How Python Emerged as the Foundation of Tech Giants: Win a Kaggle Competition in Data Science

How Python Emerged as the Foundation of Tech Giants: Win a Kaggle Competition in Data Science

Python is the most powerful, open-source, interpreted, object-oriented, and a very high-level programming language for data scientists. They need dynamic semantics, in-built data structures, a large community, and excellent libraries to deal with various data science projects/applications. 

It is a general-purpose programming language that can be used for developing both web and desktop applications, including complex numeric and scientific applications. Due to its versatility, it has become the fastest-growing worldwide use coding language and the right choice to kick-start a career in Data Science. 

So, How Python emerged as the foundation of tech giants to win a Kaggle competition in Data Science? Let’s dig deeper into the topic: Python for Data Science.

 

History of Python Programming Language

 

History-of-Python-programming-language

 

  • Python was created by Guido Van Rossum and was first released in 1991 to help programmers write a logical and clear code for small to large-scale projects.
  • Python 1.0 was issued in 1994 with new features such as map, lambda, filter, and reduce.
  • The final version of Python 2.0 came into the market on October 16, 2000, with additional features like garbage collection systems and list comprehensions.
  • Python 3.0 was published on December 3, 2008, to improve the language’s fundamental flaw.
  • Python 3.9 was declared on October 5th, 2020, and its next version is expected to come between 2020 and 2025.

Features that Attract Techies to Use Python for Data Science

 

Features-of-Python

 

Python is faster to learn and easy to use in contrast to other programming languages. Its syntax and grammar are very close to the natural language that helps programmers increase their productivity while working on Data Science.

 

Faster to Learn & Easy to Use

 

Python-fast-to-learn

 

Python for Data Science is the recommended programming language for beginners because it has no use of curly-brackets or semicolons. If a single skip in C-like languages, it would be impossible for you to compile the program.

 

Cross-Platform Language

 

Python-Cross-platform-language

 

From Google, Instagram, Facebook to Spotify like world-class companies use Python because it allows them to write a program on Macintosh computers and run it on different platforms such as Linux, Unix, Windows, and vice versa.

It is a very expressive and portable language. Overall, we can say that one language is sufficient for everything. Whether you want to build web applications and machine learning tools, it has everything you need in one language and simplifies your project using less time and money.

 

Numerous Data Science/Machine Learning Libraries

 

Python & Machine Learning

 

Python for Data Science is the key to the future. The reason for the Python popularity is its numerous free data science, data analysis, and machine learning libraries like Scikit-Learn, Pandas, Numpy, etc. All these libraries of Python help in data mining, data analysis, and data manipulation. With Python in-built libraries, you can quickly solve real-world problems and implement popular algorithms on datasets.

Apart from that, there are some deep learning frameworks in Python for data science, likewise TensorFlow, Caffee, PyTorch that allows you to build deep learning architectures by just using a few lines of Python code.

 

Hadoop Compatibility

Hadoop, an open-source software framework, supports Python for big data. The Python package labelled as Pydoop enables you to access APIs for Hadoop, and you can write several Hadoop programs using Python.

With this package, you can quickly write quality codes and solve complex problems by putting minimal effort.

 

Largest Community Support

 

 

Python-largest-community-support

 

Python has the largest community support for beginners to experience coders. The popularity of data science using Python is increasing day-by-day, but we can’t forget the language is also widespread due to its broad Python developer community.

They help everyone from Python programmers to data scientists and whenever they find it difficult while writing a code for web scraping or any other project related to Python.

 

Conclusion

 

Want to build a profitable career through Python for Data Science Training? Check upcoming classes at LearNow and give us your precious four days to prepare you for the certified Data Science exam. We have world-class instructors to teach you essential Python skills used in Data Science, and it helps you get the very tempting job of the 21st century with the highest salary in companies that you wish to work.

The hype around AI and the need for Advanced Data Science Courses

The hype around AI and the need for Advanced Data Science Courses

Every day we hear news about projects that involve Advanced Data Science and new AI advancement made in various parts of the world. The business leaders in the AI industry have worked to turn the ideas of Sci-fi movies into reality. We can now see driverless cars on the road, smart homes powered by AI, or other NLP examples in everyday use like e-Mail filters and smart assistants like Amazon’s Alexa or Apple’s Siri. But is AI actually worth the hype all around the world? Why is it that ambitious youngsters want to grab their Certificate in Advanced Data Science Course?  

