Key reasons to enroll for Python training

Here’re the most important reasons you should learn this high-level, general-purpose programming language.

• Use in data science: You’re probably aware of the fact that data science is being considered one of the most respected, prominent, and attractive career paths in the tech field these days. By completing a Python course you’ll be able to fuel your data science career as the language comes with a great number of frameworks and libraries such as NumPy, PyBrain, PyMySQL, SymPy etc that would make your job as a data science professional relatively easier.
• Use in artificial intelligence and machine learning: Similar to data science, Python is also being heavily used in machine learning as it helps to build algorithms utilizing statistics in order to enable computers to perform various actions. Some of the major Python modules that help in machine learning include Scikit-learn, Theano, Tensorflow, among others. With the help of libraries like Keras, you’ll be able to manage neural network experimentation as well.
• Simplicity: One of the biggest advantages of learning Python is its high simplicity. When you’re a beginner in coding and programming, generally you don’t want to begin with a language that comes with weird rules and tough syntax. Python isn’t only simple and readable, but it easier to setup as well as you don’t have to handle any compiler issues like C++ or classpath problems like Java. All you need to do is install Python and start exploring it.
• Robust community: Regardless of the language, a community always plays a highly crucial role when it comes to learning it. Python comes with a massive community that’ll always be there to lend you a helping hand whenever you get stuck with one or multiple issues. Today, you can easily find the solution of any of your Python related problems within minutes by doing a simple Google search.
• High prospect: As the language is growing steadily and is being adopted by a significant number of tech giants as their primary language, it makes lots of sense to obtain a Python certification in order to make your career future-proof. It’d not only make job-ready quickly but will accelerate the career growth as well.

Conclusion

Put simply, by completing a Python course you’ll never have to face a shortage of options to utilize your skills. And since a lot of tech giants rely on the language, you’ll always be in a position to make good money as a Python certification holder.

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If you’re planning to get enrolled in a Python course or online Python training program, knowing some real-world applications of Python would help you choose which specific field you want to focus on for your career. Here are the top three applications of Python:

1- Web and Internet Development

Using Python, you can easily develop web applications rapidly. Python has a number of libraries for internet protocols like XML and HTML, e-mail processing, JSON, IMAP, FTP, etc. You can even use the following libraries for a range of functions:

• BeautifulSoup – for parsing XML and HTML  documents
• Requests – a client-side HTTP library
• Paramiko – For executing the SSH2 protocol
• Feedparser – for downloading and parsing Atom/RSS feeds
• Twisted Python – for asynchronous network programming

With widely used Python frameworks like Django, Pyramid, and Flask, you can enjoy unparalleled convenience, scalability, and security when working on web development compared to starting website development from scratch. With Python, you can also get microframeworks like Bottle and Flask. You can even write CGI scripts and get advanced CMSes (content management systems) such as Django and Plone CMS.

2- Machine Learning and Artificial Intelligence

Those eyeing a career in data science, machine learning (ML), and AI (Artificial Intelligence) often take up a Python certification program to fast-track their career. Apart from being extremely user-friendly and easy to earn, Python has multiple libraries that offer support for these domains. Some of these are:

• TensorFlow – an end-to-end Python ML library for performing high-end numerical calculations.
• Scikit-learn – a well-known open-source Python ML library with a wide range of clustering, classification, and regression algorithms.
• Keras – a leading open-source Python library for building neural networks and machine learning projects.
• Theano – a renowned scientific computing library that lets you classify, optimize, and assess mathematical expressions that deal with multidimensional arrays.
• PyTorch – a production-ready Python ML library with outstanding applications, examples, and use cases supported by a robust community.

NumPy, Seaborn, and Python Pandas are some other libraries that you can use.

3- Game development

Several interactive games (like Disney’s Toontown Online, Civilization-IV, Vega Strike, etc.) and 3D graphics have been developed using Python. Libraries such as PyGame (that provides functionality via computer graphics and sound libraries) and PySoy (a 3D game engine that supports Python 3) as well as frameworks for game development like PyGame and PyKyra make game development an easy and quick job.

Final words

Education, business (e-commerce and ERP systems), software development, desktop applications, and database access are some other domains where you’ll find the use of Python. So, take your pick from these fields of Python applications when you enroll for a Python certification program. 

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How Python is used at every step of data analysis

1. Numpy and Pandas: Imagine staring at a long Excel sheet with hundreds of rows and columns, from which you want to derive useful insights by searching for a specific type of data in each row and column and performing certain operations. Since such tasks are extremely time-consuming and cumbersome, Python can come to your aid. With Python libraries such as Pandas and Numpy, you can use parallel processing for such high-computational tasks, which makes the job faster and easier.
2. BeautifulSoup and Scrapy: Using BeautifulSoup, you can parse and extract data out of XML and HTML files. On the other hand, Scrapy – which was originally designed for web scraping, can also be used as a general-purpose web crawler or to mine data using APIs. Since the necessary data isn’t always readily available, you can use these Python libraries to extract data from the internet, which would help in data analysis.
3. Seaborn and matplotlib: Instead of seeing a lot of data jumbled on a screen, it’s much easier to visualize the data in the form of pie-charts, bar graphs, histograms, etc. Such pictographic representation or visualization of the data helps in deriving useful insights quickly and easily. Here again, Python libraries can come to the rescue. Using Seaborn (which is a matplotlib-based Python data visualization library) that provides you with a high-level interface for drawing informative and attractive statistical graphics, you can easily visualize data and draw useful insights. Apart from being equipped with beautiful default styles, the statistical plotting library of Seaborn is also designed to work extremely well with the Pandas dataframe objects.

In addition, using Python would mean having scikit-learn (a machine learning library), which would help in complex computational tasks involving probability, calculus, and matrix operations over thousands of columns and rows.  For data analysis involving images, OpenCV (which is an image and video processing library used with Python) can help.

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What makes Python ideal for data science?

