Is The Data Analytics Course Worth A Shot?

Ever since Data Science came into existence, the most common buzz around has remained if Data Science is worth an opportunity! Well, it would not be an exaggeration to say that the next decade is dedicated to Data Scientists. There are ample statistics that reveal the upcoming surge in data scientist demand throughout the worldwide industries.

So let’s take a quick look at what’s happening in the Data Science domain and how you could switch your career to data science smoothly.

What is Data Science?

So, what does Data Science actually mean? Data science is an intersection of mathematics, computer science, and statistics. It deals with huge amounts of data collection, manipulation, storage, and analysis for deriving meaningful results.

Every sector-healthcare, finance, banking, media, transportation, hospitality, retail, e-commerce, and similar industries are highly dependent on data science for boosting their sales and marketing.

Why is it so important? How data science resolves those problems which are escaped by general human intelligence? Well, it's rigorous training of business models using data. Let’s learn about how Data Science is transforming the industrial methodologies for success.

Why Data Science?

The reasons why data science is so much in the face of innovation and maturity is described below.

● The billion dollar answer on the question of the importance of data science is that it reduces the errors of uncertainty in an organisation.

●  Data mining enables the exploration of useful insights and building business strategies around it

●  It is an effective way to build friendly relations with customers by understanding their needs in depth

● From banking to transport and manufacturing, data science optimises business processes

Why Become a Data Scientist/ Data Analyst?

The five best reasons to become a data scientist by switching careers in data science are described below.

Great career transition

Data scientists play a key role in organisation and add lots of value to the net worth.

Great job roles

After acquiring data science skills, a candidate can choose to become a data engineer, a data science manager, ML engineer, an analyst, and so on.

Decision maker

This is a great opportunity where a data scientist has the core responsibility of taking well-informed business decisions.

Low competition

Since this job role demands highly analytical and skilled people, the competition to get hired in such challenging roles is always very low.

Highest salaries

A data analyst or scientist can expect a great salary package depending on their critical roles and responsibilities.

Importance of Data Science Certification

Now it’s time to focus on the options available to pursue a career in data science. First of all, a certified degree stating your qualification as a data scientist is a prerequisite. Getting a degree is simple but acquiring it from reliable and renowned resources is equally important. One of the most trusted names for Data Science Certification In Bangalore is Learnbay.

Learnbay is the ideal data science Institute for Data Science Certification In Bangalore professionals wishing to switch their career into data science and take up the job profiles such as data scientist, data analyst, data engineer, ML engineer, AI engineer, domain specialisation data scientist, and many more. They can learn AI, ML, and Data science from any corner of the world.

Learnbay provides a range of data science courses for any professional with a work experience of at least a year. If your past experience lies in business, travel, hospitality, healthcare, media, or any other similar industry, you can get a specialisation in that domain.

Here is a list of offerings by Learnbay:

●       Data Science & AI Certification | Domain Specialisation For Professionals

●       Advance AI & ML Certification | Become AI Expert In Product based MNCs

●       Data Science & AI Certification Program For Managers and Leaders

●       Job Guarantee or Money Back | Data Science & AI Certification Course

●       Data Science & Business Analytics Program | Fast Track Course

●       Industrial Training in Data Science and AI for Fresh Graduates | IBM Certified

With these courses, you can get certified as a data scientist. It consists of more than 200 hours of classroom sessions in collaboration with IBM. More than 15 real-time industry projects are available. The job assistance program helps in preparing you for industrial jobs. The courses are entirely case study driven with 24/7 technical support.

If you are a beginner with no idea about Data Science and still wanting to pursue it, take up the Industrial Training in Data Science and AI for Fresh Graduates | IBM Certified course.

Scope after Data Analytics Courses

It is really interesting to note that the job market for data scientists will increase to 11.5 million by 2026. The US bureau of labour statistics has clearly stated that data science is the domain of the decade.

The average salary of a Data Scientist in the USA was $94,280 in 2019. The best-paid 25 percent Data Scientist made $125,140 in 2019. The lowest-paid 25 percent made $70,090 in the same year. The upcoming years or next decade is expected to only see a growth in salary packages.

The Data analytics courses in Bangalore by Learbay Data Science Institute can meet all the requirements of a working professional wanting to pursue/switch careers in Data Science. You can join the data science certification courses of Learnbay. To enrol for the course, book a slot for telephonic counselling and profile review here. Avail the limited discount offers now!

