DEV Community: Tejaswiniteju

DATA SCIENCE COURSE: THE NEED OF THE HOUR

Tejaswiniteju — Tue, 07 Apr 2020 09:01:25 +0000

In rural areas, people have a Samiti and ahead of the village probably known as the sarpanch, to take care of the daily system and regulations which ensures proper running of the village. The scenario of the corporate world is no different. As companies recognized the need for physical hand, to ensure the smooth sailing of an organization, entered the role of data scientists.
In this fast-paced world, it is important to keep track of how the system is to be changed according to needs and requirements. The data science course is an epitome of such a necessary change. It is the future of the business world.

OUR FUTURE-DATA SCIENCE
Being an important part of the business world, no wonder data science has a lot of scopes. But that is only possible if you have the right type of skills and capacity. Making a good career in data science is no piece of cake. Only the talented ones or ones actually interested in playing such a role and working enough to hone their skills can make a successful career in this field.
the course
Data science course prepares not only your analytical mind, but algorithms and communications form a major part of it too. It makes you skilled enough to handle and tackle the business world. The course also focuses on statistics. Emotional intelligence, mathematical mind, and good communication skills make up an ideal data scientist. Preparing you to step into the corporate world, it is the perfect course for today’s youth.
categories
Data science has four major parts and each category requires its own set of skills and requirements.
Data analyst
Machine learning engineer
Data engineer
Data scientist
data analyst
This job requires one to put their data, analyzing skills to use and bring out the best solution. One has to put their best foot forward in finding out ways to tackle a problem and become a necessity to the company. The skills required include mathematics and knowledge of the business world. The more the experience, the better the data analyst expert. Basic knowledge about SQL etc. is also a requirement.
data engineer
A data engineer is the one who has gained expertise in the arena of programming. From C, C++ to Python he needs to have knowledge about everything. His field of expertise is coding. Coding helps data science get ahead with the branch of artificial intelligence.
machine-learning engineer
A machine learning engineer understands the machine learning algorithm and works to improve task efficiency by working on algorithms. He also needs to have knowledge of mathematics and statistics.
data scientist
A data scientist is more or less a combination of all the above categories and is a top-notch job in the field of data science. A data scientist has to be well equipped with coding, data analysis skills, algorithms and communication skills. He has to convince the organization that his way of solving the problem is best.
the need of the hour
Alongside managers and engineers, data science has become a necessity for any organization. A successful organization has a force of data scientists working their mind and soul into it.
It is one of the most ideal courses the nation can take and it is a set of skills one can acquire by their interest and enthusiasm towards their job.
Some of India’s top companies recruiting data science students include HDFC, TCS and many more. Being in the era of competition, it is important for students as well to keep them updated with such unique and useful courses.

Students who pursued B. Tech, BCA or similar courses can apply for a data science course. The course is also available in a few top IIMs and IITs. You can get data science certification from 360DigiTMG to become an expert.

Click here to know more about data science course
Click here to know more about data analytics course

Address: 360DigiTMG - Data Science, IR 4.0, AI, Machine Learning Training in Malaysia
Level 16, 1 Sentral,, Jalan Stesen Sentral 5,, KL Sentral,KL Sentral50470 Kuala Lumpur, Malaysia
phone no: 011-3799 1378

Youtube: https://www.youtube.com/watch?v=UC1gHqm7WYc

CHALLENGES FACED IN THE DATA SCIENCE IN TODAY’S MODERN ERA

Tejaswiniteju — Mon, 30 Mar 2020 03:57:22 +0000

A recent survey of 16,000 data enthusiasts, concluded that the most occurring and frequent challenges faced in the data science are nothing else than the corrupt data (approx 36%), unavailability of talent in masters in data science (about 30%) and no management support (27%) at all. To study this issue, I have preferred data from Kaggle collected during August 2017.