Here’re the key reasons why Python is being considered among the fastest-growing languages across the world.

• A complete package: Apart from being a general-purpose language, Python is tailor-made for every data analysis task. Data scientists need to use lots of algorithms for which the language is tailor-made as well.
• Easy implementation: For any data scientist it’s crucial to understand the language before going into intricate details of artificial intelligence or machine learning. Since Python is popular as a beginner’s language and doesn’t come with a steep learning curve, they don’t need to think about spending time on learning how to code using this language. Python programming language is popular for making programs work well with the minimal lines of codes possible. This simplicity is one of the biggest factors that have helped Python achieve this place.
• Libraries and scalability: All the libraries preferred by data scientists are available in Python. It also comes with a significant number of exclusive libraries. In addition, Python has become the best scalable language as one can develop concrete applications with ease. This feature is ideal for data scientists working to develop certain data analysis applications and models.
• Massive community: Python is accompanied by a robust community. This community is highly cooperative and there’re people who’re making the job of data scientists easy by helping them in different ways. If you’re a data scientist and experiencing some issues related to Python, most likely you’ll get the answer by presenting your query to a Python

Apart from all these, the ability to perform machine learning tasks is probably the biggest factor that has given Python programming language a solid edge over its competitors. It’s equipped with almost all the packages a data scientist may ever need. From scientific computing and statistical modeling to linear algebra and symbolic algebra, and many more – one can find the required packages readily available.

Keeping the above advantages of Python in mind, it can be said that it has become imperative for aspiring data scientists to obtain a Python certification. As a Python certification holder, you’ll be able to add tremendous value to your resume and to make the path of becoming a data scientist somewhat easier than those who don’t. In addition, if you’re looking to switch your career and step into the field of data science, it’ll be easier to demonstrate that you’ve got the requisite knowledge to manage various data-related projects when you’ve got a Python certification. And finally, as a certified Python professional, you’ll be able to get better jobs coupled with bigger salaries.

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Recently, a lot has been discussed and written about the differences between various roles in the domain of data science. Among others, the ones that have got the spotlight on them are those that discuss and debate the differences between data scientists and data engineers. If you are wondering what triggers this tremendous interest in these roles, a change in perspective that has been felt over the years could be the driving factor.

If you step back a couple of years ago, you will find that the predominant focus was on retrieving precious insights from data. As companies and organizations started making data-based and data-driven decisions, which brought several benefits their way, the significance of data management started to sink in the industry – slowly but surely. This also made the interested parties realize that the quality of data was important to derive useful insights because it’s the principle of “Garbage In, Garbage Out” that works in the domain of data science too. Even if you are capable of creating the best models, your results are likely to be weak and ineffective in case your data isn’t qualitative. And this was what brought the role of the data engineer under the spotlight.

According to Gartner, merely 15% of big data projects ever make their way into production. According to domain experts, one of the chief reasons behind such failures is due to the inability to build a production pipeline, which is one of the principal tasks of a data engineer. In the modern age of analytics, data scientists get most of the spotlight and attention. However, the roles played by data engineers are equally important, though they are often overlooked. It’s important to realize that data science (and even data analytics) would fail to flourish if no data engineering workbench exists. If you don’t believe it, you can consider what Glassdoor’s records say.

According to Glassdoor’s data in 2018, the number of job openings earmarked for data engineers was almost five times more than that for data scientists. Elsewhere, one may find data scientist jobs exceeding the number of data engineer jobs though some say it could be because numerous organizations don’t always (or are unable to) draw a distinct line between a data scientist and a data engineer. Thus, they end up posting jobs for the former whereas in reality, the jobs should have been seeking data engineers instead. Such actions on the part of organizations are perhaps triggered by their ignorance of the significant differences between data scientists and data engineers. Many reports have revealed that the majority of organizations require more data engineers than data scientists on their team. So, the question comes to this – what exactly is data engineering and how’s the role played by a data engineer different from that played by a data scientist.

Let’s dig a little deeper to answer the questions and find out the differences between data scientists and data engineers.

1- Who is a data engineer?

S/He is a professional with specialized skills in creating software solutions around Big Data.

Another way of defining a data engineer is that s/he is an inquisitive, skilled problem-solver, who loves both data and creating things that are useful to others. Thus, along with data scientists and business analysts, data engineers form an integral part of the team effort that converts raw data in ways which offer organizations useful insights and provides them with the much need competitive edge.

To understand what the role of a data engineer is, it can be said that this professional is someone who builds, develops, evaluates and maintains architectures like databases and large-scale processing systems. In contrast, a data scientist is someone who cleans, organizes, and acts upon (Big) data.

It’s the job of data engineers to suggest and at times, even implement ways to improve data quality, efficiency, and reliability. To handle such tasks, they need to utilize a range of tools and languages to blend systems together or try to track down opportunities to get hold of new data from other systems, which can help system-specific codes, for example, to act as the basic information in advanced processing by data scientists.

A data engineer will also need to make sure that the architecture that’s in place is capable of supporting the needs of the data scientists as well as the business/organization and its stakeholders.

In order to deliver the required data to the data science team, it will be the responsibility of the data engineers to develop data set processes for data mining, modeling, and production.

2- Key differences between data scientists and data engineers

With respect to skills and responsibilities, you’ll find considerable overlapping between data scientists and data engineers. One of the key differences between data scientists and data engineers is the area of focus. For data engineers, the emphasis is on creating architecture and infrastructure for data generation.  On the contrary, the focus of data scientists is on advanced statistical and mathematics analysis on that generated data.

Though the role of data scientists demands a constant interaction with the data infrastructure that the data engineers have created and maintained, the former isn’t responsible for that infrastructure’s creation and maintenance. Rather, they can be called the internal clients, whose job is to perform high-level business and market operation research to spot trends and relations, which in turn need them to use an array of sophisticated methods and machines to interact with the data and act upon it.

