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Category: Analytics

What is the Learning Curve for Python Language?

Posted on by Imarticus Learning

What is the Learning Curve for Python Language?

Most people will tell you that Python is the easiest language to learn and should be one of the first languages that you should learn when considering a career in Python programming. Well, they are mostly right, parting with a good piece of advice. And most probably you should take these comments seriously.

However, before you kick start with unclear expectations, you should be clear about what does it truly mean by ‘learning the language’, is it being a pro and acquiring absolute knowledge of Python, or to begin with, working knowledge, that helps you start with the basics, while you can continue to learn and gain additional knowledge on the go. Python is an awesome choice, with a relatively faster learning curve, which is determined by various factors and disclaimers.

best data analytics and machine learning courses

For starters, Python should be your first programming language, simply because not only will you be able to pick up the basics quickly you will also be able to adapt to the mindset of a programmer. Python is easy to learn with a steady learning curve.

Especially when compared to other programming languages that have a very steep learning curve. Mainly because Python is very readable, with considerably easy syntax, thus a new learner will be able to maintain the focus on programming concepts and paradigms, rather than memorizing unfathomable syntax.

For those thinking that Python is said to be too easy to learn, perhaps it might not be sufficient, and hence while it could have a gradual learning curve, in terms of applicability it might not be adequate, don’t be misguided. Python is not easy because it does not have deep programming capabilities, on the contrary, Python is superefficient, so much so that NASA uses it.

So as a beginner, when you start adapting Python to your daily work, you will notice that with a combination of theoretical learning and practical applicability of the same at work, one will be able to accomplish almost anything they desire to, through its use. With the right intent, applicability, and ambition one can even perhaps design a game or perform a complex task, without prior knowledge of the language.

The learning curve for Python also depends on certain obvious factors like your prior knowledge, exposure to the concepts of programming, etc…

If you are a beginner, devoting a couple of hours on understanding the language, then say in a month, you will be able to get a good feel of the language, mostly so if Python is your first language. If you have previous knowledge of programming, Javascript, C++, or if you understand the concepts of variables, control loops, etc…, then your hold on the language is even faster.

Either way, when learning is combined with practical real-life applicability, within a few days or a month you will be able to write programs, mostly expected out of a new learner. If the same method of learning is adapted for a month or two, along with exposure in programming, one will gain knowledge of the built-in functions and general features of the language. This will help and build confidence in the new learner to enhance their capabilities in programming.

Once the basics are in place, a new learner can then delve further to leverage the power of Python’s libraries and modules which are available as an open-source.

To conclude, it is a fact that Python is designed to be used in complex programming, yet at the same time, it is easy to learn and is truly a lightweight language. And once the basics are in place you can take up tutorials and advanced courses, to enhance your understanding.

How to Up skill Your Career in Big Data Analysis?

Posted on by Imarticus Learning

We are in the midst of a digital revolution; it only means that Big Data analytics is, and will continue to be one of the most important fields, working as a catalyst, to assist businesses to unveil the insights from data and aid business growth. It is predicted that in the coming years, big data analytics will continue to grow by leaps and bounds, and will impact our lives in ways beyond our current understanding. Therefore, initiating a Career in data analytics or big data could be the wisest move that one makes. However, breaking into this field is not as easy as it sounds, as there is a combination of skills and technology available, a person planning a career in data analytics might not know which skill to pick up, and where to start from.

You need to be academically relevant to the field, like a Bachelor’s or a Master’s degree in the relevant filed like, statistics, math, or a PhD will further improve your chances of not only professional success but great salary packages as well. So if you have a chance to acquire an advanced degree please do so. If not, and you are in the mid of your career than picking up a course or certification would be the next best option. Certified Analytics Professional, Data Science Associate, PG Data Analytics by Imarticus Learning, Big Data and Hadoop by Imarticus Learning, and other similar certifications are available to choose from.

