Data Science Training in Pune
DATA SCIENCE COURSE HELPS YOU FIND OUT DATA SCIENCE ORGANIZATIONS FROM AROUND THE WORLD.
Introduction to Data Science
Data Science Course in Pune is associate Degree knowledge domain field that uses scientific strategies, processes, algorithms and systems to extract information and insights from information in numerous forms, each structured and unstructured, just like data processing. It Employs techniques and theories drawn from several fields at intervals the context of arithmetic, statistics, informatics, and engineering.
Turing award winner Jim grey imaginary knowledge science as a “fourth paradigm” of science (empirical, theoretical, procedure and currently knowledge-driven) and declared that “everything regarding science is dynamic thanks to the impact of data technology” and therefore the data deluge.
Is Big Data Training same as Analytics Training?
Advance technologies in collection and storage of data contributed to colossal amounts of data. The data can be in various forms like text, images, numeric, audio, video, social media etc. This data can be unstructured, or semi structured. Also it can be static or moving with time. What is a challenging is finding ways to extract useful information, patterns and trends from the data. Traditional data analytics techniques are often not useful because of the massive size, dimensionality or heterogeneity and complexity of the data.
For example it is now common to have a dataset with hundreds of attributes. Traditional data analysis techniques may not work well with high dimensionality or for some algorithm the computational complexity increases with increased dimensionality.
In traditional data analysis we deal with structured data and can analyse it after it is collected and organized. Here we usually deal with samples and often we know what we are looking for in the data. Sometimes we deal with data warehouses and use data analytic tools for the analysis. A non-expert user performs basic data visualization and basic analytics via front end analytics tools.
While analysing the big data sometimes we know what we are looking for and sometimes we try to answer questions raised during the analysis of massive unstructured data. Furthermore, the data sets analysed include non-traditional types of data for example web pages containing text and hyperlinks, or DNA data with sequential and three dimensional data. Sometimes new data is added incrementally taking into account the results of the previous analysis.
This makes it more challenging but at the same time giving more insight into the data. It requires changing algorithms and technology, even for basic data analysis. So the big data analytics is not the same as traditional analytics. Instead of being limited to sampling large data sets, we can now use complete data for the analysis.
NoSQl, Hadoop, and MapReduce are the technologies associated with the big data analysis. Since the traditional data warehouse are not able to store and process the big data we need to use more advanced analytical techniques, namely big data.
Hadoop is synonymous with big Data and it can rapidly process and analyse huge amounts of unstructured and semi-structured data in a cost effective manner. This allows us to do analysis iteratively for refining and testing of all the relevant data. NoSQL is a database that can process large amount of multi structured data in near real time. In order to take full advantage of big data we can use SAS or R for advance analytic techniques and visualization.
Now that we have understood the difference between traditional data analytics and big data, we will be able to put into perspective just how different analytics training is, as compared to Big data training In Pune. The former requires learning to clean, sort, analyse and interpret structured data, while the latter requires one to learn to do the same for much larger databases, that could include unstructured and non traditional data.
What is Data Science or Big Data Analytics?
The amount and variety of data and the speed at which we are generating the data is creating colossal amount of data. It is being generated from various sources like finance and marketing, health care, mobile call detail records, sensors, social media etc. The data comes in various formats like text, images, numeric, audio, video, time series, social media, streaming data etc. It can be structured or unstructured. The data is similar to small data but gigantic in size. So to find the hidden patterns, unknown relations and the useful information from the big data, new techniques, tools, and architecture is required. Big data analytics enables data scientists and other users to analyse huge volumes of data that cannot be analysed by traditional approaches.
The main goal of big data analytics is to help companies make better business decisions. The analysis can provide companies a competitive advantage over other organizations and result in business benefits, such as more effective marketing and increased revenue. Today many companies have realized the benefits of big data. Analysing data can help them to discover ways to improve customer interactions, add value and build relationships that last.
It also helps to reduce waste and fraud. Exploiting big data analytics also gives the opportunity to explore strategic options for business growth. Big data analytics help to manage and maximize operations, supply chains, and the use of assets. It also helps to reduce financial and operational risks by identifying and understanding the risk in advance risk. This helps build confidence in decision making.
Huge amount of loosely-structured data in different locations needs to be processed until the information is found. Traditional ware houses are not able to handle the processing of unstructured abstract data used for big data analysis so techniques used for advanced analytics like predictive analytics or data mining may not help to do the analysis.
Thus a new class of big data technology has emerged and is being used in many big data analytics. NoSQL databases, Hadoop and MapReduce etc. are the technologies associated with the big data analytics. The analytic models are larger and require very large amounts of memory to operate.
The tools are available in the market to analyse the data but those technologies on their own are not sufficient to handle the task. People with the talent and skills needed to leverage the technologies are essential to do effective analysis. Indeed the scope for data analysts is high today. Successful companies are aggressively hiring people with data analytics skills. However there is a big shortage of people with these skills.
For those with a mathematical bent of mind , business accumen and good communication skills now is the time to invest in some data analytics skills as well. A good career path awaits you.
2 Months | Total 72 hourse
The Data Science certificate validates a professional’s abilities in following skills:-
1.Variable declaration in R
2.Function declaration in R
3.Statistics in R
4.Machine learning in R
5.Difference in python2 and python3
6.Types of libraries in python
Who can do this course ?
- Data Analyst
- Database Administrators
- Linux administrators
This Course is Designed to Benefit the Following:
100% Job Assistance
We have dedicated a team for Job Placement that provides placement that has a provien track record to place students.
Our Mentors are more than 9-year Expertise Technology Geeks that are Highly Qualified for Delivering Training.
We provide lifetime support so that if a student get stuck in the further studies we will revise and help them on the subject.
Duration 2 months.
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