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Tag: Data science course

Why Data Scientists Should Follow Software Development Standards?

Posted on by Imarticus Learning

Introduction

Technology has become the flagbearer of changes to which we are subjected to daily. Therefore, it impacts us in every possible way. How technology comes to us should mostly positively affect us. Therefore, it becomes important for the people driving this change to adhere to some pre-defined standards for improved quality of work and standardization of the same.

Data Science has come a long way. It has become one of the most popular subjects giving people the best in class in jobs and putting them in a position of the drivers of change. A Data Science course in Chennai would help you in becoming employment ready.

Data Science has enabled handling the bulk of data with ease. With Data Science you can drive different conclusions from the same set of data. You just need to change the algorithm.

Who is a data scientist?

Your Data Science career can bring a lot to the table. Initially, the word ‘Data Scientist’ was used for people who used to organize and analyze a huge amount of data. However, the role of a data scientist has drastically evolved in its due time course.

Today, data scientists develop algorithms that make sorting, compiling, and analyzing the sets of data a cakewalk. Effective data scientists have standardized the processes and have developed a standard procedure to work things out. These data scientists are technically well-equipped and can build complex algorithms which can be repeatedly used to make a task easy.

They have a strong quantitative background and are usually result oriented. Also, they have extensive knowledge of different programming languages like R, Python, Tableau, SQL, etc. As the demand for automatization is increasing, data scientists can access more and more jobs.

The need for data scientists to follow Software Development Standards

Standardization is important everywhere irrespective of the field. Therefore, these data scientists need to adhere to a specific set of software development standards that are already in place.

In the times where cybersecurity is a major issue, it is really important to have some software development standards in place. This would ensure that the new software is being designed keeping in mind these standards which will consider the safety and security of data and information of the end-users of that particular person.

Development standards have been also designed to keep uniformity across the organization. These standards ensure that the work output is generated at a certain level. Also, with software development standards, a set of consistent rules are laid down which makes the job of a data scientist quite easy.

With Software Development standards, you can use the same algorithm for different purposes with slight modifications. Also, it ensures that the program written by a data scientist is clear and understandable and adheres to the statistical principals. With standardization, codes will be written in a language that is understood by all.

Having simple rules is important. Software development standards follow a structured approach when it comes to writing a code or designing software. It bridges the gap between your research and the final product which you want to build.

These standards are up to date and are formulated keeping in mind different quality assurance standards. This would ensure that a quality product in the form of codes is delivered. With the implementation of these practices, it would be really easy for the data scientists to meet the requirements of their customers and deliver quality results.

Conclusion

Following a set of standard procedures can make the work of data scientists’ error-free to a great extent. Also, it enables easy quality checks ensuring good delivery of an end product.

 

5 Tips To Successfully Start a Data Science Job Remotely!

Posted on by Imarticus Learning

While the news of mass layoffs has inundated the market, certain industries continue to hire with one eye on the future. The data science realm is one such job market. Quite a lot about the recruitment and onboarding processes have changed; this makes transitioning into a new role a lot more complicated.

Keeping all this in mind, it is imperative that, as a candidate, you take things into their own hands. You can prepare an action plan to approach the first day of your remote data science career with enthusiasm– and this post will help you along the way.

Tip #1: Ask for A Preview of the Process

Proactively arm yourself with a blueprint of the onboarding process– this is especially relevant in current remote working scenarios. Depending on the job role you’ve been hired for, your onboarding process may be elaborate or short and snappy. Understanding what it will look like for you is a great way to avoid spreading yourself out too thin in the first few days or virtually walking in without a clue. It will also highlight any gaps you may need to fill in your skillset, in which case you might need to enrol in a data science course.

Tip #2: Reach Out to Your Teammates

It’s much harder than usual to connect with first-time teammates and colleagues in a virtual environment; however, since someone has to do it, it can be you. Not only will this allow you to establish your presence and role in the team, but it will also paint a favorable picture of you in times when first impressions are rather restricted to screens and voice calls. Try to gauge how best your team works, what communication tools they use and what they do outside of work. This personal rapport will go a long way.

Tip #3: Ensure You Have Continuous Access to Technology

Technology is the backbone of the remote working process– especially so for data science roles. Before your first day, it is a good idea to take stock of all the tools you have and how you can add to them if required. You can first start with hardware– laptops or desktops, sufficient working space, additional accessories– before moving on to software. If you find that you need something to perform your role, it is always advisable that you reach out to the onboarding team and see if they can help.

