Is Data Scientist is Difficult to learn?

In this post you will know why becoming a data scientist is difficult. what are the reasons behind that. In my experience doing machine learning and data science, there are a lot of different elements you have to know quite well to be effective in your work. These include the following:

General programming familiarity. I consider data scientists to be engineers, where instead of coding a web app as a front-end engineer would do, they are responsible for architecting data processing pipelines, designing and implementing models, and developing infrastructure for system evaluation and metrics computation. As you can imagine, performing these tasks requires a reasonable amount of fluency with a high-level programming language (think Python, R, Matlab, or Julia), as well as data science specific libraries (think Pandas, Scikit-learn, Matplotlib, or Tensorflow). Developing this skill alone is something that can make up a year or more of an undergraduate computer science degree.

Mathematical maturity. Data scientists must be comfortable with a variety of mathematical topics. These include statistics, probability theory, and linear algebra, to name a few. Very often in a traditional data science role, you will have to read academic papers describing some model or dataset and be able to utilize and implement the key ideas of the paper. This requires being comfortable understanding mathematical concepts and notation, which can be quite hard since research papers are rarely written as if they are trying to be a New York Times popular science book, comprehensible by the general public. I would also argue that developing this skill is more important than the programming familiarity, since this is what makes data scientists…well scientists. You need to build intuition for the models you are implementing, data you are processing, and analyses you are performing, since this intuition will help you to truly do your job well.

Data insights. Using, understanding, and manipulating data is a very different skill from other traditional engineering jobs. I remember once walking into an interview for a data science role some years ago. After a brief introduction, the interviewer literally pulled up a CSV file in Sublime text with just a bunch of numbers in different columns and said “Here are our records for the past month of customers using our product. What would you do with this?” While this may seem like an unorthodox (and admittedly intimidating) interview, these are the types of situations data scientists have to deal with on a daily basis. You will often be presented with unfiltered, incomplete, and sparse datasets and expected to derive meaningful insights from them. This will require you to get comfortable asking a score of different questions about your data including:

• How do I fill in missing values?
• How do I deal with/remove outliers?
• How do I get more even label distributions to train my model?
• What kinds of features are most relevant for my model?
• How do I detect/deal with model overfitting/underfitting?
• What metrics are most useful to assess model performance?

As you can see, being a data scientist oftentimes amounts to being a jack-of-all-trades. You have to know a fair bit, which is why it seems that there is a pretty high barrier to entry. That being said, it is an extremely rewarding job, one where you are the person shedding light on the unknown, a data-whisperer of sorts :)

I’m also very confident that anyone with a disciplined course of study can achieve the skills necessary to become a data scientist, without having to spend years at some top university.

IT Skills in Demand for the Next 5 years

In this post i brought a very interesting topic that is what are the most demanding skills for the next 5 to 10 years in IT industry to survive long time. After a long Research and Analyse i list out some of the most demanding IT sills for the next 5 Years. They are

Computer Science related skills:

·         Machine Learning and Artificial Intelligence (AI)

·         Data engineering

·         Cyber Security

·         Cloud computing

·         Blockchain

·         Internet of Things (IoT)

·         Extended Reality (AR and VR)

·         Quantum computing

·         Python, Web development and Game development.

·         Digital Marketing

·         Mobile Application Development

·         How to write efficient code within deadlines.

Soft Skills:

·         Mental elasticity and ability to solve complex problems

·         Creativity

·         Communication skills, especially verbal.

·         Adaptability

·         Leadership and Social-media Marketing

Other Skills:

·         How to work in pressure.

·         Responsibility and ethics.

·         Teamwork and also, how to work alone.

·         Thinking skills (critical thinking, problem-solving, creativity, originality, strategizing)

·         Management skills.

·         Persistency.

·         Adaptability or flexibility

·         Ability to put Ego aside.

·         Emotional intelligence.