It’s the job of data engineers to provide the necessary tools and infrastructure to support data analysts and data scientists so that these professionals can deliver end-to-end solutions for business problems. Data engineers are tasked with creating high performance, scalable infrastructure that helps deliver business insights with clarity from raw data sources in addition to implementing complex analytical projects where the emphasis is on gathering, evaluating, managing, and visualizing data along with developing real-time and batch analytical solutions.

Perhaps you now understand that despite some key differences between data scientists and data engineers, the formers depend on the latter. While data scientists deal with advanced analysis tools like Hadoop, R, advanced statistical modeling, and SPSS, the focus of data engineers remain on the products that support such tools. Thus, a data engineer may deal with NoSQL, MySQL, SQL, Cassandra, etc.

In a way, you can say that in the data value-production chain, the role of data engineers is akin to the plumbers since they facilitate the job of data scientists, data analysts and other professionals working on the fed of data science. As with any infrastructure, plumbers don’t get the limelight, and yet, they are irreplaceable since nobody can get any work done without them. The same applies to data engineers as well.

3- Language, tools, and software used by data engineers

Due to the difference in their skill sets, differences between data scientists and data engineers translate into the use of different tools, languages, and software use.

For data scientists, common languages in use are Python, R, SPSS, Stata, SAS, and Julia to construct models. However, Python and R are the most popular tools without a doubt. When these data science professionals are working with Python and R, they often resort to packages like ggplot2 to make remarkable data visualizations in R or opt for the Pandas (Python data manipulation library). There are several other packages that can come for them, which include NumPy, Scikit-Learn, Statsmodels, Matplotlib, etc. The data scientist’s toolbox is also likely to have other tools like Matlab, Rapidminer, Gephi, Excel, etc.

The tools that data engineers often work with include Oracle, SAP, Redis, Cassandra, MongoDB, MySQL, PostgreSQL, Riak, neo4j, Sqoop, and Hive.

Languages, tools, and software that both the parties have in common are Java, Scala, and C#.

One of the key differences between data scientists and data engineers emerges from the emphasis given on data visualization and storytelling, which gets reflected in the tools these professionals put to use, some of which are mentioned above.

4- When organizations get the roles wrong

As mentioned before, several organizations fail to distinguish the key differences between data scientists and data engineers and often task the former with the job that the later is specialized to do. For example, asking data scientists to create a data pipeline, which is the job of a data engineer, would mean making the former function at just 20-30% of their actual efficiency. So, it becomes important to know the differences between data scientists and data engineers and hire each for roles specifically designed to match their skill sets.

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1- Key features of Python programming language

The huge popularity of Python programming language heavily relies on the unique features offered by it. Let’s have a look at the features of Python that set it apart from other programming languages.

1.1- Simple to learn

Python is highly simple to get started with. It comes with a simple setup, easy to understand syntax and many practical applications that are heavily needed in web development. The syntax is relatively simple compared to other languages and a bunch of modules can be imported to make the code much shorter. There’re some straightforward, excellent tools available to work with Python code, particularly the interactive interpreter. It eliminates the need for learning special text editor, IDE, or anything else to begin using Python programming language. All you need to have a command prompt together with the interactive editor.

1.2- A beginner’s language

For a beginner in the programming domain, starting with a language which is difficult to learn not only slows the pace of the learning but brings additional complexity also. With Python programming language, a beginner can be introduced to the fundamental concepts like procedures and loops quickly and probably can work with user-defined objects in his/her initial stage of learning. Python’s hug syntactical simplicity lets a beginner use advanced or basic concepts, without much boilerplate code which is common in many other languages.

1.3- Interpreted language

If you’re familiar with programming languages like Java or C++, you’re probably aware that in order to run them, you’ve to compile them first. But in Python programming language, there’s no need to compile it. All you need to do is run the Python code without thinking about linking to libraries. Here, interpreted means the source code isn’t executed all at once, instead it’s executed line by line, which makes it easier to debug the code. In Python, the source code gets converted into ‘bytecodes’ which is then translated into the native language of a specific computer.

1.4- Object-oriented language

A programming language, which comes with the ability to model the real world, is considered object-oriented. It concentrates on objects and merges functions and data. Python has object-oriented features. Class mechanism of Python adds classes with a minimum number of new semantics and syntax. Here, multiple inheritances are also supported.

1.5- Free and open-source

Python programming language is freely available which means it can be downloaded from the official site by anyone. Python being open-source means you can read its source code, modify it, use pieces of it, and distribute copies of it freely.

1.6- High-level language

Python is a high-level language which means programmers don’t need to remember its system architecture or manage the memory. This feature makes Python highly programmer-friendly.

1.7- Portable language

Let’s consider you’ve written code in Python programming language for a Mac machine and you want to run it on a Windows machine. There’s no need to make changes to perform it, meaning you can take a single code and run it on any other machine without having to write separate codes for separate machines. This portability is another key feature of this language.

1.8- Extensive libraries

Python programming language comes with extensive libraries which can be used to eliminate the need of writing own code for each and every single thing. There’re libraries for unit-testing, documentation-generation, regular expressions, databases, web browser, image manipulation, image, and a lot more.

1.9- Extensible language

Some of the Python code can be written in other languages such as C++ if required. This feature makes Python an extensible language which means you can extend it to other languages.

2- Why you should learn Python programming language?

We’ve already seen that Python programming language is surprisingly easy to learn and you can use it as your stepping stone into other frameworks and programming languages. Across the globe, Python is heavily used by some giant companies like Google, IBM, Nokia, Pinterest, Disney, Instagram, and many others. Since a lot of giant organizations depend on it, it’s extremely likely that you’ll never have a paucity of ways to earn good money using your Python skills. If you’re still feeling hesitant whether you should learn Python programming language, have a look at the following advantages that you’d be able to enjoy once you become a master of it.

