help them in this task are, sound knowledge of Mathematics, Computer Science, and Statistics.
The job of a data scientist is not only extracting data and analysing it, but to clean the data in such a manner that they can also predict and forecast trends for an assigned business, based on certain hypothesis or conditions. And that is the uniqueness they get to their job, the ability to accurately pre-process data and predict and forecast, sets one data analyst apart from the other.
A career in big data has become a dream choice for most job seekers these days, there is a lot that an organisation can achieve with the right application of data science. Some companies have identified this, and are either training their internal staff on the skills required to perform the job, while others are not yet too open to hire a full time resource. Although that day is not too far when the position of a data analyst will become imperative in every organisation.
If you are planning to enter the data science industry to make a great career in big data, then you need to adapt and acquire certain competencies and expertise in data analytics related tools, in addition to the above mentioned prerequisites. For example, programming languages, like R, and Python, SAS, a working knowledge of Machine Learning, and Predictive analysis. Also a sound knowledge in the industry you plan to work for, e.g., healthcare, or IT, Education etc.., will be an added advantage.
There is a huge gap between the demand and available resources in the field of data science, hence making a career shift in this direction would be wise and also lucrative, recent researchers have suggested that a data scientist earns more than experienced engineers. Clearly, this is a field with huge potential.
Do take up certifications, that will further assist you to springboard yourself in the field of data science.