Best Projects to learn Machine learning and Data science

In this post you will know some the best projects i can do to learn Machine learning and data science.The best way to build trust with a hiring manager is to prove you can do the work that they need you to do. With data science, this comes down to building a portfolio of projects. The more interactive the projects are, the more the companies will trust that you'll be an asset to the business. And the greater are your chances of getting hired.

Considering the current industry trends, the best way to showcase your Data Science skills are these 4 kinds of projects:

Data Cleaning

Data Analysis

Interactive Data Visualizations

Machine Learning

Here are a few popular projects based on these above skills that you could include in your future portfolio—

Projects based on Data Cleaning: Data scientists approximately spend up to 80% of their working time on cleaning data. So, if you can show that you’re experienced at cleaning data, you’ll eventually be more valuable. To create a Data cleaning project, you first need to pick a messy dataset and start cleaning it by removing distortions or missing data points. You can try these datasets to begin with-

Ticket Sales Data- In this you will strengthen your skills by importing and cleaning some messy online ticket sales data. This includes removing redundant information, deleting duplicates, identifying event dates and sorting them etc.

World Food Facts- The data will include some popular food items worldwide with their nutritional values and place of origin. Your goal is to eliminate useless information, remove and sort pairs of items holding duplicate values and replacing missing values.

Data Analysis Projects: This involves drawing conclusions and generating questions that lead to interesting discoveries. Here are a few popular projects you should consider-

Movie Recommendation System- Recommender systems are considered valuable even for large companies like Google or Facebook. As it is important from a perspective of revenue and engagement. So no doubt in going for this project. Beginners are able to practice their analytical skills and can build a model of their personal movie recommendation system.

Diabetes Prediction- Make analyses based on the patient’s characteristic data set to predict whether he is diabetic or not. Your goal is to build a project to analyze datasets containing attributes like glucose level, blood pressure, age, etc.

Projects based on Visualisation: Data Visualisation requires you to have a real hang of some popular tools like Tableau, Plotly, Qlikview etc. Using these tools, you have to create some interactive visualizations for a better understanding of the analysis. You can consider these projects, to begin with-

Designing a business plan for insurance distribution- In this, you would be forecasting the business for the upcoming years by exploring the hidden trends, extrapolation, assumption and finally summarizing the solutions through visualization.

Real Estate price prediction- Make predictions using Real Estate market data containing values like crime rate, age, accessibility, population, etc. Built an interactive visualization for an effective stock price.

Machine Learning projects: A machine learning project is another important piece of your data science portfolio. Now you should not approach towards every algorithm, but rather pick some basic and widely used ones like Logistic Regression, K-means clustering, Naive Bayes etc.

Twitter Stream Project- A lot of companies monitors mentions from their customers on Twitter to react to the negative ones quickly. For example giants like Uber and Airtel respond to negative tweets fast and find out what the problem is and how they can solve it. You can make this project using a convenient Twitter API and sentiment analysis algorithms to detect such tweets in the whole stream.

Spam or Ham- One of the classic data science problems is a spam detection. You can build a model for detecting spam emails and messages. Additionally, you can also add spamming user comments to hide them in the browser. This will showcase your statistical skills and some classic machine learning.

All these are some out of the box projects which can show your skills as a complete Data Scientist. Having worked on these projects will provide you an edge over the peers. Also listing these in your data scientist resume can improve your chances of landing a high-paying job. You can easily get the data sets for these projects online or at Kaggle. Also you should try to work on end to end data science projects available on public domains like Datacamp, MNIST, Github etc.

Although all these are some unconventional and easy to build projects, you should mainly focus on projects based on Data Cleaning and Analysis. Since these are the primary skills that companies usually look for before hiring freshers for data science roles.

Also you can try Edwisor,which is a popular platform for working on some modern data science projects. Here you can get a curated career path for data science in which you can learn all the tools as well as work on many industry level projects. Alongside this, a lot of top companies like American Express, Data Peace, Paisa Bazaar and many startups hire data scientists from here on the basis of the projects they do. So get some great job opportunities too. So give it a try!

