Contact Us
×

Tag: Data Science Prodegree

5 Ways Data Science Can Help You Work Smarter, Not Harder!

Posted on by Imarticus Learning

5 Ways Data Science Can Help You Work Smarter, Not Harder!

The world of decisions today runs on data. From every time we do a Google search, or use our smartphones to each of our everyday activities, we leave a trail of data on our choices, lifestyles, and habits.  The internet and total volumes of our data are being efficiently managed by the ever-adaptive data science training applications of AI, ML and Deep Learning. Data is the basis of enhancing our lifestyles and entertainment, enabling our banking and communications and empowering our financial productivity and economic growth.

What is data science?

Data science uses the large volumes of data we produce to make logical conclusions, develop models and generate forecasts and predictions through an intricate process of cleaning raw data, parsing and processing it to finally using algorithms to resolve issues and problems. Businesses thrive by using these in-depth insights from Data science training to make decisions related to their productivity, efficiency, growth, and management. It is no wonder then that many of them are heavily invested in the benefits of data science.

The five-pronged strategies for businesses:

Here are five ways in which data sciences make your operations smarter, less expensive and more efficient.

Sentiment analysis:

Sentiment analysis is fast becoming essential before taking decisions on branding, product launches, marketing areas, and even posting information on social media like Facebook, Instagram, and Twitter. Social perception analysis is easily achieved by data science that wades through very huge volumes of relevant data to provide you specific sentiment analysis to base your decisions upon. Advanced techniques and tools like RapidMiner can help you not have to rely on gut-feeling instead. With effective sentiment analysis, one can correct their test market efforts without it being an expensive waste of resources, time and efforts.

Relationship value attribution:

ROI is directly related to customer satisfaction. However, all customers, clients, products, and partners are not of equal value. ROI is determined by the resources spent and time and effort spent in acquiring the business.  Hence relationship value attribution becomes crucial in determining the allocations and budgets spent. Using logic and weights data sciences makes a distributional array of your calendar of events in professional relationships, which helps target the right customer at the right time, improve your productivity and the effectiveness of your UX experiences.

Future demand forecasts:

Demand and supply gauging is the crux of business decisions. The entire process of planning, sourcing, resource allocation and budgeting is dependent on these choices. It is improbable that you will treat such an important decision lightly. Data analysis and data science training when done right and on sufficient relevant data can be very accurate in predicting demands, making forecasts, improving your stock and inventory, tweaking the logistics, providing the metrics for efficient performances and enabling all decisions that lie in between. Of particular use in e-commerce platforms and stock market-based products stocking, the price differences and rates are constantly changing and too little or too much can have a tail-spinning effect.

Fault finding analysis:

No organization is perfect and has tremendous scope to discover ways to encash its strengths and counter its weaknesses. The larger the growth of an enterprise the more difficult it is to spot weaknesses much less rectify them. Data analysis can fill this gap and provide a complete weakness analysis reports to help with rectifying the fault-finding analysis insights. It provides you with the overall view and how each of the departments dovetail together to spot the weaknesses early on.

There have been many instances of these inter-relationships not being corrected in time resulting in over-production, product starved markets, errors in logistics leading to rejections and losses and so on. Underperformance is quickly spotted by data science techniques and applications.

Gauging trends:

Data science can monitor large volumes of data effectively to spot even distant emerging trends.  Since the process goes on continuously and behind the scenes due to automation and AI the algorithms can find and highlight them with little or no manual investigation. Trend analysis is one of the biggest benefits that can help you revise business strategy and models while staying ahead of the curve of competitors.

Conclusions:

All businesses can benefit from data science used effectively. It is the platform on which you can base your new products, build brands, strengthen the lacunae, and make effective allocations of finance and resources. The timely decision of data science training is enabled by putting forecasts and predictions which are data-based in the hands of decision-makers. If you are interested in learning more about data science do a training course at Imarticus Learning the pioneers in data science education. Why wait?

 

How do data scientists publish their work?

Posted on by Imarticus Learning

A data scientist is someone who has a skill set and qualification in interpreting and analyzing complex data that a company deals with. This interpretation and analysis make it easier for the general public to understand it. In a company, the primary function of a data scientist is to interpret data and help the company make its decision in accordance with that data.
Data scientists are intelligent individuals who pick up a huge mass of complex or messy data and apply their mathematical, statistical and programming skills to organize, interpret and analyze it to make it understandable for the general people.
A data scientist is a profession which is needed a lot by companies. In this era, where technology is everything, companies need the data to be simplified to understand it and make crucial decisions accordingly.
In this article, we will talk about how data scientists tend to publish their work. Keep reading and find out!
Share through a beautifully written blog!
Data scientists can be working in an academic field or a company. Inside an industry, the data scientist shares his work through an internal network limited to the employees of the company or the ones to which it the data concerns. In a company, where the main purpose of a data scientist is to recommend a change in decision making in accordance with the data, the task of a data scientist working in an academic field is entirely opposite.
The research and research paper does not stay limited to a section of people, but it is available to the general public. Data scientists usually present their work through blogs. Data is something which might not be interesting to all; hence, they try to make the blog as exciting as it can be for the general people to understand.
Social media is a platform for almost everything
The data might be shared through public repositories and other social platforms such as Google, Facebook, Twitter etc. The research of learned and experienced data scientist can be looked upon by the people who aim at getting the data science training.
A research paper shared by a data scientist consists of a lot of complex data converted into organized beauty! New scientists can get an idea by these research papers from experienced data scientist about how the data needs to be sorted.
Emails have a lot of conveniences!
Last but not least, data scientists also tend to share their work and research papers through emails. If a company does not have an internal network, emails serve the need. If a data scientist is working as a freelancer for a company or individual after completing Genpact data science courses in India, he will have to use the email services to share the work with concerned people.
Working as a freelancer is something which a data scientist can easily do. They generally prefer working from home because most of the work they do is typically done on computers. Big companies who employ data scientist permit the convenience of working from home to the data scientists.
Takeaway:
The sharing of the research data or research papers depends wholly on the data scientist. If working for a company, the company may not allow the scientist to share the data publicly but internally. If you are thinking to make this well-paid post as your profession, there are a number of Genpact data science courses in India after taking which, you can get the data science pro degree which makes you eligible to be hired by a company dealing with complex data.