Tip #4: Be Forthcoming in Your Questions and Help

A virtual environment makes it significantly more difficult to read and react to facial or virtual clues. If you’re curious about something or don’t understand a task, it is best to be forthcoming about it. This tactic leaves no grey areas or causes for misunderstanding. Similarly, don’t hesitate to offer help where you feel like you have more to offer. This tip will make you a more valued member of your team as well as cement the skills and talents you bring to the table.

Tip #5: Weigh in Your Emotional Responses, Too

When starting a data science career remotely, it is easy to feel lonely and disconnected with your teammates despite working on the same projects. However, it is always recommended that you check in with yourself periodically and understand if you are adjusting. Reach out to colleagues to build a friendly rapport with them. Take time away from the computer and stick to strict work hours as much as possible so you don’t burn out.

Industries across the board have shifted operations to a work-from-home basis in order to cease the spread of COVID19. If you’ve been lucky enough to land a remote data science job, it’s best to head into it with a determined mind and an action plan in hand!

Does Data Science Require Coding?

Posted on by Imarticus Learning

Data science has been gaining significant traction over the past few years. Myriads of people working in several areas including, business and IT look to shift to this emerging career option. Moreover, individuals with immense expertise (over 10 years) want to switch to data science.

Going for a data science course begins with numerous queries such as “Does data science require coding?”, or “What are the requisites to learn data science?” It is not necessary to be an expert coder to become a data scientist. Insufficient coding skills should not preclude people from pursuing a data science career. There has been a notion recently that people need excellent coder to become a data scientist.

Undoubtedly, coding is essential in data science but that does not imply that you need to be a hardcore coder to go for a career in the field. Industry executives reckon that anyone who knows the fundamentals of coding – functions, loops, and programming logic – can flourish as a data scientist. Having coding skills already is a plus point in a data scientist job but not compulsory. Then, what about those who have never learned to code earlier? Is there any other way they can become data scientists?

Tips for Non-Coders Learning Data Science

Become an Excellent Storyteller

If you think that vital business decisions rely on data and other quantitative parameters, you are wrong. Even after a machine learning (ML) model is developed and assessment is done by people expert in coding, somebody has to present the outcomes to the shareholders who are well oblivious to programming languages or statistical models. This accents for the need of a story woven around the insights to convince shareholders quickly. You can become that individual with exceptional storytelling abilities in spite of having mediocre programming skills.

Get a Grip on GUI-based Tools

If you are not much of a coding person, then the first thing you can do is to learn the application of GUI-based tools. There are many graphical user interfaces (GUI) supported data science tools that exclude the coding aspect and offer a user-centric interface that aids everyone with the fundamentals of algorithms.

The tools are quite easy to use to develop top-notch ML models sans coding. The majority of these GUI-based tools can be accessed for free and allow you to assess and elucidate data via charts, graphs, and other special graphics.  You do not have to display exceptional coding skills to efficiently leverage these tools but instead having a knack of visualization does help.

Enhance your Credibility with Business Intelligence

If you an expert in insurance or have comprehensive experience working in the retail sector, it is good news for you. You certainly are aware of the nitty-gritty and intricacies of businesses compared to expert coders. If you are highly skilled in areas such as healthcare, and e-commerce, you will be an asset to any company. No certification of expert coding skills can beat business intelligence in a particular area for a long period. Capitalize on your domain expertise and abilities and become the data science wizard.

Final Words

At present, the success mantra to grab a data scientist job in any company is, “The More You Know, The Better It Is.” Although companies prefer professionals with specialized coding knowledge, they are increasingly channelizing their attention towards candidates exhibiting a diverse skill set.

To wrap up, you need not be a die-hard coder or programmer to become a great data scientist.

Is Data Science a Good Career in India?

Posted on by Imarticus Learning

In India, a lot many academic decisions are taken on the basis of how good a career one will have after graduation. This is all the more true for STEM streams like data science and data analytics that make up some of the hottest courses (and professions) in the country.

Aspiring students are signing up for these courses across universities in large numbers. But is a data science career in India a good bet? Can you lead a prosperous and content life by becoming a data scientist? Let’s find out.