Top 5 Technologies to learn in 2019

In this post you will know the best programming languages/technologies should learn in 2019 to survive in IT industry with high Package.In present IT,it is very difficult to determine one particular technology as the best among others,because everyday is a evolution in computing and every single paves a way for a new technology.

But,according to the present job scenario and stack overflow popularity,the below technologies have good growing opportunities:

1. Artificial Intelligence:

It covers technologies that are used for prediction purpose.The technology stack of AI constitutes

Machine Learning

Deep Learning

Computer Vision

Human Computer Interaction

Robotics

2. Data Science:

Data Science is all about cleaning,analyzing,organizing,preparing and visualizing the data.

It requires the following things to be included:

Statistics

Machine Learning

Data mining

Data Analytics

3. Big Data and Cloud Computing:

These are another boom areas to be considered as the trending technologies in the present IT sector . It is because of the importance of data in the life of every individual and consistent improvement in social networks and eCommerce traffic.

4. Android Development:

As the internet users are more comfortable with using android apps than websites,the demand of android development becomes very high.The two popular ways of building android apps are through:

Java

Kotlin

5. DevOps:

Devops is the combination of Development and operations team in a software organization,which is advanced version to agile development.

Highest Paying Companies for Data Scientist

In this post look at some of the highest paying companies for Data Scientist.A data scientist is a professional responsible for collecting, analyzing and interpreting large amounts of data to identify ways to help a business improve operations and gain a competitive edge over rivals.

These are some of the top paying companies for data scientist-

Amazon

Microsoft

IBM

Google

So there you have the big names in data science, but if you’re planning to start your career at one of them, I would rather suggest you to drop the big names like ones given above and rather concentrate on smaller companies and startups. The reason why I am saying this is that big product based companies look for a lot when it comes to hiring their data scientist.

Apart from skills and technological know how you need to have a lot of experience working as a data scientist over the trending technologies. I do not mean to demotivate you but it would be extremely difficult to get placed at one of them. So, I would rather suggest you to aim for smaller companies and startups like-

Zomato

Karvy Analytics

Mu Sigma

Social Cops

MobiKwik

These are few among the popular startups that hire data scientist at good salaries. Apart from these, there are some leading product based companies like Zoho, Freshdesk, Wingify, Haptik, etc. that can offer a great start to you data science career.

So, how can one get into these companies as a fresh data scientist?

Although these are smaller companies and startups, but getting hired at them too would not be too easy. In fact, some of these companies have quite strict hiring practices. But such companies provide good learning exposure, culture, growth opportunities and decent salary packages. To be precise, the starting salary package for fresh data scientist at such companies is 7 L.P.A and above. So, what do these companies seek in their data scientists?

To get hired at one of these companies as a data scientist, you need to have strong logical and analytical skills, reasoning abilities, and of course decent soft skills. Apart from these skills, you need to have the a great hold over the tools and technologies that are being used currently in data science.

So, what are those tool and technologies that you should know?

Basic knowledge of Statistics and Statistical analysis

R & Python

In depth knowledge of machine learning algorithms like Logistics regression, Linear Regression, etc.

Knowing tools like Rapid Miner, Map Reduce and Tableau would be an added advantage for you. Also, learn data cleaning, data mining, Visualization and deployment so as to get placed at a good job in data science.

Once you have learned them all, move on to working on projects. But why?

As you already know, good product based companies and startups usually prefer to hire experienced professionals. This way they can be sure of the competency of the candidate. But being a fresher it might be difficult for to get hired.

The best thing that can prove your skills and competency at this point is-Projects. Pick up a lot of challenging real data projects and build a strong portfolio through them. Remember, the better portfolio you have, the higher number of good opportunities you would get. For this, you can pick up real data projects from platforms like Kaggle, etc. Here, you would get to work on public data sets. Summing this up, for getting a good paying job, follow this approach-

Acquire the skills in relevant tools and technologies that are used by good product based companies

Start working on projects and build a strong portfolio using them

Start applying for jobs at smaller product based companies and startups like Zomato Karvy Analytics, Mu Sigma, Social Cops, MobiKwik, etc and eventually get placed.