How Does Facebook Identify Where You Are From Your Profile Photo?

Posted on by Imarticus Learning

We all know that Mark Zuckerberg of Facebook is strongly passionate about Machine Learning and Artificial Intelligence, but how has that impacted our everyday online social life?
You may think you’re just uploading a photo, but facebook knows how many people are there, whether you’re outside or inside, and if you’re smiling.
The technology that Facebook uses, Artificial Intelligence, is a rigorous science that focuses on designing systems that make use of algorithms that are much similar to that of our human brain. AI learns to recognize patterns from large amounts of data and come up with a comprehensive conclusion.

What does that have to do with how Facebook knows if I’m smiling or not?

Facebook is constantly teaching their machines to work better. By using deep learning, they train AI to structure through various processing layers and understanding an abstract representation of what the data could be. By using their system called “convolutional neural network”, the computer is able to go through layers of units and understand whether there is a dog in a photo.

Follow Us on Facebook

Facebook works through layers. In the first layer, it is able to identify the edges of objects. In the second layer, it is able to detect combinations and identify it to be an eye in a face or a window in a plane. The next layer combines these further and identifies them to be either an entire face or a wing on a plane. The final layer is able to further detect these combinations and identifies if it is a person or a plane.
The network needs to be able to read the labels on the database and identify which of these are labeled as humans or plants. The system learns to associate the input with the label. The way facebook works is that it is able to now identify not only that there are humans in a photo, but how many humans, whether they are indoors or outdoors, and their actions, i.e. if they are sitting or standing.
However, a photograph that has been uploaded may need to be completely zoomed in for Facebook’s AI to understand intricacies if a person is smiling or not.

It may not always be perfect in its recognition, but it’s getting there.
A lot of information can be extracted from a photograph. Facebook is only going to get better with its AI and making use of big data.

Artificial Intelligence and Machine Learning is a concept that will be looked at in Imarticus’s Data Science Prodegree. This course is a cutting-edge program designed and delivered in collaboration with Genpact, a leader in Analytics solutions. Students get their hands-on learning with 6 industry projects and work with industry mentors.

Written by Tenaz Shanice Cardoz, Marketing & Communications.

Loved this blog? You might like these as well-
Machine Learning Demystified
Importance of R Programming As a Tool in the Machine Learning Spaces
Best Books To Read In Data Science And Machine Learning

Advantages Of R Programming Language

Posted on by Imarticus Learning

R is a programming language, mainly dealing with the statistical computation of data and graphical representations. Many data science experts claim that R can be considered as a very different application, of its licensed contemporary tool, SAS. This data analytics tool was developed at Bell Laboratories, by John Chambers and his colleagues.
The various offerings of this tool include linear and non-linear modeling, classical statistical tests, time-series analysis, clustering, and graphical representation. It can be referred to as a more integrated suite of software facilities, for the purpose of data manipulation, calculation and data visualization. The R environment is more of a well-developed space for an R programming language, inclusive of user-defined recursive functions as well as input and output facilities. Since it is a relatively new data analytics tool in the IT-sphere, it is still considered to be very popular amongst a lot of data enthusiasts.

There are a number of advantages of this data analytics tool, which make it so very popular amongst Data Scientists. Firstly, the fact that it is by far the most comprehensive statistical analysis package available totally works in its favor. This tool strives to incorporate all of the standard statistical tests, models, and analyses as well as provides for an effective language so as to manage and manipulate data.
One of the biggest advantages of this tool is the fact that it is entirely open sourced. This means that it can be downloaded very easily and is free of cost. This is mainly the reason why there are also communities, which strive to develop the various aspects of this tool. Currently, there are about some 19 developers, including practicing professionals from the IT industry, who help in tweaking out this software. This is also the reason why most of the latest technological developments, are first to arrive on this software before they are seen anywhere else.

Why Learn R Programming

When it comes to a graphical representation, the related attributed to R are extremely exemplary. This is the reason why it is able to surpass most of the other statistical and graphical packages with great ease. The fact that it has no license restrictions, makes it literally the go-to software, for all of those who want to practice this in the earlier stages. It has over 4800 packages available, in its environment which belong to various repositories with specialization in various topics like econometrics, data mining, spatial analysis, and bioinformatics.
The best part about R programming is that it is more of a user-run software, which means that anyone is allowed to provide code enhancements and new packages. The quality of great packages on the R community environment is a testament to this very approach to developing certain software by sharing and encouraging inputs. This tool is also compatible across platforms and thereby it runs on many operating systems as well as hardware.
It can function with similar clarity for both the Linux as well as Microsoft Windows Operating Systems. In addition to this, the fact that R can also work well with other data analytics tools like SAS, SPSS and MySQL, have resulted in a number of takers for this data analytics tool. Imarticus Learning The Data Science Prodegree powered by KPMG is one such course which offers both SAS and R along with the opportunity to be a Data Scientist at KPMG.