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.
Day: February 13, 2019
How to get a Better Job in Data Science?
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
The Advantages of Learning Fintech
In any market, when a certain industrial product or service is launched, there are a variety of reasons why it may work or may not work for the industry. This holds especially true in terms of any of the new technologies entering the market. The growth of such technological industries determines the influx of new players, some of whom may come in with a silver spoon in their mouths, basically turning everything they touch into gold, great turnovers, fabulous mergers and acquisitions and so on. At the same time, there are also a number of failures and flounders in the industry.
Also Read: Introduction to Fintech
The fintech industry is not a very new one and has been through its fair share of ups and downs. In spite of a little while of a slump in the middle, there are many ways in which the fintech industry has strived and turned its trade positive in order to come to be thriving in the present times. One of the main reason why this industry has seen more ups is that it catered not just to the big guns but also to the small business owners which included all the disadvantaged sections of the society, women, minorities and immigrants.
This industry is going to grow on and be stronger, which is why there are many experts who believe it is advantageous to get certified and get into this industry.
Here are a few reasons why we believe there are great advantages in learning Fintech;
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- Financial technology has been criticised in the sense that it would not see a large number of growth and developments in the future. But the majority thinks otherwise. The reason that fintech is actually going to be there in the industry for a much longer time is the very fact that banks are getting digitalized and they are doing so at an alarmingly fast rate. The industry is no longer at the innovator stage and is more at an adoption stage. Thus getting certified to work in this industry will prove extremely advantageous.
- Fintech companies or those organizations that are involved in this industry don’t really have the rules of capitalism apply to them. This is mainly because of the fact that, in capitalism, there is the chance of companies getting explosive dividends and later on also going bust. But in case of fintech whenever a void is created, there would be a number of alternative lenders to jump in to fill it up. This is the reason why the field of fintech which does not follow a ‘growth at all costs’ mentality will definitely flourish both in terms of growth and job opportunities.
- The world is getting digitalized on a massive scale and today no one really has any time for actually getting their transactions done from slower, larger financial corporations anymore. Technology has touched every single sphere of life including the sphere of banking. This is why learning the various nuances of fintech would be most definitely an advantage for candidates entering into this new world of technology.
- Financial technology has been criticised in the sense that it would not see a large number of growth and developments in the future. But the majority thinks otherwise. The reason that fintech is actually going to be there in the industry for a much longer time is the very fact that banks are getting digitalized and they are doing so at an alarmingly fast rate. The industry is no longer at the innovator stage and is more at an adoption stage. Thus getting certified to work in this industry will prove extremely advantageous.
The above reasons are why learning fintech is a great option for candidates looking to get into the industry today.
Related Article: What are the career opportunities in the Fintech sector in India?
How to Become A Data Scientist?
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?
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.