Is Data Science Career in India Promising?

The short answer is yes.

There is enough research to support that a career in data science is promising and that it is a high-demand skill right now. Why? Because it’s a stream that has seen a spike in demand globally since the start of the 2010s decade.

A lot of data is being generated, but the number of people who can process them is still relatively negligible. Although tech startups are mushrooming in every corner of the world, there is still a gap in demand and supply. New technologies, tools, and systems are being invented in the stream but there is still a lack of talented and skilled professionals who can drive them. This points to the reason why there is a growing need for data scientists.

Companies across sectors need insights to aid in their decision-making. This is why industry analysts around the world are regularly commenting on the gap between demand and supply. The demand is only growing – from behemoths like Amazon.com to smaller local startups needing data and insights – while the supply of professionals remains truncated.

A reason for the dim supply is the overall poor understanding of data science as an academic field. It is a complex course and one that requires in-depth academic proficiency and experience.

What Does a Career in Data Science Look Like?

Before becoming a data scientist in India, one should understand what the stream is and what all will entail in the profession. Only then can one make a decision if they are fit for the role.

As noted above, it is a STEM degree. And according to Northeastern University, a data scientist is a ‘computing professional who has the skills for collecting, shaping, storing, managing, and analyzing data [as an] important resource for organizations to allow for data-driven decision making.’ It then takes the example of Amazon as one of the largest organizations utilizing data science for its business development.

Take, for example, all the purchases made on Amazon.in in a single day. The data that is generated from these purchases – types of product, prices, the weight of the package, locations, etc. – are then collated to understand user buying behavior among other things. These insights are then used to better the process.

When you become a data scientist, you will begin as an analyst and mainly work with streams of huge data. It can be purchases made on an eCommerce website as in the example above or the data about Covid-19 cases in a specific geography. Whatever it may be, your role as a data scientist will involve collating and analyzing data. Tools like Microsoft Excel, Tableau, Power BI, MATLAB, and Apache Spark will be heavily used.

To conclude, a data science career in India is a good bet considering the world is quickly moving into a data-driven world. If you have an academic background in mathematics and engineering, then it’s time to take it up seriously and become a data scientist.

A Step-By-Step Guide For A Smooth Career Transition To Data Science!

Posted on by Imarticus Learning

Are you an electronics engineer looking to move to a data science career for a better-paying job? Or are you a commerce student who has recently taken to data science? This simple data science career guide will help you transition to the field and become a data scientist.

Step-By-Step Guide to Becoming a Data Scientist

Follow these four steps to move from any field to a data science career.

Step 1 – What field are you in right now?

The first and the most important step in this process is to understand your current academic and professional standing.

Are you still a student? Or are you working at a job in another field right now?

Step 2 – Gain Data Science Qualifications – Study or Work

If you are still studying, you have two options. Either wait and complete your existing degree (especially if only a few months are left) or immediately switch to a data science course. The latter can be a difficult situation, but when you look at the number of years you will further waste in that degree, it will make sense to you.

As a student, your goal should be to gain at least a degree, a certificate course, or a diploma in the field. Without an educational qualification, becoming a data scientist will be challenging. Then you will need a lot of hands-on experience and projects in your CV to prove that you are an able data scientist.

If you are working, the transition will take a bit longer for you. As noted, gaining some education in the field should be your higher priority. In case it is not possible due to financial or any other reasons, you can look at online courses.

Whatever you do, make sure you have some experience – academic or professional – before you move to step 3. In an ideal scenario, a little bit of both will improve your chances considerably.

Step 3 – Create a Solid Data Science CV

Making a data science career for yourself should start with you making a CV of yourself. Once you are satisfied with your own academic and professional qualifications, a CV is your pathway to reach out to potential employers.

Here is a list of activities that you need to do one after other:

  • Create a detailed CV that is no longer than two pages
  • Update and build your LinkedIn and Naukri.com profiles (among other job websites)
  • Create a web portfolio that lists our your academic and/or professional projects in the data science field

Once you have any or all of these three, move to the next step.

Step 4 – Apply for a Dozen Jobs Daily

The fastest way to build a data science career is to aggressively apply for jobs. You need to send out at least a dozen applications – via LinkedIn, Naukri.com, company career pages – every day to even get a response.