According to Gartner, the IT department’s number of AI jobs has tripled between 2015 and 2019. Also, the most substantial demand for AI–skilled Data Scientists comes from the non-IT departments. A recent report by Statista states that the AI job titles are the highest paid jobs in the US as of 2019, with an annual income of around 143 thousand US dollars.

The above stats are evident from our daily life as well. We all have become slaves to our AI devices like Alexa or Google Assistant, and are falling for the genie inside our smart speakers. Humans are social animals, and unlike other living species, we are blessed with the power of speech. That, too, is not restricted to human beings. Machines are programmed to give an intelligent response to your questions. But, their answers are not 100% accurate to answer all the questions asked by humans. A report published by Business Insider in 2018 stated that “Alexa answered informational questions accurately 91% of the time, up from 78% in July.” The information questions are related to general questions, like discovering a country’s population to identifying how long it takes water to freeze.

So, if you ask, “Can AI perform all my tasks as efficiently as I can do?” The answer would be a straight, “No!” But, if the question is, “Can AI simplify my task related to Data handling and analysis?” Then the answer is clearly, “Yes!” AI has techniques to handle such situations effectively and effortlessly. 

 

What is AI or Artificial Intelligence?

What-is-Artificial-Intelligence

 

Artificial Intelligence refers to a case where a computer can do anything that would otherwise require human intervention. It includes Machine learning, Planning and scheduling, Game playing, Machine Vision, and of course, NLP. Today, AI is used for Sentiment Analysis, and machines try to imitate human capabilities. But, devices cannot match the ability of a human mind and heart. The response of an engine is quite alienated. 

For example, if you ask a person and a machine to express a specific tweet’s sentiment, the answers will indeed vary. A person will read and understand the whole tweet, whereas a machine would consider the length of the words, their distribution, and the punctuation used. The sentiment analysis can reliably predict the text’s sentiment, which can be used for Advanced Data Science models. Still, human research will give a deeper understanding of the particular text. It is because humans and machines have a different approach to handle things. With increasing data volumes and the growing need for quick and accurate data analytics for better decision-making, we can switch to AI for Big Data Analytics. Businesses must understand the facts, statistics, and data for an effective and result-oriented decision-making process.

 

Cases where AI plays a crucial role

 

AI-Plays-crutial-role

 

Look for the following guidelines in your project, and see if AI will play a critical role in it.

  • Can be performed by a human
  • The process is descriptive and formal
  • The solution can be categorized as positive/negative
  • Can be expensive if done by humans
  • Time-consuming
  • Machines can perform the task effectively in a short period

A few examples where AI is significantly used, based on the above guidelines, are online conferences, prediction analysis, summarization of text, etc.

 

Cases where AI does not help

 

AI-Cant-Help

 

There a few cases where AI will not be useful, and the social role will be more important than machines. AI is not the best tool in every task and cannot replace human intelligence. There are a few cases where the workflow cannot be defined, and the job cannot be expressed formally. AI needs strict rules for the completion of a task and decision making. Also, the human cost may be negligible for a task compared to the implementation of Advanced Science models.

 

Benefits of using AI

 

Benefits-of-using-AI

 

AI is used to solve problems quickly and helps in better decision making. The following benefits will help you realize the importance of AI in the coming years:

1) Reduced Human Error

2) Overcome human inhibitions and take risks

3) Available 24/7

4) Performing Repetitive Tasks

5) Digital Assistance for social interaction

6) Quicker Decision making

7) Used in Daily Applications

8) Emoweirng Innovative developments

 

Drawbacks of using AI

 

Draw-backs-of-using-AI

 

Like they say, “There are two sides of a coin”, people are sceptical about the increasing popularity of AI in the industry. The disadvantages of using AI are:

1) Creation needs huge investment

2) Human learning and working capabilities may stagnate

3) Threat to employment

4) No Emotional bond with humans

5) Machines can’t think Out of Box 

Like every new development in technology, AI has massive potential advantages but may threaten human development. But, we must realize that the “rising robots” can no-way enslave the human race.

One of the major concerns these days is that automation will replace human resources and threaten the workforce. But, organizations and business leaders are working in this direction and using the social intelligence of their resources for other creative tasks. It will also take a lot of time for humans to build complete trust in machines and AI. Of course, one can afford to buy and rely on a driverless car, but it will take at least a decade for people to board a driverless bus.

Over the years, the demand for a Certificate in Advanced Data Science Course has increased tremendously, and there are many job opportunities in various industries. Netflix, Intel, Google, and Microsoft have made the most investments in AI startups. Microsoft, Nvidia, ServiceNow, DocuSign are leading AI companies.  