2.1- Data science

This is the biggest reason you should focus on learning Python programming now. Though R was considered perfect for data science tasks till sometime back, with lots of frameworks and libraries Python has become the most preferred language among data scientists across the globe these days. In addition, you can a lot more with Python than R. For instance, you can automate staff, create scripts, step into web development etc once you become a master in Python programming language.

2.2- Machine learning

This is the second most important reason to learn Python now. The exponential growth of machine learning has attracted a huge number of businesses to leverage this technology. With algorithms becoming sophisticated day by day, we can expect to see more advanced and diverse implementations of machine learning in the technology domain. If you want to play around machine learning or do a pet project, Python is the only major language that makes it easy.

2.3- Web development

With lots of good frameworks and libraries, Python programming language makes web development actually easy. For example, you can complete a task in minutes on Python, whereas it’ll take hours to be completed in PHP. There’s a huge number of web developers who’re using Python frameworks like Flask and Django to develop web applications in minimum time.

2.4- Massive community

This is another key reason to start your programming career with Python programming language. Presence, as well as the size of the community, can act as a great differentiator when you’re learning something new. You can find the answer to almost any of your queries related to Python in minutes with the help of its communities.

2.5- Multipurpose

Another key factor behind the huge popularity of Python is its multipurpose nature which means it’s not tied to only a single thing. For example, R is good on machine learning and data science but fails to add any value when it comes to web development. By learning Python programming language you’ll be able to do a lot of things: from doing data analysis to creating web applications to automating tasks to writing scripts.

2.6- It pays well

When it comes to salary, Python professionals belong to some of the highest paid groups in the industry, especially those working in the machine learning, data science, and web development domains. While tech salaries greatly differ from one region to another, they stay in a very good range, anywhere between 70K and 150K based on the domain, location, and experience.

2.7- High demand

Based on popular job portals, it looks like having a Python programming certification under your belt can greatly help you get a decent job within a short time. In addition, the demand has increased exponentially these days because of the widespread implementation of data science and machine learning.

3- Things to know about the content of Python certificate

Having a Python certification gives you a competitive advantage when you appear in front of the hiring managers. While competitions in the technology domain are rigorous, having such a certification can change the scenario completely for you. A Python programming certification demonstrates that you not only have a robust understanding of the language but are determined to improve your skillset as well. In addition, having a certification greatly helps you move up the corporate ladder quickly. Lastly, by obtaining a Python programming certification you’ll be able to earn significantly more than those who don’t. If you’re interested to know about what you’ll learn by completing a Python certification course, here’s an overview of the content of Python certificate programs.

• Introduction to and importance of Python
• Introduction to Python programming language semantics, syntax, runtime environment etc
• Understanding operations, functions, class defining etc
• Understanding Hadoop deployment
• Understanding machine learning algorithms in the context of Python
• Working on real-life projects running on Python

Python programming certification course can be taken by Project Managers and BI Managers, Software Developers, Big Data Professionals, Analytics Professionals, and anyone interested to build a career in Python. Beginners without any previous programming experience can also take up a Python certification course to step into the technology field quickly.

Conclusion

However, it’s extremely important to complete your Python programming certification course from a reputed institute. Things you need to review meticulously include the content of Python certificate program, whether the course is designed by industry professionals, facility to receive hands-on experience, job assistance after successful completion of the program, among others.

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Before we delve deeper into how should you learn programming, we should give you an overview of why you should learn Python and R.

1- Key reasons to learn Python and R

If you’re planning to learn programming and start with Python, here’re the reasons you should dive in without any hesitance.

• Easy to learn: Though Python is a highly powerful language, its simplicity makes it one of the most preferred languages among aspiring programmers.
• Data science: Python is the language of choice of a huge number of data scientists. Though private researchers and academic scholars have been using the MATLAB language, the scenario started to change with the emergence of Python numerical engines like Matplotlib.
• Machine learning: This is one of the biggest reasons to start with Python when you’re planning to learn programming to step into the field of machine learning. If you want a pet project in machine learning or just wish to play around it, Python is the only prominent language that makes it easy.
• Widely used: Python is used in a wide range of software industries and packages. For example, it powers Reddit, Quora, Google’s search engine, Dropbox, just name a few. As a skilled Python programmer, you may be able to grab a job at one of these giant companies.
• An object-oriented language: A solid grasp of the basics of Python can help you migrate to any object-oriented language of your choice as you’d only need to learn the new language’s syntax.
• Huge community: Whenever you’re trying to learn programming, a robust community becomes your biggest asset. Thanks to its vast community, you’re most likely to find the answers to any of your Python related queries in minutes.
• Professional growth: Python is growing pretty fast and it makes sense to start your journey to learn programming with this language. Once you become an expert, you can not only get a job quickly but will be able to accelerate your career growth. And Python developers belong to the league of highest paid developers, especially in the fields like web development, data science, and machine learning.

R programming language is specialized for statistical computing and was designed by statisticians. With the advancements in technology, the data captured by businesses has become highly complex, and R has become the most preferred language among professionals need to analyze data. If you’re still thinking of why you should learn R, here’re some key reasons that you can consider.

• A powerful scripting language: R can handle complex, large datasets and is a great language to use for resource-intensive simulations.
• Data visualization: Data visualization is considered to be easier with R as it comes with a lot of useful tools and built-in functionalities which make performing certain tasks more straightforward.
• Statistical packages: When it comes to statistical packages, R is absolutely free. All you need to do is download it and get started. On the other hand, software like SAS, STATA, and SPSS can cost you heavily for a license.
• Robust statistical community: R is supported by a solid widespread community which maintains lots of user groups that allow you to learn and exchange ideas.

Since R is extremely flexible, both as a programming language and a statistical package, its usages continue to increase as a reliable tool for an array of statistical computations.

If you hold limited programming language or are planning to learn programming, familiarizing yourself with languages like Python and R can prove to be the best initial step in advancing your career.

2- Different approaches to learn programming

If you’re planning to learn programming, both Python and R both can greatly help you in taking your career to the next level. When it comes to learning these languages, there’s a wide range of resources available. It actually comes down to the learning method that suits you the best. Here’re the most popular resources from which you can take your pick based on your preferences.