Conclusion

Businesses are busy in ranking up their business goals. They are extremely invested in improving their data science workforce and come out with better planning. Every department of every business requires at least one efficient Data Scientist. Now you can imagine the workload on Data Scientist and their demand for job roles.

We hope this blog helps you in choosing the right data science course. This might have helped you in clearing the doubts and questions surrounding data science.

Learnbay offers all the basics to advance courses in Data Science to working and non-working professionals. The experts at Learnbay have made many successful careers in Data Science. The aspirants have got their degrees and are working at highly successful companies. Learn more about the professional guidance to the learners on our website.

For similar blogs and content, follow our website now. You can also find us on these social media platforms- Linkedin, Facebook, Instagram, Youtube,  Twitter, and Medium. Stay connected with us for all your data science related job queries and career counselling.

AI and Machine Learning Courses, Data Science Training in Bangalore

 

Artificial Intelligence :-

Today, AI is one of the most important new-age technologies, with applications in a wide range of sectors across the world, and the possibilities in AI are growing rapidly.

This is concerned with giving robots the capacity to think and operate in the same way as humans do. Artificial intelligence (AI) refers to computers that are capable of doing tasks that are typical of human intelligence. However, it encompasses skills such as preparation, language understanding, object perception and sound perception as well as listening and problem-solving abilities among other things.

Machine learning :-

Machine learning is an artificial intelligence subset that uses a computer algorithm to analyze data syntactically in order to identify or predict anything in a text.

 TYPES OF MACHINE LEARNING ALGORITHMS:

     ●    Supervised Machine Learning Algorithms

●    Unsupervised Machine Learning Algorithms

●    Semi-supervised Machine Learning Algorithms

●    Reinforcement Machine Learning Algorithms

 

 The best courses Artificial intelligence and Machine learning :-

 

●   Artificial Intelligence and Machine Learning course By Learnbay:-Machine Learning  courses by Learnbay  This is one of the best ways to do this career. IBM-certified  data science and ai course , Around a hundred students are put in exciting data-science positions companies. Through authentic real-time industry projects. Choosing Learnbay, you can accomplish the most ambitious current and potential data scientist jobs in india

 

○    Duration: 9 Months

○    Fees: Rs.75,000.

○    Mode of Learning: Online

 

●        Post Graduate Program In AI And Machine Learning by Simplilearn

This course was designed by Simplilearn in partnership with Prude University and collaboration with IBM to give comprehensive knowledge about artificial intelligence. 

●      Duration: 12 Months

●      Fees: Rs. 2,30,000

●      Mode of Learning: Online

●    Andrew Ng Courser Deep Learning Specialization:-

Andrew Ng's Courser Deep Learning specialty is a more advanced course series for individuals interested in learning about AI and deep learning, how to use it to solve issues, and how to start a career in AI.

●        Mode of Learning: Online

●        Duration : 4 months 

Data Science:- In recent years, there has been a significant rise in the need for data science graduates. Nowadays, companies and organizations are seeking experts that understand their requirements and can identify the best data-driven solution and then execute it. In recent years, both government and commercial companies and agencies have relied on big data analysis to better serve their users. 

Top Data science courses in Bangalore:- 

●    Data science and Artificial Intelligence course by Learnbay :-

Learnbay data science courses is a one-stop solution for all your data science and ai course-related queries, as we are specialized in data science training in bangalore and data science courses for managers and globally for the professionals who want to pursue their career in Data Science and AI. 

●        Course fees: - Rs. 59,000.

●        Mode of Learning: Online 

●    Post Graduate Program in Data Science By Simplilearn:-This postgraduate course in Data Science offered by Purdue University in collaboration with IBM. 

●      Duration: 12 Months

●      Fees: Rs. 2,15,000

●      Mode of Learning: Online 

●      PG Diploma in Data Science by Upgrade :- Upgrade IIITB data science courses are one of the best resources available today, upgrade Global university of information Technology Bangalore is a scientific and research university in Bangalore, founded on 15 September 1999 first as Indian Institute of Information Technology, with a campus in the International Tech Park. 

○    Mode of Training:- Online and Classroom Training.