The Covid-19 situation can be a challenge here but staying aggressive is the only solution. Since a data science career entails working on a computer, employers are still looking for talent.

Following these four critical steps can pave your way to the goal of becoming a data scientist. It is not as easy as it looks and will require a lot of hard work on your part. Nor is it a quick way to get into a data science career.

On average, you can expect to spend at least 2 to 4 years in this process if you are looking for a successful transition regardless of your current professional situation.

What Are The Steps To Become a Data Scientist From a Non-Technical Field?

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A data scientist is a professional who is in the authority of collecting, analyzing and understanding large amounts of data. This job deals with advanced analytics technologies like machine learning and predictive modeling. Some of the basic responsibilities of this role include collecting and analyzing data, using different types of reporting tools to detect the trends, patterns, and relationships in various data sets. In today’s scenario, a data scientist is one of the best professions to pick as a career.

Scope of a non-technical person as a data scientist

To understand the scope, firstly, the term non-technical person should be defined. It refers to a person from a non-engineering background. Basically, the person may be from any educational background but should have the right approach. He or she should be ready to put in a lot of time and effort. Self-motivation is a must to mentally prepare oneself to learn whatever is essential to become a successful data scientist.

Try enrolling in a good data science course to give shape to the career. Eventually, you will realize the time invested in learning the matter will prove to be one of the best long term investments in your career.

Steps to become a data scientist from a non-technical field

Coming from a non-technical background, to become a successful data scientist following this six-step guide can prove to be really helpful:

  • Broaden the skill level with the help of a planned course – For those who are a fresher in the area of data science, they should enroll in a well-structured course. An ideal syllabus should cover the basics of the programming languages R and Java, Big Data handling, deep learning a part of machine learning, data visualization, probability, and statistics.
  • Get in touch with some mentors in this field – When venturing into any new field, a mentor’s guidance plays an important role in guiding one through the best path. Getting in touch with an experienced person helps in building a network and getting valuable lessons.
  • Try to attend every event held in the town or online – Attending such events are a great way to gather information from industry experts in this In-person events are better as that has a scope of open conversations.
  • Appear for mock interviews – Only preparations are not enough. When one is looking for a job opportunity in data science, having a basic idea of what the hiring managers are seeking, is very important. Attending mock interviews is the best way to measure one’s expertise level.
  • Never compromise with the basics – If one is serious about a career in data science, then familiarity with any one of the basic areas is really important. This helps in tuning the intellectual capacities of analyzing and interpreting data.
  • Stay open to learning new things – In the field of data science, remember there is no end to learning. So, don’t hesitate to learn new things from your peers or seniors as you move ahead in your career path. But remember there should be a methodical approach in whatever you do. Keep strengthening your basic knowledge and read books related to data science.

Conclusion

Following these simple steps will make one’s transition from a non-technical field to the domain of data science not only interesting but also hassle-free. To get the real feel of this process of shifting, it is recommended that one checks out the online videos of some real-life examples of people who made it possible and are successful data scientists today.

How Is Data Science Changing The Process Of Web Design?