2.1- Online resources

Whether you like a hands-on approach or a textbook approach or a lecture hall approach to learn programming, you can find it online. Courses revolving around hands-on approach usually include projects, lessons, and quizzes to help you learn Python and R. In general, with a free account you can access interactive lessons and exercises, but a pro account is required to practice with project challenges and quizzes. There’re some websites that are considered goldmines of articles, tutorials, and documentation on programming languages. These are usually free resources and can greatly help you in learning Python and R, regardless of whether you’re a beginner or looking to learn complex topics. You can also learn programming by opting for MOOCs (massive open online course). Usually, there’re no entry requirements for MOOCs and you can participate regardless of your financial circumstances or where you live. It’s important to note that because of the large number of people attending MOOCs, you’ll usually receive support from different communities of educators, as well as other learners. And your progress on learning Python and/or R might be assessed through computer-marked tests or peer-reviewed written assignments, rather than by tutors. You can also use GitHub to learn programming through an exploratory approach. However, this method is ideal for people who’ve some experience in languages. Anyone can use this centralized repository to house and maintain code. And you can find lots of Python and R projects which are available for exploration. GitHub can be a great resource if you’re looking for a guide that offers a comprehensive overview of different Python and R concepts, from debugging to installation to writing documentation.

2.2- A programming bootcamp

If you don’t want to take the online or self-learning route, joining a programming bootcamp seems to be your best bet. These bootcamps enable participants to focus on the most crucial aspects of programming and apply the new skills to handle real-world problems. Let’s have a look at why programming bootcamps have become one of the most sought after avenues to learn programming.

• High-impact learning: Unlike other approaches to learn programming where you some of the aspects you learn may not be relevant in the present business world, programming bootcamps only teach the skills that are most relevant in the real world. These programs exclusively focus on the skills that the learners will need to become successful programmers in the professional world. In addition, the duration of these bootcamps is relatively shorter than other avenues to learn programming, which is ideal for people who want to gain programming skills to become employable within a short time frame.
• Secure Return on Investment: When it comes to attending a bootcamp to learn programming, it’s important to remember the old adage – you get what you pay for. Programming bootcamps may seem to be a little expensive but then you need to consider that you’re likely to get a nice job soon after you complete the program. In addition, most of the bootcamp organizers offer job assistance, so your investment remains secure to a great extent.
• Immersive learning: People often get stuck along the way without great support and give up eventually. In a bootcamp, if you get stuck somewhere, instructors are readily available to help you out. In addition, if you choose online or self-learning approaches to learn programming, you’ll be missing out on the additional features offered by a bootcamp. These include presenting in front of a group, pitching ideas, interviews with potential employers etc.

Regardless of whether you want to learn Python or R in a bootcamp, you should always keep in mind that the more effort you invest in the program, the more you’ll get out of it. Most of the students who do well in a bootcamp keep programming outside class hours, and thus you’re expected to. You should also understand that importance of programming languages in the professional world will keep on changing. The crucial part is adapting how programmers look at problems and think about those. Once you learn this, you’ll be able to learn any programming language you want.

Conclusion

Both Python and R can be learned relatively easily as long as you’re motivated and have a clear set of goals in mind based on your background. For instance, if you’re coming from a statistical background, R should be the best option for you. If your goal is to become a data science professional, starting your journey to learn programming with Python should be ideal for you. At the rate the demand for skilled programmers is increasing, now seems to be the best time to learn programming. So, assess the above-mentioned approaches, pick one that suits you the best, and start learning to gain a competitive edge over others.

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To accomplish this goal, data science professionals need the finest tools to leverage advanced techniques that can turn data into actionable insights. There’re some prominent languages like Java, C, C++ etc that can be used to make meaning out of data. However, Python has emerged as the most popular programming language used for data science, a StackOverflow survey revealed.

1- How is Python related to data science?

In the coding world, Python is considered as a kind of Swiss Army Knife. It supports structured programming, object-oriented programming, functional programming patterns, and more. For example, Google has developed TensorFlow, a deep learning framework that has been created using Python as the primary language.

Apart from Google, other tech giants like Netflix, Facebook, NASA etc have been using Python as a prominent language for a long time. There’re some particular situations where this language is the most appropriate data science tool to perform the job. For example, it’s perfect when statistical code is needed to be incorporated into the production database or when data analytics tasks need integration with web apps. The full-fledged programming nature of this language makes it an ideal fit for implementing algorithms.

2- How is Python involved in every stage of data science?

Let’s consider the goal of data science professionals once again – derive actionable insights from data. To accomplish this, some computational tasks are needed to be performed. Here, Python libraries like NumPy and Pandas can be used to perform the job quickly.

Data may not be readily available to data science professionals, so it needs to be scraped from the web. Here Python libraries like BeautifulSoup can be used to extract data from the web.

In order to drive insights, visualization of the data is a must. Here, libraries like Matplotlib are used to represent data in the forms of pie charts, graphs, and other formats.

The next stage is machine learning where tasks are made efficient and easy by using Python libraries like Scikit-learn.

3- Why is Python heavily preferred in the data science landscape?

Python is open source and free, and thus anyone can write a library package in order to extend its functionality. And data science is the field that has experienced the advantages of these extensions. Just to give you an idea of the popularity of Python in the data science field – 66% of data scientists reported using it daily, in 2018. Now, you may ask that what’s so special about Python? Let’s have a look.

3.1- Easy to learn

Python is widely considered as a beginner’s language because it doesn’t have any difficult learning curve, and a developer with fundamental knowledge can work with Python. If you compare it other languages used in data science like R, Python comes with a shorter learning curve and beats the competition by offering an easy-to-understand syntax. In addition, code implementation is less in Python, so data science professionals can spend more time to focus on the algorithms.