○    Course Fees:- Rs.2,85,000

Conclusion:-

Several such institutions and universities are offering some of the best knowledge-based resources for improved technological development in this era of digital transformation, when every business needs highly skilled Data Scientist abilities. There are many possibilities to become a data scientist of this kind in the business, making it difficult to decide which path to follow. In this article, I've collected a list of some of the top courses that may help you become an expert in machine learning and data science .

Know-How to win over the 3 key provocations against securing Your First Data Science Job

Nowadays, doing a data science certification course is a very common practice. But does getting certified mean getting a job? In reality, this is not so easy. Aspirants are struggling to land their first data science job for a long time. Read this article to know why (3 key provocations)? And what are their prerequisite solutions?  

Provocation 1: Plenty of Interview calls but no offer letter

Almost all the Data Science Courses in Bangalore now advertise for a job assistance programme on their certification course. So getting an ample amount of interview calls is not a matter of surprise. But, cracking the interview depends on your efficiency.

There are mainly three inferences about why the recruiter turns down your application to the rejection list after the first round of interviews.

●     Lack of strength in your project portfolio

●     Lack of programming proficiency from the perspective of statistical concept application

●     Ineffective domain knowledge

To win over these challenges, you need to consider the following precautions. 

●     Ensure that you have done the creditable real-time industrial project and documented it properly on online platforms like Linkedin or GitHub. Also, make sure that your project benefits are in-lined with your domain expectations. For example, being from the pharmaceutical industry, doing a project on Uber analytics does not hold any significance during a job interview.

●     In the case of codding efficacy, your entry-level designation does not demand hard-core programming knowledge. Rather, what it demands is the creditable application of your basic programming knowledge on making effective ML models and algorithms. So practice more on this aspect.

●     Ensure that you apply to the right job position that equally gives credit to your newly earned data science knowledge and previously earned practical domain-based knowledge. 

Provocation 2: Getting offer letter but with degradation of designation/salary package

At this point, I am not talking about the fresh graduates. Rather my focus is on the aspirants seeking for career transition. Unfortunately, after completing your data science certification, companies offer your job but sometimes with lower designation and sometimes with a lower salary package. The key reasons for such ill-incidents may be as follows.

●     You are not targeting the right job role.

●     Your certification learning module was not effective according to your domain knowledge or working experience.

●      Your project is not all a creditable one.

To win over this challenge, the best option is

●     Do extensive research on your targeted job role and the skills you currently hold to ensure that you can meet recruiters' expectations.

●     Choosing a course randomly is the biggest mistake most of the aspirants make. Not all the courses are suitable for all. As domain knowledge holds a crucial position in the data science domain, choose such a course that makes you a competent data science candidate in your domain.

●     Ensure that your project is perfectly showcased so that the talent acquisition team gets convinced enough about your ability to help their company grow.  

Provocation3: Feeling low about job sustainability

Even after grabbing your first data science position, if you remain doubtful about your job sustainability, then it might be possible that

●     You have started finding a gap in your learning while doing practical things.

●     You lack soft skills like communication, critical thinking, and time management.

●     Either you are low in conceptual knowledge or ineffective in tool handling. 

To win over such situations, you need to take part in competitions like the Kaggle competition, do some soft skill up-gradation courses, and discuss with your organisational domain experts and seniors. Just don't give up without trying to evaluate the problem. 

The above solutions will help you fight back with your career transition challenges, but the best way is to take precautionary measures by choosing the right course at the initial stage. Then, you can join Learn bay Data science certification courses, where you will be trained against all the aspects associated with the above challenges. Finally, with a comprehensive data science course syllabus and expert job assistance, you will become job market competent in the real means.

What is data science?

 

Data science is a discipline that integrates subject understanding, computer skills, and math and statistics knowledge to derive useful information from data. Beginners and those considering self-learning will find it challenging to grasp these concepts, so I suggest consulting with experts. Learnbay (an IBM-certified programming course) offers a wide range of data science classes, from beginner to advance.

Machine learning algorithms are applied to numbers, text, images, video, audio, and other data to create artificial intelligence (AI) systems that can perform tasks that would otherwise require human intelligence. As a result, these programmes produce insights that analysts and market customers will use to create real business value.Importance of data science

Mostly data science is used to forecast the sale and the growth of any firm by using data science types those are:

●       Descriptive analysis (used to analyze the past data)

●       Predictive analysis (used to analyze the future by analyzing  past data)

●       Prescriptive analysis (used to analyze the current data which helps in decision making)

Data technology, AI, and machine learning are becoming increasingly important to businesses. Organizations who want to stay competitive in the era of big data, regardless of market or scale, must build and incorporate data science skills quickly or face being left behind.