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A data science course will cover a lot of ground in learning about data sciences and its interlinkages with the creation and management of data and involves research and studies into how data is used to accomplish tasks that can change processes in web management and design.
Let us now explore how AI and data sciences have actually impacted web design.
AI and data analysis:
Today’s AI devices no longer depend on humans for the input of data or limit their insights to only the data inputs provided. Rather, self-learning AI devices are scoring and ML algorithms can not only clean and format large volumes of data they have turned self-learning and making predictions from data across the board in different formats and from different sources a breeze. This impacts web design too, as like AI it depends on the human interpretation of data and making gainful insights.
The web design process:
The normal process of web design will start with seeking information from various surveys and focus groups and this data is banded together, organized, cleaned, formatted, assimilated and organized by a team of human beings. Next, the coding process begins. A model or prototype for coding is drawn up and this is tested through beta-testing.
Once it emerges successfully from testing the software is declared ready for use. However, many inadvertent flaws end up in the code and land in the final design for want of effective prevention methods to vulnerabilities in coding. These are set right while in use through newer versions and updates. The modern Data Science Course uses AI and data science to create and reuse code to make it secure against such vulnerabilities.
Focus on AI and Data Analysis:
A change from the current methods of web design allows AI and data analysis to function differently from the current focus groups. Data that is collected is taken up by AI for in-depth data analysis using the vast swathes of Internet resources. This provides for the ability to overcome human errors, improve coding, streamline the creation process and create more security. This, in turn, increases the traffic of users, figures in the search engines with SEO optimization and enhances the web designs.
Use of Code:
The use of coding is an integral part of web design. What will, however, change with AI handling the coding from scratch is that one would no longer look for solutions to fix vulnerabilities. It would just mean perfect coding without vulnerabilities and most users, governments, businesses, and enterprises using such data science course solutions will no longer have to worry about Internet threats and hackers or data breaches and unauthorized tampering of data. Web designing stands to gain in many ways like keeping the app developers and owners of the websites free from the problems of the past, ensuring the protection of proprietary data, and encouraging the online conduct of business.
Apps and new versions:
In the near future, it will be AI that decides in conjunction with ML, DL and more how new software gets designed, why a website menu needs to be a certain way, which apps are best suited and whether updates or re-versioning is needed for any particular app. With the increased sophistication brought in more repetitive tasks will be handled by bots and apps based on AI and thus leave more quality time for business decisions and doing business online. At the end of the day, manual human intervention will also be more sophisticated and need more in-depth skills to handle such changes. Web designing is surely set to become possible on much smaller budgets.
Opportunities for web designers:
A very potent question doing the rounds today is that AI will eventually cost the jobs of current web designers and coders. Just remember that technological advances do mean some jobs will be displaced but at the same time newer requirements and jobs are being created for those willing to tweak and make good use of their skills. AI is never about replacing human intervention. Rather it is about aiding human intervention in data sciences, ML, DL, and other emerging technologies. Experienced web designers should do a re-skilling course to stay abreast of the changes and be exposed to newer emerging jobs where the payouts and demand are bound to be much higher.
The Bottom Line
On the never static Internet subjects like AI, DL, ML and such emerging technologies hold great potential and should be embraced by doing a data science course. The reputed Imarticus Learning Institute is where you should head to for comprehensive learning and skill assimilation in emerging technologies.
For more details, you can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon, and Ahmedabad.

Evolution Of Data Science In India

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Evolution of data science in India
In today’s bustling world an unprecedented amount of data is being generated by businesses and firms. The pinnacle of data proliferation pressurized on the businesses to employ specialized professionals apt for the tasks. The amount of digital data that exists today is at a staggering rate and is bound to increase by a large extent in the future. Data science is considered a young profession or a relatively new concept which became popular after a decade in 2010.
Initially, statistics and statistical tools were implanted in data science which later clubbed with the growing technology and newer and sophisticated concepts like Artificial Intelligence, Internet of Things and Machine learning for better results. Data science is basically the study of data which is in structured or unstructured form. It is a process of collecting, storing, analyzing and processing the data using various statistical tools and machine learning for producing meaning insights.
Revolutionary data science in India
Before the arrival of data analytics, the blue-chip companies and consulting businesses ruled the analytics market. By removing the barriers in the industry cloud technology has made it possible for start-ups to emerge in this field fearlessly. Big businesses to start-ups all rely largely on data science for solving their simple to complex problems.
Hence, it is appropriate to say data is the new oil for businesses. Many developed countries like USA, UK, Singapore, and Australia are a favorite destination for Indian analytic experts next to software professionals. A study by Analytics India Magazine reveals that the US pays $11b to India for data analytics annually.
The evolution of data science in India is tremendous and hence India is amidst top 10 countries for analytics in the world with over 600 analytic firms out of which half of them are start-ups and the number is expected to increase with the increased efficiency in its solutions and products.
Banking and finance are the prominent industries that use data analytics and revenue generated from these industries is more than 30% in India. Apart from finance and banking, marketing, pharma, advertising, and healthcare are other sectors which rely on data analytics by a large extent. Thus, data analytics, data science, and big data industry are set to double in India by 2020.
Digitalization banking data science to a great extent
In the present business, data is considered the most valuable thing even more vital than money, since data analysts use them for figuring more opportunities. Data science is a multi-disciplinary subject which effectively produces smarter business moves convincing the customers generating more revenue. Digital business is able to build a larger customer base by utilizing smarter data analytic techniques to identify the tastes and preferences of individual customers in India and brings about a satisfying solution.
Once online retailing has established a positive outcome from using big data to understand its customers, the same big data technique is applied in different sectors like engineering, medical, academic research, and social science to name a few. The availability of the huge amount of data has led to its widespread application for arriving at effective and efficient solutions or products.
Cloud and its impact on millennials favorite data science 
Cloud computing has created a revolution in data sciences which has made data centers more accessible at moderate prices thus creating a boom in the Indian market. The trending fact is that earlier only Bangalore and Delhi were major contributors in this sector in India but Pune, Chennai, and Hyderabad are not far from joining the racing market.
Data science is a vast sector with the widespread application of its techniques business identify opportunities, frame better goals and create productive solutions. This has created more demand for professionals who can analyze and scrutinize the data by understanding better insights within the data.
To flourish in this exciting field full of challenges one needs Data Science Training for gaining a comprehensive knowledge of the subject. The millennials like data science as it does not pose and restrict their functionality. Data science and millennials are interconnected as highly responsive and engaging marketing based on their preferences using data analytics drive today’s marketing with the use of advanced cloud technology.
To sum up
As you can see the evolution of data science over the last ten years has been tremendous and will continue to do so with the splurging demand for data analytics across many sectors. Data science is a promising sector which got prominent attention with the advancement in technology. As consumers are embracing digitalization, more scope of data science in India is inevitable.

