Must’ve skills for a career in Data Science and their free-resources
In my previous article, we went over some of the foundational topics used by Data Science professionals. In order to develop the must-have’s for this profession, we must use our time to the fullest and upskill.
Must’ve skills
Whenever someone applies for a job opportunity, there are always a set of skills that the hiring team looks for, could be some specific softer skills or some technical skills. And so, we ought to prepare well with those skills. We would be able to upskill better, once we know the various stops of a Data Scientist.
Soft Skills
- Recognizing the problem
Data scientists, use data to solve some problem, this makes their work vital to understand the flaws in the system. Once the problem is identified, finding the potential causes and then fixing is the next step. This skills builds up with experience.
Technical Skills
1. Fundamentals of Statistics
Virtually, the job of a data scientist is to number crunch, build a model and logically predict the future. All the basic concepts of Data science and prediction, stem from high sophisticated Statistics. To be able to stand out of the crowd and know the deeper meaning of the tools, we must know the intuitive reasons to everything we apply.
Since, performing the data cleaning and exploration is a significant component, concepts like Outlier Analysis, Data Manipulations, etc., play a vital role.
2. Basic Programming
With the large datasets, we cant’ be analyzing data on pen and paper. Here, comes the role of programming. Programming gives us the power to conduct the analysis more accurately and in significantly less time. Hence, making us efficient and accurate.
3. Model Development
As seen above, this stage comes only later, but tit is important to be technically aware of the various model, their assumptions, their application and their fit to the kind of data. This helps us fit a model suitable to our data, culminating into an efficient model.
4. Story Telling
Once you have the skills as above, you now have to communicate your findings and present them. You should have communication skills to be able to do this.
Now, these are some of the major technical skills that can get you started. But, this list is never ending, we would have to study and read everyday. Because learning never stops.
Free Resources Online
In this day and age, there are numerous resources one can refer to while upskilling.
1.Published Research Paper
There are numerous research papers that contain model development and predictive modelling. Reading through them and understanding their data is one great way of learning the fit of a specific kind of model.
Resources: Google Scholar (no doubt, is a waterfall of information)
2. Student Journals
Student Journals give us perspective into the various problem statements, their relevance their approach of solving it. Right from the type of data used (secondary or primary) to the methods, tools and tests applied. It also helps us look at their proposals critically and maybe build something better.
Resources: imstat, stemeducationjournal, tandfonline, jse.amstat.
3. Online Communities
These are an excellent way of learning and helping peers with their conceptual doubts. Not only does it force skewed thinkin but also bizarre answering. It helps us look at a problem more objectively.
Resources: stackoverflow, Quora, study.com, etc.
4. Youtube
No doubt, this resources comes in handy for many skills from cooking, sewing to programming. Spending sometime on the content here, helps in learning more, learning diverse, learning fast and learning better.
5. Books /Novels and Websites
These are also a great way of understanding the great minds, fictional or non-fictional. There are great books for statistics and predictive modeling which are a great start. This is a treat to those who enjoy reading.
Books from Hans Rosling are a great start.
Going over websites related to Data science is also a great way of kicking start. Like: https://www.gapminder.org/resources/