Steps to analyze or process the data:

●       Identifying the problem

●       Collect the data needed to solve the problem

●       Analyze the data

●       Clean the data

●       Create a model

●       Evaluation

●       Deployment

●       Monitoring

Major subset of data science

AI – Artificial intelligence

Artificial Intelligence enable the machine to think and act without any human intervention.

AI are into 3 types

1. ANI (Artificial Narrow Intelligence) which is goal-oriented and programmed to perform a single task.

For example – automatic washing machine 

2. AGI (Artificial General Intelligence) which allows machines to learn, understand, and act in a way that the machine is programmed

For example – auto pilot car

3. ASI (Artificial Super Intelligence) is a hypothetical AI where machines are capable of exhibiting intelligence that surpasses brightest humans.

For example – automation robots

ML- Machine learning

Machine Learning (ML) is a subset of artificial intelligence (AI) that uses statistical learning algorithms to build smart machine or model. The (ML) Machine Learning systems can automatically learn and improve without explicit being programmed.

Machine Learning (ML) is commonly used along with artificial intelligence (AI) but it is a subset of artificial intelligence (AI). Machine Learning (ML) refers to an artificial intelligence (AI) system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is Machine Learning (ML). 

For example - suggestions in youtube videos

DL- Deep learning

Deep learning is also a subset of artificial intelligence. The deep learning is inspired by the way a human brain analyze the information. Deep Learning Courses help the AI model to analyze the input data to forecast or classify the information.

Deep learning analyze the data in multi-layer neural network

            ANN(artificial neural network) information input in the from of numbers

            CNN( convolution neural network) information input in the from of images

            RNN(recurrent neural network)information input in the from of time series data

There are few simple facts about the data science subsets of AI, ML, and DL. I recommend taking a Learnbay course to create a model using data or software like Python. Learnbay in Bangalore offers a wide range of data sciences, artificial intelligence, machine learning, python, R-programming, and Data Analytics Courses.

Power Of Automation

One of the very important application areas for automation technology is manufacturing, to several people automation means only the manufacturing automation. In reality there are different kinds of automation which are defined in this blog and the uses of automation is briefly discussed. Generally there are three types, which are automation in production (fixed automation), programmable automation and flexible automation.

Note: To study Machine Learning and Artificial Intelligence please checkout Learnbay, it provides one of the best Machine Learning courses in Bangalore and AI courses in Bangalore.

Fixed automation which is also referred to as “hard automation,” refers to an automatic production facility within which the sequence of processing operations is fixed by the equipment configuration. In effect, the programmed commands are contained within the machines within the kind of cams, gears, wiring, and other hardware that could not be easily changed over from one product style to another different style. This kind of automation is usually characterized by high initial investment and high production rates. It’s therefore suitable for products that are made in large volumes. Samples of the fixed automation includes machine transfer lines found within the automotive industry, automatic assembly machines and other certain chemical processes.

Programmable automation could be a style of automation for producing products in batches. The products are generally made in batch quantities starting from numerous dozen to several thousands of units at one time. For every new batch, the assembly equipment must be programmed again and altered over to accommodate the new product style. This reprogramming and changeover takes time to accomplish, and there’s a period of non-productivity followed by a production to endure each new batch. Usually, the production rates in programmable automation are generally less compared to fixed automation, because the equipment is meant to facilitate product changeover instead of product specialization. A number-control machine may be an exemplar for programmable automation. The program is generally coded in most of the computer hardware for every different product style, and therefore the machine is controlled by the system program. Industrial robots could be taken as an example here.

Learnbay is a training center which provides one of the best Machine Learning courses in Bangalore and AI courses in Bangalore.

Mathematics in Data Science

Even though mathematics is different from a field from Machine Learning, it still is greatly involved in the layers and inches of it. We know that logical reasoning is a skill that should be mandatorily possessed by engineers or programmers but factually, specific areas of mathematics are highly important in Machine Learning and those are highly necessary to be learnt by Machine Learning engineers. Read on to know what areas they are and why they are so important.