BARRIER AND HINDRANCES CREATED AT WORK
It has been asked in the survey by the respondents that what are the major problems faced by you all in the past years. Results appear and the most important 10 challenges were as follows:
Corrupt Data (36%)
Unavailability of talent in data science (30%)
Politics in the companies (27%)
Unclear questions (22%)
Can’t access data (22%)
Privacy problems (18%)
Inexperienced experts in the domain (16%)
Small organization and unable to afford a data science team (14%)
Explanation of data science in front of others (14%)
Results were not used by during the decision making process (13%)
After going through the results, it has been concluded that on an average a data scientist is facing three (median) barriers in previous years. The challenges faced varied on the basis of the job profile a person is having in the field of data science. Those who are programmer reported one and those who are data scientist reported 4 platforms.
PLATFORMS GROUPED TOGETHER BY PCA
A survey conducted in which the Principal Component Analysis has been calculated for 20 challenges in order to group the considered challenges.
The top 5 components are as follows:
RESULTS NOT UTILISED IN THE DECISION PROCESS:
These types of challenges come under the politics which occurs in a company, unavailability of the management support and inability to utilize the findings earned during the decision making.
DATA PRIVACY AND UNAVAILABILITY:
These types of challenges dealt with the data only that how much clean data is available, what are the privacy aspects of the data and the availability of the data.
UNAVAILABILITY OF FUNDS:
Challenge under the lack of funds relates to the organization that how much the organization buy from the out sources, domain expertise of the organization, and the data science talent.
WRONG QUESTIONS ASKED:
Not having clearance in the question to be asked and to be answered in the data science field in order to go in a clear direction to the available data.
LIMITATIONS OF THE TOOLS USED:
These types of challenges include the tools which are used for the extraction process, deployment of the available models as well as for the scaling of the database to a higher level.

Data scientists are the bridge between the IT sector and the Management department. Hence, there is a need to manage these challenges for future development.

Resource Box:
If a person really wants to become a good Data Scientist then he/she needs to go through both the good as well as the bad aspects of the field and try to get rid of these challenges faced. data science course malaysia institute is one of the best institutes for the data science course. Hence, apply over it.

Click here to know more about data science course

Applications of Data Science in Real-time world

Tejaswiniteju — Thu, 19 Mar 2020 09:00:45 +0000

No matter what the stream/industry is, data is very important. Data collected from a particular industry can be used for the analysis of patterns, trends, etc. This aspect of data leads to innumerable applications. This, in turn, has led to the development of data science as an important job avenue. Not only are the jobs created by data science considered to be lucrative, but data science also paves a way to probe the innards of modern technology. The jobs related to the field of data science are a direct manifestation of its real-time applications. Due to the continuous development of computing technology, the field of data science has gotten even more interesting. This means being a data scientist is not only rewarding in terms of financial aspects but also in the aspect of job satisfaction. Some of the real time applications/possible avenues of data science are:

Intelligent internet searches:
This is a relatively well-known application of data science. Over the years, like many other internet technologies, the simple internet search has seen monumental advancements. The people behind major search engines like Google, Yahoo, Bing, etc. have altered their search algorithms from time to time to make searches more user-friendly. Initial searches were cumbersome. The user has to be certain of every keyword entered because a single error in a keyword would pop up a wrong search. The data collected by search engines and other online companies have been used in the determination of the nature of the searches and supplying the user with intelligent recommendations. This is the reason why almost all the related options related to a particular topic appear before typing the entire search string. Moreover, data science has also enabled using data from a particular location to determine the searches trending in a particular area and recommend it to users.
Image recognition:
Image recognition has been around for quite a long time. However, the full potential of image recognition and advanced image recognition technology has been used by various social media platforms. Facebook is a good example of a social media site employing facial recognition. For uploading a photo on Facebook with friends, Facebook immediately suggests the user to tag his/her friends from the photo. This is made possible only by means of cutting edge image processing and recognition technology. Not satisfied? Facebook is currently working on applying the same technology to videos too. Once fully developed, the technology can detect the faces of the people known to the user from uploaded videos. Another example is the image search option provided by Google and certain online shopping platforms. This technology enables the user to search for a particular product by means of its image rather than the actual product name. Pretty cool, right?

Advertising:
Modern advertising is a great application of data science and assorted data analytics. Of late, advertisements have become target-specific. Advertisements are shown to the user based on past interests or on the search of related items. This has enabled many companies to prosper by making people want to buy products by means of user-intensive advertising. This is made possible from the application of advanced data science. Data science techniques are used to collect information from various users over varying periods of time. This data that is obtained is used for analysis further on. Based on the analyses, user-intensive recommendations are forwarded to the user. This phenomenon is known as target advertising. This is prevalent nowadays. Many online sellers and online shopping platforms have used target advertising to rake in a large number of customers. This has led to the meteoric rise of online businesses.

Online recommendations:
This digital age has seen widespread changes in even the smallest units of the internet. In order to meet the needs of the present and the future, almost everything has changed. This includes a change in our browsing patterns too. Everything on the web nowadays is recommended to the user based on his/her interest. This is again a direct result of advanced data science and analytics. Basically, recommendations are forwarded to a particular user based on the data collected regarding their usage of various online services. The times and patterns of the usage data are studied and recommendations are made. The YouTube algorithm is a great example for a recommendation making algorithm. It tries to make intelligent recommendations and, in its course, has become a host for various jokes relating to its complexity and randomness. Many websites are also recommended to users based on such data.