A career in data science will need to be of two types 

Technical Skills

You will need to upscale or build on the already existing technical skills to succeed in this ever-evolving field. Working knowledge of Statistics, understanding the concepts of Data Mining, Data Science with R, Python or SAS, Application of Machine Learning, Deep Learning and Artificial Intelligence, Software Programming Languages and Database Management, and lastly Data Visualisation, are a few skills, of these you could choose to build deep knowledge in a few or all skills, depending upon the scope of work interest and business requirement.

Business Skills

A person planning to work in the analytics field, irrespective of their level and designation, should know how to correlate their tasks with the larger picture, hence Business Acumen, Inquisitiveness in Problem Identification, Approach and Solution, Creativity and most importantly Communication are a few non-negotiable skills to be developed, so that in this ever competitive data world you can get a hold on the desired position.
This field of big data and analytics is not static, in the coming years, there will be a technology explosion, demanding change and flexibility from professionals. If your intent is to enter the field or sustain your position in the field, then upgrading your skill-set becomes essential.
No matter how analytically sound you are, and how great your knowledge is in advanced analytics, nothing can teach or replace the ability to think through a situation. The way big data functions now, is changing, and new technologies will replace the ones mentioned above. But if you are naturally inquisitive and motivated to dig deep, with rock-solid determination to find answers or solutions, then you will always be a desired candidate for corporations.

How Should You Learn Python For Machine Learning And Artificial Intelligence?

Posted on by Imarticus Learning

In an era where Machine Learning/ML and Artificial intelligence/AI rule the roost of technology and analytics one can understand why Python experts are most sought after. With the advent and use of AI and ML in everything you do, there is an urgent need for collaborators who can tweak software, create new applications, use the predictive and forecasting alerts and insights gainfully to improve profits, efficiency and save time, effort and costs. It is still early days and the right time to upgrade and re-skill with machine-learning courses that will enable smart and creative use of Machine learning benefits. Big-data Hadoop training courses are also required to help ML understand and use the mind-boggling quantities of data that is now usable. Without the will to effectively use data and the training required to adapt you will be left far behind. The situation today is adapting, or die!
Python’s library versatility:
Learn-by-doing for tasks involving data analytics in Python machine learning which will help in the following.
Web development is simplified with Bottle, Flask, Pyramid, Django, etc especially to cover REST APIs at the backend.
Game development is not so difficult with Pygame where you can use the Python modules to build video and animated games.
Computer VisionTools like Face detection, Opency, Color detection and more are available for specific tasks in the Python suite.
Website Scraping that cannot expose data without an API is regularly undertaken using Python libraries like Requests, BeautifulSoup, Scrapy, Pydoop, and PyMongo by e-commerce sites for price-comparison, data and news aggregators and others.
ML algorithmic tasks like predicting stock prices, identification of fingerprints, spam detection and more using AI and ML is enhanced in Python’s modules and libraries like Scikit-learn, Theano, Tensorflow, etc. Even Deep Learning is possible with Tensorflow.
GUI desktop cross-platform applications can easily be developed with the Python modules of Tkinter, PyQt, etc.
Robotics uses Raspberry-Pi as its foundation for coding in Python.
Offline/online data-analytics needing data cleaning and being sourced from various databases can be achieved using Pandas. Find patterns and data visualization with Matplotlib which is an essential step before executing the ML algorithm.
Automation of browser tasks like FB posts, browser opening, and checking of status are rapid in Python’s library Selenium.
Tasks in Content- Management including advanced functions are quicker executed in Django, Plone, CMS, etc.
Big-Data handling libraries in Python are more flexible and can be used as effective learning tools.

Why Python?

Data Science and its analytics require good knowledge and the flexibility to work with statistical data including various graphics. Python is tomorrow’s language and has a vast array of tools and libraries. Its installation program Anaconda works with many operating systems and protocols like XML, HTML, JSON, etc. It scores because it is an OO language well-suited for web development, gaming, ML and its algorithms, Big Data operations, and so much more.
Its Scipy module is excellent for computing, engineering and mathematical tasks allowing analysis, modeling, and even recording/ editing sessions in IPython which has an interactive shell supporting visualization and parallel computing of data. The decorators of functionality are a good feature in Python. Its latest V3.6 features the a-sync-io module, API stability, JIT compiler, Pyjion, and CPython aids.