In a demographic of the mathematical knowledge you need to know to learn Machine Learning. 60% of Mathematics in ML is concerned with Probability, Statistics and Linear Algebra. Algorithms and Complexity is something that you would have learnt in your undergraduate (if you have a CS background) and Multivariate Calculus comes into picture when you deal with a lot of features and very huge data.

Start with Linear Algebra at the Kindergarten level and then focus on basic Probability which covers all Bayesian methods and then move on to Statistics. Once you cover the basics then comes the next biggest thing in ML. Applying the Basics!

All ML algorithms have libraries that make them run in seconds but you don’t know the underlying mathematics that’s happening in the algorithm. Try to implement basic mathematics by coding a ML algorithm from scratch. execute and see line by line to derive the basic mathematical process behind an algorithm. After doing this, you go for core Linear Algebra, Applied Probability(Descriptive and Predictive Modelling) and Applied Statistics.

Note: Start your journey in Data Science with Learnbay as it provides Big Data Analytics Training and Data Science Courses In Bangalore.

Essential Concepts To Study To Become A Data Scientist!

 For a very long time I have been noticing that most people have notions about data science, they think by studying and learning to work on python, R and other tools, they can become a data scientist. Python, R, SQL, are all important in data science but these are all could be learnt easily once you start working in them, it is really the least challenging thing to learn all these tools and languages for a data scientist. What really matters is the knowledge in and about data, a data scientist should have the intelligence to vision through the raw data.

Let us focus on the real thing, we need to think beyond the tools and start concentrating on developing relationships with data because that is the key to become a data scientist. 

A data scientist must mainly possess the skill in understanding the potential of data, its value, threshold and flexibility. We call this as Data Processing. In data science, data will be the dish for which data itself will be the ingredient, which means, the main goal of data processing is to find salutary data by crunching and filtering the raw data.

The process of Data processing:

1.     Gathering of the data. By various platforms, surveys and mediums data(in all forms) will be gathered, this data will not be validated while being picked and that is why it is called as raw data. It is the data in its raw form.

2.     Cleansing of data. The gathered raw data will go through a validation process in which the useless data will be eliminated from the main data, only the useful data will be filtered through this process.

3.     Modification of data. The thoroughly validated data will be rebuilt, manipulated and will be merged with other data if necessary.

4.     Processing phase. This is the ultimate phase where the processing of data takes place, here is where the final solution for a problem will be found. Machine learning algorithms and methods are used in this phase.

5.     Interpretation of data. The final solution of data could be easily read by the data scientists but for the non-data scientists the interpretation of salutary data into an easily readable and understandable way is important. Data visualization is used in this phase of data processing.

6.     Data Storage. It is extremely necessary to store the data especially to store the statutory data so that it could be reused in future whenever necessary. But storing data was a huge concern for the businesses but due to the concept of hadoop in big data this concern has been easily resolved.

This is probably the simplest and shallow explanation on the phases of data processing.

A data scientist must have knowledge in both technical and non-technical aspects of computer science and data science.

Note: Start your journey in Data Science with Learnbay as it provides big data analytics training in Bangalore and Data Science Courses In Bangalore.

Technical aspects that one must learn to become a Data Scientist:

1. Linear algebra- Singular value decomposition, optimization and probability theory.
2. Mathematics, Stats, Data structure, data analytics and algorithms.
3. Programming languages like python, R, SQL, Java, C++, but python and R are mainly necessary. Python is majorly used in various phases and processes of data science like in importing the SQL tables into code, to create tables, etc. R programming should also be necessarily learnt because 43% of data scientists believe in the potential of the language while solving statistical problems.
4. Data visualization is the other essential part in data science, it is imperative for the data scientists to have thorough knowledge in it.
5. Machine learning and AI algorithms ofcourse.

Always remember that you will not really be a data scientist until you enter into the field in that designation, only after years of proper experience you could be called as the ideal data scientist. So do not expect more from anything, make sure you will keep up with your perseverance towards learning the field.

Learning data science can be tricky, difficult and confusing all at the same time but with the right source of assistance you can easily follow up with the field. I recommend you the data science course of Learnbay, it is really a good source to learn data science as it provides big data analytics training in Bangalore and Data Science Courses In Bangalore.