Gaming:
Gaming is one of the fields in which data science and other advanced technologies were used. Now the gaming industry has gained notoriety for being the house for cutting-edge science and data technologies. Games are only fun if they are made more competitive and more interesting than usual. This can be done using data science. Many new generation games use data collected from the user in a particular level to understand the user's level of playing to plan the progressive levels. This smarter system helps in making the game competitive without making it unplayable. Motion gaming is also another real time gaming application. In motion gaming, the computer attempts to learn the player's movements using a series of motion detection sensors. Using the player’s movements, the computer manages to up the ante and provide a stiffer competition to the gamer. Many prominent gaming companies are using these technologies for providing a better experience to customers.

Speech recognition and synthesis:
Speech synthesis and recognition is something that has amazed people over the years. Many forms of these technologies have existed over time. The past few years have seen a rise in the number of speech recognition software used like Google Voice, Microsoft Cortana and Apple Siri have gained prominence. These types of software generally can recognize a particular user's command and perform functions if connected to the internet. They are compatible with various mobile devices and affect people's daily lives. Another speech technology is speech synthesis. Text-to-speech synthesis is something that has never taken off. The technology involved in transforming written text into speech is very complicated. Until now, the proper transformation of text into speech without errors has not been made possible. A voice synthesizer will surely make a great gadget, directly from James Bond 007.
Risk detection:
Risk detection is a major field of interest for many management firms like banks and insurance companies. One of the earliest places where data science found this application was finance. Many finance companies and banks were tired of sanctioning unpaid loans and other frauds which caused them to lose money. So to cut costs, these companies decided to make dire decisions. As a result, they decided to employ data scientists to help them make even better decisions. This was due to the fact that banks had all sorts of data regarding customers, but were unable to contain frauds. Along with the help of the data scientists, banks were able to analyze data patterns and come up with better decisions. Needless to say, this scheme worked and banks were significantly able to reduce loss of money. As a result of this success, many companies began to use data science for better results.

Augmented reality:
One of the latest applications of data science is augmented reality. Many augmented reality games and apps have become the norm today. Such augmented reality apps and technologies collect real time data and are able to incorporate imagination with reality. Games like Pokémon Go, which was trending some time ago, is an excellent example of augmented reality games. The app employs the camera of the user's mobile device to capture real time data regarding the surroundings. Then the app projects the various game members, i.e. Pokémon at various locations for the user to literally 'catch' it. Many similar games and apps have also been developed. Augmented reality is surely the technology of the future. The ramifications of augmented reality being extended into various fields can lead to interesting consequences. From surgeries to construction, many technology firms have started employing augmented reality to meet with rapid prototyping demands.
Healthcare:
Data collected over time has been efficiently utilized by various hospitals. Previously collected data can be used for rapid diagnosis and providing immediate remedies to patients. Medical imaging recognition software is used to identify various diseases from imaging techniques like X-rays and MRI’s (Medical Resonance Imaging). This can help doctors in making better decisions and accelerate healing processes. Another use of data science is the development of various drugs and medicines. For the development of new compounds and medicines, thorough knowledge of existing chemicals is really important. This part of the job is undertaken by data scientists instead of doctors. This reduces the workload on doctors considerably.

Future:
The future does present a lot of opportunities for data science and data scientists. Many high-end government and private scientists are tirelessly working on high- impact data science projects. The future of data science is very promising. As quoted by an eminent data scientist, “This is a great time to be a data scientist.”

Click here to know more about data science course
Click here to know more about data analytics course

Youtube : https://www.youtube.com/watch?v=QOrtxgzY28A

WHO IS USING AWS?

Tejaswiniteju — Wed, 18 Dec 2019 06:37:41 +0000

Here are the names of some big organizations which are currently working with Amazon Web Services.
Playfish
Instagram
Etsy
SmugMug
Zoopla
Ftopia
Pinterest
Dropbox
Talkbox
Netflix

ADVANTAGES OF AWS
Here are some advantages of Amazon Web Services listed below:
The AWS provides a friendly environment to its customers. The AWS does so by providing those tools to its customers to which they are already familiar, for example, models, databases, operating systems, architectures, etc.
The services offered by the AWS are very cost-effective. Here, the user has to pay only for those services which they are using. The AWS won't force you to make commitments for the long-term.
If the user is using Amazon Web Services, then he/she doesn't have to spend their money on various data centers.
The services of AWS provide faster deployment of the products.

With the AWS, the user can easily remove or add the storage capacity according to his/her requirements.
more info: https://360digitmg.com/course/amazon-web-services/