Learning Python Step-by-Step

Become a Kaggler on Python from an absolute newbie using the step-by-step approach to emerge complete with skills in Python tools and ready to kick-start your career in data-sciences.

  • Step 1: Read, learn and understand why you are using Python

Zero in on your reasons for learning to use Python, its features, functions and why it scores in the various verticals of data sciences like ML, AI, financial applications, Fintech applications and more.

  • Step 2: Machine set-up procedures

Firstly use Continuum.io to download Anaconda. Just in case you need help, refer to complete instructions for the OS by just clicking on the link.

  • Step 3: Python language fundamentals learning:

It is always better to gain experience from a reputed institute like Imarticus Learning for doing a Machine learning course on data analytics and data sciences. Their curriculum is excellent and includes hands-on practice, mentoring and enhancing practical Python machine learning skills. The topics covered include linear and logistical regression, decision trees, K-clustering, dimensionality reduction, Vector Machines, ML algorithms and much more.

  • Step 4: Use Python in interactive coding and ‘Regular Expressions’:

When using data from various sources the data will need cleaning before the analytics stage. Try assignments like choosing baby-names and data wrangling steps to become adept at this task.

  • Step 5: Gain proficiency in Python libraries like Matplotlib, NumPy, Pandas, and SciPy.

Practice in these frequently used libraries is very important. Try out these following tasks and resources like NumPy tutorial and NumPy arrays, SciPy tutorials, Matplotlib tutorial, the ipython notebook, Pandas, Data munging and exploratory analysis of data.

  • Step 6: Use Python for Visualization

A good resource is linked in the CS 109 lecture series.

  • Step 7: Learn Scikit-learn and ML

These are very important data analysis steps.

  • Step 8: Practice using Python and then practice more

Try webinars, hackathons like DataHack, Kaggle, and such fun Python machine learning resources.

  • Step 9: Neural networks and Deep Learning

Do short courses on the above topics to enhance your skills.
Concluding note:
Machine learning and AI in data processing have changed drastically the way things work in enterprises and even our daily lives. Digital technology has been able to enable machines with ML software and algorithms to process intelligently and unsupervised the large volumes of data generated. The advent of the internet and such limitless uninterrupted data processing has generated many an error-free gainful insight. Businesses can use the Python programming language and shift gears to the high-efficiency mode where profits increase and employee-time is well-used in creatively use of forecasts and insight provided by data analytics, ML, big-data processing, and concise clear predictive analysis.
The Python machine learning course at Imarticus offers certification and other advantages such as global updated industry-relevant curriculum, learning through convenient modes and timings, extensive hands-on practice, mentoring, etc that ensure you use the mentorship to be career and job-ready from the very first day.

What is The Meaning of Business Analytics?

Posted on by Imarticus Learning

What is Business Analytics?

According to the definition of business analytics, it means, “The study of data through the statistical and operations analysis, the formation of optimization techniques and the communication of these results to customers, business partners, and college executives.”

Business analytics is supposedly a quantifying mechanism, which works with big data and performs various functions of business modelling and decision making for furthering the business prospects of various companies.

Big data basically refers to a huge volume of data, meaning information that could be both in a raw format or a properly structured format. This is also known as unstructured data and structured data in the business analytics jargon.

This information that is available on such a massive scale to companies, is usually used by them by the method of analyzing of the same. When firms analyse this information, they are basically making use of business analytics to do so. This way once the process of analysis is done, the companies are able to get the proper insights that are required for them in order to take more effective business decisions and strategist in terms of their future endeavors.

More and more companies have begun to make use of business analytics, in order to make data-driven decisions these days. As the process of business analytics is actually able to mine this data, process it and derive the proper kind of insights, which then help the companies in achieving the much sought after competitive edge over their contemporaries.

This is the reason why more and more firms and professionals today are getting attracted to the process of Business Analytics or as it is popularly known BA.

Business Analytics gains importance. It is business analytics courses that makes it possible for the organisations to connect to their data, and use it effectively to identify new opportunities.