What Are The Best Online Courses in Data Science Using Python?

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Truth be told, today it is all about data and using it to further business growth and productivity using cutting-edge technology to sift through huge volumes of data. The data and its analysis provide gainful insights that are used in strategic decision making, various business-related predictions, and for gaining foresight into market conditions.

Online Vs Classroom courses:

Online courses are trending and are the latest in a series of measures to equip yourself with new skill sets. You may be either looking to make a career or change professions. Such courses fill the gap in addressing the chronic shortage of trained personnel in the data science field. While the online courses do not make experts of you, they serve the purpose of giving you an overview of the subjects involved and making a generalist of you. You may also find a few free online data science certificate courses.

In contrast to the online data science certificate, the paid classroom certification courses help acquire hands-on practical-learning applications,  an improved skill set, an effective framework for learning and mentorship,  and boosts your confidence. Many also help you acquire certifications that come in handy as measurable proof of your being able to practically apply your skill sets to work issues and industry-relevant applications. However, in terms of being useful to get your dream career and job, remember that many aspects of learning about data sciences and programming languages are best learned through the hands-on approach.

Why Python?

Python is a free open-source general-purpose programming language that is very useful in data sciences. It allows you to create CSV files that help read data in spreadsheets. It also permits other complicated outputs using computational ML clusters. It has a wide range of available libraries like Pandas for tasks like data import from Excel sheets, processing time-series analysis and everything in between. Its ML libraries like PyBrain and Scilkit-Learn provide ready-made components and modules for data processing and neural networks. It is a bit slow but makes up for its excellent compatibility with other languages, simple syntax, and applicability to varied verticals.

Requisite Educational Qualifications:

Most online data science courses are introductory and fundamental in nature and do not require any formal qualification or degree. Data analysts can definitely use the resources online to learn data analytics when they have a good understanding of subjects like Computer Science, Mathematics, Statistics, Economics, Engineering, etc. Allied subjects like Machine Learning, Neural Networks, Deep Learning, and such courses are also available in the mode of online courses.

Online courses do improve your foundation in theory. Many career aspirants pursue such online data science certificate courses at reputed training institutes to equip themselves on how to apply their learning to different situations and verticals. Classroom learning, however, becomes essential to pick up crucial skills like comprehensive data capture, organization of databases, cleaning of data, applying insights to business decisions and strategy and the effective presentation of the insights gained. Proficiency in Microsoft Excel techniques, having good mathematical abilities and the knowledge of statistics are huge advantages.

  • The criteria for selecting these courses are that the online data science certificate
  • Cover all relevant data science process topics.
  • They use free open-source libraries and tools.
  • Basic machine learning algorithms are covered.
  • It covers basic applications and the theory behind them.
  • Projects, assignments and hands-on supervised practical sessions are provided.
  • Lead instructors are certified, engaging and presentable.
  • Courses are rated at least 4.5 on a scale of 5.
  • Frequency of courses can be on-request or monthly.