3.2- A wide range of data science libraries

One of the major factors that helped Python to take the most sought after place in the data science field is its wide range of libraries that can be used for analysis, visualization, scientific computing etc. Let’s quickly discuss some of them.

• Pandas: This Python library is used for data analysis and manipulation and it’s well suited for different data like tabular, matrix, among others.
• NumPy: NumPy or “Numerical Python” is a core Python library used for data science. It’s used for scientific computing and as a multi-dimensional container where data science professionals can perform various NumPy operations together with special functions.
• Scikit-learn: It’s one of the major attractions of Python where machine learning can be implemented. This free library contains efficient and simple tools for data mining and analysis purposes.
• Matplotlib: This is a plotting library for data visualization in Python. It’s used in Python scripts, web application servers etc.
• PyBrain: It’s another machine learning library of Python that offers modules for developing neural networks.

3.3- Scalability

Python has emerged as a scalable language compared to other languages like R. Python’s scalability lies in its flexibility that it offers to solve problems. As a result, it has been used by different industries to develop tools and applications of almost every kind.

3.4- Python community

One of the biggest reasons behind the exponential growth of Python is its massive community. There’re millions of users who’re happy to offer suggestions or advice when a Python learner get stuck on something. And chances are, someone else has already been stuck there at some point of time.

3.5- Huge amount of Python resources

As Python has become extremely prevalent in the field of data science, there’re lots of resources which are specific to using Python in the context of data science. Meetup groups for data science professionals using Python can be found across the globe.

3.6- Python and machine learning

In the data science field, machine learning is one of the major elements utilized to maximize the value from data. With Python as a major data science tool, exploring the fundamentals of machine learning becomes effective and easy. Put simply, machine learning heavily encompasses mathematical optimization, statistics and probability, and Python has become one of the most sought after machine learning tool that lets aspiring professionals do the math easily.

Apart from all these, Python comes with varied visualization options that help in creating graphical layouts, web-ready plots, charts, among others.

4- Simple steps to learn Python for data science

Today it’s evident that the future is extremely bright for data science professionals and learning Python is just the right thing to get your journey toward the field started. Let’s have a look at the steps.

4.1- Master Python basics

First of all, you need to get the basics right to learn Python. There’re lots of ways to accomplish this – from taking a course to self-teaching to watching tutorials. However, we strongly suggest taking a course for this purpose. And if you’re looking to enter the data science field, look for courses that are particularly designed to teach you Python in the data science context. During this stage, try to join a learning community where you can find like-minded people passionate about Python.

4.2- Learn Python libraries used in data science

Once you’ve gained a solid understanding of Python fundamentals, it’s time to learn Python libraries that are used in data science. The most important of these include Pandas, NumPy and Matplotlib. If you get stuck somewhere, seek help to a Python community and most likely you’ll get it.

4.3- Develop a data science portfolio

Assuming you’re planning to enter the data science field, a proper portfolio is a must. Your experience in working on different datasets should be clearly mentioned. This not only gives your fellow learners something to collaborate on but also demonstrates the future employers that you’ve actually invested your time to learn Python. During this stage, you should start working on developing other data science essentials like soft skills.

4.4- Learn advanced data science techniques

This is the stage where you should be learning advanced Python and data science techniques. Ideally, you should take an advanced course from a reputed institute. There’re different options available like taking free online courses, learning by reading books, attending an immersive data science bootcamp etc. However, if you truly want to ensure that you’ve covered all the points and want to be job-ready quickly, enrolling with a data science bootcamp should be your best bet. That way, you’ll not only be able to pursue your dream at a relatively affordable rate but will be able to develop some greatly useful connections as well.

4.5- Keep on learning

The field of data science is evolving quickly and the technologies and skills that are necessary to become a data science professional may not be the same tomorrow. So, you need to continue learning for both Python and data science fields to maintain a competitive edge.

In conclusion 

For the above reasons and others, Python is so much beloved by data science professionals and programmers. Data science aspirants often come from different backgrounds other than computer science and feel extremely overwhelmed by the difficulty level of the field. But Python’s inherent simplicity and readability make it comparatively easy for them to pick up the learning pace. Also, the huge number of available dedicated analytical libraries means that data science professionals in almost every industry will find packages tailored to their needs already.

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To learn more about Python, click here and read our another article.

The post How and Why Python is a part of Data Science? appeared first on Magnimind Academy.

Today, big data is being heavily used almost everywhere – from the concept of self-driving cars to healthcare industries to tech giants, just to name a few. All these requirements raise the demand for efficient data scientists to a great extent. In addition, the role of data scientists, which has been proclaimed to be the 21^st century’s hottest job, will only continue to get bigger and better in the upcoming years.

1- Things that attract people to become a data scientist

There’re enough facts that indicate a clear lack of competent data scientists for filling the increasing market demand. So, it’s no surprise that people from different fields are trying hard to step into the field and to become a data scientist. If you too belong to this league, you may be wondering what would one gain from becoming a data scientist, especially when he or she needs to spend thousands of dollars together with a significant amount of effort and time?

Let’s explore seven major advantages of becoming a data scientist.

1.1- You’ll get to enjoy more employability

In today’s data-driven world, almost every industry needs to take advantage of data in order to sustain and grow. So, knowledgeable and proficient data scientists are required by employers in every industry – from tech to healthcare to finance to automobile, and many more.

There’re thousands of vacant data scientist positions available for competent people, who can demonstrate that they’ve the credentials and skills to fill them. It’s important to note that many of these positions are high-paying, coveted jobs with giant companies. So, for candidates who’re looking to take their career to the next level, becoming a data scientist is most likely the best way to enter an extremely rewarding field. Also, for employees who’re stalled in their present position, becoming a data scientist can greatly help them move up the ladder quickly in the field where they’re already working.

1.2- You get to become non-negotiable for companies and businesses

This is one of the biggest reasons why people are striving to become a data scientist. Put simply, success in today’s business landscape heavily depends on comprehending and making the right decisions based on insights derived from data captured through different sources. Companies or businesses that don’t accomplish this at the right time will fail to stay in the competition for long and may have their business prospects severely damaged.