This further leads to a better business environment, where you can make informed decisions, create an efficient operations environment, gain higher profits, and acquire happy customers.

Companies using Big Data Analytics gain value by :

Cost Reduction, Cloud-Based Analytics, Hadoop, and similar technologies can not only bring down the cost of storing large amounts of data but at the same time they can bring about more ways of doing business.

Quicker Decision making, with the availability of in-memory analytics, and to add to that the ability to analyse the new sources of data, businesses can analyse real-time data, and make immediate decisions based on what they have learnt.

Segmented Products and Services, the power of analytics is that it can gauge customer needs so that organisations can create product catering to specific needs of the said customers.

Business Analysis
The only pitfall that organisations applying big data analytics initiatives need to be alert on, is the lack of internal analytics skills and the high cost of hiring experienced data scientist and data engineers to bridge the gap. If you are a fresher looking at career opportunities, with interest in data analytics, then getting yourself trained in techniques of Big Data Analytics, will be a fantastic career choice.

For companies, in particular, this field offers a host of benefits in terms of conducting various procedures such as data mining, completion of statistical and quantitative analysis in order to discern how results actually are achieved. Business analytics even helps companies by testing all of the previous decisions by using multi-dimensional testing methods as well as predictive modelling and predictive analysis in order to forecast future results.

It is this very field of operations which provides firms with the support that they need in order to make the most proactive and tactical decisions and also to make their decision making processes automated, in order to enable them to support all the real-time responses.
Business Analytics
Another concept which is quite similar to business analytics is business intelligence. Here it refers to only and only the process of collection of data from all of the sources that are available and only preparing it for Business Analytics. This includes automating and monitoring of all the sources basically for the main purpose of analyzing them and then being able to provide proper results and insights for the same.

Today as the presence of data in our world becomes quite strong enough, there are a number of professionals who actually go get certified in business analytics from many esteemed institutes like Imarticus Learning in order to enter this field.

How Does a Beginner Start To Learn Machine Learning, Having Some Knowledge of Programming Languages?

Posted on by Imarticus Learning

So you have learnt to programme and were having a happy time at work, by putting into practice what you learnt. Suddenly, out of nowhere, this huge wave of Machine Learning comes up, and you are all at sea! You know programming, but now you need to learn machine learning to stay relevant. And you are staring at a Herculean task, sifting through all the available machine learning courses online – unable to decide, whether any of them can actually help you out!
The first question you should ask yourself is what the kind of role that you see yourself playing in the future is? Yes, before actually diving into the time and effort consuming task of attending machine learning courses, you should make an effort to read as much as possible. Read articles, participate in forums, talk to people and find out, what are the kind of roles on offer in this field. See which of these matches your aspirations and career goals. Before you start to learn machine learning, you should know the field, however abstract it may seem.
Once you know, what the hullabaloo is all about, it is extremely crucial to assess your current skills. Yes, you know programming, but which languages are you familiar with? Can they help you in getting deeper in the domain of machine learning and artificial intelligence? If your answer is no, the first set of machine learning courses you should look into are the ones that familiarize you with such languages like R or Python.
As you become familiar with the programming languages that will help you learn machine learning, do an honest assessment, whether you really like programming in them and have you decided to pursue the career as a programmer. In that scenario, you should look at more and more advanced machine learning courses that teach you the intricate details of programming in R or Python. Also, you should undertake some of the basic machine learning courses that help you to familiarize yourself with algorithms and statistics.
In case programming is not what you want to pursue, but analytics is where your heart and ambition lies; you should look at machine learning courses or even generic artificial intelligence courses dealing with statistics, algorithms, when and where to use them. This will give you a much better grip on which algorithm to apply in which situation and the logic behind it.
As you become more and more proficient in the logic, you should also be able to develop quick prototypes of your proposed solutions with your understanding of the programming languages. This is the benefit of your efforts to learn machine learning in a streamlined manner. You can easily relate to the problem, identify solutions, build prototypes, evaluate and improvise. No one can actually stop you.
And lo! You have crossed the hurdle with the help of machine learning courses online. All you needed was a firm decision to learn machine learning and determination to achieve your goals with dedicated efforts.