 

The AI hype is real

 

AI-Hype

 

With many organizations adopting AI and Advanced data science models for their business operations, the AI hype is justifiable. Our fascination with scientific fiction movies is becoming a reality. AI is silently operating a large section of the businesses and astonishing the users with its unimaginable intelligence. AI brings along a lot of excitement, but we should be realistic and understand the real prospects.

How to Become a Data Scientist in 2020

How to Become a Data Scientist in 2020

Data Science has been one of the trending technologies for more than a decade now. The science of all data involves analysing millions of data units to find hidden information. The insights gained from the information help the stakeholders make informed decisions. With the increase in the demand for data, the demand for data science tools and applications has also increased. This domain has also spurred the development of other technologies such as artificial intelligence, machine learning, deep learning, IoT, robotics, and neural networks. With such a wide scope across all sectors and industries, there is always a huge demand for skilled Data Science professionals.

Advanced Data Science

Advanced data science course curriculum includes basic data analytics tools and techniques as well as advanced ML and DL tools and technologies. The advanced training program helps you specialise in niche ML concepts that will help you learn to create ML algorithms. In the training program, you will also learn all about neural networks and NLP.

Steps to Gain Data Science Expertise:

One of the chief challenges of data science aspirants is to bridge the gap between knowledge and application. In order to build a successful career in Data Science, aspirants should have the essential skills, portfolio, and certification.

Learn the skills

The mandatory skills required to become a data science professional are:

  • Knowledge of programming languages such as Python and R
  • Knowledge of databases, especially SQL
  • Ability to use Data Science tools and techniques
  • Knowledge of advanced mathematics, statistics, and probability
  • Machine Learning algorithms

If you come from a non-computer science background, then you can do basic training in Python programming before you take up the advanced data science program. Then, you can learn the other technical skills essential for a career in Data Science by enrolling in one of the Data Science certification programs from LearNow. There are different  courses where you will learn all about data science, from the basic to advanced levels. The training will equip you with the working knowledge of Excel, Hadoop, and other data analytics tools. You will also learn about deep learning and machine learning in the training.

There are many ways you will benefit by joining a Data Science course. The first benefit is that you will be trained by expert academicians and industry experts. Also, interactive training sessions will help you understand the core concepts of technology. You will earn an industry-recognised certification, which will enhance your prospects for building a career in this domain.

Build your portfolio

The second step to gaining expertise in data science is to build a portfolio. A certificate can get you inside the door. But a well-done portfolio can help you secure the job. It is because the project work showcases your learning and understanding better than answering a hundred questions at the interview. When you’ve done the project from the start to finish, you will be able to confidently answer all the relevant and related questions. The confidence in your subject knowledge will make you a winner.

Why Get Advanced Data Science Certification?

The first step to build a career in the field of Data Science is to earn an Advanced Data Science Certification from a reputed institute. The certificate will help you stand apart from the hundreds of others who have applied for the position. Also, the training program will equip you with the essential skills and working knowledge of data science tools and technologies. The in-depth training will make you job ready. The best thing about this domain is that anyone with the right skills and abilities can work irrespective of your background. Even if you are from a non-technical background, if you learn the basics of Python or R programming, you can join the Data Science course and start your career

There are numerous career opportunities for professionals in the field of data science. A few of the job profiles are

  • Data scientist
  • Data architect
  • Data analyst
  • Research analyst
  • Business analyst

Data Science tools and applications are now being used in various sectors including education, healthcare, research and development, infrastructure development, and even marketing. So, there is a huge scope for certified professionals in this domain.

Just as the demand for data science professionals is more, the competition is also equally high. A strategic job application will help improve your prospects of finding a good company in this field. First, you need to update your LinkedIn profile and follow the right companies to get updates. Second, make your profile “All-star” by building a contact list of at least 500 members. Follow niche-career boards. If the organisation is good and there are prospects for career development, accept the position. You can move further as you build your professional experience.

Earn an Advanced Data Science Certification from LearNow

The certification at LearNow will you bridge the gap between learning and practical application. To do this, we follow an intensive, regularly updated curriculum that is designed by industry experts. We also use interactive teaching methodologies in our training program. The online training sessions allow aspirants to learn at their own pace. We also organise live webinars with Q&A sessions that allow students to interact with the trainers. On completion of the training program, you will receive an industry recognized certificate, which can improve your career prospects.

Contact us to know more about our Data Science Certificate Programs.