Also, once you get placed at one of such companies, gain a few years of experience and you can always make a switch to a better product based company.

Did you know that data scientist with 5 years of experience are getting as high as 75 Lakhs per annum currently? So, no doubt you do have great earning opportunities in this field.

If you are yet to start learning these tools and technologies, here are a few platforms that you can use-

Edureka - Here, you would get to learn data science skills and tools through pre-recorded video lectures. You also get certificates here but if your goal is to get placed at a good paying job, I am not sure whether this platform would help you or not.

Simplilearn - Here, too you would get skill learning courses and certifications but let me tell you good product based companies look out for candidates having a good knowledge and hands on experience/practical exposure rather than meagre certifications.

edWisor - Here, you would get to acquire skills and technological know how through a specifically curated career path in Data Science. You would also get to work on real data projects here and also get job assurance that I don’t think any other platform provides. A lot of product based startups and companies hire their data scientist from here on the basis of projects individuals do here.

What is the hiring Process of Data Scientist?

In this post you will know the hiring process of Data Science or Data analyst job. There are 2 kinds of companies that are looking for data scientists- Startups and MNCs.Every company has a different stack and a different dataset. This means the technical portion of a data scientist job interview is the part that will vary most between companies, and is the hardest part for which to create universally relevant questions. That said, it’s also likely to be the easiest portion for which to conclude an objective fit: they can solve problems, or they can’t.

MNCs have a bit easy work culture while on the other hand, startups will make you work day and night relentlessly.

STARTUPS:

Generally they will have an Aptitude test which includes Probability, Linear Algebra, Statistics, Distance-Speed related problems and some logical reasoning questions.

This is coupled with a programming test to check your coding prowess in SQL, R or Python to ensure you have sufficient knowledge in machine learning. If selected you will move forward for a final F2F round where you can discuss the JD and further details of the profile. Some might even have a F2F technical round before this.

MNCs:

They start with an aptitude test which includes the syllabus mentioned above and if qualified you will have a technical F2F round with a senior data analyst who will analyze you for your knowledge in data science along with your approach to some analytical problems.

Getting a job in Data Science is not difficult considering the number of hirings currently happening. Always remember-PRACTICE IS THE KEY TO BEING A KICKASS DATA SCIENTIST, just hang in there while you get the right opportunity!

What are the Hottest Jobs in Technology Market?

In this post you will know the hottest jobs in Technology Market right now. We don’t know what these fields will be called 20 years down the line but right now they’re the most trending jobs in the IT market. Right now it’s Data Science. Call it Machine Learning, Deep Learning or anything you want.

Which jobs will be employable till 2025?

These are the skill areas that the experts recommend which also has the strongest job categories - according to Bureau of Labor Statistics

Technology

Software developers (Front-end, Back-end and MEAN Stack developers): software developer jobs will grow 18.8% between now and 2024, according to the BLS, while computer systems analystjobs will increase 20.9% by 2024. Market research analyst and marketing specialist jobs, which also require those analytical skills, will increase 18.6%.

Data Scientist: Forrester predicts that by 2025, the cognitive era will create 8.9 million new jobs in data science, robot monitoring, automation specialization, and content curation. Now more than ever, we need workers who understand the technology long before we can think about how they are going to be replaced by it. Read here..

Caregiving

Related jobs: Medical technicians, Physical therapists, Workplace ergonomists
Social intelligence and Media Literacy

Related jobs: Sales representatives, Marketing specialists, Customer service representatives
Lifelong Learning

Related jobs: Teachers and Trainers
Adaptability and Business Acumen

Related jobs: Management analysts, Accountants and Auditors


The SKILLS you need to LEARN to remain FUTURE SAFE today:

The jobs that are seen in the IT sector is beating the rest of the economy by 3:1 ratio, the technology sector has grown as a pace which is faster than the private sector as a whole and has proved more resilient through the recession-and-recovery period.