How to get a Better Job in Data Science?

Posted on by Imarticus Learning

Job hunting in itself is a very daunting task, almost all of us have experienced the anxiety over acquiring enough knowledge, and getting through the challenging task of landing ‘The Dream Job’. Add to that the desire to get into the ever-evolving world of data science, let’s admit, it can be very intimidating, more so at the entry level. Preparation is the key to success, it is what takes your dependency away from luck and progresses towards definite success. This blog is a small effort in the direction of laying down the guide which could perhaps clear the vision of people, thinking of starting their career in data science or analytics. It is aimed at assisting you in decoding the framework which can help you to learn the relevant skills in data science.
Also Read : How to Build a Career in Data Science?

Clarity in Goal

First thing first, be clear what do you need to advance in, the field of data science is very vast and varied. Data analyst, Data Engineer, Machine Learning Analyst, Data Architect, Data analyst, or the revered data Scientist, as you can see there are many options available in the field of data science. The choice also depends on factors like your academics, interest, work experience, etc…, for example, if you are a software developer, getting into data engineering would be the most obvious choice. Hence clarity of though is imperative to avoid confusion and fluctuation from one vertical to another. You can gain clarity by talking to people from the industry, career counseling, conduct your own research and choose the role that suits your interest and field of study. Do not make a hasty decision.

Upscale with the Help of a Course

Once you have decided on the role, assess what lacks and accordingly pick up a course that will help you reach the goal. Taking up a course not only adds to your skills but it will help you network with like-minded people, further making it that much more possible to land your dream job. Data science is considered the coolest field to work in, so clearly, besides the high demand, there is also a high number of applicants, hence ensure you are skilled and networked enough to grab the opportunity when you see one. 

Pick up a Tool/ Language and practice

Breaking into the data science industry is tough, hence it is important that you have an understanding of what lies underneath data science. Statistics, Machine Learning, Software Engineering, Math, Data Mining, Data Mugging, Probability, Predictive Analytics, Sentimental Analytics etc…, are some areas that you need to have knowledge in. Fluency in Programming Languages like R and Python, GUI tools, and coding is a must have.

Practical Application

Even when undergoing a course, ensure you learn with practical experience as opposed to theory, this way you will not only understand the concept but will also have a sense of applicability. A tip is to practice what you learn in theory as a continuous process. Continue to research and read in the area of interest, blogs and white papers by the most influential data scientist can be a starting point.

Soft Skills

In the effort of acquiring all the technical know-how do not overlook sharpening your soft-skills. A person working in the data science field needs to possess a combination of technical, analytical and presentation skills. It is not only required from you to be a creative problem solver but to be able to communicate the solution to a non-technical audience to get their buy-in.
Data science is an evolving field, and there are no signs of it slowing down or becoming redundant. Perks such as big pay packs, and job security aside, you also get to make a big impact in the organisation, by solving complex problems. Overall it is indeed a rewarding career choice. So to advance in this field take the right steps to refine your skills to inch towards securing the job of your dreams.
Related Article: Top 5 Data Science Trends in 2018

How to Become A Data Scientist?

Posted on by Imarticus Learning

A data scientist is a new trend, and everyone has been working to find a stable place in it. It is ranked on the top as one of the hottest jobs in the Harvard business review. This guide will help you to know about the details of becoming a data scientist.

Who is a Data Scientist?

Before going towards the steps, it is mandatory to know what it means: Data scientists are people expert in analyzing data and have practical technical skills to tackle complex technical problems. They are a unique blend of a mathematician, computer scientist, and trend spotter.
They are professionals in both it and business sector, and that’s why they earn more than enough.

Also Read: What is the Role of a Data Scientist?

How to become one?

Learn statistics, ml, and algebra. A good data scientist can solve any problem better than a computer science engineer. They are well learned in the statistics and algebra. That is why deepening the knowledge about the mentioned subject is a fundamental need to start the journey as a data scientist.

