The current trend in Data science with R and Python for Healthcare Data

The data scientist job is definitely one of the most lucrative and hyped job roles out there. More and more businesses are becoming data-driven, the world is increasingly becoming more connected and looks like every business will need a data science practice. So, the demand for data scientists is huge. Even better, everyone acknowledges the shortfall of talent in the industry.

But, becoming a data scientist is extremely complicated and competitive. The career path of a data scientist is not going to be easy. It needs a mix of problem-solving, structured thinking, coding, and various technical skills among others to be truly successful.

The Field of Data Science is Broad and Varied

There is no single definition of data science, as it varies with industry, specific business, and what the purpose of the data scientist’s role is. Different roles require different skill sets, therefore the educational and training path is not uniform.

The role the data scientist is to play is now generally broken down into two large categories:

Type A: Data science for people – data collection and analysis to support decision-making based on the evidence

Type B: Data science for software – for example, the recommendations one might get for books or movies from Amazon or Netflix, based upon past behaviors.

Industry Demand from a Modern Data Scientist

In the current job market, Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning.

Being a Data Scientist is much more than a glamorous job title and a generous salary. It takes serious commitment to become a great Data Science practitioner in this competitive, candidate driven market we’re seeing grow exponentially today.

Studying further degrees are extremely time-consuming and take a huge commitment. To then go into the commercial sector, the expectations here can be much more demanding. In a commercial environment, pressure can come from a variety of sources – time, colleagues, money, answers, the list goes on.

Data plays a huge part in business decision-making and the skills required to manage these data sets fall well outside of the remit of managers and executives. This means a lot of pressure can be felt by Data Scientists who are working for companies with shareholders expecting to see profit and business input directly from your insights.

Most organizations will expect some quick results so picking the projects with low hanging fruit becomes important. This can be daunting for a rookie Data Scientist, so some guidance from the wider Data Science team could be key to initial success. Whereas in a research environment, the pressures – whilst still demanding – are perhaps not as pointed.

Required Skills for Data Science Job Roles

Jeff Hale looked at general data science skills and at specific languages and tools separately. He searched job listings on LinkedIn, Indeed, SimplyHired, Monster, and AngelList on October 10, 2018. Here’s a chart showing how many data scientist jobs each website listed.

Source: KDnuggets

As per Jeff’s analysis, machine learning, statistics, and computer science skills are the most frequent general data scientist skills sought by employers.

Source: KDnuggets

It is interesting that communication is mentioned in nearly half of job listings. After all, data scientists need to be able to communicate insights and work with others.

Source: KDnuggets

Among Tech skills, Python is the most in-demand language. The popularity of this open-source language has been widely observed. R is not far behind Python. It once was the primary language for data science. I was surprised to see how in demand it still is. The roots of this open source language are in statistics, and it’s still very popular with statisticians.

Python or R is a must for virtually every data scientist position.

Apart from Py...(more)

Here is how to Become a data scientist in 4 steps. This is how I did it.

Learn Statistics First

I did this too late, you can start early. It so easy, I dont know why I hesitated. Perhaps the mental block we all(well not all) have with maths.

Descriptive statistics

Types of data variables

Central tendency measures

Spread of data, skew of data

Measures of dispersion

Inferential statistics

Population and sample (Sampling methods is optional but read it : simple random sampling and stratified random sampling

Random variables, Probability distributions - normal, Poisson

Estimation and Hypothesis testing.

Hurrah! and now you are ready for the next step and its not R or python! ha ha !

Learn Excel & Power BI next : 750 million users globally, the tool and platform that has seen more data than any other. Also the mom of Power BI, Yes! Power BI is advanced excel features evolved into a software.

Relevance of inserting tables in excel - 9 great reasons to insert tables in excel for data analytics professionals.

Consolidating data with compatibility view features

Data manipulation with machine learning in excel using xlstat

Analysis toolpak for descriptive and inferential statistics

Power View, Power Query, Power Pivot and Power Maps.

Learn R and Python (Yes its and not or)

Learn Tableau (it is integrated with Python and R)

Why? Its too hot to ignore. Too easy to not try on. You see Data science has data exploration, data analytics, and data presentation. Tableau and to an extent Power BI is great with exploration and presentation. R and Python are super for analytics that comes in between.