Top 5 Data Science Trends of 2020

Top 5 Data Science Trends of 2020

The world revolves around Data! Companies need customer data to check if their products or services meet customer expectations. Pharma companies need data to find out if their new product has been successful or not. Educational institutes need data to evaluate the reach of their new training program. These are just a few examples of how data is being used across the sectors. In fact, we can say that data is the key enabler that organisations use to evaluate their performance and identify ways of improvement and expansion.

Data science is more about data and numbers. It is an umbrella technology that contains various other technologies such as artificial intelligence, machine learning, deep learning, neural networks, business analytics, and lots more. As technologies emerge, data science trends evolve. The top 5 data science trends of 2020 are:

1. Access to Artificial Intelligence and intelligent apps

Small and large companies have already started using AI-based applications. In 2020, we will see a rise in intelligent applications designed using ML, DL, and AI technologies. Automated machine learning will be used along with data science to enable efficient data management.

2. Rapid growth in the IoT

According to a survey conducted by IDC, the investment in IoT apps and solutions will be nearly $1 trillion by 2020. Until now, IoT was used in home automation, self-driving cars and automating manufacturing processes. However, this year, we can see more research and development in the applications of IoT in agriculture, city management, retail industry, and other sectors.

3. Evolution of Big data analytics

Businesses have found the huge benefits of Big Data analytics. This year will see more companies use data analytics to improve their profit margins. Predictive analysis will also become more popular. Businesses don’t just want to use data to understand what has happened, but also predict what could happen in the future.

4. Edge Computing will be on the rise

Edge computing is a process that analyses data in real time. The data is stored near the source itself so that it can be analysed just as it is being produced. The increasing applications of IoT have risen the demand for real-time data analysis. In IoT, sensors and devices collect data, which is immediately transferred to Cloud storage. Using edge computing, the data is immediately analysed and actions are taken. This synchronised structure speeds up the decision-making process and at the same time ensures it is accurate. It also allows applications to react immediately, which is one of the essential characteristics of an Internet of Things system.

5. Demand for Data Science Security Professionals

With huge volumes of data being used across various systems and applications, data vulnerability increases. Though there are numerous applications of data science, many are still sceptic of using it because of the security challenges. So, there will be a huge demand for data science security professionals this year. These professionals should be experts in the latest data science technologies and have in-depth knowledge of Python concepts. So, they can deal with data issues with confidence.

Benefits of Learning Data Science

Data is a vital component of any organisation. Data science is one of the fast-growing technologies that is finding applications across all horizontals and verticals. Organisations rely on data to make informed decisions. For example, customer data is used to evaluate the performance of an organisation and at the same time used to identify ways in which a product or service can be improved. Data captured from social media help organisations gauge people’s opinions about their company, services, or products. Data science applications are used not just in product-oriented organisations, but also in service-oriented organisations. So, you can see that the scope of job opportunities is enormous. Even if you are from a pure science background or medical background, you can learn data science and find better career opportunities in your field itself. Data science is the present and the future. So, you will have many opportunities for career growth once you complete data science certificate course.

Advanced Data Science Courses

The in-demand skills for a data scientist are knowledge of programming languages such as R, Python, and Java. Data scientists should also know about applied mathematics and statistics. They should have in-depth knowledge of databases, machine learning, deep learning, and working knowledge of the data science tools. Advanced data science courses should equip you with these skills and capabilities. The curriculum of the advanced data science course offered at LearNow includes

  • Introduction to machine learning
  • Linear regression
  • Data visualisation using python
  • Decision Trees
  • Logistics Regression
  • Clustering
  • Text Mining
  • NLP
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent and Recursive Networks

The advanced course will be suitable for a working professional who wants to upskill themselves and enhance their career prospects in this domain.

Career Opportunities in Advanced Data Science

Once you have earned a certification in advanced data science, you can apply for various job profiles such as

  • Data architect
  • Data scientist
  • Data statistician
  • Data analyst
  • Data engineer
  • Data administrator
  • Business intelligence manager
  • Data analytics manager

Join Advanced Data Science courses at LearNow

LearNow offers Data Science Certification Course for graduates and working professionals. This professional course is designed and delivered by experts who come with nearly a decade of industry experience. Aspirants can join the Executive program in Data Science or Advanced Data Science course based on their preference and eligibility criteria. The curriculum includes 4 to 6 industrial projects, which will give students a working knowledge of the concepts they learn in the training. LearNow offers online training sessions that are interactive with use cases, discussions, quizzes, and more. Students can join the live training sessions from anywhere in the world. In case, you miss a session, you can watch the recorded session or opt to join a live session being conducted for another batch.

Upskill yourself in the advanced concepts of Data Science by joining our online training program. Contact us for more details.