Learn more about databases

The database is a frequent subject in computer science. A data scientist, however, makes you safer in securing your high paying job. This can only happen if a thorough study of databases is done.

Coding

It is a common part of the computer science world. It is, however, important to note that unless one becomes a better coder, they cannot become a data scientist. A data scientist has efficient experience in coding, and they have deep knowledge about it.

Practice and Work on Projects

Whosoever has achieved the status of becoming a data scientist; they will recommend you to start practising your coding and programming skills in real time. Practice makes a man perfect, and a data scientist is an ideal computer science engineer.

Practice on big data software

The data scientist has to deal with various non-segregated and segregated data. To make things easy, many data scientist use big data software such as MapReduce or Hadoop. Becoming an expert in using the software can help you in achieving your desired goal.

Become expert in data munging

It is a process in which raw data is further converted into easy to study and well-analyzed form. A data scientist is an expert in it. You have to practice more to become one.

Learn more!

It is one of the critical processes of becoming a data scientist. It is known to everyone that a computer scientist has to be updated about the new languages in the field. A data scientist is also the same. They have to be well learned in their area and become experts. This can happen by being in the association of like-minded people and developing the curiosity to learn more.

Development of Powerful Communication Skills

How will you communicate with people if you fear to go in front of an audience? Communication skills, therefore, most important part of any job profile. An expert data scientist has practical communication skills that separate them from others.

Apply for Jobs

After learning, practising and studying thoroughly, if you are sure of your talent and skills, you can start applying for the jobs by making an attractive portfolio.

Conclusion

Becoming a data scientist is not an easy task.
However, the situation can be tackled by using the process of learning and practice. The more you practice, the better you become. Even if you fail in interviews, you can still practice and learn. Who knows? You can be the next data scientist expert.
Related Article: What a Data Scientist Could Do?

How to learn Artificial Intelligence?

Posted on by Imarticus Learning

The final goal of AI/ Artificial intelligence is to create a robot or computer system that works as a human does and has the same or more intelligence in handling data rapidly. Obviously, the term is simple enough to read, in that it says the intelligence is artificially induced. From calculations, data-handling, drawing inferences, automated processes, self-driven automobiles to path-breaking machines in healthcare, customer service, and more AI is scoring over human abilities. However, they are meant to aid humans and not replace them. Have you seen the movies based on AI like Lucy, Terminator, and Chappie?
AI goals:
AI mimics the human brain in its concepts, building methodology and functioning logic. What it does include is the ability for NLP, deduction, planning, reasoning logic, inferential techniques, learning from data, and the ability to move or control objects. To this exhaustive list try adding creativity, sociability and intelligence or wisdom and you have the direction that AI is bound to grow into.
AI varieties:
Artificial intelligence itself is a very broad definition that can be subdivided as follows
Reactive applications: Such basic applications do not really count as having memory or self-learning capacities. Ex: Calculators.
Strong application: Such applications are the ‘smart’ variety and very close to human intelligence levels. They are multi-tasking and can network efficiently. A lot of development has to still come but the Robot is a good example. They understand emotions, can interactively respond to queries and can self-learn from data.
Weak application: Such applications are the single-tasked, lack self-awareness and have limited intelligence. Try having a conversation with the Google assistant to see its weak application limitations. Please note that even such applications have several embedded AI programs to help it achieve its single task of being an assistant. These applications may develop memories for a limited time-period and act driven from the memory. For example traffic signals, chatbots, automatic self-steering vehicles and industrial lifting arms.
Super application: These applications combine human-like intelligence with superb ability to calculate, strategize, plan and execute decisions and tasks. Perhaps they are a tad better than the human’s limited ability to process very large volumes of data, recognize patterns over large databases and use the information for self-learning. It does have a fine line to its limitations and learned people have expressed concerns of being completely taken over by machines. Many movies have also raised the issue of rogue machines running amok!

Here’s An Easy Learn Artificial Intelligence Tutorial to Follow

  • Start with learning programming languages like Python, R etc.
  • For this, you will need to refresh your fundamentals in algebra, mathematics, calculus, statistics and probability theory.
  • Then do formal training in getting up to speed with your programming and application skills.
  • Start applying your knowledge to practical applications and build your bot in Python.
  • Between Python and R the syntax in Python is more user-friendly and it has great libraries and supported applications that need very little coding.