Conclusion:

Learning reinforcement and hands-on practice scores! With so many resources and a learning data science in Python for free and on one’s own is never easy. It emerges that the paid-courses are better than the online courses in Data Science for their widely accepted certification, mentorship by certified trainers, personalized personality-development modules, a skill-oriented approach with tons of practice and assured placements.

The Imarticus Learning courses deliver skilled well-rounded personnel in a variety of latest technology courses which are based on industry demand. If you want to be job-ready with data science certification from day one, then don’t wait.

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Delhi, Gurgaon. Hurry and enroll.

Good Ways to Learn Data Science Algorithms, if Not From IT background?

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At the beginning of your career in data sciences, algorithms are hugely over-rated. Every routine task, every subroutine, every strategy or method you do or write is because of an effective algorithm. In essence, all programs are formed of algorithms and you implement them with every line of code you write! Even in real life, you are executing tasks by algorithms formulated in your brain and just remember that all algorithms are simulations of how the human brain works.
Just as you begin with baby-steps and then worry about speed and efficiency it is a good routine to start your Data science career with the algorithms if you are not from a computer science background. And there are hordes of resources online that you can start with. Some people prefer the Youtube tutorials to reading books or even a tandem process including texts and videos which is fine.
As a beginner of a Data Science Career, your focus should be on making your algorithm work. Scalability comes much later when you integrate writing programs for large databases. Start with simple tasks. You will need to learn by practice and with determination laced with dedication. Don’t give up, as you never did, when you started walking or talking in English!
At the onset of learning, you will need to:

  • Understand and develop algorithms.
  • Understand how the computer processes and accesses information.
  • What limitations does the computer face when executing the task on hand?

Here’s an example of how algorithms work. Though huge amounts of data are stored and processed almost instantly, it can process/access only one/two pieces of information every time. This is the basis that algorithms use for simple tasks like finding the lowest/ highest number. An algorithm is essentially a series of sequential steps that helps the computer perform a task.
Starting with very basic algorithms for finding maximum/ minimum numbers, identifying prime numbers, sorting a list, etc will help understand and move to more complex algorithms. Modern times computer scientists use the suite and libraries of optimized and developed algorithms for both basic and complicated tasks.
For one who is not from a computer science background here are the basic steps to learn algorithm writing.

  • Begin with basic mathematics needed for algorithmic complexity analysis and proofs.
  • Learn a basic computer language like the C Suite.
  • Read about data science topics and the best programming practices:
  • Study algorithms and data structures
  • Learn about data analytics, databases and how the algorithms in CLRS work.

Learning algorithms and mathematics:
All algorithms for a  data science career requires proficiency in the three topics of Linear Algebra, Probability Theory, and Multivariate Calculus.
Some of the many reasons why mathematics is crucial in learning about algorithms are: 

  1. Selecting the apt algorithm with a mix of parameters including accuracy, model complexity, training time, number of features, number of parameters and such.
  2. Selecting the validation of strategies and parameter-settings.
  3. Using the tradeoff of Bias-Variance in identifying under or overfitting.
  4. Estimating uncertainty and confidence intervals.

Can you learn Math for data science quickly? The answer is that it is not required for you to be an expert. Rather understand the concepts and applications of the math to algorithms.
Doing math and learning algorithms through self-learning is time-consuming and laborious. But, there is no easy way out. If you want to quicken the process there are short and intensive training institutes to help.
While there may be any number of resources online, mathematics and algorithms are best learned by solving problems and doing! You must undertake homework, assignments and regular tests of your knowledge.
One way of getting there quickly and easily is to do a Data Science Course with a bootcamp for mathematics at Imarticus Learning. This will ensure the smooth transition of math and algorithmic data science applications. At the end of this course, you can build your algorithms and experiment with them in your projects.
Conclusion:
Algorithms and Mathematics are all about practice and more practice. However, it is crucial in today’s modern world where data sciences, AI, ML, VR, AR, and CS rule.
These sectors are where most career aspirants are seeking to make their careers because of the ever-increasing demand for professionals and the fact that with an increase in data and development of these core sectors, there are plentiful opportunities to land the well-paid jobs.
At the Imarticus Learning, Data Science career course, you will find a variety of courses on offer for both the newbie and tech-geek wanting to go ahead in his/her career.
For more details, you can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.
Start today if you want to do a course in the algorithms used in data sciences. Happy coding!