Data Science In 6 Months…Why A Big No…!

what do you think ? Can Any One From Non Tech Background Learn Data-Science Within 6 Months ???

“It’s just a recipe, only knowing a recipe doesn’t make you a good cook” -keep practising

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Table Of Content

1. Data Science In Nutshell
2. Mistakes follows by Professionals
3. Ground Truth and Learning Path
4. If Not 6 Months Then How Many ?

Data Science In Nutshell

The key point you need to know is assortment of several tasks and then attention on that, depends on the present applications.
Below mentioned categories and tasks itself is very huge and deep to learn to get a better proficiency which will actually define you a Data Scientist at minimum level.

What are those tasks which actually defines Data Science ?

1. Getting and collecting Data— This is the very basic skill and basic section in data science which every Data Analyst or Data Scientist should know that how to do research for data and how to be able to scrap that data for further data operations.
2. After collecting, Storing the Data — From this part the real journey starts of a Data Science enthusiast. It is a well known fact that how much of huge amount of data is generating nowadays so someone is required for the administration and management of this huge data. The most important part is learning these skills definitely isn’t a rapid phenomenon. You need to understand and get more efficient with it. You need to know some common key domains and it’s normal understanding as follows,
   a. Relational Database (Understanding Structured data, SQL queries).
   b. Data Ware Housing (Structured and curated approach, optimised analytics).
   c. Data Lakes (Big Data, Unstructured Data)
3. Treating and Processing Data — One of the most important task that takes as bigger amount of time as its importance. I would primarily divide this category in two major sub categories,
   a. Data Wrangling or Data Munging — In which you need the understanding of extracting data, transforming data and further loading the data.
   b. Data Understanding and Data Cleaning—In which you need to understand each and every term of given data and treat it for it’s correctness like scaling,normalising,standardising etc.
4. Describing and Questioning Data and of course ! the insights — Yes !! This is the most and the most difficult and Most Most important part of Data Science. When actually a Data Scientist starts speaking and having conversation with given data, this is the skill for which the Data Scientist get paid such higher money. The time when you get some magical answers and patterns from it, you actually becomes a true analyst and with more creativity and innovation, it leads you to a scientist profile.
   Trust me this process take longer period. then you could ever imagine.
5. Modelling — It simply the relationships between data which we define by some statistical or any algorithmic approach. To master it you need to be very efficient with statistics,calculus and liner algebra which is again a longer run to learn.

Mistakes follows by Non-Tech Professionals

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It’s a continuous process of learning. If you are thinking that doing some 6 months of practice or any fascinating course by some high paid platform will make you a data scientist within short span of time you are fooling your self.
No one will tell you the core things. It needs a lot of dedication specially in critical thinking, logical reasoning and questioning and answering the hidden patterns. I am trying to list down some mistakes follows by non-tech professional which they don’t think significant in this domain.

• Programming — Data Science was never about Python,R,Scala, Matlab or any other specific programming language. It is all about programming as a tool to perform statistical and analytical tasks and the logic, if you master any of it’s logic and concepts, it doesn't make any difference whether you do in any of the programming language, its just that Python have more libraries inline with Data Science, we choose it over others.
• Maths— Data Science was all about maths from a long ago. Using IT tools and programming languages are just making these all things easier then before. It is like knowing how to drive a Car but not knowing where to drive it, when to drive it. Tools and technology are very important but ignoring the maths is definitely not a good idea.
• Its Not a Subject — Understanding it as a subject is definitely not gonna work for long run, It is a skill which you need to master by doing it.
• Relying more on Abstract approach — Only relying on theoretical and conceptual would not work efficiently; you need to understand, what is more heavier between “knowing the concept without implementation” and “implementation without knowing the concepts”.
• Attitude for seeking guidance and mentor ship — In IT industry the most newest thing especially for non-tech professionals is the expectancy to get help or support from their manager or lead but ground truth is you have to do it by your own research and approach.

Ground Truth and Learning Path

Being a non IT professional, some of the ground truth I’ve encountered which I will be sharing further but most important thing is to know a generalised and standard learning path and timeline to put in to this.

Learning path for foundation

• Dynamic or competitive programming.
• Data structures and Algorithms.
• Statistics and Probability theory.
• Python with OOP and with at least one framework (Flask or Django)
• Intense learning Pandas Numpy and Matplotlib
• Understanding the ML algorithms without Scikit-Learn (initially)
• Relational and Non-Relational Databases (like, SQL and MongoDB)
• Normal Understanding HTML CSS JS and web scraping is plus.
• Understanding the CRISP-DM flow and agile methodology
• Doing as many possible projects and datasets from kaggle or any other sources at least 100 to 150 data sets.

after following it with a intense dedication you will have the understanding & confidence and only this confidence will bring you excitement and interest in Data Science which will make u a long run and worthy contender in this domain.

If Not 6 Months Then How Many ?

So if 6 months aren’t enough then what’s the ideal time period to master this skill ??

As I already mentioned in above text that it is a continuous process to be in learning but nowadays some of the institutes and online forum don’t tell the reality that investment and dedication of time is the most key factor in this whole process,

Yet, 10 to 18 months are really required to get proficient with Data Science and Analytics.

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[P.S.] All the best learners for your Data Science journey.