As revealed by various reports, there was an acute paucity of data scientists in the U.S. alone. Such a paucity is a key barrier to innovation and can easily truncate the prospect of businesses. As a result, the role of data scientists has become non-negotiable for companies looking to rise above the competition.

1.3- You get to grab the top-paying job

The emergence and prospect of big data has generated a significant number of job titles that pay attractive salaries compared to other tech-related jobs. We’ve already discussed that data scientists are needed by almost every industry. Today, the requirement isn’t only limited to the IT domain, but it has spread across all the major industries.

So, it can be said that for a data scientist, sky is the limit. In addition, data scientists who hold a Master’s degree or a Ph.D. degree can earn more than their relatively lesser educated peers and with a couple of years of experience, the pay packet would become even fattier.

1.4- You get to work in a rapidly evolving field

The field of data science is developing quickly and the necessary skillsets are also changing fast over time. Skills that are necessary to become a data scientist today may not be of that much importance tomorrow. So, as a data scientist, your learning never becomes stagnant.

On a continuous basis, you get the chance to work with new technologies and tools that are developed and encouraged. This kind of an exciting work environment is another key factor that motivates many to become a data scientist.

1.5- You get to work with multiple high-end tools

There’re very few fields where professionals get to work with multiple, advanced tools. Once you’ve succeeded in your effort to become a data scientist, you hold the chance to gain a huge amount of proficiency over multiple tools by working with them during various stages of your job responsibilities. For example, you may use SQL to capture and fetch information, process it using Pandas, develop machine learning models with the help of Python’s learning packages, use D3.js to demonstrate the results, and many more.

You may also need to use big data tools like Spark and Hadoop for performing certain tasks. This ability to work with a diverse range of high-end tools is another major triggering factor that encourages lots of people to become a data scientist.

1.6- You get the chance to work with the giants and at different roles

You can apply for jobs in giant companies like Apple, Amazon, Uber etc as a data scientist. For example, Apple utilizes big data to decide on its product features, while data science is used by Amazon to sell products by strategically recommending them to consumers. The surge pricing of Uber is a great example of how the company uses data science. Also, as a data scientist, you become able to fit into different roles when it comes to solving real-world problems.

Every single day, a huge amount of data is generated by people as well as large and small companies. To help the companies get useful and actionable insights from the captured data, the expertise of data scientists is required at different stages. Not every job in today’s world offers this level of flexibility and such vast and exciting learning opportunities.

1.7- You get to build a safe career

We’ve seen that many new technologies come and go, and that’s exactly why many people think that every technology that shines in the tech domain comes with a somewhat fixed tenure of existence. But this isn’t the case with data science. As a data scientist, you should always try to put your best foot forward to learn new technologies and tools, as and when they arrive. It has become evident that different aspects of today’s tech landscape, including data science, have started becoming automated. What it actually means is that some areas will experience automated processes but the data science field will continue to grow and the need for data scientists will continue to increase as well. Data scientists with the right skill and mindset will continue to be in demand, probably in wider fields than today.

Apart from these seven reasons, there’re some other factors as well that attract people to become a data scientist. For example, data scientists need to work with different technologies and different programming languages, and use different tools to solve real-world business problems. During those processes, they achieve mastery of various aspects. When you’ve this amount of expertise together with the knowledge of operational methods of different industries, it becomes much easier to establish own business. There’re lots of people who always dream about developing their own businesses and working as a data scientist can immensely help them in accomplishing their endeavors.

2- Things to keep in mind when you’re trying to become a data scientist

If the above reasons have convinced you enough to become a data scientist and you’re ready to start your journey, there’re some things that you should keep in mind to succeed. First of all, there’re different avenues to become a data scientist. You can take the traditional route or attend a data science bootcamp or be a self-taught data scientist. Among all these, the second route i.e. a data science bootcamp is the most popular and probably the most effective one to become a data scientist. When you’re getting enrolled in a bootcamp, go through the curriculum offered meticulously, and compare the cost, the success rate, and the post-program guidance with other data science bootcamps. Though by attending such a bootcamp, you’ll be able to become a data scientist in a shorter period of time and at a lesser cost, it’ll still cost you thousands of dollars and a significant amount of time and effort. So, it’s strongly recommended to take your pick wisely.

Conclusion

Finally, you should try to become a certified data scientist as a majority of the giant companies prefer certified candidates over non-certified ones. As a certified data scientist, it will not only become easier to get a coveted job but also greatly help you in gaining smooth promotions in the organization. Certification brings enhanced trust in a candidate as it’s believed that he or she will be able to handle the job responsibilities more efficiently than his/her non-certified peers.

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To learn more about data science, click here and read our another article.

The post 7 Things That Convince People to Become a Data Scientist appeared first on Magnimind Academy.

In fact, if you consider the cost of a data bootcamp with the big amount of money that you’ll need to spend on an equivalent master’s degree, the former would be just a fraction of the latter. And when you consider two years of education for the latter and weigh it against the fast-paced data bootcamps that can take anywhere from 12 to 24 weeks (or a bit more depending on the curriculum), it may all seem worth your money as well as time and effort.

The key in determining whether you should invest money on a data bootcamp depends on a lot of factors. Unless you have considered them all, making a decision simply on the basis of the cost factor alone wouldn’t be prudent.

But before we talk about the key factors on which the cost of data bootcamps depend, it’s important to ask yourself why you would like to join such a bootcamp. It’s equally important to know why learning new data skills as well as enhancing what you already know makes sense in today’s data-driven world.

So, let’s take a look at some statistics to understand why data science has become such a hot topic today, and the reasons behind the paucity of qualified data scientists despite a rapidly rising market demand.

1- What makes data science an interesting field to enter?