Here is a step-by-step approach to learning Python for newbies complete with tools as they do in an Artificial Intelligence Course.
Step 1: Read, learn and understand why you are using Python
Learn artificial intelligence and zero in on your reasons for learning to use Python, its features and why it scores in AI and data science applications.
Step 2: Machine set-up procedures:
Download Anaconda from the net/ Continuum.io. Also, refer to complete instructions for the OS by just clicking on the link.
Step 3: Python language fundamentals learning:
It is always a wise option to gain experience from formal learning at a reputed institute like Imarticus Learning for doing a course. Their curriculum is excellent and includes hands-on practice, mentoring and enhancing practical skills in Python.
Step 4: Use Python in interactive coding and Regular Expressions:
When using data from various sources the data will need cleaning before the analytics stage.
Step 5: Gain proficiency in Python libraries like Matplotlib, NumPy, Pandas and SciPy. Try out these following tasks and resources like NumPy arrays, tutorials for NumPy, Matplotlib, SciPy, the notebook ipython, Data munging, Pandas, and exploratory data-analysis. Practice these frequently used libraries which is essential and very important in AI.
Step 6: Use Python for building your first bot
Step 7: Imbibe ML and Scikit-learn:
These are very important data analysis steps.
Step 8: Use Python and keep practicing:
Try hackathons like Kaggle, DataHack and many others.
Step 9: Neural networks and Deep Learning
Try out short courses on the above topics to enhance your skills.
Interesting daily applications:
Very widely popular are the smart devices from phones, TVs, burglar alarms, security systems etc. Assistants like Google, Alexa, Cortana, Siri are being used by millions globally on Windows, Android devices and the i-OS. We also have self-driven cars, motion detectors, talk-back and voice recognition features and more incorporated into household appliances.
Organizations use Artificial intelligence technology for a variety of applications like CAD, CAM, robotic steering, warehousing, logistics, planning, monitoring, risk management and many, many, more applications. The health sector has seen an overhaul with MRI, CAT scans, Lasers, VR equipment for operations, and such. Fintech startups have even cracked the stock-price predictions and are set to revolutionize financial transactions with blockchains. Gaming, VR, AR, Visualization and nearly every vertical has benefited from AI applications.
Concluding notes:
Artificial intelligence has permeated nearly all fields of life and the human brain remembers innovations only when new. This permeation of all markets and the never-ending upgrades to technologies, software and programming languages implies ample scope for those who choose to make a career in AI. Yes, jobs are aplenty and would need you to match your technical skills to the role. Learn artificial intelligence by doing a practically-oriented course at Imarticus, which can help you get the formal education required, the practical experience in building bots, and of course certifications which are an endorsement of your job-readiness. The salaries are generous and get better with experience. AI can also be clubbed with other skills like ML, AR, VR etc which can offer career choices of your interest and lateral options. Be the early bird and begin your career journey in AI today.

Which Skills are Required for Machine Learning Jobs?

Posted on by Imarticus Learning

Machine language is generally related artificial intelligence, which provides the machine or computers with the ability to complete certain tasks like diagnosis, planning, prediction, recognition or robot control. It consists of different algorithms, which you can use to teach the machines to change and grow when exposed to new data.

The process of implementing machine language is somewhat similar to data mining because the process looks through data and searches for the same pattern. Now that you have an idea of what machine learning is, let’s have a look at the skills that are required to get a machine learning job.

Also Read: Future of Machine Learning in India

Computer Science & Programming Skills

Some of the fundamentals of computer science are essential when you are looking to learn machine learning. Concepts like data structure, algorithms, complexity and computability, along with computer architecture are essential for artificial intelligence. In fact, you should also have knowledge of programming languages like C, C++, Java, Python and R, among others. A little bit knowledge of assembly language doesn’t hurt either.