The best way that one can cope up with the world is by learning the next level technology that the IT industry demands right now. You must be aware of the happenings taking places in most of the work places in your field and be prepared for it. In this way you’ll be ahead of everyone and you’ll be prepared when technology changes around you.

Currently Full Stack/MEAN Stack developer and Data Scientist tops the list of the most demanded job role with the highest salaries. Click to know more..

Talking more about the domain:

Web development:

Front-end developer - HTML, CSS, Jquery , and Angular are used for developing dynamic website interfaces. The average salary ranges from 4–5 LPA.

Back-end developer - PHP, Django, Node JS, etc. and frameworks/technologies such as NodeJS, ExpressJS, MongoDB are used in back-end development. The average salary ranges from 5–6LPA.

Full Stack/MEAN Stack developer - MongoDB , ExpressJS, AngularJS , NodeJS. The average salary ranges from 5–7LPA.

Data Scientist - R, Python, Machine Learning, Predictive modeling, Data modeling, Data Visualization, Statistics, Data Mining, Data Analysis etc. The average salary ranges from 8–9LPA.

AND TILL 2035-

1. IoT
2. Artificial Intelligence
3. Driver-less Cars
4. Block Chain Technology
5. Edge Computing
6. Data Science will still remain till then
7. Human like Robots
8. Augmented Reality

What Should I learn first Machine Learning, AI or Data Science?

In this post you will understand which course you should learn first, how to learn these course and what is average salary of these courses.Machine Learning is a specific set of techniques that enable machines to learn from data, and then make predictions. What you must be concern right now is to first understand the point in time when machines reach a higher level of intelligence than humans and will in turn take over the world -

However, before getting into Deep Learning and AI, you better learn Machine learning first.

You can think of Deep learning, Machine Learning and Artificial Intelligence associated within each other. If you look at it, Deep learning is a subset of Machine Learning and Machine Learning is a subset of AI, you can call it an umbrella term because it is a computer program that does something smart.

Machine Learning is basically a current application of AI based around the idea that we should be able to give machines access to data and let them learn for themselves. In addition to this, another field of AI – Natural Language Processing (NLP) – has become a source of hugely exciting innovation in recent years which heavily relies on ML.

Deep Learning is a subset of Machine Learning, mostly when people use the term deep learning they are referring to deep artificial neural networks. Deep artificial neural networks are sets of algorithms that sets new records in accuracy for many important problems i.e. image recognition, sound recognition, recommender systems, etc.

Artificial Intelligence is defined as the engineering and the science that is required to build intelligent machines.

All these are basically the branches of data science and how it is compared with fields mentioned above, e.g. machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.

So, if you’re looking to get into machine learning/AI or Deep Learning, my best suggestion for you is to first learn data science, then maybe you could get into depth with machine learning and deep learning.

So how can I start with data science?

The first step to learning data science is by asking yourself, “how do I actually learn data science?”

In order to learn data science you must know these:

Do you love data or numbers? - You must love numbers if you plan on taking data science. Without motivation you might end up halfway there believing you can’t do it.

You can learn data science by doing it - 90% of the work will be data cleaning. Having a grasp of few algorithms is better than not knowing anything at all.

You should know how to code using R and Python.

Where can you learn data science?

These are some helpful resources:

Dataquest - you can learn data science in your browser, work on projects and build a portfolio.

Elements of statistical learning - good machine learning books.

Udemy - online courses but only certification.

Khan Academy - good statistics and algebra content.

Udacity - upgraded online courses but just nano degree and certificates and no job assurance.

edWisor - online career path, good learning skills, job assurance - job oriented platform.

Resources on their own aren't useful you need to find a context for them - research on all the above resources and choose the best option.