According to a McKinsey Global Institute report, the demand for qualified data scientists was growing yearly at about 12%, which was far above the available supply. Since then, the situation hasn’t improved. According to a LinkedIn report (August 2018), almost every large city in the US faces a severe shortage of data science skills. The national shortage of people with data science skills stands at a staggering 1,51,717. The McKinsey report predicts the US to be reeling from a shortage of 250,000 data scientists by 2024. And such shortage would exist despite the field of data science paying extremely well, much more than its similar counterparts.

You can understand how well paid data science professionals are by considering a few statistics. In 2018, data scientists’ mean base salaries stood at $95,000 for those who had 1 to 3 years of experience (non-managers), while those with over 9 years of experience earned more ($165,000). Those working as managers earned more with their mean base salaries varying between $145,000 (for having 1 to 3 subordinates reporting to them) and $250,000 ((for having more than 10 subordinates reporting to them).

2- What triggers the paucity of data scientists?

You may be wondering – if the field pays so well, why is there a huge shortage of professionals having the requisite skills? The reasons are many. Many aspiring candidates take up full-time data science courses and enroll into data bootcamps driven by false and misleading promises, but discover midway through the program that they don’t like it, aren’t cut out for it, or simply can’t handle the tremendous stress as well as put in the desired efforts to finish it. As a result, they quit or drop out.

Then there are some others who finish their courses but aren’t job reedy, either because they haven’t got the hands-on experience, or lack the skills required to handle real-world problems. In such cases, they have to look for specialized courses or data bootcamps that will help them bridge this skill gap and make them employable.

The ability to adapt quickly to changing technology, methods, and tools is another important skill to excel in the field of data science, which is again found lacking in many professionals that make them fall behind the competition, and ultimately, bow out. So, what’s the answer?

3- Why you should seriously consider attending data bootcamps?

If you have a background in computer science, mathematics, or statistics, you can think of entering the field of data science. Having some programming knowledge, especially in Python and R would be an added advantage for sure, but even if you don’t have it, you can always learn provided you’re serious about improving your chances of being a successful data science professional. And if you’re searching for the fastest and most effective way of getting your brain up to speed and ready for a data science career, hardly anything else can match data bootcamps.

If the word “bootcamp” makes you think about a military-style training camp where you would be rousted out of bed early on in the morning and made to do a two-mile jog, take heart for this isn’t it. The word “bootcamp” signifies an intensive, short-paced, extremely targeted curriculum with the aim of drilling a lot of useful information in your mind, which can be extremely overwhelming and hectic, just like a military camp is.

Yet, you won’t find a more practical and effective way to learn about and use the tools and techniques that data scientists use every day. And as you gain knowledge of the field, you’ll also get to meet and collaborate with a lot of link-minded people, some of whom are already working in the field while others are hoping to enter it soon. You’ll also get to learn from industry experts, who give you useful insights into what works in the real world and what doesn’t. By networking with a varied group of people coming from diverse backgrounds, you can end up making some good connections, which in turn might help you further your own career aspirations.

Unlike two-year data science master’s degree courses or Ph.D. in data science that would take longer, you can learn and master the key concepts of data science much faster by enrolling into a bootcamp that focuses on the field. And the best thing is the lot of choices that you can take your pick from.

When bootcamps took off in the 2014, they were originally all-day long, short-term, and on-site courses. But the scenario has changed a lot since then. Now, as a data bootcamp participant, you have a variety of options – from weekend and evening data bootcamps to the ones that are conducted totally online. Even in terms of duration, you have several options – from short-term ones that run for 8 weeks to the long-term ones that could be 20 weeks or even more. You can even take your pick from full-time programs or their part-time counterparts, the latter being most suitable for those who’re employed and don’t want to quit their jobs while learning new skills or honing the skills they already have.

Irrespective of your skill level and experience, you can be sure of finding matching data bootcamps (provided you can tick the basic requirement checklist) that let you learn your desired techniques and tools of the trade. So, apart from helping you with fast-track, effective learning programs, data bootcamps also offer you the flexibility in term of program choices, duration, mode of learning etc. But what to do if you are concerned about the big amount of money that you need to invest into such data bootcamps?

4- Should the high cost of data bootcamps deter you?

The answer is a resounding “No”. If you’re thinking of attending data bootcamps, you should be already aware that they virtually always cost money, and often, a big amount of money that can go up to $16,000 (or even more) in case of some highly rated and extremely popular ones.

However, you may also find some others that are usually called “fellowships” and could be offered for free to the qualified candidates. But be ready to face a tough and intense competition as typically, some of the brightest minds compete to get an entry to such fellowship programs. You can also search online to find data bootcamps that may offer financing options for candidates who can’t afford to pay the total amount at one go.

And if you’re wondering whether paying a big amount of money for a reliable and reputed data bootcamp is really worth it, we would say it is indeed. After all, if you choose your bootcamp carefully, it would deliver you the results that are a few times more than the cost you pay to get it done.

Final words

With the huge shortfall of data scientists and other data professionals, the need has arisen to get professionals with the right skillsets ready at a fast pace. And this is where data bootcamps are miles ahead of their traditional counterparts. If you have the right background (math/statistics/computer science etc), have the zeal and interest to make it big on the field of data science, are ready to toil hard to learn and master the required tools and techniques, and can’t wait to implement your knowledge for solving real-world problems, you should definitely consider attending a data bootcamp.

And once you’ve decided in favor of a data bootcamp, you shouldn’t defer it just because they involve a big amount of money. If you’re really driven, it won’t be that hard to arrange for the money or look for financing options. After all, once you land that coveted data science job, you would soon repay the loan and debt, if any, and start your journey toward financial independence that comes with a lot of perks, a major one of which is working with a futuristic technology that’s set to only grow bigger and better in the forthcoming years. So, if you’re in a dilemma about attending a data bootcamp, go for it now!

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To learn more about data science, click here and read our another article.

The post Do Data Bootcamps Require a Big Amount of Money? appeared first on Magnimind Academy.