Probability & Statistics

Conditional probability, and its characteristics and the techniques derived from it plays a key role in the machine learning algorithms. Moreover, you should also know about the different terms of statistics like mean, median and mode along with variance and standard deviation. These are all necessary to not only observe the pattern but also validate the data that is received through different means. Some machine learning algorithms are in essence an extension of the common statistical operation procedures.

Applied Mathematics & Algorithms

You need to know not only how to solve a problem but also how to implement it in short executable steps when it comes to machine learning. Algorithms help you to understand how to break down a problem into executable steps, and that is why this is important. In addition, you also need to know about gradient, convex optimization and its application in daily life, so that you can implement it in machine learning.

Operating Systems

When it comes to machine learning, most of the coding is done in Linux or some version of it. So, you need to be versatile with Unix or a version of Linux, which is in use presently. You also need to know about the Linux tools, which will make your life easier in the long run. Some examples include grep, find, sort and tr.

Software Engineering & Designing of Systems

When you are designing a machine learning tool, you are also designing an advanced software. So, at the end of the day, you need to know how to design system, and how you can implement your ideas in that. You also need to understand how different algorithms interact with your system, and how you can speed up the process without compromising on the resource space.

Now that you know about the skills required for machine learning jobs, it is time to get started on acquiring these skills. In case you have some of these skills, make sure you hone them so that you can implement it and build a great system when the time comes. Good luck!

Related Article: What is The Easiest Way To Learn Machine Learning?

Tips that Will Help Elevate Your Career as a Data Scientist

Posted on by Imarticus Learning

For a data scientist, appearing smart should be the topmost priority. It will not only elevate your self-esteem among others but will also keep you self-motivated. It’s not just for the data scientists but also for the individuals employed in different sectors as well. Even if you have a great knowledge and depth in your field, may not be able to reach the heights of your potential.
In this case, having the right attitude, personality, and communication skill helps you to achieve your targets. Also, the job market for the data scientists is getting tougher day by day. Thus, in order to bag the right job for you, only if you learn data science, that may not be enough. That is why in this article, let’s explore four tips that will make you stand above the rest in your data scientists career.
Have a Sporting Attitude towards Competitiveness
There is no doubt today, wherever you go, you will face competition. Even if you are looking for a job or in a company, you will face tough competition from others. If you feel scared or if you just back off from that competition thinking that you are beyond it or don’t have the ability to compete then I am afraid you will never going to make it. If you want to have a successful career as data scientist after completing data science courses then you have to embrace the competition that you have in front of you. Only having the knowledge of statistics for data science will not going to help your cause because, at this age, your attitude plays a key role.
Look Beyond the Job Title
 Data science salary and job titles may vary from company to company. By just hearing the name of the position, it will be hard for you to make out whether the position you are getting in that company is the position you wanted for yourself after completing the data science courses. Asking about what would your responsibilities be in the company and what methodologies you will be using to fulfill your responsibilities will help you to get an idea of your job responsibility. However, if you are looking to have a good career as a data scientist, then you should be selecting your company solely based on the job title.
Having Multiple Skills is a Plus Point
This is an age where companies look for specialists. However, if those specialists have other skills that might be helpful for the company then that is a plus point for the candidate. There are companies looking for other skills as well apart from the knowledge you have in data science. Sometimes you may miss out on grabbing the job opportunity even after knowing statistics for data science just because you don’t have the required skill set the company is looking for. Also, having an extra skill might have an impact on your data science salary.
Be Prepared to Face the Tests
Anyone can put data scientist on their resume. But, that does not mean that they have the required skill set to handle the work of data scientists. That is why even if you have put data scientist on your resume, the companies will test how much of that is actually true. They will test whether the candidate knows his or her R or Python or you have a vague idea of these terms. That is why you need to learn data science in a robust manner so that whichever test companies put in front of you, you will have the confidence of passing them.
Final Thoughts
The career as a data scientist is very lucrative and everyone knows that. However, in this competitive world, many fail to establish themselves as data scientists simply because they don’t have the X-factor that will elevate their career. Following the aforementioned tips will surely help you to succeed as data scientists.