Analytics Society of India
In 4 Analytical Tools
Analytics Society of India
In 4 Analytical Tools
The PGP in Business Analytics and Data Science is a highly crafted course that promotes critical & analytical understanding of Big Data to introduce data-fluidity into daily business decisions. It is a highly industry responsive program with a balanced curriculum that weaves together theoretical classroom training with practical project based learning for mastering the skills.
The curriculum is designed in a well-thoughtout manner, making it acceptable to students coming from non-technical backgrounds as well, offers deep understanding of analytics along with certifications in Python, SAS , Tableau , & Power BI.
15 years of education with 2 years of work experience.
The global business analytics industry is expecting to grow at 30.8% CAGR
The total projected revenue from business analytics industry is pegged at $224.6 million in 2020
The global expected shortfall of qualified business analytics professionals in 2020 is projected at 700,000
In India, there will be additional 200,000 job vacancies for business analytics professionals in 2020
Both belong to the Analytics world. The only difference is that Data Science uses some complex models and tools to function, while the focus of the Business Analytics is to make data driven decisions by converting the data into values.
R is a programming language which has lot of inbuilt statistical packages. One can learn R in order to work on large datasets. But if you want to develop models, then you should also learn ML.
One can build career in profiles like Data Scientist, Business Analyst, Data Engineer, BI developer, Product Manager, ML Engineer, etc.
Analytics in Supply chain can increase accuracy in Planning, predicting future demands, eliminating and mitigating the risk from the current process by making patterns from the data.
You can use multiple tools like Excel, Tableau, Power Bi to visualize the datasets.
Hadoop comes into the picture when you are dealing with Big Data. SAS comes into the picture when we want to read and access data through multiple applications and files.
Yes it is. Python is the leading programming language in the field of Data Science.
Every programming language has its own benefits and limits. One can learn both Python and R and use it as per case wise.
Business analytics is the process of collating, sorting, processing, and studying business data, and using statistical models and iterative methodologies to transform data into business insights. A Business Analyst is the person who bridges the gap between the Business and the Technology. Business Analyst is responsible for understanding customer requirements and their business and accordingly offer the services.
Yes they are eligible for this kind of course.
Coding background is not a pre-requisite, but if you know coding it will be an added advantage. One can get into Data Science if the person is from non-coding background also.
No, it's not limited to one field. These days Data Science is being applied and practiced in multiple sectors including IT, Banking, Finance , E-Commerce, Healthcare etc.
Python can be used as Data Analysis tools in CA industries where the libraries like Numpy and Pandas would be very useful .
Healthcare is the industry which is making the most from Data Analytics. It analyses patient details like past medical history in details through models and recommends the treatment. This has helped doctors to understand internal diseases more easily.
It depends on the interest an individual has in any of the domains. It can be banking, finance, technology etc.
Yes. One can do predictive analysis and market analysis to see if their product is fit for the market & work around other aspects.
Yes it’s possible. Analysis does not mean that you must do coding. It simply means you must analyse the data and it totally depends on which tool you use it to analyse.
-Data Science is the extraction, manipulation, visualization of data in order to understand the data and drive meaningful patterns from it in order to make better decision with the help of statistics, machine learning and programming languages like Python. -AI is a collection of mathematical algorithms that make computers understand relationships between different types and pieces of data such that this knowledge of connections could be utilized to come to conclusions or make decisions that could be accurate to a very high degree.
Yes, there are BI (Business Intelligence) tools and are used extensively for visualization and understanding graphs and charts.
One can begin to start with a problem statement he/she wants to solve. The best way is to pick a problem statement and understand the dataset of it and then follow the steps to create meaningful pattern from it and use some tools like Tableau, excel to visualize and make decisions from it.
Six Sigma is a process which is performed by the organization in order to eliminate waste, i.e. unwanted steps in their process and increase productivity of the organization. It is used immensely in the manufacturing industries with contribution to some other sectors also.Data Science is the extraction, manipulation, visualization of data in order to understand the data and derive meaningful patterns from it in order to make better decision with the help of statistics, machine learning and programming languages like Python.No. In Six Sigma you mostly work on Excel and SPSS. Data Science requires some advanced tools. It also depends on your dataset which you want to analyze.
Yes, LinkedIn does use Data Science It uses various machine learning, recommended Engine and Deep Learning models to provide with the insights and information for an individual.
In the current situation organizations collect the data from their customers and then process it to understand the customer requirements in detail and launch the product as per the customer need with focus on cost reduction.
Tableau and Power BI will help you to analyze the data through visualization in the form of Graphs and Charts which would be effective in decision making. It has the flexibility to work in combination with AI, but you must link your model.
Yes. One can get into Data Science from any domain.
Yes, it is progressing with multiple labs and test centers being opened to fight with Covid-19 across the world.
We must analyze the technical and fundamental parameters of stocks and analyze the Covid-19 data and derive the relations and patterns between them.
Analytics helps to understand the data with respect to risk, deals, M& A, IPO and also gives business insights in order to make effective decisions.
Data Scientist rely on blockchain to track and authenticate data at every data point. Blockchain’s immense security is the driver for the DS to use this mechanism. This is mostly used in Financial Industries.
There are multiple variables one can use to predict the financial up and downs. Lot of Micro and Macro economic variables will also be a part of this analysis.
One must extract the data clearly and understand it correctly. If the information is incomplete or incorrect, then it will lead to wrong decision making.
It depends on which problem statement are you working. But yes if you have domain expertise for that particular domain then you always has the upper hand.
There is no rule for it. These skills get developed and matured only by practicing and solving more problems.
Digital Marketing has taken Data Analytics to a next level which involves YouTube Analytics, Google Analytics, Mobile Analytics, etc.
Yes. Deep Learning is required to create facial recognition model.
Sports Industry is coming with different ways to present Scorecard, Probability of a team winning, creation of a bat which gives the details of how the batsman plays a shot, etc.
One should look at the curriculum first and the interest of the participant and most importantly the zeal to learn.
Yes it can be . There’s lot of Covid-19 authentic information present, one can use it to understand and predict.
You must take the Covid-19 data and the employment data and then perform a comparative and impact analysis to understand the cause and effect of the same.
Data Science is used extensively for Election Exit polls. Lots of concepts of statistics like Clustering and Sampling comes into this picture while predicting the winner.
Yes we can.
Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle-of-the-road decisions.
You can choose the verticals such as Risk Management, Research in Investment Banks.
This course will help you gain knowledge of data in all aspects right from analyzing the data to decision making and how you can also pull business insights from it.